comprehensive statewide forest inventory analysis and ...€¦ · the methods used for the project...

Comprehensive Statewide Forest Inventory Analysis and Study (CSFIAS)

Technical Report

Prepared for:

Florida Forest Service

August 25, 2013

Revision 3

Prepared by: Andrew Brenner and Emilly Foster Photo Science John Cothrun F4 Tech Brian Condon, BRM Inc.

2 Florida Forest Service

Table of Contents

1 EXECUTIVE SUMMARY .................................................................................................... 4

2 INTRODUCTION ................................................................................................................ 6

3 OVERVIEW OF PROJECT ................................................................................................ 7

3.1 ORIGIN AND DESCRIPTION OF MAIN SOURCE DATASETS USED IN THIS PROJECT ................. 8 3.2 GEOSPATIAL DATASETS CONSTRUCTED FOR THIS PROJECT ............................................. 8

4 STEPWISE DESCRIPTION OF METHODS USED FOR ALL DATA DEVELOPMENT AND DATA SOURCES .....................................................................................................................11

4.1 BIOMASS CLASSIFICATION SCHEME ..............................................................................11 4.1.1 Classification Ruleset.............................................................................................11

4.2 DATA SOURCES ..........................................................................................................12 4.2.1 Imagery Used ........................................................................................................12 4.2.2 Other Data Used ....................................................................................................13

4.3 STATEWIDE FORESTLAND COVER DATA LAYER ...............................................................16 4.3.1 Image Preprocessing .............................................................................................16 4.3.2 Crosswalk ..............................................................................................................16 4.3.3 Creation of Maximal Potential Forestland Mask .....................................................18 4.3.4 Creation of Forest-Land Cover Masks ...................................................................18 4.3.5 Final forestland cover map assembly .....................................................................20

4.4 STATEWIDE TIMBER STAND AGE CLASSES DATA LAYER ...................................................21 4.4.1 Change detection 1996-2011 .................................................................................21 4.4.2 Change detection 1972-1996 .................................................................................22

4.5 STATEWIDE ORIGIN OF THE FORESTS DATA LAYER .........................................................23 4.5.1 Creation of Plantation Mask ...................................................................................23

4.6 STATEWIDE TIMBER PRODUCT CLASSES DATA LAYER .....................................................24 4.7 ACCURACY ASSESSMENT OF LAND COVER, STAND AGE, AND STAND ORIGIN DATASETS .....24

4.7.1 Methodology ..........................................................................................................24 4.7.2 Results ..................................................................................................................27

4.8 STATEWIDE OWNERSHIP OF FORESTLAND DATA LAYER ..................................................35 4.8.1 Data Source and Development ..............................................................................35 4.8.2 Accuracy Assessment............................................................................................36

4.9 STATEWIDE PRIMARY WOOD-USING PLANTS DATA LAYER ................................................38 4.9.1 Documenting wood using plants in Florida .............................................................38 4.9.2 Estimating timber demand, sustainability and availability .......................................38

4.10 TIMBER RESOURCES DISTRIBUTION IN GREEN TONS .......................................................41 4.10.1 Calculation of standing timber biomass ..........................................................41 4.10.2 Equations, processes and programs used for stratified inventory ...................42 4.10.3 Stratified Inventory Process ...........................................................................44

5 LESSONS LEARNED, STRATEGIES FOR FUTURE UPDATES AND RECOMMENDED NEXT STEPS ...........................................................................................................................51

5.1 LAND COVER, AGE AND ORIGIN LAYERS .......................................................................51 5.2 OWNERSHIP LAYERS ...................................................................................................52 5.3 MILL LOCATION AND DEMAND LAYERS ..........................................................................52 5.4 STANDING BIOMASS, GROWTH, SUSTAINABILITY AND AVAILABILITY LAYERS ....................53


6 APPENDIX A: CROSSWALKS .........................................................................................55

7 APPENDIX B: STRATA TABLES .....................................................................................62


1 Executive Summary The Comprehensive Statewide Forest Inventory Analysis and Study (CSFIAS), authorized by the 2012 House Bill HB7117, provides a comprehensive statewide inventory of timber biomass resources on forestlands for the State of Florida using a stratified inventory approach. This approach provides an overall estimate of timber volume, net growth, timber demand levels, and their spatial distribution in the state. In addition, the project identifies forest ownership across the state. The primary plot information used for the assessment was from the United States Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program. The mapping of forest cover types used remote sensing and geographic information system (GIS) approaches and leveraged off existing available GIS data sets from a variety of sources within Florida. The main mapping source was Landsat Thematic Mapper (TM) imagery with a spatial resolution of 30 meters (m). The methods used for the project are presented in this report and the results are provided in the Executive Report and Map Book. This report details the approach which used circa-2011 Landsat TM imagery in combination with existing Florida land cover datasets to create forestland cover, stand age, and stand origin data layers. The forestland cover layer was created mainly by combining and updating three existing statewide data layers – (1) the Florida Land Use and Cover Classification System (FLUCCS), (2) the National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center’s Coastal Change Analysis Program (CCAP), and (3) the Florida Forest Service’s (FFS) Florida Risk Assessment Canopy Inventory Project (FRACIP) – to a 2011 vintage and classification system required by the project. Other datasets, such as soil type and habitat land cover, were also used to support the analyses. To build the stand age dataset, the team performed change detection and change vector analyses using Landsat TM and Multispectral Scanner (MSS) imagery and existing CCAP datasets to create a time series of forestland cover change. By identifying changes in forest stands, such as clear-cutting or natural disturbances, it was possible to estimate an approximate date of origin, and therefore age, for current stands. Areas of cleared forestland which were not converted to urban uses were considered to still be forestland. The stand origin data layer was created using FLUCCS, state lands data obtained from FFS, and photo interpretation to identify areas planted with trees versus areas with naturally regenerating vegetation. Forest cover data layers were accuracy assessed using a stratified random design. Each accuracy assessment area was photo-interpreted. The resulting overall accuracies were 84.4% for the forestland cover, 79.0% for forest sub-type, 84.0% for stand age, and 93.4% for stand origin. Detailed accuracy assessment tables are provided in this report. The project also developed forestland ownership datasets for the entire state categorized by federal, state, local government, and private ownerships, all of which were further sub-categorized. These datasets were derived from the Florida Department of Revenue (DOR) parcel data overlain on the forestland cover dataset. A single layer that combined forestland cover (B), age (A) and origin (P) data layers was created and used for the stratification of the FIA plots; this layer was called the BAP layer. The overlay was conducted by FIA personnel to protect locations of the FIA plots from disclosure. Each plot was labeled with a BAP strata label (a unique combination of its mapped forest cover, age and origin), as long as that label could not be used to determine the location of the plot. Each FIA plot in the FIA plot database was grown using the Forest Vegetation Simulator (FVS) that


models tree growth to its estimated status for 2011, since plots were measured between 2006 and 2011. The plots were grouped together based on their BAP labels and strata tables for each BAP class were calculated. These strata tables were linked to the BAP map and used to estimate standing timber biomass for three timber product classes: sub-merchantable, pulpwood and sawtimber, and for both softwood and hardwood species groups. The net timber growth data was also produced in this fashion. However, due to sample size and potential overlay errors, the net growth rate calculated using this method appeared unreliable and is not reported here. As a result, net growth rate for each stratum was calculated using measured FIA plot growth for each FIA forest type, age class (0 – 20, 20 – 40 and > 40 years), and FIA Unit. Florida contains four FIA Units which were aggregated into North Region comprised of Units 1 and 2, and South Region comprised of Units 3 and 4. These data were linked to the BAP strata map and estimates of net timber growth for each 30 m pixel were calculated and summarized by county for the entire state of Florida. The demand for forest products was estimated using known mill locations and mill capacities. These demand surfaces were integrated for the known mills to provide information for each county. The sustainability indices (net timber growth to timber demand ratios) and the timber availability estimates (net timber growth minus timber demand) for pulpwood and sawtimber timber product types in each softwood and hardwood species group were also developed for each Florida county. The final section of this report describes lessons learned from conducting the project, outlines strategies for future updates, and provides recommended next steps.


2 Introduction The Comprehensive Statewide Forest Inventory Analysis and Study (CSFIAS), authorized by the 2012 House Bill HB7117, was developed to facilitate study of the growing demand on Florida’s timber biomass resources for both traditional forest product industry and energy generation. The needs of new industries require a balance against current demand on timber biomass from established forest product industries and the ability of the ecosystem to sustainably support timber demand. The risk of over-utilization has been identified by the State of Florida as a potential problem. This project resulted from understanding the need to assess the current timber biomass resource and continue to assess the spatial distribution of the resource as a result of annual changes brought by stand growth as well as natural disasters such as fires, floods and hurricanes. To address this issue, the project provides a comprehensive statewide inventory of timber biomass resources for the State of Florida using a stratified inventory approach. The approach not only provides an overall quantity but also provides the spatial distribution of this information. However, as it applies to any stratified inventory, the validity of the assessment increases as the assessment area increases. The primary plot data used for the assessment was from the United States Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program. The mapping of forest cover types used remote sensing approaches and leveraged off existing available geographic information systems (GIS) data sets from a variety of sources within Florida. The main mapping source was Landsat Thematic Mapper (TM) imagery which has a spatial resolution of 30 meters (m). The Landsat sensor adequately detects changes over wide-scale landscapes but is not optimal for detecting changes over smaller areas. The historic record of Landsat makes it appropriate for this type of project, and it aligns well with federal programs such as the National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center’s Coastal Change Analysis Program (CCAP) which is also based off Landsat data. In addition to timber biomass mapping and inventory, this project estimates demands on timber resources and identifies forest ownerships across the state. Demand on timber resources is driven by activities in mills in the state and what type of timber products they consume. Therefore, mill locations, woodsheds, and wood products were taken into consideration when estimating timber demand and calculating sustainability index and availability. Not all timber biomass was available for harvest as there are large areas of the state that are protected/ preserved. Forest ownership is identified across the state and mapped into federal, state, local government and private categories to support the current analyses. The following report focuses on the technical approach and includes the look-up tables, imagery scenes used, and details on the processes employed. There are two companion reports that can be obtained from this web address http://www.FreshFromFlorida.com/Forest_Inventory :

1) The Map Book containing 11” x 17” maps and tables presenting the results of the analyses in an easy-to-view format.

2) The Executive Report discussing the results of the analyses within a narrative with smaller format maps, charts, and selected tables.

http://www.freshfromflorida.com/Forest_Inventory


3 Overview of Project This section provides an overview of the project to help the reader understand the full scope prior to reading the detailed approach for each task. This project leveraged off data already available in Florida in order to meet the requirements and timeline of the project. The overall approach was to create the forestland cover, stand age and origin layers from a combination of GIS analyses and satellite remote sensing. This resulted in a forestland cover x age x origin layer that was used for stratifying the existing Florida FIA data. In addition to the land cover mapping, the team gathered information on ownership and mill locations to facilitate other components of the project. These elements were then used to calculate the standing timber volumes and tonnages, estimate timber demand, and then calculate sustainability indices and estimates of timber biomass availability in Florida.

Figure 1. A flowchart depicting the overall approach to creating the deliverables for the project.

1. Forest Land Cover

Imagery Databases

Land Cover Databases

Remote Sensing and GIS Analyses

2. Timber Stand Age

3. Timber Product

Class

4. Origin

5. Ownership

6. Wood Plants

7. Timber Resources

9. Executive Report

10. Technical Report

Ownership Databases

Wood Plants Databases

FIA Databases

GIS and statistical analyses

Forest Cover x

Age

Data Layers


A short summary describing source datasets and datasets created for this project is provided below. More details can be found in the next section.

3.1 Origin and description of main source datasets used in this project

• FLUCCS: The Florida Land Use and Cover Classification System (FLUCCS) was originally established by the Florida Department of Transportation (DOT) in response to the need for a statewide, uniform land classification system that could be applied at varying landscape scales and meet different user needs. Currently, data are maintained by Florida’s five water management districts: Northwest Florida (NWFWMD), Suwannee River (SRWMD), St. John’s River (SJRWMD), Southwest Florida (SWFWMD) and South Florida (SFWMD). FLUCCS includes over 200 vegetation and land use classes arranged in four hierarchical levels with each level increasing in detail. Depending on the district, the data vary in vintage between 2008 and 2011 and in level of detail.

• FRACIP: The Florida Forest Service (FFS) Florida Risk Assessment Canopy Inventory Project (FRACIP) dataset, created in 2008, divides the landscape into vegetation classes that relate to canopy fire behavior. The dataset includes 3 layers - species groups, canopy closure and tree size - all of which are based on 30 meter Landsat imagery. The species layer contains 18 forest type classes.

• CCAP: The NOAA Coastal Services Center creates and maintains a standardized land cover database of the coastal United States based on 30 meter Landsat satellite imagery as part of their Coastal Change Analysis Program (CCAP). Since 1996, this database is updated every five years by mapping land cover change and then updating the previous land cover map with the change data which includes 25 vegetation and land use classes.

• FIA: The Forest Inventory and Analysis (FIA) Program of the USFS provides the information needed to assess the nation’s forests. FIA reports on status and trends in forest area and location; in the species, size, and health of trees; in tree growth, mortality, and removals; in wood production and utilization rates by various products; and in forestland ownership. It consists of a uniform grid placed over the entire United States (U.S.), including Florida, where forested plots are sampled with a well-defined protocol. These plots are permanent and are re-measured every 5 years. Their exact locations cannot be disclosed per U.S. privacy and confidentiality laws, so there are limitations on the use of these data which will be further explained in this report.

3.2 Geospatial datasets constructed for this project 1. Forestland Cover: The forestland cover dataset was developed through using the

FLUCCS, FRACIP and CCAP datasets. This was achieved by crosswalking the classes in each dataset to the project classification scheme, resampling the FLUCCS to the 30 m grid that the other data were in, and then overlaying the datasets in combinations and looking for agreement between data layers. Where disagreements were found, a series of logical models were developed to deal with the differences in resolution and classification schemes. The land cover dataset was updated to 2011 conditions using 2011 Landsat TM imagery and the 2011 CCAP update. The Landsat imagery was also used to classify specific classes where there was uncertainty in the way these classes were mapped in the base datasets. Longleaf pine was one such class. Since the minimum mapping unit (MMU) was 10 acres, specific classes in the map were filtered to remove speckle and small inclusions. Some classes, however, were preserved, such as


cypress domes, because they often occur in small areas and filtering them out would impact the results of the analyses. Urban areas were based on urban FLUCCS codes, but increases in urbanization were added via the 2011 CCAP update and manual review. Once completed, the whole dataset was reviewed against high resolution aerial imagery from Bing and Google, and specific areas were hand edited prior to delivery.

2. Forest Stand Age: The change detection used two approaches to create a time series of information about forestlands between 1972 and 2011. Forest layers already existed for 1996, 2001 and 2006 as part of NOAA CCAP, although the definitions of forest differ between that classification scheme and this project. The change between 2006 and 2011 was analyzed using the approved USGS and NOAA Multi-Index Integrated Change Analysis (MIICA) methodology. This methodology has been used for the current NOAA CCAP approach nationwide, and it is being used for the update of the National Land Cover Dataset. Tracking forestland changes provided information that could be used to date the clearing of land and it was assumed that replanting would occur soon after that time unless the land was converted to urban land uses. For areas of forest that existed before 1996, a change vector analysis for seasonally matched Landsat scenes was conducted. The change vector allows the analysis of both the magnitude and direction of change between the two datasets. For the period between 1986 and 1996, Landsat TM imagery was used. For the period between 1972 and 1986, Landsat Multispectral Scanner (MSS) imagery was used. The 60 m MSS imagery was interpolated to a 30 m resolution to make it compatible with the rest of the data. The interpolation combined logical rules with spectral analysis. The 1972 date was created by separating young and old pine stands from the 1977 MSS imagery.

3. Timber Product Class: The timber product class was created by overlaying the FIA data on a raster layer produced by combining the forestland cover type (B), age (A) and origin (P) classes. This combination layer was termed the BAP dataset with a pixel resolution of 30 m. Since the location of the FIA plots cannot be revealed/ distributed, the BAP dataset was provided to USFS FIA staff to summarize. The FIA staff overlaid the plots on the BAP layer and joined the cover type x age x origin strata label to each overlying FIA plot. Timber product values were established according to the diameter of the inventoried trees and the general species group that the inventoried trees fell into. By summarizing the FIA data by the BAP strata label, it was possible to develop strata tables that could be linked back to the BAP dataset and then viewed spatially. The strata tables included four timber product types: softwood pulpwood, softwood sawtimber, hardwood pulpwood and hardwood sawtimber. Since some BAP strata labels had more than one timber product, a map was produced for each of the four timber products in green tons per acre.

4. Origin: Tree plantations were identified using two main datasets: FLUCCS and FFS state-owned lands. The original FLUCCS layer identified tree plantations and the state-owned lands dataset contained stand origin as being planted or natural. Many stands did not have an origin type. In these cases, the origin was photo-interpreted with the help of high resolution imagery from Google.

5. Ownership: Forest ownership was determined by examination of statewide parcel shapefiles and databases maintained by the Florida Department of Revenue (DOR). Parcels containing forest were first identified and were classified at a primary and sub-category level. The primary ownership category identified federal, state, and local/municipal government, and private ownerships. The sub-category ownership detailed individual agencies or departments within public ownership categories, as well as a host of private corporate and other ownership types. Ownership was determined by searching records for specific keywords indicating specific categories and was also refined by consultation with outside data sets and publications.


6. Wood Mills: The FFS primary wood-using mills list was the starting point for the analysis of timber demand. The type and quantity of products generated by each mill were identified along with the timber species and size class employed as raw material. Conversion factors were utilized to move from finished product volume to timber product volume requirements, a representation of each mill’s demand for timber products when operating at full capacity. Woodshed boundaries were then determined for each mill as a function of mill size and product types. Each mill’s demand for timber was then distributed across its woodshed, a geographic area defined by distances from 75 to 90 road miles from the mill location. This distribution of demand was applied in further analyses of timber resource sustainability and availability. Details of this approach are covered later in this report.

7. Timber Resources: The distribution of timber resources was mapped for each of the 67 Florida counties and included layers with values for standing timber, net timber growth, timber demand, sustainability index, and timber availability. These values were calculated for each of the four timber product types: softwood pulpwood and sawtimber, and hardwood pulpwood and sawtimber. Standing timber, in green tons, was summarized in each county using the statewide timber product maps created earlier. The other four timber resource data types were calculated, per county, using the statewide timber product maps in addition to the mills and woodshed data.


4 Stepwise description of methods used for all data development and data sources

4.1 Biomass Classification Scheme The first element of any map is its land cover classification scheme. This scheme needs to be hierarchical, totally exhaustive (i.e. every location can be classified), mutually exclusive (i.e. no location can get more than one label), and have a ruleset defining the classes. The classification scheme developed for this project was based on discussions with FFS and review of the datasets. The decision rules are shown below and are used for dividing the land area into classes.

4.1.1 Classification Ruleset The classification ruleset was set up like a key with “if” and “else” statements, so when a user ran through the key, he/she could identify what cover type the area fell into. -------------------------------------------------------------------------------------------------------------------------- If land is under urban land uses (defined by the FLUCCS definitions of urban), then Urban Else if land is used for agricultural production or grassland (tree canopy < 20%) and land not considered forestland (does not include orchards), then Agriculture/Grassland If areas are cultivated, then Row-crops Else Pasture and range Else if land is covered by water, then Water Else if land is wetland (as defined by Florida Statutes, from FLUCCS or National Wetlands Inventory (NWI)), then Wetlands If woody canopy is > 20%, then Forested Wetlands If > 75% area is covered by Cypress wetland complex vegetation, then Cypress (as defined by FLUCCS) Else if > 75% area is covered by the Mangroves, then Mangroves Else Other Forested Wetlands (e.g. Wet hardwoods, Bays) Else Non-Forested Wetlands Else if land is used or has been used for forestry and is not urban, then Forestland

If > 75% of canopy is conifer, then Pine If pine is < 10 years old, then Young Pine Else if > 75% of the canopy is Sand Pine, then Sand Pine

Else if in South Florida (defined by division line) and if > 75% of the canopy is Longleaf and S. Florida Slash, then Longleaf/S. Florida Slash Else if in S. Florida Loblolly and North Florida Slash (will include Pond, Shortleaf etc.)

Else if in N. Florida and > 75% of the canopy is Longleaf, then Longleaf Else Loblolly and N. Florida Slash Else if > 75% of canopy is broadleaf then Hardwood Forest Else Mixed Pine-Hardwood Forest

Else if land is used for tree fruit or seed production, then Tree Orchards If >75% of the tree canopy is used for Forest Seed Production, then Forest Seed Production (Note this class has been edited in based on ancillary data)

Else Fruit Production Orchards (include Citrus and Pecan) will also include nurseries Else Other (non-vegetated areas). -------------------------------------------------------------------------------------------------------------------------------


4.2 Data Sources The next section reviews the multiple imagery and GIS datasets that were used in this project.

4.2.1 Imagery Used

4.2.1.1 Landsat Imagery The scenes used for the 2011 land cover map and the age analysis followed the same Landsat path-row structure as can be seen in Figure 2.

Figure 2. Landsat TM scene boundaries, including path/row label, required to cover the project area.

4.2.1.2 Years 2006 – 2011 change classification The scenes used for the 2006 to 2011 change detection and the 2011 cover classification are detailed in Table 1 below.


Table 1. Landsat imagery used for 2006-2011 change detection and classification.

Path/Row Earliest Date Latest Date p15r41 25 Jan 2005 13 Jun 2009 p15r42 25 Jan 2005 5 Feb 2009 p15r43 25 Jan 2005 8 Feb 2010 p16r39 13 Sep 2005 30 Sep 2011 p16r40 5 Mar 2005 22 Mar 2011 p16r41 19 Jan 2006 16 Dec 2010 p16r42 1 Feb 2005 16 Dec 2010 p17r39 23 Jan 2005 13 Mar 2011 p17r40 12 Mar 2005 23 Oct 2011 p17r41 12 Mar 2005 13 Mar 2011 p18r39 11 Sep 2005 30 Oct 2011 p19r39 9 Feb 2006 22 Jan 2011 p20r39 1 Mar 2005 6 Jun 2011

4.2.1.3 High Resolution Imagery Sources High-resolution imagery was used as a reference dataset for photo-interpretation and quality control of the training and accuracy assessment sites. Google Earth and Bing provide imagery from many sources for many different dates that support this analysis. The majority of the State of Florida is covered by imagery flown by the United States Geological Survey (USGS) and the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP). Google Earth’s multi-date capability, often extending backwards to the early 1990s, was frequently used for photo-interpretation of stand age.

4.2.2 Other Data Used Statewide land cover was obtained from three primary sources: NOAA CCAP, FLUCCS, and FFS FRACIP.

4.2.2.1 NOAA CCAP The NOAA Coastal Services Center creates and maintains a standardized land cover database of the coastal United States based on 30 m Landsat satellite imagery as part of CCAP. This database is updated every five years by mapping land cover change and then updating the previous land cover map with the change data which includes 25 vegetation and land use classes. The CCAP land cover data layers are available for 1996, 2001, and 2006. Photo Science recently completed the 2011 CCAP land cover update for the entire state of Florida that was used in this analysis. The base dataset for CCAP is in North American Datum 1983 (NAD83) Albers and was reprojected to Florida Department of Environmental Protection (FDEP) Albers HPGN/HARN1 for the project as requested by the FFS.

1 High Precision Geographic Positioning System (GPS) Network (HPGN); High Accuracy Reference Network (HARN)


4.2.2.2 FLUCCS FLUCCS was originally established by the DOT in response to the need for a statewide, uniform land classification system that could be applied at varying landscape scales and meet different user needs. Currently, data are maintained by Florida’s five water management districts: NWFWMD, SRWMD, SJRWMD, SWFWMD, and SFWMD. FLUCCS includes over 200 vegetation and land use classes arranged in four hierarchical levels with each level increasing in detail. Depending on the district, the data varied in vintage (2008 to 2011) and classification detail. FLUCCS data are delineated based on 1-foot orthophotos (an aerial photograph with a square pixel resolution of 1 foot that has been projected and rectified so that image displacements caused by camera tilt and terrain relief are removed) and are provided in vector format. The most current FLUCCS shapefiles for each district were reprojected to FDEP Albers HPGN/HARN, converted to 30 m resolution raster format, and mosaicked into a single raster layer covering the state.

4.2.2.3 FFS FRACIP The FFS FRACIP dataset, created in 2008, divided the landscape into vegetation classes that relate to canopy fire behavior. The dataset includes 3 layers - species groups, canopy closure and tree size - all of which are based on 30 m Landsat imagery. The species layer contains 18 forest type classes that include a breakdown of pine types into classes that were similar to those used by this project.

4.2.2.4 Florida Forest Service and USFS Stand Data Forest cover types for State-owned and National Forestlands were used as a guide for photo-interpretation and as training data for a spectral analysis among pines, specifically Longleaf pine. The database also contained stand age and plantation/non-plantation descriptions that supported quality control (QC) of the dataset and accuracy assessment sites. These datasets were not burnt into the classification because they were not statewide datasets.

4.2.2.5 CRIFF Soil Gridded Soil Survey Geographic Database (SSURGO) soil data were downloaded from the Natural Resources Conservation Service (NRCS) web site. Next, the SSURGO soils were grouped into CRIFF groups, or Cooperative Research in Forest Fertilization Groups, which is a soil classification system developed by the University of Florida related to soil drainage, and texture and depth of the subsurface soil layers. The dataset was then rasterized to 30 m resolution file. For our purposes, the CRIFF soil classes A and B were grouped into a single class, as were C and D, and E and F. Soils data were absent for the Everglades National Park. CRIFF soil groups were going to be used as a stratifier but there were not a sufficient number of plots to justify it. Therefore, the CRIFF dataset was used for some modeling to help differentiate between upland and wetland areas.


Figure 3. The CRIFF forest soil classification system (copied from Jokela and Long 20002).

4.2.2.6 FWC Habitat and Landcover Florida Fish and Wildlife Conservation Commission (FWC) Habitat and Landcover data (circa 2003) was used to aid photo-interpretation and help delineate agriculture areas. The statewide data layer contains 26 natural and semi-natural vegetation types plus other classes such as agriculture and urban. The layer is based on 30 m Landsat Enhanced Thematic Mapper+ (ETM+) satellite imagery.

4.2.2.7 FNAI Conservation Lands In a few areas, identification of natural vegetation versus plantation was facilitated by the Florida Natural Areas Inventory (FNAI) data layer known as Florida Managed Lands (FLMA), last updated in 2012. This layer demarcates Florida conservation lands and provides a general description of the natural plant communities.

4.2.2.8 Slash Pine habitat range The delineation between typical northern variety of Slash pine (Pinus elliottii) and South Florida Slash pine (Pinus elliottii var. densa) ranges was heads-up digitized using a map created by Little (1971)3,4.

2 Jokela, E.J.; Long, A.J. 2000. Using soils to guide fertilizer recommendations for southern pines. University of Florida, Institute of Food and Agricultural Sciences Extension Circular 1230. 11 p. 3 Pinus elliottii Engelm. http://www.ncsu.edu/project/dendrology/index/plantae/vascular/seedplants/gymnosperms/conifers/pine/pinus/australes/slash/habitat.html, North Carolina State University. 4 Little , E.L., Jr., 1971. Atlas of United States trees, volume 1, conifers and important hardwoods: Misc. Pub. 1146. Washington, D.C.: U.S. Department of Agriculture. 9 p. 200 maps.


4.3 Statewide forestland cover data layer

4.3.1 Image Preprocessing The 2011 Landsat data were preprocessed to obtain image derivatives. The derivatives used for spectral analysis in this project were Tasseled Cap, Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and texture. The Tasseled Cap methodology is a useful tool for compressing spectral data into a few bands associated with physical scene characteristics. The transformation has potential applications in revealing key forest attributes including species, age and structure. The NDVI is a simple numerical indicator that can be used to analyze remote sensing measurements and assess whether the target being observed contains live green vegetation or not, and to a certain extent, quantify the vigor and health of the vegetation. The formula for NBR is very similar to NDVI although the index highlights the location and severity of burns that occurred in the landscape. The Texture layer gives a measure of the variability in pixel values around a pixel in the near-infrared band and, thus, a measure of the homogeneity or heterogeneity around the pixel. Other preprocessing of the Landsat data included reprojection from NAD83 Albers to FDEP Albers HPGN/HARN. The Landsat scenes were not “balanced” with one another, as spectral balancing would alter the imagery data, thus diluting the ability to discriminate the individual species.

4.3.2 Crosswalk The classification schemes of the three main data layers used for this analysis – CCAP, FLUCCS, and FRACIP – have similarities but are not identical so crosswalks to the project classification system were developed to integrate them. The crosswalks for FRACIP and FLUCCS are provided in Appendix A, Tables A1 and A2, respectively. The 2011 CCAP update and the 1996, 2001, 2006 CCAP layers were crosswalked as listed in Table A3. The agriculture classes in FWC Habitat and Land Cover were also crosswalked (Table A4). The FLUCCS and FRACIP forested classes are more detailed than CCAP. In particular, the CCAP palustrine scrub/shrub wetland class varies in species and canopy cover in different latitudes of the state, with the north being more mixed scrub/shrub wetland and young hydric pine flatwoods, and the south being freshwater marsh. Therefore, a dividing line between the panhandle of Florida and the peninsula was digitized (Figure 4). Shrub wetlands in the northwest were crosswalked to Forested Wetlands while in the south these were crosswalked to Non-Forested Wetlands. In addition, because of the difficulty in differentiating typical northern Florida Slash pine variety and South Florida Slash pine in the South, a dividing line between North Florida and South Florida Slash pines was drawn based on research by Little (1971). This was digitized and approved by FFS before use (Figure 5).


Figure 4. Boundary separating forested vs. non-forested wetlands when crosswalked from CCAP “palustrine scrub/shrub wetlands”.

Figure 5. Boundary separating North Florida vs. South Florida Slash pine when crosswalked from FRACIP “wet flatwoods”, as shown by Little (1971).


4.3.3 Creation of Maximal Potential Forestland Mask After the CCAP, FLUCCS, and FRACIP layers were crosswalked to the Forest Cover classification system of this project, a forest mask was created from each layer, including the three historic and the most recent CCAP layers, by grouping cypress, mangrove, other forested wetlands, pines, mixed pine-hardwood, and hardwood stands into a single “forested” class and having all other classes as “non-forested”. The four CCAP masks were unioned into a single, maximal layer. Then, the CCAP, FLUCCS, and FRACIP masks were overlaid to form an overall maximal Forestland mask which, theoretically, included all possible forest stands that were either currently forested or were recently cut then replanted or left to naturally regenerate. The maximal Forestland mask contained three classes: “definitely forestland”, “possibly forestland”, and “not forestland”. If two or more of the overlaid forest masks had “forested” for a pixel, it was called “definitely forestland”. If just one forest mask had an area mapped as “forested”, then it was called “possibly forestland”. This layer was filtered three ways to reduce speckle, i.e. smooth the image. First, holes in the forestland less than four pixels were filled with the majority. Next, clumps of “definitely forestland” less than 5 pixels were changed to “non-forestland”. This removed speckle of around an area of 1 acre. Last, large clumps (<20 pixels) of “possibly forestland” were filtered to the majority of the surrounding pixels. Finally, Urban, Water, Tree Orchards, and “definitely Other” were masked out of the Forestland mask. The creation of those four masks is described in the following subsections.

4.3.4 Creation of Forest-Land Cover Masks The 2011 CCAP, FLUCCS, and FRACIP layers were used to create individual masks for each of the forestland cover classification groups: Urban, Agriculture/Grassland, Water, Wetlands, Forestland, Tree Orchards, and Other. Pixels in each of these classification groups, if present, were extracted from CCAP, FLUCCS, and FRACIP. The creation of each mask is described in the following sections.

4.3.4.1 Urban Mask The Urban mask was created by mosaicking the urban classes from CCAP and FLUCCS. Holes in the mask < 5 pixels were filled in with urban. Single-pixels were not removed across the map in order to preserve roadway networks. At the end of the mapping process, areas of urban openland (including forest and nonurban) < 10 acres were reclassified to urban. Some large forested areas within or adjacent to urban areas were retained if they were > 10 acres.

4.3.4.2 Agriculture/Grassland/Tree Orchards mask The Agriculture/Grassland/Tree Orchards mask was created using CCAP and FLUCCS since FRACIP did not contain these classes. In cases where both CCAP and FLUCCS overlapped and they had the same Agriculture/Grassland type (AG-type) of row-crops or pasture and range, they were considered “definitely” that AG-type. FLUCCS was the only dataset to contain orchards data so all FLUCCS orchards were considered “definitely” tree orchard and were defaulted to the fruit production orchards Orchard-type. Where AG classes occurred in any of the data layers they could be considered “possible” AG-types. Where CCAP and FLUCCS had opposing AG-types, the FWC Habitat and Land Cover layer was used to break the tie. The remaining ties were broken by using FLUCCS as the master. FLUCCS class 2100 Cropland and Pasture was crosswalked to the Agriculture/Grassland group but not to an AG-type, so if CCAP overlapped the same area, it was used as the master. The remaining areas from the FLUCCS 2100 crosswalk were assigned to pasture and range after a review by photo-interpretation. Holes in the mask less than 5 pixels were filled in using a majority filter. Also, single isolated pixels were removed. The Forest Seed Production Orchards were added at the end of the mapping process based on known locations of forest seed


orchards. These orchards did not have a unique spectral signature and so would have been impossible to identify from either Landsat or aerial imagery.

4.3.4.3 Water The Water mask was primarily created using CCAP and FLUCCS because FRACIP tended to over-estimate the water extents, so water from CCAP and FLUCCS were mosaicked to create the Water mask. Single-pixel holes in the mask were filled in with water. Also, single isolated pixels were removed, although they may have represented small water bodies.

4.3.4.4 Other CCAP and FLUCCS data layers were used to create the Other mask. The “other” class in the FRACIP layer was not usable since it broadly consisted of urban, agriculture, other, non-forested wetlands, and forest with < 20% canopy. When overlapped, if both FLUCCS and CCAP had “other”, then the pixel was assigned “definitely other”. If just one layer had “other”, then it was assigned “possibly other”. No filtering was performed on the mask. Areas classified as other were reviewed to see if they should be assigned to different classes, which was the case for many areas. The areas that were left were, in many cases, mining sites.

4.3.4.5 Wetlands A basic, overall Wetlands mask was created using CCAP, FLUCCS, and FRACIP. If two or more overlapping datasets had a wetland class type, then the pixel was called “definitely wetlands”. If only one layer had a wetland class type, the pixel was labeled “possibly wetlands”. Next, a more detailed Wetlands sub-type mask was built in descending layers using a series of else-if steps involving overlaying combinations of CCAP, FLUCCS, FRACIP, and the Forestland mask. This Wetlands sub-type mask contained classes for cypress, mangrove, other forested wetlands and non-forested wetlands. Other forested wetlands and non-forested wetlands are further divided into “possibly” and “definitely” using the first basic mask. First, all new 2011 CCAP updates to “palustrine emergent” were assigned “definitely non-forested wetlands”. Both cypress and mangrove from FLUCCS and CCAP were added as “definites”. If two or more main datasets were forested wetlands, then “definitely other forested wetlands” were added. The pixels were categorized as “definitely non-forested wetlands” if “definitely wetlands” and “non- forestland” overlapped. Next, if “possibly wetlands” overlapped with “non-forestland”, then “possibly non-forested wetlands” was assigned. Lastly, if “possibly wetlands” overlapped with “possibly non-forestland”, then pixels were added to the “less possibly other forested wetlands” category. The resulting Wetlands sub-type mask was filtered several times. First, the “possibly wetlands” class clumps less than 5 pixels were absorbed into the “definite” class clumps, pending if majority. Then, clumps less than 5 pixels of “definitely non-forested wetlands” and “definitely other forested wetlands” were filtered into other “definite” classes. Holes in the mask less than 3 pixels were then filtered into their majority.

4.3.4.6 Pine The Pine sub-types mask consisted of four pine categories: Longleaf, Longleaf/South Florida Slash Pine, Sand Pine, and Loblolly/North Florida Slash Pine. Similarly to the Wetlands sub-type mask, the pine classes were mapped by overlaying FLUCCS, FRACIP, and CCAP and assigning different combinations to a pine class using if-else statements. The pine classes were


divided into “definites” if two or more datasets agreed on a class and “possibles” if one dataset had a pine class. CCAP does not classify to the species-level, but the evergreen was incorporated by first producing a single, maximal Pine mask by unioning evergreen from the four CCAP data layers. The maximal CCAP pine mask was used to help “validate” an overlapping pine species class from FLUCCS and FRACIP and make it a “definite”. For example, if CCAP “Pine” overlapped with FLUCCS “Sand Pine”, the pixel was coded as “definitely sand pine”. If CCAP “Pine” did not overlap with a pine class in FLUCCS or FRACIP, it was coded as “definitely pine” temporarily. If there was a 2- or 3-way tie consisting of different pine classes from overlapping FLUCCS, FRACIP, and CCAP layers, then it was coded as “possibly pine” temporarily. The ruleset looked at the species in each layer, its origin and location in the state, and this determined the pixel classification within the pine categories. By reviewing the information from the National and State Forests, it was apparent that the low density of the longleaf pine was leading it to be underrepresented in the map. To address this, we utilized remote sensing approaches using the training data from the National and State Forests and a classification and regression tree (CART) approach for the Northern Florida Landsat Scenes that was focused just on areas that were identified as pine stands. Where areas were classified by the image analysis as longleaf pine, these areas were recoded to longleaf. These data were compared to stand data from the FFS State-owned forests and National Forests and visually inspected.

4.3.4.7 Hardwoods All three datasets were used to create the hardwood mask. When overlapped, “definitely hardwood” was assigned if two or more datasets agreed on hardwood, while “possibly hardwood” was assigned if one dataset had hardwood. Since CCAP contains very few hardwoods mapped in Florida, they were all put into the “definitely hardwood” group. Ultimately, it was determined that hardwoods were underrepresented in the dataset, this may have resulted from large areas of hardwood being classified as “Other Wetland Forest”, since they were occurring on areas considered to be lowland or wetland sites. Where hardwoods had been mapped in the FLUCCS dataset, they were also brought in to the land cover layer as hardwoods in this classification. CRIFF group soils were also used to identify areas of lowland forest that should be coded to an upland forest class such as hardwoods or mixed.

4.3.4.8 Mixed Pine-Hardwood Stands All three datasets were used to create the Mixed mask. When overlapped, “definitely mixed” was assigned if two or more datasets agreed on mixed, while “possibly mixed” was assigned if one dataset had mixed. It was determined that the “possibly mixed” was overall capturing the mixed class and so with some exceptions it was mapped as “definitely mixed”. Exceptions to this rule were areas considered plantation.

4.3.5 Final forestland cover map assembly All the data layers were assembled using a series of decision rules that would maximize the accuracy of the overlay, taking into account origin, location in state, and expected area of coverage, with reference to FIA acreages. The final data layer was reviewed multiple times for errors, although pine species were difficult to photo-interpret. Very small clusters of pixels (less than 5) for most of the land cover classes were filtered to reduce speckle in the layer, keeping in mind the MMU was 10 acres, or 45 pixels, for each land cover class. The final map was provided to the FFS, who provided comments and suggestions that were incorporated into the final deliveries.


4.4 Statewide timber stand age classes data layer In order to capture the stand age, a number of approaches were used and are described below.

4.4.1 Change detection 1996-2011 The change detection approach for updating the 2006 CCAP dataset to 2011 used the approved USGS and NOAA multi-index integrated change analysis system (MIICA) methodology. This methodology has been used for the current NOAA CCAP approach nationwide and is being used for the update of the National Land Cover Dataset. A brief description of this approach is provided below.

4.4.1.1 Task 1: Data Acquisition and Preprocessing The first step in the processing chain is to quality control (QC) the datasets provided. These datasets were reviewed for:

1) Geographical Scope: Does this dataset cover the project area? 2) Data integrity: Do all datasets have valid attribution based on metadata

provided and are the attributes understandable? 3) Currency: When was the data created, how will this impact the analysis? 4) Spatial accuracy: Does the data overlay the base dataset (i.e. Landsat

imagery) and if not, what will it take to co-register it? The imagery was processed per Landsat path/row scene, and used the preprocessing steps outlined in section 4.3.1.

4.4.1.2 Task 2: Change Mask Creation The images were then run through the MIICA system that identifies spectral changes between image pairs. These spectral changes represent changes in land cover but may not necessarily represent changes in land cover class. The thresholds were set to capture all change at the risk of capturing some non-change areas. The key to change detection is to be able to focus attention on areas of change rather than completely re-map the area. The risk of using a change mask is that some types of change are not captured within it. Photo Science’s approach was to ensure high change areas were included into the mask; areas that were unchanged that entered the mask were removed manually or using models at the classification step. To improve the mask, land cover classes were analyzed separately with individual spectral thresholds of change, rather than applying the same threshold to all land cover classes. The draft change mask was reviewed against the two dates of imagery in a stratified manner to ensure that no significant changes were missed. Once the review was completed, the mask was edited where necessary. The change areas were then classified into their respective CCAP classes using unchanged areas of the 2006 land cover dataset as training sites. Clusters of similar pixels were sampled across the production area, creating the training sites for the CART classification. The independent variables were based on the 2011 imagery and image derivatives and other thematic data (e.g. elevation, soils, etc.), where appropriate. We used primarily spectral variables for the initial CART classification using the thematic variables in subsequent logic models to refine the classification. This approach, termed as “super sampling,” has been shown to provide good initial results. The classification was applied to the change mask resulting in a full classification of areas that had changed. The areas where classes had not changed were dropped out. Each change dataset was then run through a number of steps to remove any misclassification, first using modeling and then using manual editing approaches. Prior to the final delivery, the draft dataset was reviewed and then edited based on the review comments.


The CCAP layers used for tracking changes between1996 – 2001 and 2001 – 2006 used similar but not identical approaches. The project used the change analysis described above for forestland between 2011-2006 to age stands < 5 years, change analysis for 2006 – 2001 to age stands 5 – 10 years, change analysis from CCAP data 1996 – 2001 to age stands 10 – 15 years.

4.4.2 Change detection 1972-1996 For areas of forest that existed before 1996, Photo Science conducted a change vector analysis for seasonally matched Landsat scenes. The change vector allowed the analysis of both the magnitude and direction of change between the two datasets. For the period between 1986 and 1996, Landsat TM imagery was used. For the period between 1972 and 1986, Landsat MSS imagery was used. The 60 m MSS imagery was interpolated to a 30 m resolution to make it compatible with the rest of the data. The interpolation combined logic rules with spectral analysis. Each layer created through this change detection had up to four categories

• 1 = forest between time 1 and time 2 that was unchanged • 3 = forest that we were very confident appeared to had changed within the time interval • 5 = forest that we were less confident appeared to had changed within the time interval • 9 = forest that was obscured during the time interval

Category 3 consisted of mainly obvious, probably recent, clear-cuts. Category 5 consisted of mainly harvests, but of more varying types and appearances. Backgrounds could be dark or wet soils, or recently burned. If an area was cut shortly after the first observation date, considerable vegetation could have regrown by the time of the second observation, which would mean it would not be picked up easily. If a plantation was only thinned, this would also cause problems in identification. Category 9 consisted mostly of clouds, on either observation date, but occasionally included dense smoke. The information in the last two time intervals, 1986 to 1991 and 1991 to 1996, was derived from comparison of Landsat TM data. The information from the 1981 to 1986 time interval was derived from a comparison of Landsat MSS and TM data. The information from the 1977-1981 time interval was derived from a comparison of MSS data. Since no imagery existed for 1972, the only way to date the remaining stands that were planted before 1977 was to analyze the 1977 and 1981 MSS imagery to determine the age of the stand in 1977. The age of stands is easier to determine in their earlier years because of the rate of change of canopy cover and NDVI; as canopies close, the NDVI becomes stable. The same codes were used in this layer as were used in the other forest change detection product. Anomalies were noticed in the results between some Landsat path/rows creating what appeared to be “fringing” due to mis-registration. This was caused by a one-pixel shift between the two forest strata used. This mis-registration was minimized using the autosync module in ERDAS IMAGINE. Most of these problems were removed through filtering although they could have remained in some areas of the final age map.


Table 2. Landsat TM and MSS imagery used for the 1976-1996 change detection. Path/Row 1996 1991 1986 1981 1976

p15r41 18 Feb 1996 13 May 1992 11 Apr 1986 n/a n/a p15r42 18 Feb 1996 15 Oct 1990 11 Apr 1986 n/a n/a p15r43 18 Feb 1996 15 Oct 1990 11 Apr 1986 n/a n/a p16r39 12 Mar 1996 11 Feb 1991 2 Apr 1986 n/a n/a p16r40 12 Mar 1996 11 Feb 1991 2 Apr 1986 20 Dec 1981 11 Apr 1976 p16r41 12 Mar 1996 11 Feb 1991 2 Apr 1986 20 Dec 1981 11 Apr 1976 p16r42 12 Mar 1996 11 Feb 1991 2 Apr 1986 20 Dec 1981 20 Feb 1977 p16r43 n/a n/a n/a 20 Dec 1981 20 Feb 1977 p17r39 16 Dec 1996 16 Oct 1991 25 Apr 1986 15 Nov 1981 9 Feb 1976 p17r40 17 Dec 1996 16 Oct 1991 25 Apr 1986 15 Nov 1981 9 Feb 1976 p17r41 18 Dec 1996 16 Oct 1991 25 Apr 1986 15 Nov 1981 9 Feb 1976 p17r42 n/a n/a n/a 15 Nov 1981 9 Feb 1976 p18r39 13 May 1996 9 Feb 1991 16 Apr 1986 23 Sep 1981 17 Mar 1976 p18r40 n/a n/a n/a 23 Sep 1981 17 Mar 1976 p18r41 n/a n/a n/a 23 Sep 1981 17 Mar 1976 p19r39 27 Oct 1996 14 Oct 1991 16 Oct 1986 15 Apr 1981 23 Apr 1976 p20r39 25 Apr 1996 6 Nov 1991 7 Dec 1985 21 Feb 1981 3 Mar 1976 p21r38 n/a n/a n/a 26 Sep 1981 9 Jun 1976 p21r39 n/a n/a n/a 26 Sep 1981 9 Jun 1976 p22r38 n/a n/a n/a 20 Nov 1981 23 Oct 1976 p22r39 n/a n/a n/a 20 Nov 1981 23 Oct 1976

4.5 Statewide origin of the forests data layer

4.5.1 Creation of Plantation Mask Tree plantations were identified using two main datasets, FLUCCS and state-owned lands provided by the FFS. The original FLUCCS layer identified tree plantations, specifically Coniferous Plantations, Hardwood Plantations, and Forest Regeneration Areas. Furthermore, the state-owned lands dataset contained stand origin as being planted or natural. Several of the stands did not have an origin type though, so the origin was photo-interpreted with the help of Google Earth. The state-lands “planted” stands and the FLUCCS plantations were mosaicked to form the Plantation mask. Single-pixel holes in the mask were filled in with “plantation” to smooth the raster layer and reduce speckle. The Plantation mask helped improve the Forestland mask. Anywhere “plantation” overlapped “possibly forestland”; the Forestland mask was changed to “definitely forestland”. The plantation mask was used to identify areas where stands were considered plantations. These stands were reviewed and verified by visual analysis against Bing and Google imagery. Plantations were also compared to the forest cover classes and where these were illogical; the pixels were changed to natural, which generally cleaned up speckle in the dataset.


4.6 Statewide timber product classes data layer The statewide timber classes were produced from the overlay of the FIA data on the Forestland Cover (B) – Age (A) – Origin (P) layer. The overlay process is described in more detail in Section 4.10. Since the primary focus of this project was biomass of each timber product class, the amount of product in each BAP strata class was assessed. Since many strata have multiple product classes, it was decided to map each product class, in tons per acre, per BAP strata pixel across the state. So for strata that had only softwood pulpwood, that area would be shown by a softwood pulpwood tonnage. However, for stands like “mixed forest; age 30 – 35 years” that had both hardwood and softwood products and both pulpwood and sawtimber products, those same stands would be represented in all four maps but with the corresponding product value. The BAP strata tables used for this map are in Appendix B in Table B2.

4.7 Accuracy assessment of land cover, stand age, and stand origin datasets

4.7.1 Methodology The purpose of the accuracy assessment is to provide a measure of confidence in the map, which is helpful for the map user and supports the decision-making process. For this study, Photo Science used a stratified approach that weighted each land cover class by its acreage across the state so that the overall accuracy of the map was assessed without bias. The analysis was conducted over the whole of the State of Florida. Therefore, the statistics are valid over the whole state and not at an FIA Unit or county level. The processes followed for this analysis are based on the methods outlined in Congalton and Green (2009)5.

4.7.1.1 Unit of Analysis: Sample plots for the accuracy assessment were collected from the BAP strata layer covering 16 land cover types, 9 stand age classes, and 2 origin classes. Since product classes were derived through a look up table of the combination of the above datasets, it was not appropriate to test it for accuracy since it was not a source product. The MMU for the project is 10 acres, or 45 pixels, so sample sites with acreages less than the minimum mapping unit were not used in the accuracy assessment. Furthermore, a cap of 50 acres, or 450 pixels, in size was used per class. To find areas in the map suitable for random sampling for the accuracy assessment, a filter was performed on the BAP layer isolating class clumps greater than 45 but less than 450 pixels across the whole state. These clumps were then converted to a polygon shapefile, retaining the BAP class label. The overall number of polygons and acreage for each land cover class were recorded to ensure sufficient observations per class.

4.7.1.2 Stratification and Sampling Design: The advantage of a stratified accuracy assessment is that it samples rare classes and therefore provides class accuracy assessment on all classes within the classification scheme and not just the common classes. Although this is an advantage from a class accuracy perspective, it means that the sampling scheme is biased towards the accuracy of the rare classes, rather than the common classes 5 Congalton, R.G. & Green, K. (2009). Assessing the accuracy of remotely sensed data, principles and practices. 183 pp, CRC Press, Boca Raton, FL.


that make up most of the map. To allow the assessment of the whole map in an unbiased manner, it is necessary to weight the class accuracies by the extent of each class on the map. One way is to use the area that each class represents on the map to weight the class accuracies so that the overall accuracy of the map can be understood. This is the approach taken in the confusion matrices presented in this report.

4.7.1.3 Number of Points: The sampling of 1,000 plots covered each of the datasets: forestland cover, stand age, and stand origin. Fifty (50) samples were randomly selected from each of the 16 forestland cover classes using a stratified random sampling tool “r.sample” in Geospatial Modelling Environment (Version 0.7.2.0) resulting in 800 sample plots. Two hundred (200) additional plots were sampled after calculating the weights of each land cover class based on their overall acreage and translating the weight to number of plots per land cover class. The 1,000 total sample plots were stripped of their BAP strata codes, loaded into a reference geodatabase, and given to the Florida-based photo-interpretation (PI) expert. It should be noted that one class, Forest Seed Orchards, was not sampled. This class is included in the forestland cover map but was derived from existing datasets, not from remote sensing or GIS modeling. The distribution of the accuracy assessment sample sites throughout the state of Florida is shown in Figure 6.

Figure 6. The distribution of 1,000 Photo Science accuracy assessment sites in Florida


4.7.1.4 Method of Collection of Reference Sites: The PI expert conducted a review of the dominant land cover within each sample plot in ArcGIS using high resolution Bing imagery and Google Earth’s historical imagery and Street View capabilities, when available. FLUCCS data and state-owned lands managed by FFS were also referred to. Several descriptive variables were collected for each sample plot including major land cover type, primary land cover sub-type plus possible alternative sub-types, plus the following:

• Canopy cover occupied by species or species group, • Product type for each species group (sapling, pulpwood, sawtimber), although product

type used in the analyses was just pulpwood and sawtimber, sapling data were estimated on each site,

• Whether the area was a plantation, natural or of unknown origin, • An estimate of stand height and an estimate of age based on that height, • Any uncertainties associated with the interpretation to support the fuzzy accuracy

assessment.

4.7.1.5 Fuzzy Accuracy Assessment: The use of fuzzy accuracy assessment protocols allows the user to understand the uncertainty in the reference dataset. Since accuracy sites could not be visited and were photointerpreted, there was some degree of uncertainty associated with the labels of the reference sites. The fuzzy approach allowed that uncertainty to be captured in the final statistics. The accuracy of the Forestland Cover, Stand Age and Stand Origin was assessed with this approach, using photointerpretation points that were quality controlled by our local experts. During the data collection, uncertainty was captured at the same time as the site was interpreted by adding secondary labels to the sites when the photointerpreter thought that either the primary or secondary label could be assigned to the site. When assessing accuracy using both the primary and secondary labels, this is referred to as fuzzy accuracy assessment. When the secondary labels are not taken in to account for accuracy analysis, this is referred to as deterministic accuracy. Stand age was assessed through the review of time series analyses in Google Earth and photo interpretation of the Landsat imagery.

4.7.1.6 The Confusion Matrix: Photo Science has presented the data in a confusion matrix format to provide the map with a good basis for making a decision and understanding the uncertainty associated with it. The confusion matrix was developed for land cover types, age classes and stand origin. The matrices present deterministic and fuzzy accuracies and include area weighted and unweighted overall accuracies. The weighting removes the sampling bias inherent in the stratified sampling approach. Although all matrices are included for completeness, the overall accuracy of the map should be judged on the weighted fuzzy accuracy assessment since this provides the best overall understanding of the accuracy of the map and takes into account uncertainty in the reference dataset, which is inherent in any photo-interpreted dataset, and the bias in the sampling system. Each cover type has two accuracies. The cells at the bottom of the sheet are the producer’s accuracy, and these values quantify the probability that the cover type on the ground is correctly classified in the map. The cells on the right-hand side of the matrix are called the user’s accuracy, and these values quantify the probability that the cover type found on the map is actually what the cover type is on the ground. In other words, looking at the producer’s accuracy, for any site you visit on the ground, you know what the probability is that the site is labeled correctly in the map. And for user’s accuracy, if you look any site in the map, you know what the probability is that the site in the map is correctly labeled as its actual ground cover. So according to the reference data, there could be one class that has a high degree of accuracy in


the map (high producer’s accuracy) but there could be a high degree of other classes being incorrectly classified as that class (low user’s accuracy).

4.7.2 Results Table 3 shows the forestland cover class name and their abbreviation in the confusion matrices. Also shown in the table are the forestland cover type and forestland cover subtype used in the accuracy assessment. Table 3. Land cover type and sub-type abbreviations. The shading indicates a common forestland cover type.

Land Cover Class Forestland Cover

Type Forestland Cover

Sub-type Urban Urban Row Crops Crop Pasture/Grassland Pasture Fruit Production Orchards Orchard Hardwood HW Young Pine Pine YP Sand Pine Pine Sand Loblolly/N. FL Slash Pine Pine LNFS Longleaf Pine LL Longleaf Pine/ S. FL Slash Pine Pine LLSFS Mixed Pine-Hardwood Mixed Water Water Cypress Wet For Cypress Mangrove Wet For Mangrove Other Forested Wetlands Wet For OFW Non-Forested Wetlands NFW Other Other


4.7.2.1 Forestland Cover In the following sections we present two tables for each dataset. In the first table, the deterministic accuracy assessment is made assuming no uncertainty in the reference dataset. The second table shows the results when that uncertainty is made explicit, called the fuzzy accuracy assessment. There are also two numbers that are highlighted in yellow and they show the overall map accuracy. The number closest to the total number of observations shows the unweighted or biased accuracy and the second highlighted number shows the weighted or unbiased accuracy. Table 4. Deterministic accuracy assessment for forestland cover types.

Urban Crop Pasture Orchard HW Pine Mixed Water Wet For NFW Other Area WeightedUrban 42 1 4 0 0 0 1 0 0 1 0 49 86% 16.9% 0.145Crop 0 28 14 2 1 0 0 0 0 0 4 49 57% 4.9% 0.028Pasture 0 2 37 0 0 2 0 0 0 2 5 48 77% 11.9% 0.092Orchard 0 2 4 42 0 0 0 0 0 0 11 59 71% 2.7% 0.019HW 0 0 1 0 46 2 9 0 2 0 0 60 77% 2.4% 0.018Pine 0 1 8 0 7 303 28 0 9 0 4 360 84% 22.3% 0.188Mixed 0 0 3 0 7 9 48 0 1 0 0 68 71% 4.1% 0.029Water 0 0 0 0 0 0 0 48 0 1 1 50 96% 4.2% 0.040Wet For 0 0 1 0 9 14 6 1 127 6 0 164 77% 18.0% 0.140NFW 0 0 4 0 0 2 0 4 2 38 2 52 73% 12.2% 0.089Other 0 0 4 0 0 3 1 0 1 2 34 45 76% 0.4% 0.003

42 34 80 44 70 335 93 53 142 50 61 1004100% 82% 46% 95% 66% 90% 52% 91% 89% 76% 56% 79% 79.08%

Area 16.9% 4.9% 11.9% 2.7% 2.4% 22.3% 4.1% 4.2% 18.0% 12.2% 0.4%Weighted 0.1691 0.04 0.05525 0.02591 0.0157 0.2017 0.0211 0.038 0.16113 0.09275 0.0022 82.27% 80.67%

Map

Reference


Table 5. Fuzzy Accuracy assessment for forestland cover types.

At the forestland cover type level, the overall accuracies reached 84%. The lowest accuracies were in the mixed classes where there was confusion between mixed, pine and hardwood. This often relates to the density of the pine and hardwood in the stand. Since the classification was based off a variety of datasets, it was probable that specific datasets used did not capture the distribution of the pine and hardwood components in the same way as the classification system defined it. This could be one source of the confusion between these classes. The other low accuracy classes were the pasture that was confused with agriculture and young pines, and other that was confused with the agricultural and orchard classes primarily. Since these classes are spectrally similar and often start to differ once the crop or tree canopy starts to develop, this is also not a surprising confusion. There was a balance between the user’s and producer’s accuracy that indicated that classes were overall well balanced between omission and commission errors, and that one class was not over-represented in the classification. Tables 6 and 7 represent the deterministic and fuzzy accuracy assessments (as discussed above), respectively, for the forestland cover types at the sub-type level.

Urban Crop Pasture Orchard HW Pine Mixed Water Wet For NFW Other Area WeightedUrban 46 1 0 0 0 0 1 0 0 1 0 49 94% 16.9% 0.159Crop 0 31 11 2 1 0 0 0 0 0 4 49 63% 4.9% 0.031Pasture 0 1 38 0 0 2 0 0 0 2 5 48 79% 11.9% 0.095Orchard 0 2 4 43 0 0 0 0 0 0 10 59 73% 2.7% 0.020HW 0 0 1 0 49 2 6 0 2 0 0 60 82% 2.4% 0.019Pine 0 0 7 0 7 311 25 0 8 0 2 360 86% 22.3% 0.193Mixed 0 0 2 0 5 7 53 0 1 0 0 68 78% 4.1% 0.032Water 0 0 0 0 0 0 0 48 0 1 1 50 96% 4.2% 0.040Wet For 0 0 1 0 9 13 6 1 131 3 0 164 80% 18.0% 0.144NFW 0 0 3 0 0 2 0 3 1 41 2 52 79% 12.2% 0.096Other 0 0 0 0 0 3 0 0 1 1 40 45 89% 0.4% 0.004

46 35 67 45 71 340 91 52 144 49 64 1004100% 89% 57% 96% 69% 91% 58% 92% 91% 84% 63% 83% 83.16%

Area 16.9% 4.9% 11.9% 2.7% 2.4% 22.3% 4.1% 4.2% 18.0% 12.2% 0.4%Weighted 0.1691 0.043 0.06775 0.02594 0.0164 0.2039 0.0238 0.0387 0.1639 0.10212 0.0025 85.71% 84.44%

ReferenceM

ap


Table 6. Deterministic accuracy assessment for forestland cover layer at forest sub-type level.

Urban Crop Pasture Orchard HW YP Sand LNFS LL LLSFS Mixed Water Cypress MangroveOFW NFW Other Total Area WeightedUrban 42 1 4 0 0 0 0 0 0 0 1 0 0 0 0 1 0 49 86% 16.9% 0.145Crop 0 28 14 2 1 0 0 0 0 0 0 0 0 0 0 0 4 49 57% 4.9% 0.028Pasture 0 2 37 0 0 1 0 0 1 0 0 0 0 0 0 2 5 48 77% 11.9% 0.092Orchard 0 2 4 42 0 0 0 0 0 0 0 0 0 0 0 0 11 59 71% 2.7% 0.019HW 0 0 1 0 46 0 0 2 0 0 9 0 0 0 2 0 0 60 77% 2.4% 0.018YP 0 1 7 0 1 43 0 12 5 2 0 0 0 0 1 0 2 74 58% 2.8% 0.016Sand 0 0 0 0 2 2 28 35 5 0 3 0 0 0 0 0 0 75 37% 1.3% 0.005LNFS 0 0 0 0 0 3 0 71 17 1 2 0 0 0 4 0 2 100 71% 14.4% 0.102LL 0 0 1 0 2 1 0 16 21 0 14 0 0 0 1 0 0 56 38% 2.2% 0.008LLSFS 0 0 0 0 2 1 1 2 2 35 9 0 0 0 3 0 0 55 64% 1.7% 0.011Mixed 0 0 3 0 7 1 0 6 1 1 48 0 0 0 1 0 0 68 71% 4.1% 0.029Water 0 0 0 0 0 0 0 0 0 0 0 48 0 0 0 1 1 50 96% 4.2% 0.040Cypress 0 0 0 0 0 0 0 0 0 0 2 0 52 0 8 0 0 62 84% 2.6% 0.022Mangrove 0 0 0 0 0 0 0 0 0 1 0 0 0 6 0 1 0 8 75% 2.0% 0.015OFW 0 0 1 0 9 3 0 4 1 5 4 1 6 0 55 5 0 94 59% 13.4% 0.079NFW 0 0 4 0 0 2 0 0 0 0 0 4 0 0 2 38 2 52 73% 12.2% 0.089Other 0 0 4 0 0 3 0 0 0 0 1 0 0 0 1 2 34 45 76% 0.4% 0.003Total 42 34 80 44 70 60 29 148 53 45 93 53 58 6 78 50 61 1004

100% 82% 46% 95% 66% 72% 97% 48% 40% 78% 52% 91% 90% 100% 71% 76% 56% 67% 72.09%Area 0.1691 0.0486 0.119459 0.02714 0.0238 0.0283 0.0126 0.1436 0.0216 0.017 0.0408 0.0419 0.02639 0.019586 0.1342 0.122 0.0039Weighted 16.9% 4.0% 5.5% 2.6% 1.6% 2.0% 1.2% 6.9% 0.9% 1.3% 2.1% 3.8% 2.4% 2.0% 9.5% 9.3% 0.2% 72.08% 72.08%

Map

Reference


Table 7. Fuzzy accuracy assessment for forestland cover layer at forest sub-type level.

At the forest sub-type level the Sand Pine and Longleaf-South Florida Slash Pine are well classified, both these sub-types have very distinctive locations where they grow. Longleaf Pine has been classified as other types of Pine and some mapped Longleaf stands were photo-interpreted as Loblolly-NF Slash and Mixed. Ground verification would really need to establish whether these stands are actually Loblolly/Slash as opposed to Longleaf. In hardwoods, 9 out of 71 stands (13%) were mapped as Other Forested Wetlands which would go some way to explaining the high acreage of Other Forested Wetlands when compared to Hardwoods; however, it does not explain it all. Other Forested Wetlands were confused (9%) with Cypress, whereas most of the Cypress was captured as Cypress. Given that the time and budget did not allow field verification of these sites, it is likely that some of the pine types were incorrectly photo-interpreted and it would be valuable to verify some of the calls with field visits at a later date. The overall accuracy of 84% for the forestland cover is at the level expected for this type of approach. Given additional time and as the project moves forward, errors in the map can be corrected and ground verified accuracy assessment sites will improve the confidence of the accuracy assessment. This assessment should be considered when evaluating the area and green tons estimates made for the state.

Urban Crop Pasture Orchard HW YP Sand LNFS LL LLSFS Mixed Water Cypress MangroveOFW NFW Other Total Area WeightedUrban 46 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 49 94% 16.9% 0.159Crop 0 31 11 2 1 0 0 0 0 0 0 0 0 0 0 0 4 49 63% 4.9% 0.031Pasture 0 1 38 0 0 1 0 0 1 0 0 0 0 0 0 2 5 48 79% 11.9% 0.095Orchard 0 2 4 43 0 0 0 0 0 0 0 0 0 0 0 0 10 59 73% 2.7% 0.020HW 0 0 1 0 49 0 0 2 0 0 6 0 0 0 2 0 0 60 82% 2.4% 0.019YP 0 0 7 0 1 48 0 10 5 1 0 0 0 0 1 0 1 74 65% 2.8% 0.018Sand 0 0 0 0 2 2 57 6 5 0 3 0 0 0 0 0 0 75 76% 1.3% 0.010LNFS 0 0 0 0 0 0 0 86 7 1 2 0 0 0 3 0 1 100 86% 14.4% 0.123LL 0 0 0 0 2 1 0 16 25 0 11 0 0 0 1 0 0 56 45% 2.2% 0.010LLSFS 0 0 0 0 2 0 1 2 1 37 9 0 0 0 3 0 0 55 67% 1.7% 0.011Mixed 0 0 2 0 5 1 0 4 1 1 53 0 0 0 1 0 0 68 78% 4.1% 0.032Water 0 0 0 0 0 0 0 0 0 0 0 48 0 0 0 1 1 50 96% 4.2% 0.040Cypress 0 0 0 0 0 0 0 0 0 0 2 0 53 0 7 0 0 62 85% 2.6% 0.023Mangrove 0 0 0 0 0 0 0 0 0 1 0 0 0 6 0 1 0 8 75% 2.0% 0.015OFW 0 0 1 0 9 3 0 3 1 5 4 1 5 0 60 2 0 94 64% 13.4% 0.086NFW 0 0 3 0 0 2 0 0 0 0 0 3 0 0 1 41 2 52 79% 12.2% 0.096Other 0 0 0 0 0 3 0 0 0 0 0 0 0 0 1 1 40 45 89% 0.4% 0.004Total 46 35 67 45 71 61 58 129 46 46 91 52 58 6 80 49 64 1004

100% 89% 57% 96% 69% 79% 98% 67% 54% 80% 58% 92% 91% 100% 75% 84% 63% 76% 79.04%Area 0.1691 0.0486 0.119459 0.02714 0.0238 0.0283 0.0126 0.1436 0.0216 0.017 0.0408 0.0419 0.02639 0.019586 0.1342 0.122 0.0039Weighted 16.9% 4.3% 6.8% 2.6% 1.6% 2.2% 1.2% 9.6% 1.2% 1.4% 2.4% 3.9% 2.4% 2.0% 10.1% 10.2% 0.2% 78.93% 78.99%

Reference

Map


4.7.2.2 Age There were nine age classes mapped for this project in 5-year increments and these nine classes are shown in the matrices below. However, based on the format requested for the final deliveries, the nine age classes were further grouped into six the age classes, which are represented on the maps in the executive report and the map book delivered to FFS. The assessment of age for this assessment it was based primarily on photo-interpretation. This interpretation was supported by the fact that imagery dating back to 1994 was available on Google Earth for reference. Before 1994, whether an area was forested could be observed from the Landsat imagery. Any age estimate was considered to be ± one age class from an uncertainty perspective. So the difference between the deterministic and fuzzy accuracy assessment was based on including sites that were ± one age class as correct. Table 8. Deterministic accuracy assessment for stand age.

Row Labels 0-5 years 5-10 years 10-15 years 15-20 years 20-25 years 25-30 years 30-35 years 35-40 years 40+ years Unknown-N/A Total Area weight Weighting0-5 years 38 7 9 3 4 2 2 0 2 0 67 57% 7.1% 0.0405-10 years 6 6 5 1 1 2 0 0 1 0 22 27% 4.2% 0.01110-15 years 0 5 11 1 1 0 1 0 3 0 22 50% 4.7% 0.02315-20 years 0 2 7 13 3 2 1 1 8 0 37 35% 6.4% 0.02220-25 years 1 0 2 7 7 4 0 0 6 0 27 26% 4.2% 0.01125-30 years 0 0 0 4 3 4 3 0 2 0 16 25% 2.9% 0.00730-35 years 0 1 0 0 2 12 3 0 3 0 21 14% 2.4% 0.00335-40 years 0 0 1 0 2 2 0 0 10 0 15 0% 2.1% 0.00040+ years 0 5 1 5 4 11 24 8 318 0 376 85% 66.1% 0.559Unknown-N/A 6 0 1 0 0 0 0 0 3 0 10 N/ATotal 51 26 37 34 27 39 34 9 356 0 613 67.82%

75% 23% 30% 38% 26% 10% 9% 0% 89% N/A 0 65%Area Weighting 7.1% 4.2% 4.7% 6.4% 4.2% 2.9% 2.4% 2.1% 66.1%Weighting 0.053 0.010 0.014 0.024 0.011 0.003 0.002 0.000 0.591 70.75% 69.29%

Reference

Map


Table 9. Fuzzy accuracy assessment for stand age.

Row Labels 0-5 years 5-10 years 10-15 years 15-20 years 20-25 years 25-30 years 30-35 years 35-40 years 40+ years Unknown-N/A Total Area weight Weighting0-5 years 45 0 9 3 4 2 2 0 2 0 67 67% 7.1% 0.0485-10 years 0 17 0 1 1 2 0 0 1 0 22 77% 4.2% 0.03210-15 years 0 0 17 0 1 0 1 0 3 0 22 77% 4.7% 0.03615-20 years 0 2 0 23 0 2 1 1 8 0 37 62% 6.4% 0.03920-25 years 1 0 2 0 18 0 0 0 6 0 27 67% 4.2% 0.02825-30 years 0 0 0 4 0 10 0 0 2 0 16 63% 2.9% 0.01830-35 years 0 1 0 0 2 0 15 0 3 0 21 71% 2.4% 0.01735-40 years 0 0 1 0 2 2 0 10 0 0 15 67% 2.1% 0.01440+ years 0 5 1 5 4 12 2 0 347 0 376 92% 66.1% 0.610Unknown-N/A 6 0 1 0 0 0 0 0 3 0 10 N/ATotal 52 25 31 36 32 30 21 11 375 0 613 84.29%

87% 68% 55% 64% 56% 33% 71% 91% 93% N/A 0 82%Area Weighting 7.1% 4.2% 4.7% 6.4% 4.2% 2.9% 2.4% 2.1% 66.1%Weighting 0.062 0.028 0.026 0.041 0.023 0.010 0.017 0.019 0.612 83.74% 84.02%

Reference

Map


The overall accuracy for the age classes was around 84%. The analysis put many stands into the 40+ category that was well represented on the map. Many of the errors in the other classes were based on younger stands being considered 40+ stands. Apart from the 25 – 30 age class, all class producer’s accuracies were above 50% and class user’s accuracies were above 60%. Determining age is generally difficult even when on the ground, so the overall accuracy was at a level that was reasonable for the approach taken and available input data. The age classification in the pine plantations was fairly well distributed through the age classes, except in natural stands where the information differs from that in FIA. In these stands, the classification generally calls these areas as older than they would be typed in the field. This is because of the method relied on ground clearance to set the date of stand origin. If that clearance did not occur or the stand was mixed age, then the method primarily identified the oldest trees in the stand.

4.7.2.3 Origin The origin analysis classification exceeded 93% accuracy. Given that there were only two classes, this was not unexpected. Although origin was relatively easy to tell in young stands, once a natural stand had been thinned and managed, especially a slash pine stand, it became more difficult to identify as natural since it started to look like a plantation from the photo interpretation stand point. The origin identifier was based on planted or not planted rather than whether it was managed. The stands that were identified as plantation, but were mapped as natural, could have had natural origins but now appeared to be plantation like. Table 10. Deterministic accuracy assessment for stand origin.

Table 11. Fuzzy accuracy assessment for stand origin.

Row Labels Natural Plantation Total Area WeightedNatural 341 63 404 84% 66.7% 0.563Plantation 6 194 200 97% 33.3% 0.323Total 347 257 604

98% 75% 89% 88.60%Area 66.7% 33.3% 263Weighted 0.655 0.251 90.68% 89.64%

Reference

Map

Row Labels Natural Plantation Total Area WeightedNatural 368 36 404 91% 66.7% 0.608Plantation 6 194 200 97% 33.3% 0.323Total 374 230 604

98% 84% 93% 93.06%Area 66.7% 33.3% 236Weighted 0.656 0.281 93.72% 93.39%

Reference

Map


4.8 Statewide ownership of forestland data layer

4.8.1 Data Source and Development FFS provided land ownership data as individual county polygon shapefiles obtained from the DOR. The data provided included the NAL (name, address, legal) file compiled for each county joined to individual parcel polygons. Ownership was classified according to a primary category and a set of sub-categories. The primary category indicated whether ownership was private or public, and in the latter case whether it was owned by federal, state, or local government. Ownership sub-categories indicated specific types of private landowners or the agency in the case of public ownership. All primary and sub-categories are detailed in Table 12. Ownership was identified statewide for all parcels that contained forest. Individual fields within the NAL file were screened for keywords indicating primary and sub-categories of ownership. Several dozen keywords were utilized, and included such words or phrases as ‘county’, ‘city’, ‘WMD’, ‘LLC’, or ‘Inc’ among many others. Other keywords were specific to known large landowners within the state, such as timber REITS and TIMOs. The NAL owner name field generally provided sufficient information to classify each parcel to the sub-category in the case of private ownership. The exception was with TIMOs, where the owner name typically was an entity such as a limited liability company (LLC) with some distinctive name not necessarily indicative of its TIMO status. In these cases, owner address information usually revealed that the owner was an individual investment fund managed by a TIMO. State and federal ownership were also generally difficult to classify into sub-categories based on owner names included in the NAL file. The owner was often identified simply as ‘U.S.A’, ‘State of Florida’, or ‘Trustees of the Internal Improvement Trust Fund’ without any indication of department or agency affiliation. In these cases, the FNAI 2012 Florida Conservation Lands data layer was consulted. This is a statewide polygon data layer containing parcels managed at least partially for conservation purposes, and in most cases specifies ownership sub-categories. Once primary and sub-categories of ownership were determined for all forest parcels, the polygon layer was intersected with the statewide forest cover raster layer in order to assign ownership to all forestland within the state.


Table 12. Land ownership categories used for the project.

Code Primary category Sub-category

F1 Federal USDA National Forest

F2 Federal United Stated Department of the Interior (USDI) Fish and Wildlife Service

F3 Federal USDI National Park Service F4 Federal USDI Bureau of Land Management F5 Federal U.S. Department of Defense F6 Federal Bureau of Indian Affairs (BIA) and other Indian lands F7 Federal Other or unknown agency Federal

S1 State Florida Department of Agriculture and Consumer Services (FDACS) Florida Forest Service

S2 State FDEP Division of Recreation and Parks S3 State FDEP Office of Greenways and Trails S4 State FDEP Office of Coastal and Aquatic Managed Areas S5 State FDEP Division of State Lands S6 State FDEP Northwest District S7 State FDEP Bureau of Mine Reclamation S8 State Fish and Wildlife Conservation Commission S9 State Babcock Ranch (Babcock Ranch Management, LLC) S10 State Department of Corrections (PRIDE) S11 State Department of Military Affairs S12 State State Universities and Colleges S13 State Water Management Districts S14 State Other state, including Undesignated State Lands or unknown agency L1 Local/Municipal Municipal L2 Local/Municipal County

P1 Private Nonindustrial private forest (NIPF) non-corporate (individual and family)

P2 Private NIPF corporate Real Estate Investment Trust (REIT) P3 Private NIPF corporate Timber Investment Management Organization (TIMO) P4 Private NIPF corporate Other P5 Private Forest products industry P6 Private Private conservation lands P7 Private Private other U1 Unknown Unnamed owner U2 Unknown Parcel not present in DOR data, added

4.8.2 Accuracy Assessment

4.8.2.1 Accuracy Requirements The 95% accuracy requirement for the forestland ownership layer is based on accuracy of three elements: (1) spatial completeness of the parcel data, (2) currency of the data, and (3) completeness of ownership attributes at the primary level of ownership (federal, state, local, and private categories). Because FFS provided the ownership parcel data to the project, element (2) is assumed satisfied.


4.8.2.2 Potential Sources of Error Errors in the finished ownership data layer may have originated from two sources: errors in the original DOR data set or errors generated in classifying property records to the ownership categories utilized here. Original data source errors. From county to county we observed varying levels of errors in the DOR data, for the most part limited to a very small fraction of any given county’s land area. The most common observed error was a blank NAL record associated with a parcel polygon. In a few cases, there were missing polygons within the boundaries of a county. We did not attempt to correct the former type of error, although when detected we did draw in a parcel but did not determine ownership. Errors originating from ownership classification procedures. The general procedure to determine ownership was (1) reduce the number of parcels being classified by (a) eliminating parcels from urban or other agreed-upon land use classifications that were outside the scope of the task, or alternately (b) once the forest cover layer became available mid-project, intersecting the forest cover layer with DOR parcel shapefiles to identify the parcels needing classification, (2) screen NAL content to identify key words indicating ownership categories, (3) assign primary and sub-categories to each record, and (4) visually inspect the resulting map of classified parcels. Errors could arise at each of these steps. Some parcels containing forest could have mistakenly been eliminated. Our screening process may not have correctly identified some ownership. Misspelled words in the NAL record may have handicapped the screens. Ownership codes may have sometimes suffered from keystroke errors. Last, ownership may simply have been misidentified, among other potential mistakes.

4.8.2.3 Error Detection in the Ownership Data Layer Since the DOR dataset was used as the basis for the ownership classification, our classified parcel dataset was compared to the source DOR data. A random draw of points from within the forest cover data layer was made with one or two points drawn for a 5 kilometer (km) x 5 km statewide grid. This layer of points was used to select its corresponding parcel polygon from the original DOR shapefile as well as classified ownership data parcel shapefile. The DOR shapefile parcels were then classified to the primary and sub-category level. This classification was done by individuals not involved in the development of the statewide ownership data parcel shapefile layer. Finally, the independent classification was compared to the final ownership data parcel shapefile classification to evaluate the level of agreement between the two. Where primary ownership classification of the check data layer disagreed with the statewide ownership data layer, or if a parcel polygon was missing from the statewide data layer, that point was considered an error. Once the comparison was completed, the accuracy percentage was calculated using Equation1. Equation 1: Accuracy % = [1 – (number of disagreeing points) / (total number of points)] x 100

4.8.2.4 Results A total of 3,328 ownership parcels were drawn and independently evaluated for the accuracy analysis. Ownership classification at the primary level was in agreement for 98.9% of all parcels, while the sub-category classification was in agreement for 94.5% of all drawn parcels.


4.9 Statewide primary wood-using plants data layer

4.9.1 Documenting wood using plants in Florida FFS provided a statewide list of mills containing location and mill type information. The operational status of these mills was determined via industry contacts and in some cases outreach to the mills themselves. Some additional mills were identified through additional research.

4.9.2 Estimating timber demand, sustainability and availability The demand analysis matched sustainable levels of timber supply with existing mill demand for raw materials to generate timber sustainability and availability maps for four product categories (softwood and hardwood in pulpwood and sawtimber size classes). These maps identified areas where utilization may approach or exceed sustainable levels as well as areas presenting opportunities for additional utilization.

4.9.2.1 Timber supply: net growth The maximum sustainable level of timber availability was taken to be the annual net growth for each of the four categories of material: softwood and hardwood pulpwood and sawtimber size classes. The starting point for timber supply was the statewide BAP raster layer that was coupled to net growth estimates for each of the strata. Net timber growth information for each pixel was aggregated at the county level.

4.9.2.2 Timber demand Mill demand for each of the six product categories (for this analysis softwood was divided between pine and cypress adding two more classes) was distributed across a woodshed determined for each individual mill. Demand levels contained in each woodshed were then partitioned into the same polygon cells as the timber supply volumes described above.

The development of the geographic distribution of mill demand is summarized by the following steps:

1. Mill locations described in 4.9.1 represented the origin of each individual supply shed. 2. One or two end product types were identified for each mill, the mill’s production capacity

for each product type was estimated, and the attendant raw material demand for each product estimated.

3. Mills were classified as small, medium and large/specialty mills based on their product type and capacity.

4. Mill woodshed boundaries were delineated according to their classification. 5. Mill raw material demand was distributed into 2 or 3 zones within each woodshed.

Mill locations. FFS mills data were used as the starting point for this analysis, and the data set was reviewed to identify any new entrants into the market or mill closures. Mill product types and raw material demand. The physical capacity of a mill to generate finished products such as dimensional lumber, wood pulp, or mulch establishes the basis for each mill’s raw material demand. Mill capacity was estimated using published sources (Lockwood-Post Pulp and Paper Mill Directory, USFS publications, etc.) when possible, and these sources were


complemented by information gleaned from company websites and local contacts within the industry. Conversion factors were used to translate finished product capacity levels to raw material demand. Sources for conversion factors were similar to those utilized for mill capacity. Two satellite chip mills in and around the state satisfy a portion of the raw material needs of pulp mills, and each has its own woodshed. Chip-n-saw mills also generate clean chips for pulp mills, and likewise have their own woodsheds. As a result, the raw material demand of pulp mills was adjusted downward from that indicated by its finished product capacity to account for the contribution of satellite chip mills and chip-n-saw mills. Recall that raw material demand estimated as a function of mill capacity differs from actual raw material utilization, since mills do not typically operate at full capacity year-round. Operating levels vary by industry and reflect prevailing economic and market conditions, and as a result actual raw material utilization (e.g., as reported by bi-annual USFS Timber Product Output reports) may approach capacity-based raw material utilization or may differ significantly at times. Mill classification. All mills were assigned to one of three categories based on individual capacity and product type: small, medium, and large/specialty mills. The size classes generally follow those contained in the FFS mills database. Categorizing mills was done in order to define the dimensions of each mill’s woodshed. Larger mills necessarily draw on a larger geographic area than small mills, and this is reflected in the size of their woodshed. Some mills, for example pole mills, utilize a timber resource that is relatively scarce due to its demanding specifications, and as a result also draw upon a larger geographic area. Furthermore, freight is a smaller component of delivered cost for these specialty mills, and as a result, they have a greater ability to absorb relatively higher freight costs. This larger draw is also reflected in a larger woodshed for specialty mills. Woodshed boundary delineation. All mills preferentially seek nearby material due to its lower freight cost, and all mills confront an outer boundary where elevated freight results in a prohibitively high delivery cost of raw materials. The prevailing local practice is to price freight on a per-mile rate, with a minimum freight distance of 40 miles. That is, all material sourced within 40 miles has the same freight cost assigned to it, while wood sourced from greater distances incurs an additional incremental freight cost for each mile beyond 40. The 40 mile zone thus defines a ‘core’ woodshed for each mill, regardless of mill type or size. Economically feasible freight distances may be as much as 100 miles for some mills, and some mills may need to source wood from such great distances in response to wet conditions that temporarily prohibit logging activity in nearby timber tracts. In this analysis, a woodshed outer limit of 90 miles was used for large/specialty mills, while 75 miles was used as the outer boundary for medium and small mills. Mill raw material demand distribution. The actual sourcing of mill raw material is determined by the distribution of timber resources, competition for those resources, and other factors. While different mills range more or less widely to satisfy their raw material demand, industry reporting indicates that in the Southeast U.S., the average haul distance from timber tract to mill is typically 55 miles (see, e.g., Timber Mart South reporting), and this distance was utilized as a guide to distribute each mill’s demand across its woodshed.


This analysis partitioned each mill’s demand into woodshed zones reflecting varying freight distances from the mill (measured in road miles from the mill location) using the distribution in Tables 13-15. The right hand column indicates the share of the overall mill demand that is estimated to originate from each woodshed zone described in the left hand column. The distributed demand was plotted along with the outer woodshed boundaries in the Mill Woodshed and Demand Level map series. Demand was summed for overlapping woodsheds to indicate the total estimated demand for any given location. Mill demand was then aggregated at the county level to generate the Timber Demand map series for softwood and hardwood species groups each for sawtimber and pulpwood product types. Table 13. Percentage of raw material furnished in three concentric zones extending from large/pole/veneer mill types.

Table 14. Percentage of raw material furnished in three concentric zones extending from medium mill types.

Table 15. Percentage of raw material furnished in two concentric zones extending from small mill types.

4.9.2.3 Sustainability Index and Availability Maps The Timber Sustainability Index map evaluated the ratio of net growth to estimated timber demand at the county level for each of the four product categories: softwood and hardwood, for sawtimber and pulpwood. Sustainability indices with values greater than 1.0 indicated that net growth exceeded estimated demand for that species group and product type in that county. Conversely, sustainability indices less than 1.0 indicated greater demand than net growth of

LARGE/POLE/VENEER MILLRadius (miles) % raw material furnish0-40 25%41-65 40%66-90 35%

57.9 estimated average mileage

MEDIUM MILLRadius (miles) % raw material furnish0-40 35%41-60 40%61-75 25%


SMALL MILLRadius (miles) % raw material furnish0-40 45%41-75 55%



timber and therefore an unsustainable situation. When the sustainability index is equal to 1.0 , it means that timber net growth is equal to the demand in that county. The Timber Availability map series depicts availability or deficit of timber at the county level. Each map in that series started with timber net growth at the county level and subtracted the estimated timber demand for that county for each species group (softwood or hardwood) and product type (pulpwood or sawtimber). This difference defined in green tonnage values which counties had net surpluses of net timber growth over-estimated timber demand, and which counties were in timber deficit for any species group product type combination. In Southern Florida, net timber growth and/or timber demand were found to be low in some cases. This made it necessary to apply cut-offs to the calculation of sustainability indices and timber availability to avoid undue distortions in map depictions of timber supply and demand. Generally, if the timber demand was less than 100 tons per year, the timber sustainability index and timber availability were not estimated. Similarly, where this low timber demand was accompanied by negative timber growth, these situations were indicated on the maps with different symbols.

4.10 Timber resources distribution in green tons

4.10.1 Calculation of standing timber biomass The statewide timber product class data layers were a series of maps generated through the use of the BAP (Forest Cover x Age x Origin) stratification layer and a stratified yield lookup table. The BAP stratification layer was a combination of the statewide forestland cover data layer (described in section 4.5), the statewide timber stand age classes data layer (described in section 4.4) and the statewide origin of the forest data layer (describe in section 4.6). The stratified yield lookup table was generated from data collected by the USFS FIA Program and its synthesis is detailed in this section. Each 30 m pixel of the BAP stratification was given a series of values derived from the stratified yield lookup table. Maps were produced that displayed the amount, in tons per acre, which each pixel represented for each product class across the state. This method was determined to be more desirable than a single value such as quadratic mean diameter (QMD) or arithmetic mean diameter (Mean), and a single resulting map due to the large geographic distribution of sample points and strata. A single value often underestimated the resident size classes of the strata even when using a measure of low sensitivity like QMD. Product classes were established according to the diameter of the inventoried trees and the general species group that the inventoried trees fell into. The general species groups were identified as softwood species and hardwood species using the Forest Inventory and Analysis species list in Table 16 (O’Connell et al. 2012) 6. It is important to note that while each tree’s product class was determined using a general species group, its growth and volume were modeled using the actual species of the tree. Within each general species group, three product classes were established which included sub-merchantable, pulpwood and sawtimber. Product classes by diameter at breast height (DBH) are in Table 17. 6 O’Connell B. M.et al., 2012, FIA Database Description and Users Manual for Phase 2, version 5.1.4, United States Department of Agriculture, Forest Service, Forest Inventory and Analysis Program, USA.


Table 16. FIA Species Groups (including species not growing in Florida). Species Group Species Code Eastern softwood species group Longleaf and slash pines 1 Loblolly and shortleaf pines 2 Other yellow pines 3 Eastern white and red pines (not growing in Florida) 4 Jack pine (not growing in Florida) 5 Spruce and balsam fir (not growing in Florida) 6 Eastern hemlock (not growing in Florida) 7 Cypress 8 Eastern hardwood species group Select white oaks 25 Select red oaks 26 Other white oaks 27 Other red oaks 28 Hickory 29 Yellow birch 30 Hard maple 31 Soft maple 32 Beech 33 Sweetgum 34 Tupelo and blackgum 35 Ash 36 Cottonwood and aspen 37 Basswood (not growing in Florida) 38 Yellow-poplar 39 Black walnut 40 Other eastern soft hardwoods 41 Other eastern hard hardwoods 42 Eastern noncommercial hardwoods 43

Table 17. Minimum DBH values for product classes in general species groups.

Product Class Softwood Hardwood Minimum DBH Maximum DBH Minimum DBH Maximum DBH

Sub-merchantable 1 4.9 1 4.9 Pulpwood 5 8.9 5 10.9 Sawtimber 9 -- 11 --

4.10.2 Equations, processes and programs used for stratified inventory

The following section details the process, programs and equations used to create the stratified inventory lookup table which contributes to the creation of deliverables including the Statewide Timber Product Classes Data Layer and the Timber Resources Distribution in Green Tons. The process was completed using a number of programmatic tools including the Forest Vegetation


Simulator, TCruise Desktop, the USFS Forest Inventory and Analysis Program’s FIDO and EVALidator online tools, and SilvAssist 3.0 for ArcMap 10.1.

4.10.2.1 Forest Vegetation Simulator The Forest Vegetation Simulator (FVS) is a predictive model used extensively in the United States. It is the standard modeling system for a number of federal agencies including the USFS and the USDI Bureau of Land Management as well as state agencies including Washington Department of Natural Resources (Dixon 2002) 7. It is a semi-distance dependent individual tree growth and yield model which models the behavior of individual trees in context of the larger surrounding population dynamic. The population is defined as the stand or strata that contain a plot and standard forest inventory data is utilized in the growth model. For this reason, FVS could model a wide range of forest types and stand structures ranging from even-aged to un-even aged and single species or cohort to multi-species and multi-cohort (Dixon 2002).

4.10.2.2 TCruise Desktop TCruise Desktop is a full featured Microsoft Windows 95/98/NT/2000 timber cruise program that supports stratified and un-stratified plot, point, and double point sampling for standard cruises and stump cruises. The program allows site index, reproduction survey, and growth projection data to be collected and processed in conjunction with the main cruise plot data (Matney 2000)8. For this project, TCruise was primarily used to generate new volumes from grown trees. All of TCriuse’s tree volume computation and merchandising routines were tree profile/taper function based. Because tree profile functions allowed maximum flexibility for implementing virtually any set of merchandising standards and computing product volumes in any units, TCruise can be configured to most local timber market conditions (Matney 2000). For this project, the actual FIA volume equations and product classes defined by the Florida Forest Service were used in the calculations of tree volumes.

4.10.2.3 USFS FIA Data and Online Tools This project was conducted using FIA data collected in the state of Florida. Since FIA has transitioned from a periodic inventory to an annual inventory, we were able to use these plots to calculate standing volumes as well as the rate at which the standing timber was growing. Florida has approximately 7,200 continuous inventory plots that are re-measured at the approximate rate of 1/5th per year. Each inventory plot is composed of a cluster of fixed radius sub-plots, which collectively represent approximately 5,700 acres of forest as well as non-forest conditions. The plots are laid out in a systematic polygon grid across the state (O’Connell 2012). Data collected by FIA includes tree level information such as DBH and height as well as plot level information such as ecoregion used later in our calculations. The Forest Inventory and Analysis Program made a number of tools available online to generate reports from collected FIA data. During this project, two of these tools were used to calculate strata growth rates. The Forest Inventory Database Online (FIDO) and EVALidator tools were used to generate tables on annual growth rates and acreage which were used to calculate volume growth per year by BAP strata levels.

7 Dixon, Gary E., 2002 (revised 2010), Essential FVS: A user’s guide to the Forest Vegetation Simulator: Internal Report, United States Department of Agriculture, Forest Service, Forest Management Services Center, Fort Collins, CO 8Matney, Thomas, 2000, Timber Cruise Program User’s Guide, Heuristic Solutions, Starkville, MS


4.10.2.4 SilvAssist 3.0 SilvAssist 3.0 is a proprietary tool developed by F4 Tech to streamline the process of forest inventory collection, processing and reporting. It allows the user to allocate points, load data, produce reports and track events in a forest inventory database. In this project, the primary function of the SilvAssist 3.0 toolbar was to create yield tables and statistics reports from data which was previously processed by FVS.

4.10.3 Stratified Inventory Process

4.10.3.1 Import and Format FIA data To ensure that sufficient samples were available for analysis, it was determined that all FIA plots from 5 inventory sub-cycles would be utilized. To match the 2011 vintage of the imagery, the most recent inventory sub-cycle utilized was 2011. Each incrementally older inventory sub-cycle was grown to the same 2011 vintage. Due to the difference in collection time of these plots, the data were from 0 to 5 years old in 2011, which was the base year for this analysis. FIA points were linked by a series of unique identifiers which allowed trees, plots and inventory cycles to be linked via SQL in Access database software. The following unique survey identifiers or “SRV_CN” were used to isolate plots from the previous 5 inventory sub-cycles:

• 128092297010854 • 128092296010854 • 128092295010854 • 20344092010478 • 20218603010478

This filtering resulted in an initial sample size of 7,129 fixed radius sub-plot clusters. Due to legal restrictions dictating the public availability of plot locations, it was determined that treating individual sub-plots from within clusters as separate entities would result in the loss of too many samples due to FIA filtering. This filtering was enacted to ensure that no plot location could be reverse engineered from data provided about it. For this reason, each sub-plot cluster was aggregated into a single fixed-radius nested plot which FIA overlaid with our BAP strata map. Overstory data plots represented 1/24th of an acre with a radius of 24 feet and sub-merchantable data plots represented 1/300th of an acre with a radius of 6.8 feet after aggregation. Data was imported to a personal geodatabase using a series of SQL statements in Access software to a format compatible with SilvAssist and TCruise. The relationship between plots and trees were maintained through the unique “CN” values assigned to each item from the FIA database. Plot data contained an approximate lat/long value which was used to create a spatial plot feature containing each of the 7,129 plots. This approximate location was used as a placeholder and storage location for calibration/location data during the modeling phase of the project. As a final step in formatting the data for use in FVS, the SilvAssist FVS Converter was used to produce the necessary input, location and key files for a modeling session in FVS.

4.10.3.2 Grow FIA data to the current year It was necessary to grow the data to a common year to ensure that reports were generated from consistently aged data. The common year selected for growth was 2011 because the imagery utilized was sampled in 2011. Without the modeled growth, data from sample years previous to


2011 would represent trees smaller than current conditions. These smaller trees would represent less volume and bias the per acre tree volume values. To grow data to 2011, a series of models had to be constructed using FVS. Multiple models were necessary due to plots originating in 5 different data collection sub-cycles. To construct these models, three steps were taken. First, the data was calibrated by location, then it was imported into a format compatible with FVS (as described above), and lastly it was constrained to meet the projection goals. Model calibration was accomplished in multiple ways. Broad calibration was done by using regional variants which have been calculated for most forested regions of the United States. For this project, the model was calibrated using the Southern Variant (SN) which was fit to data from the states within the USDA Forest Service Southern Region. This region consists of 13 Southern states including Florida (Keyser 2008). Within the SN variant, more refined calibration was implemented by associating each plot’s approximate location with one of the following range or location codes:

• Apalachicola - 80501 • Lake George - 80502 • Osceola - 80504 • Seminole - 80505 • Wakulla - 80506

These location codes are associated with National Forests in Florida and each plot was assigned to the location code of the National Forest which was closest in proximity. Further, local calibration was done by associating each plot with an ecoregion. The ecoregion variable placed the plot into a relatively small geographic and topographic area and was derived from the FIA plot data. This fine geographic calibration allowed the model to account for very small fluctuations in resource availability and growing conditions due to geographic region or provenance. The model constraints were relatively basic, because there was not a practical method to account for harvest or silvicultural behavior outside of plantation vs. natural origin. The major model constraint for each iteration was the time horizon and cycle length. The time horizon was set to be the difference between measurement year and Landsat imagery collection year (2011) or 5 years whichever was greater. This accounted for the bias associated with model sessions shorter than 5 years when using the Southern Variant (Keyser 2008)9. Cycle length was set to 1 year so that growth iteration would be output for each year from the collection date to the end of the horizon. This allowed on-the-fly checks of the data. The model is relatively complicated and including all the equations and process that make it function would be impractical as well as redundant. A number of publications have described all the different components of the FVS model as well as the Southern Variant. Please reference the “Southern (SN) Variant Overview” and “Essential FVS” if additional information is required.

4.10.3.3 Aggregate growth datasets Output data was contained in multiple database outputs and each separate database contained growth results from multiple output years. A series of SQL statements were used to parse the 9 Keyser, Chad E., 2008 (revised 2011), Southern (SN) Variant Overview - Forest Vegetation Simulator: Internal Report, , United States Department of Agriculture, Forest Service, Forest Management Services Center, Fort Collins, CO


data and output all data associated with the project year of 2011 into a single personal geodatabase. This database was formatted to import data to TCruise where it could be volumized and prepared to load into the final database by SilvAssist. Within TCruise, the aggregated data was processed to properly identify product class for each tree and to assign volume to each grown tree. To maintain consistency across the project TCruise was set up to calculate all tree volumes with species specific FIA equations. The volume equations that are used are for the Southern Region and can be found in the “Southern Forest Inventory and Analysis Volume Equation User’s Guide” (Oswalt 2011).10 Table 18. List of FIA equations used for 71 species or species group within TCruise. The first number before the colon indicates the equation number from FIA documentation.

American beech 531:9:SWide:AmerBeechAll:n=119 (221) American elm 970:9:SWide:ElmAll:n=159 (335) American holly 591:9:SWide:AmerHollyAll:n=40 (232) American hornbeam 500:9:SWide:HardHwsAll:n=5943 (374) American sycamore 731:9:SWide:SycamoreAll:n=126 (264) apple spp 500:9:SWide:HardHwsAll:n=5943 (374) baldcypress 221:9:SWide:BaldcypressAll:n=41 (189) basswood spp 950:9:SWide:BasswoodAll:n=71 (333) bigleaf magnolia 652:9:SWide:SMagnoliaAll:n=20 (248) black locust 901:9:SWide:BlackLocustAll:n=208 (330) black oak 837:9:SWide:BlackOakAll:n=321 (320) black walnut 602:9:SWide:BlackWalnutAll:n=75 (234) blackgum tupelo spp 693:9:SWide:UpBlackgumAll:n=186 (256) blackjack oak 835:9:SWide:PostOakAll:n=215 (315) boxelder 316:9:SWide:RedMapleAll:n=711 (195) Carolina ash 540:9:SWide:AshAll:n=182 (226) cherry plum spp 762:9:SWide:BlackCherryAll:n=68 (269) cherrybark oak 813:9:SWide:CherrybarkOakAll:n=94 (289) chinkapin oak 827:9:SWide:WaterOakAll:n=299 (298) common persimmon 500:9:SWide:HardHwsAll:n=5943 (374) cottonwood spp 300:9:SWide:SoftHwsAll:n=4231 (367) cucumbertree 652:9:SWide:SMagnoliaAll:n=20 (248) eastern hophornbeam 300:9:SWide:SoftHwsAll:n=4231 (367) eastern redbud 300:9:SWide:SoftHwsAll:n=4231 (367) elm spp 970:9:SWide:ElmAll:n=159 (335) Florida maple 316:9:SWide:RedMapleAll:n=711 (195) flowering dogwood 491:9:SWide:FlringDogwoodAll:n=43 (220) green ash 300:9:SWide:SoftHwsAll:n=4231 (367)

10 Oswalt, C.M., Conner R.C., 2011. Southern forest inventory and analysis volume equation user’s guide, Gen. Tech. Rep. SRS–138, Department of Agriculture Forest Service, Southern Research Station, Asheville, NC: U.S.


hackberry 460:9:SWide:HackberryAll:n=46 (217) hickory spp 400:9:SWide:HickoryAll:n=655 (210) honeylocust spp 500:9:SWide:HardHwsAll:n=5943 (374) laurel oak spp 820:9:SWide:LaurelOakAll:n=224 (292) live oak 838:9:SWide:LiveOakAll:n=36 (326) loblolly bay 555:9:SWide:Lob-BayAll:n=39 (230) loblolly pine 131:9:SWide:LobPineNat:n=2100 (180) longleaf pine 121:9:SWide:LonglfPineNat:n=933 (163) other hardwood spp 500:9:SWide:HardHwsAll:n=5943 (374) other tree spp 300:9:SWide:SoftHwsAll:n=4231 (367) overcup oak 822:9:SWide:OvercupOakAll:n=17 (294) pond pine 128:9:SWide:PondPineAll:n=317 (171) pondcypress 222:9:SWide:PondcypressAll:n=129 (191) post oak 835:9:SWide:PostOakAll:n=215 (315) pumpkin ash 540:9:SWide:AshAll:n=182 (226) red maple 316:9:SWide:RedMapleAll:n=711 (195) red mulberry 300:9:SWide:SoftHwsAll:n=4231 (367) redbay 300:9:SWide:SoftHwsAll:n=4231 (367) redcedar spp 060:9:SWide:RedCedarAll:n=133 (143) river birch 370:9:SWide:BirchAll:n=163 (204) sand pine 107:9:SWide:SandPineNat:n=150 (147) sassafras 300:9:SWide:SoftHwsAll:n=4231 (367) shortleaf pine 110:9:SWide:ShortlfPineNat:n=1195 (149) Shumard oak 833:9:SWide:NRedOakAll:n=310 (310) slash pine 111:9:SWide:SlashPinePln:n=1086 (155) slippery elm 970:9:SWide:ElmAll:n=159 (335) sourwood 500:9:SWide:HardHwsAll:n=5943 (374) southern crab apple 500:9:SWide:HardHwsAll:n=5943 (374) southern magnolia 652:9:SWide:SMagnoliaAll:n=20 (248) southern red oak 812:9:SWide:SRedOakAll:n=336 (284) spruce pine 115:9:SWide:SprucePineAll:n=12 (161) swamp chestnut oak 825:9:SWide:SwampChestnutOakAll:n=118 (296) swamp tupelo 694:9:SWide:LowBlackgumAll:n=359 (262) sweetbay 653:9:SWide:SweetbayAll:n=124 (251) sweetgum 300:9:SWide:SoftHwsAll:n=4231 (367) turkey oak 820:9:SWide:LaurelOakAll:n=224 (292) water oak 827:9:SWide:WaterOakAll:n=299 (298) water tupelo 691:9:SWide:WaterTupeloAll:n=166 (254) white ash 540:9:SWide:AshAll:n=182 (226) white oak 802:9:SWide:WhiteOakAll:n=1060 (273) willow spp 300:9:SWide:SoftHwsAll:n=4231 (367)


winged elm 970:9:SWide:ElmAll:n=159 (335) yellow poplar 621:9:SWide:YellowPopAll:n=710 (243)

4.10.3.4 Overlay BAP strata with FIA plot locations To connect the growth and yield data to the physical map, it was necessary to identify the location of the FIA data plots in relation to the BAP strata layer. Since FIA plot locations cannot be published, it was necessary to rely on the staff at the Forest Inventory and Analysis Program in Knoxville, TN for this function. The plots were overlain with the strata values and a table was provided by FIA that included plot identification (ID) numbers with connected BAP strata values. A number of filters were applied that reduced the total number of sample plots. The plot ID was withheld by FIA staff and not returned to the project in areas where identifying the strata would potentially compromise the confidentiality of the plot location. This initial filtering of the data reduced the sample size from 7,129 to 6,269. Finally, an analysis was conducted on the remaining plots belonging to any stand re-measured between 2006 and 2011. On these plots, the quadratic mean diameter was calculated from plot basal area and trees per acre using Equation 2. Since harvest cycles are short in Florida and there is no way of knowing whether a plot measured in 2006 has been harvested by 2011 (the base year for the map), it was necessary to identify these plots and remove them from the analysis so that the inventory would not be biased. To achieve this, any plot that had a young age, indicated by change management analysis, in the maps and had a quadratic mean diameter greater than 5 inches, which indicated an average product class outside of sub-merchantable class, was removed. This filtering reduced the plot count by 108 resulting in a final data plot count of 6,181. Equation 2.

𝐷𝑞 = �� 𝐵𝐴𝑇𝑃𝐴�

0.005454�

0.5

4.10.3.1 Calculating Net Growth Rate Timber net growth rate was calculated to determine the amount of biomass available after mills’ demand and the relative level of sustainability of an area. The net growth was calculated differently from the standing biomass strata tables while growing plots to 2011 condition. Additionally, in this part of the analysis the total tree growth in Northern Florida (FIA Units 1 and 2) and Southern Florida (FIA Units 3 and 4) were calculated separately. This was determined to be necessary due to the different growing conditions and soil profiles present in Northern vs. Southern Florida. Growth was calculated using measured growth information based on all FIA plots and the FIA Program analysis of growth available for download online. The FIA growth data was used because it was determined that modeled growth rates across such large tracts of land would not accurately represent the available biomass. Reports were generated from the FIA online tools that gave total growth rates in cubic feet for FIA forest types across the state of Florida. These reports were filtered to account for broad species groups, pine vs. hardwood, as well as age class in 20-year increments. Additional reports were created that gave the acreage that was present in each of the forest types by age class in 20-year increments. By tabulating each set of information according to general species


and age class, growth rate was calculated per acre for each general species group and each forest type in 0 – 20, 20 – 40 and 40+ age groups. This growth rate was further differentiated into separate rates for naturally regenerated stands and artificially regenerated stands. This was accomplished in a method similar to the previous report aggregation but used total growth by forest type, age class and stand origin. From these tables, an average growth rate was calculated for all stands, for naturally regenerated stands and for artificially regenerated stands. From these results, a ratio was calculated that showed the average rate of growth for planted stands vs. all stands and natural stands vs. all stands. This ratio was used to divide the growth rates by forest type, general species group, age class, and forest origin. Finally, these growth rates were weighted by the acreage they represented in the statewide sample and aggregated into BAP groups according to the forest type definition. These growth rates were multiplied by the acreage of each stratum for each county. This provided the biomass growth per county that was used in the sustainability assessment. Note that for this analysis, it was important to use the most accurate map available. Improvements to the map were made after the map was overlain with the FIA plots, as a result of some additional analysis. This updated map was used for the final assessment.

4.10.3.2 Yield Tables and Statistics The final step was to develop the yield lookup table and statistical analysis by aggregating tree and plot level data on BAP strata. This was accomplished using the reporting and analytics tools from SilvAssist 3.0. The yield lookup table contains per acre values for basal area, trees per acre, sawtimber tons per acre, sawtimber cubic feet per acre, pulpwood tons per acre, pulpwood cubic feet per acre and top-wood tons per acre for each strata present in the BAP strata map. This tabular data was used as the basis for the Standing Timber Distribution Maps. The statistical report was produced for each of the values present in the yield table lookup at a confidence interval of 90% and a percent error of 10%. The fields in the statistical report are defined below:

Figure 7. A frequency diagram of a normal distribution, showing the fraction of a population that falls within 1, 2 and 3 standard deviations (σ) of the mean (µ).

• Degrees of Freedom: Sample size (n) -1; In this case sample size refers to count of inventory plots in the stratum; Degrees of freedom are essentially the number of samples that are free to vary within the constraints of the system.

• Lower Limit: Lower limit is the Mean sample value, in this case TONS, minus the Half Confidence Interval; See Definition of Half Confidence Interval below

• Mean “Tons” Per Acre: Mean Per Acre is the arithmetic mean of the sample metric; In this case the sample metric is “Tons”


• Upper Limit: Upper limit is the Mean sample value, in this case TONS, plus the Half Confidence Interval; See Definition of Half Confidence Interval below

• Standard Deviation (σ): The standard deviation is the amount of distance, or dispersion, of the population from the mean; It assumes a normal distribution and a predictable percentage of the population will fall within each standard deviation from the mean (see the image above)

• Standard Error: Standard error is the deviation of the sample mean within the population. In other words, if other samples were taken from the population how different would they be when compared to the sample mean. It can be called the standard deviation of the sample mean within the population. The smaller the standard error the less variation exists within the population.

• Coefficient of Variation: The coefficient of variation is the ratio of standard deviation to the mean expressed as percent. It shows the variability in relation to the mean, or the sensitivity of the measure to an additional sample point.

• Half Confidence Interval: The half confidence interval is one half of the total confidence interval, which is dependent on the confidence level. Confidence interval is the distance around the mean that randomly selected values will fall within according to the confidence level. For example, if statistics are calculated at a 90% confidence level, the confidence interval will contain 90% of all randomly sampled values from that population.

• Confidence Interval % Error: The percent error is the accuracy of a measurement when it is normalized to the size of the sample.


5 Lessons learned, strategies for future updates and recommended next steps

5.1 Land Cover, Age and Origin Layers The mapping of the land cover, age and origin layers relied heavily on existing datasets and their crosswalk to the land cover and origin layers. Both the comparison of FIA derived forest cover, age and origin acreages to the mapped areas and the accuracy assessment identify areas where the map could be improved. The biggest items that need investigation are:

1) Why did the hardwood class appear to be under-represented and the other wetland forest class over-represented when compared to FIA data, and

2) Is it possible to identify age classes better in non-pine plantation forests? Stratified inventories compensate for errors in the map, because the plots used to build the strata tables are grouped together by the map. So if there are hardwoods classified as pine, there will also be hardwood plots included in the pine strata means. So when the strata means that include softwood and hardwood biomass are multiplied by the area of that stratum from the map (that has some pine where there should be hardwood) the estimate of green tons of hardwood for the area will be correct. This is the approach that was taken to estimate standing biomass. The growth estimates on the other hand were not compensated for in this manner and so incremental improvements to the map will be important for improving the net growth estimates and acreage information in the future. Hardwood identification. The low acreage of hardwoods was identified early in the process and effort was made to identify other areas of hardwoods across the state. Using all the datasets available, hardwood acreage was maximized. However, this still resulted in an underestimation when compared to FIA estimates. In order to understand the significance if this difference, it is necessary to identify areas of hardwoods that are missed in this analysis (and therefore are also not classified into upland hardwoods in the FLUCCS, CCAP, FRACIP and FWC Habitat and Land Cover maps). This information could be used to focus attention for the update of the forestland cover map. Although there is confusion between Other Forested Wetlands and Hardwood classes, this discrepancy did not explain the full difference in area estimates. So, upland hardwoods would need to be identified from other classes to bring the area estimates in line with FIA estimates. Field review of the map was not possible for this study as a result of the short timeline, but would be very valuable for the revision of the map. Field review could evaluate whether there are significant areas of upland hardwood confused in the map, or whether the map represents the actual extent of upland hardwood as defined by the classification scheme. Age identification. Since remote sensing to determine stand age directly is not possible, the only way to remotely sense the age of stands is through change detection. It is possible that age classes in non-plantation pine stand are more complex. In non-plantation situations, some species are removed and other species are left rather than clear-cutting, as is seen on plantations. A more complex harvesting regime would require a more detailed biomass change assessment between imagery dates. This approach was not possible given the timeline and budget of this project. Similarly, the hardwoods are not always clearcut and so the simple change detection methods used may not have picked up selective harvests. In the future, using Landsat 8 imagery and some more sophisticated change analyses, it may be possible to identify more complex harvest regimes.


Improving the accuracy of reference data collection. There were a number of sites, especially in the pine classes, where the species composition of ground reference sites was difficult to classify. The creation of a set of reference sites for future maps that are ground verified would be valuable to ensure the accuracy of the pine type classification. The identification of reference sites for non-pine classes, although valuable, are not as critical since these sites are more easily identified through photo-interpretation. These sites could remain in place for measuring the accuracy of the updates, assuming that new high resolution imagery would be available to make sure that those sites had not changed. Segmentation vs. pixels. The project was conceived and implemented based on a pixel approach to remote sensing. This makes sense for many of the cover types, such as urban, and land cover types that occur in small extents such as cypress domes. However, this approach results in many edge pixels that are in the transition zone between forest and non-forest. These pixels can constitute a significant area on the map and may not be correct because they fall into a transitional class between types. Although filtering was used to reduce the occurrence of these pixels, it may be preferable to use a segmentation approach that groups together adjacent pixels based on spectral similarity and spatial adjacency. It may make sense to test out this approach for specific forested and non-forested classes in the next iteration of the forestland cover map, since it would provide more stand-like information. End user review. This dataset is the first of its kind produced for Florida. It was produced in a short time-frame with very little end-user review. Over the next year, it is essential that the layers are reviewed by end users and the good and poor elements of the forestland cover, age class and origin maps identified. Once these issues are identified, a strategy can be developed to address them in the next update of the project. End user review comments should be recorded in a structured approach so that they can be easily incorporated into the update process.

5.2 Ownership Layers Classification of ownership was made more difficult because all parts of this project were initiated simultaneously due to the relatively short timeframe for completion. This could have been avoided if it had been possible to wait until the forest cover layer was completed prior to intersecting ownership parcels. Since the ownership classification had to be completed before the forest cover layer was complete, many non-forest parcels were classified that were later deleted from the data set. This was very costly in terms of time and effort. Going forward, the update to the data layer will rely on change detection in the parcel ownership that reflects the sale of parcels between 2013 when the current dataset was taken to the new date of the dataset. This will significantly streamline the updating of the ownership layer.

5.3 Mill Location and Demand Layers Once mill locations and their operational status are known, the estimation of their raw material demand is relatively straightforward. That is also true of the determination of their woodshed boundaries. Although these datasets do change from year to year, the changes are relatively small and should be much easier to capture when compared with the effort of compiling the initial datasets.


5.4 Standing Biomass, Growth, Sustainability and Availability Layers

Standing Biomass. Analysis and subsequent reporting of the standing biomass was heavily dependent on the data available for analysis. Due to the large geographic region of the project, the only feasible source of data was the Forest Inventory and Analysis (FIA) Program. These data were available statewide and were systematically allocated. This is more applicable to this type of analysis than other inventory datasets that tends to be in specific forest types and small geographic extents. Though the FIA data was essential to the analysis, it has significant downsides as well. Despite being systematically located, FIA data plots occur over a wide grid, with each plot representing approximately 5,700 acres. As a result, there was significant chance of underrepresentation of rare strata. This under-representation coupled with potential Forestland Cover map errors could lead to uncertainty associated with the standing biomass and growth rates of these rare strata. This was represented by the merging of strata together and large standard errors associated with these strata. To mitigate this negative aspect, it is possible to install supplemental plots in areas that were deemed poorly represented, though this would necessitate a significant investment. Existing plots could also be verified for correct classification but that cannot be done because of the privacy requirements for the plots. This limited our ability to evaluate points based on their location on the map or evaluate the map based on the actual data represented by the FIA plots. Since this was a matter of legality, there was little that can be done to mitigate this restriction. However, supplemental plots, as suggested above, would allow for verification of the data and the map. Having said that, the analysis is dominated by the common and not the rare strata, and therefore errors in the rare strata are not expected to impact the overall results significantly. Overall, using the FIA database for this project analyses is still the best option available to produce the requested results. Moving forward, the lessons learned would be to rely less on the physical location of the FIA plot data and to utilize the attribute information from the FIA plot. The primary reason for disconnecting the plot data from its physical location is that we cannot guarantee the accuracy of the overlay and cannot ground truth the data that were collected. A more disconnected method of data analysis could potentially minimize these errors by grouping plots based on some data driven algorithm which determines forest type. The other option, which represents a significantly higher monetary investment, would be to supplement the existing FIA plot information with additional data collected specifically for this project. Growth Rate. The original evaluation of growth rate was done using the Forest Vegetation Simulator to determine average annual growth of the trees on each plot. These data were then aggregated to the strata level. When the results of this method were ultimately evaluated, it was determined that a number of anomalies existed in the model output regarding predictions of annual growth rates when aggregated to the strata level. This was particularly evident in areas where sample size was small due to small strata extent or where strata potentially aggregated a large number of disparate ecological communities. As a result of the FVS approach evaluation, it was determined that a new method for timber growth calculations would be utilized, and that growth rate would be calculated separately for the northern and southern parts of the state due to large growth conditions differences between FIA Units 1 and 2 (North Florida) versus FIA Units 3 and 4 (South Florida).


As described in section 3.10.3.5, the growth rate was calculated based on FIA reported growth rates in stand types. This growth rate proved to be much more representative of the landscape at a small scale when compared to existing FIA growth reports. The negative aspect of this new process was the disconnection of plot data from the map data. Instead of relying on the strata value from the overlay to group plots, they were instead grouped by a forest type classification based on the attributes of the database that was then aggregated based on the best fit to the BAP strata. We do not expect to update the approach used in the update of the dataset. However, an evaluation of the accuracy of the classification routine should be assessed to make sure that each plot is associated with the correct stratum. The addition of supplemental plots as mentioned in the Standing Biomass section would also benefit this process as this approach would increase the number of samples present in underrepresented areas. Sustainability Index and Timber Availability. These two elements of the project were derived from the other data layers; any lessons learned are largely applicable to the timber demand and net growth layers. Mill demand and woodsheds were completed by pine, hardwood, and cypress types, but net growth was only classified to the level of softwood and hardwood. This somewhat complicates the development of sustainability index and timber availability layers for a critical product type of interest to the project, pine pulpwood, since standing timber volumes and net growth of softwood include both cypress and pine. Although most softwood in the calculations was pine, it may make sense to separate the cypress biomass from the pine biomass in terms of sustainability index and timber availability assessments in the future.


6 Appendix A: Crosswalks Table A1. FRACIP crosswalk to Florida Forest Cover classification. FRACIP Class Florida Forest Cover Crosswalk Non Flammable Hardwood and will include Cypress and Orchards

Non Canopy Other (Urban, Agriculture, Other, Non-Forested Wetlands, Forest < 20%)

Water Water Cabbage Palm Hardwood Eucalyptus Hardwood Long Needle Pine Longleaf and S. Florida Slash Pine Sand Pine Sand Pine Other Upland Pine Loblolly and N. Florida Slash Pine Palm Mix Mixed Titi Mix Mixed Long Needle Pine - Oak Mixed (could also go to Pine) Sand Pine - Oak Mixed (could also go to Pine) Other Pine - Oak Mixed (could also go to Pine) Melaleuca Hardwood Wet Flatwoods North or South Florida Slash Pine Melaleuca-Pine Mixed (could also go to Pine) Other Lowland Forest Forested Wetland Melaleuca Mixed Forested Wetland Mixed Wet Flatwoods Mixed (could also go to Pine) Lowland Mixed Forest Forested Wetland

Table A2. FLUCCS crosswalk to Florida Forest Cover classification.

FLUCCS Code and Description Florida Forest Cover Crosswalk

1100: Residential, low density - less than 2 dwelling units/acre Urban 1110: Fixed Single Family Urban 1120: Mobile Home Units Urban 1130: Mixed Units, Fixed Urban 1180: Residential, rural - one unit on 2 or more acres Urban 1190: Low density under construction Urban 1200: Residential, medium density - 2-5 dwelling units/acre Urban 1210: Medium Density, Fixed Single Family Units Urban 1220: Medium Density, Mobile Home Units Urban 1230: Medium Density, Mixed Units (Fixed and Mobile Home Units) Urban 1290: Medium Density, Under Construction Urban


1300: Residential, high density - 6 or more dwelling units/acre Urban 1310: High Density, Fixed Single Family Units Urban 1320: High Density, Mobile Home Units Urban 1330: High Density, Multiple Dwelling Units, Low Rise (Three Stories or Urban 1340: High Density, Multiple Dwelling Units, High Rise (Four Stories or Urban 1350: High Density, Mixed units (Fixed and Mobile Home Units) Urban 1390: High Density, Under Construction Urban 1400: Commercial and Services Urban 1411: Shopping Centers Urban 1423: Junk Yards Urban 1454: Campgrounds Urban 1460: Oil and Gas Storage Urban 1480: Cemeteries Urban 1490: Commercial and Services Under Construction Urban 1500: Industrial Urban 1510: Food Processing Urban 1520: Timber Processing Urban 1523: Pulp and Paper Mills Urban 1530: Mineral Processing Urban 1532: ODC - Phosphate Urban 1533: ODC - Limerock Urban 1540: Oil and Gas Processing Urban 1550: Other Light Industrial Urban 1551: Boat building and Repair Urban 1552: ODC - Electronics Urban 1560: Other Heavy Industrial Urban 1561: Ship Building and Repair Urban 1562: Pre-stressed concrete plants Urban 1564: Cement Plants Urban 1590: Industrial Under Construction Urban 1600: Extractive Other 1610: Strip mines Other 1611: Strip Mines - Clays Other 1612: Peat Other 1613: Heavy metals Other 1620: Sand and Gravel Pits Other 1630: Rock Quarries Other 1631: Limerock Other 1632: Limerock or dolomite Other


1633: Phosphates Other 1640: Oil and Gas Fields Other 1650: Reclaimed Lands Other 1660: Holding Ponds Water 1700: Institutional ( Education, Religious, Health) Other 1710: Educational Facility Other 1730: Military Other 1750: Governmental - for Kennedy Space Center only Other 1760: Correctional Other 1800: Recreational Other 1810: Swimming Beach Other 1820: Golf Courses Urban 1830: Race Tracks Other 1831: Automobile Tracks Other 1833: Dog Tracks Other 1840: Marina's and Fish Camps Other 1850: Parks and Zoos Other 1860: Community Recreational Facilities Other 1870: Stadiums - facilities not associated with high schools, colleges, or university Other 1890: Other Recreational (Riding Stables, Go Cart Tracks, Skeet Ranges) Other 1900: Open land (urban) Urban 1910: Undeveloped Land Within Urban Areas Urban 1920: Inactive Land with Urban 1940: Grass Surface Urban 2100: Cropland and Pastureland Agriculture 2110: Improved pastures (monocult, planted forage crops) Pasture/Grassland 2120: Unimproved Pastures Pasture/Grassland 2130: Woodland Pastures Pasture/Grassland 2140: Row Crops Row Crops 2143: Potatoes and cabbage Row Crops 2150: Field Crops Row Crops 2153: Hay Fields Row Crops 2156: Sugar Cane Row Crops 2160: Mixed Crops Row Crops 2200: Tree crops Orchards 2210: Citrus Groves Orchards 2230: Other Groves (Pecan, Avocado, Coconut, Mango, etc.) Orchards 2240: Abandoned Groves Orchards 2300: Feeding Operations Other


2310: Cattle Feeding Operations Other 2320: Poultry Feeding Operations Other 2400: Nurseries and Vineyards Orchards 2410: Tree Nurseries Orchards 2420: Sod Farms Pasture/Grassland 2430: Ornamentals Row Crops 2431: Shade Ferns Row Crops 2432: Hammock Ferns Row Crops 2450: Floriculture Row Crops 2500: Specialty Farms Pasture/Grassland 2510: Horse Farms Pasture/Grassland 2520: Dairies Pasture/Grassland 2540: Aquaculture Other 2550: Tropical Fish Farms Other 2600: Other Open Lands (Rural) Pasture/Grassland 2610: Fallow Crop Land Pasture/Grassland 3100: Range Land, Herbaceous (Dry Prairie) Pasture/Grassland 3200: Shrub and brushland (wax myrtle or saw palmetto, occasionally scrub oak) Pasture/Grassland 3210: Palmetto Prairies Pasture/Grassland 3220: Coastal Scrub Hardwood 3300: Mixed Rangeland Mixed 4100: Upland Coniferous Forests Pine 4110: Pine Flatwoods Loblolly/N. FL Slash Pine

4120: Longleaf Pine - Xeric Oak Longleaf Pine/ S. FL Slash Pine

4130: Sand Pine Sand Pine 4140: Pine - Mesic Oak Loblolly/N. FL Slash Pine 4200: Upland Hardwood Forests Hardwood 4210: Xeric Oak Hardwood 4220: Brazilian Pepper Hardwood 4240: Melaleuca Hardwood 4270: Live Oak Hardwood 4271: Oak - Cabbage Palm F Hardwood 4280: Cabbage Palm Hardwood 4340: Hardwood Coniferous - Mixed Mixed 4370: Australian Pine Mixed 4400: Tree Plantations Pine 4410: Coniferous Plantations Pine 4420: Hardwood Plantations Hardwood 4430: Forest Regeneration Areas Pine 5100: Streams and Waterways Water


5110: Natural River, Streams Water 5120: Channelized Waterway Water 5200: Lakes Water 5250: Open water within a freshwater marsh / Marshy Lakes Water 5300: Reservoirs - pits, retention ponds, dams Water 5400: Bays and estuaries Water 5410: Embayments opening directly into the Gulf of Mexico or the Atlanta Water 5420: Embayments not opening directly into the Gulf of Mexico or the Atlanta Water 5430: Saltwater Ponds Water 5500: Major Springs Water 5600: Slough Waters Water 5710: Atlantic Ocean Water 5720: Gulf of Mexico Water 6100: Wetland Hardwood Forest Forested Wetlands 6110: Bay Swamps Forested Wetlands 6111: Bayhead Forested Wetlands 6120: Mangrove Swamp Mangrove 6130: Gum Swamps Forested Wetlands 6140: Titi Swamps Forested Wetlands 6150: Stream and Lake Swamps (bottomland) Forested Wetlands 6170: Mixed Wetland Hardwoods Forested Wetlands 6172: Mixed Shrubs Forested Wetlands 6180: Cabbage Palms Forested Wetlands 6181: Cabbage palm hammock Forested Wetlands 6182: Cabbage palm savannah Forested Wetlands 6191: Wet Melaleuca Forested Wetlands 6200: Wetland Coniferous F Cypress 6210: Cypress Cypress 6215: Cypress- Domes/Heads Cypress 6216: Cypress - Mixed Hard Cypress 6220: Pond pine Pine 6240: Cypress - Pine - Cab Cypress 6250: Hydric Pine Flatwoods Loblolly/N. FL Slash Pine 6260: Pine Savannah Loblolly/N. FL Slash Pine 6300: Wetland Forested Mixed Forested Wetlands 6410: Freshwater Marshes Wetlands 6411: Freshwater Marshes-S Wetlands 6420: Saltwater Marshes Wetlands 6430: Wet Prairies Wetlands


6440: Emergent Aquatic Vegetation Wetlands 6460: Mixed Scrub-shrub Wetland Wetlands 6500: Non-Vegetated Wetlands Wetlands 6510: Tidal Flats Wetlands 6520: Shorelines Wetlands 6530: Intermittent Ponds Wetlands 6540: Oyster Bars Wetlands 6600: Salt Flats Wetlands 7100: Beaches other than Swimming Beaches Other 7200: Sand other than beaches Other 7300: Exposed Rock Other 7400: Disturbed Lands Other 7410: Rural land in Transition Other 7420: Borrow Areas Other 7430: Spoil Areas Other 7450: Burned Areas Other 7470: Dikes and Levees Other 7500: Riverine Sandbars Other 8100: Transportation Urban 8110: Airports Urban 8113: Private Airports Urban 8115: Grass Airports Urban 8120: Railroads Urban 8130: Bus and Truck Terminals Urban 8140: Roads and highways (divided 4-lanes with medians) Urban 8150: Port Facilities Urban 8160: Canals and Locks Urban 8170: Oil, Water or Gas Long Distance Transmission Lines Urban 8180: Auto Parking Facilities Urban 8200: Communications Urban 8300: Utilities Other 8310: Electric Power Facilities Urban 8320: Electrical Power Transmission Lines Other 8330: Water Supply Plants (Including pumping stations) Urban 8340: Sewage Treatment Urban 8350: Solid Waste Disposal Urban 8360: Treatment Ponds Urban 8370: Surface water collection basins Urban 8390: Utilities Under Construction Urban 9999: Missing LUCODE Other


Table A3. CCAP crosswalk to Florida Forest Cover classification.

CCAP Class Florida Forest Cover Crosswalk

High Intensity Developed Urban Medium Intensity Developed Urban Low Intensity Developed Urban Developed Open Space Urban Cultivated Row Crop Pasture/Hay Pasture/Grassland Grassland Pasture/Grassland Deciduous Forest Hardwood Evergreen Forest Pine Mixed Forest Mixed Scrub/Shrub Forest Palustrine Forested Wetland Forested wetlands

Palustrine Scrub/Shrub Wetland

Forested Wetlands (North Florida); Non-Forested Wetlands (South Florida)

Palustrine Emergent Wetland Non-Forested Wetlands Estuarine Forested Wetland Mangroves Estuarine Scrub/Shrub Wetland Mangroves Estuarine Emergent Wetland Non-Forested Wetlands Unconsolidated Shore Other Bare Land Other Water Water Palustrine Aquatic Bed Other Estuarine Aquatic Bed Other

Table A4. FWC Habitat and Landcover crosswalk to Florida Forest Cover classification

FWC Habitat and Landcover Class Florida Forest Cover Crosswalk

Grassland Pasture/Grassland Improved pasture Pasture/Grassland Unimproved pasture Pasture/Grassland Sugarcane Row Crops Citrus Orchards Row Crops Row Crops


7 Appendix B: Strata Tables The numeric codes used in the tables below and in the BAP data layer are made up of three components, the first three digits represent the forest cover layer, the 4th digit represents the age class and the final letter represents whether the stand is planted or natural. The three tables below show the class names and their alpha-numeric codes Table B1. Alpha-numeric codes for reading strata tables. Forestland Cover Class 100: Urban 200: Agriculture 210: Row Crops 220: Pasture/Grassland 500: Water 600: Wetlands 610: Forested Wetlands 611: Cypress 612: Mangrove 613: Other Forested Wetlands 620: Non-Forested Wetlands 400: Forestland 420: Pine 421: Young Pine 422: Sand Pine 423: Loblolly/N. FL Slash Pine 424: Longleaf 425: Longleaf Pine/ S. FL Slash Pine 410: Hardwood 430: Mixed 230: Tree Orchards


231: Forest Seed Production 232: Fruit Production Orchards 700: Other

Age Class 1: 0-5 yrs 2: 5-10 yrs 3: 10-15 yrs 4: 15-20 yrs 5: 20-25 yrs 6: 25-30 yrs 7: 30-35 yrs 8: 35-40 yrs 9: 40+ yrs

Origin Class P: Planted N: Natural


Table B2. Standing Biomass Strata Table.

Total Tons/Acre Stand Totals

Hardwood Softwood Total Trees/Acre

Total Basal Area

Total Tons/Acre

Total CFIB/Acre

BAP Group Pulpwood Sawlog Topwood Total Pulpwood Sawlog Topwood Total

1000N 1 1.21 1.61 0.01 2.83 0.88 2.36 0.02 3.26 58.22 8.72 6.09 169.75

2100N 2 0.15 0.42 0.00 0.57 0.09 0.22 0.00 0.30 7.35 1.36 0.87 24.03

2200N 3 0.77 2.06 0.01 2.85 0.62 2.00 0.02 2.63 56.91 8.69 5.48 152.07

2320N 4 0.11 0.25 0.00 0.36 0.20 0.13 0.00 0.33 5.81 0.93 0.70 19.32

4101N 5 0.00 0.00 0.00 0.00 0.00 3.39 0.02 3.42 4.50 3.76 3.42 98.24

4102N 5 0.00 0.00 0.00 0.00 0.00 3.39 0.02 3.42 4.50 3.76 3.42 98.24

4103N 6 0.00 0.00 0.00 0.00 0.00 6.79 0.05 6.84 9.00 7.52 6.84 196.48

4104N 6 0.00 0.00 0.00 0.00 0.00 6.79 0.05 6.84 9.00 7.52 6.84 196.48

4105N 7 9.98 36.14 0.15 46.26 3.18 0.00 0.00 3.18 413.32 63.28 49.45 1332.98

4106N 7 9.98 36.14 0.15 46.26 3.18 0.00 0.00 3.18 413.32 63.28 49.45 1332.98

4107N 8 31.22 45.84 0.28 77.33 0.88 6.76 0.04 7.68 525.17 91.73 85.01 2296.05

4108N 8 31.22 45.84 0.28 77.33 0.88 6.76 0.04 7.68 525.17 91.73 85.01 2296.05

4109N 9 14.22 33.73 0.15 48.10 2.54 10.66 0.05 13.24 393.36 88.71 61.34 1671.50

4109P 9 14.22 33.73 0.15 48.10 2.54 10.66 0.05 13.24 393.36 88.71 61.34 1671.50

4211N 10 2.73 1.93 0.01 4.67 45.09 19.80 0.21 65.11 383.62 80.45 69.78 1996.80

4211P 10 2.73 1.93 0.01 4.67 45.09 19.80 0.21 65.11 383.62 80.45 69.78 1996.80

4221N 11 3.72 1.30 0.01 5.03 4.15 4.62 0.03 8.80 735.99 34.32 13.83 387.85

4222N 11 3.72 1.30 0.01 5.03 4.15 4.62 0.03 8.80 735.99 34.32 13.83 387.85

4223N 12 0.00 0.00 0.00 0.00 10.59 1.75 0.02 12.35 2021.19 113.96 12.35 354.97

4224N 12 0.00 0.00 0.00 0.00 10.59 1.75 0.02 12.35 2021.19 113.96 12.35 354.97

4225N 13 0.00 0.00 0.00 0.00 36.98 22.49 0.17 59.63 507.97 69.67 59.63 1714.03

4226N 13 0.00 0.00 0.00 0.00 36.98 22.49 0.17 59.63 507.97 69.67 59.63 1714.03

4227N 14 0.00 0.00 0.00 0.00 12.97 0.00 0.00 12.97 953.99 70.07 12.97 372.83

4228N 14 0.00 0.00 0.00 0.00 12.97 0.00 0.00 12.97 953.99 70.07 12.97 372.83

4229N 15 4.11 1.98 0.02 6.11 27.20 33.94 0.22 61.36 587.20 83.80 67.47 1927.51


4221P 16 0.48 0.00 0.00 0.48 0.00 0.00 0.00 0.00 53.00 2.15 0.48 12.76

4222P 16 0.48 0.00 0.00 0.48 0.00 0.00 0.00 0.00 53.00 2.15 0.48 12.76

4223P 17 1.02 0.00 0.00 1.02 31.60 4.98 0.04 36.62 882.96 85.78 37.63 1079.75

4224P 17 1.02 0.00 0.00 1.02 31.60 4.98 0.04 36.62 882.96 85.78 37.63 1079.75

4225P 18 0.35 0.00 0.00 0.35 72.36 14.01 0.14 86.51 804.14 111.81 86.86 2495.90

4226P 18 0.35 0.00 0.00 0.35 72.36 14.01 0.14 86.51 804.14 111.81 86.86 2495.90

4227P 19 0.25 0.00 0.00 0.25 37.42 29.44 0.37 67.24 524.76 82.22 67.49 1939.42

4228P 19 0.25 0.00 0.00 0.25 37.42 29.44 0.37 67.24 524.76 82.22 67.49 1939.42

4229P 20 6.89 0.00 0.00 6.89 37.52 27.45 0.24 65.21 697.55 93.50 72.10 2059.22

4231N 21 3.62 6.94 0.00 10.56 5.50 5.30 0.05 10.86 454.65 65.90 21.42 595.45

4232N 21 3.62 6.94 0.00 10.56 5.50 5.30 0.05 10.86 454.65 65.90 21.42 595.45

4233N 22 4.58 12.28 0.04 16.89 4.10 15.32 0.10 19.52 465.99 53.58 36.42 1014.47

4234N 22 4.58 12.28 0.04 16.89 4.10 15.32 0.10 19.52 465.99 53.58 36.42 1014.47

4235N 23 8.38 0.00 0.00 8.38 4.44 15.20 0.10 19.74 139.49 36.51 28.12 792.41

4236N 23 8.38 0.00 0.00 8.38 4.44 15.20 0.10 19.74 139.49 36.51 28.12 792.41

4237N 24 2.67 8.29 0.04 11.00 8.22 19.17 0.18 27.57 240.65 45.33 38.56 1087.48

4238N 24 2.67 8.29 0.04 11.00 8.22 19.17 0.18 27.57 240.65 45.33 38.56 1087.48

4239N 25 10.34 9.74 0.07 20.15 8.58 45.00 0.27 53.84 464.27 81.15 73.99 2088.35

4231P 26 3.05 5.89 0.03 8.97 31.12 25.24 0.27 56.63 332.97 79.27 65.60 1868.43

4232P 27 1.76 1.15 0.01 2.91 4.08 9.16 0.09 13.33 356.33 30.96 16.25 461.39

4233P 28 1.64 0.80 0.01 2.44 17.76 4.84 0.05 22.64 587.22 61.19 25.08 716.32

4234P 29 2.07 1.88 0.01 3.96 33.96 8.53 0.11 42.60 684.49 81.16 46.56 1330.84

4235P 30 2.41 2.02 0.01 4.44 34.23 12.47 0.15 46.85 615.23 77.91 51.29 1465.77

4236P 31 4.17 4.68 0.02 8.86 29.38 29.14 0.36 58.88 479.34 83.39 67.74 1930.17

4237P 32 3.10 0.64 0.01 3.74 26.13 37.60 0.43 64.15 649.47 82.04 67.90 1944.37

4238P 33 4.30 4.37 0.02 8.69 26.96 52.75 0.52 80.23 421.96 95.76 88.92 2539.18

4239P 34 6.17 6.97 0.03 13.17 20.94 29.29 0.27 50.50 512.71 78.96 63.67 1804.94

4241N 35 1.60 1.79 0.01 3.39 2.15 19.08 0.10 21.33 185.99 27.37 24.72 704.17

4242N 35 1.60 1.79 0.01 3.39 2.15 19.08 0.10 21.33 185.99 27.37 24.72 704.17

4243N 36 0.45 0.00 0.00 0.45 19.04 48.98 0.36 68.39 186.64 63.78 68.84 1977.76


4244N 36 0.45 0.00 0.00 0.45 19.04 48.98 0.36 68.39 186.64 63.78 68.84 1977.76

4245N 37 1.70 0.00 0.00 1.70 7.20 10.40 0.10 17.70 339.49 38.55 19.40 554.47

4246N 37 1.70 0.00 0.00 1.70 7.20 10.40 0.10 17.70 339.49 38.55 19.40 554.47

4247N 38 5.86 13.32 0.07 19.25 10.60 18.99 0.18 29.77 344.34 65.85 49.03 1372.41

4248N 38 5.86 13.32 0.07 19.25 10.60 18.99 0.18 29.77 344.34 65.85 49.03 1372.41

4249N 39 8.06 7.39 0.04 15.49 6.48 36.73 0.21 43.42 336.19 66.44 58.91 1663.62

4241P 40 2.15 0.29 0.01 2.44 21.36 3.98 0.02 25.37 611.97 61.15 27.81 794.56

4242P 40 2.15 0.29 0.01 2.44 21.36 3.98 0.02 25.37 611.97 61.15 27.81 794.56

4243P 41 4.29 0.57 0.01 4.88 42.72 7.96 0.05 50.73 1223.95 122.30 55.61 1589.12

4244P 41 4.29 0.57 0.01 4.88 42.72 7.96 0.05 50.73 1223.95 122.30 55.61 1589.12

4245P 42 8.25 0.00 0.00 8.25 32.87 4.69 0.08 37.65 448.63 68.93 45.90 1303.50

4246P 42 8.25 0.00 0.00 8.25 32.87 4.69 0.08 37.65 448.63 68.93 45.90 1303.50

4247P 43 7.27 0.57 0.00 7.84 17.82 25.06 0.17 43.05 575.81 66.26 50.89 1447.68

4248P 43 7.27 0.57 0.00 7.84 17.82 25.06 0.17 43.05 575.81 66.26 50.89 1447.68

4249P 44 6.28 1.13 0.01 7.42 2.77 45.42 0.26 48.45 702.98 63.60 55.88 1591.86

4251N 45 2.49 9.68 0.06 12.22 22.49 45.27 0.38 68.15 379.96 98.88 80.37 2286.71

4252N 45 2.49 9.68 0.06 12.22 22.49 45.27 0.38 68.15 379.96 98.88 80.37 2286.71

4253N 46 1.18 3.03 0.01 4.22 6.23 12.20 0.11 18.53 183.49 38.37 22.75 645.92

4254N 46 1.18 3.03 0.01 4.22 6.23 12.20 0.11 18.53 183.49 38.37 22.75 645.92

4255N 47 0.00 0.00 0.00 0.00 0.72 10.65 0.08 11.45 171.00 25.55 11.45 329.12

4256N 47 0.00 0.00 0.00 0.00 0.72 10.65 0.08 11.45 171.00 25.55 11.45 329.12

4257N 48 0.00 0.00 0.00 0.00 2.24 18.02 0.15 20.41 73.33 29.36 20.41 586.52

4258N 48 0.00 0.00 0.00 0.00 2.24 18.02 0.15 20.41 73.33 29.36 20.41 586.52

4259N 49 4.65 6.28 0.03 10.95 7.58 23.71 0.17 31.46 314.22 65.06 42.41 1198.13

4251P 50 0.20 1.91 0.00 2.11 4.73 27.15 0.20 32.08 425.99 55.78 34.19 978.67

4252P 50 0.20 1.91 0.00 2.11 4.73 27.15 0.20 32.08 425.99 55.78 34.19 978.67

4253P 51 2.18 0.00 0.00 2.18 14.76 1.57 0.03 16.36 347.41 43.47 18.54 528.77

4254P 51 2.18 0.00 0.00 2.18 14.76 1.57 0.03 16.36 347.41 43.47 18.54 528.77

4255P 52 0.00 0.00 0.00 0.00 11.43 0.00 0.00 11.43 683.98 55.15 11.43 328.50

4256P 52 0.00 0.00 0.00 0.00 11.43 0.00 0.00 11.43 683.98 55.15 11.43 328.50


4257P 52 3.18 0.00 0.00 3.18 10.44 6.10 0.07 16.61 505.98 50.44 19.79 562.78

4258P 52 3.18 0.00 0.00 3.18 10.44 6.10 0.07 16.61 505.98 50.44 19.79 562.78

4259P 54 6.37 0.00 0.00 6.37 9.45 12.20 0.14 21.79 327.98 45.72 28.15 797.06

4301N 55 3.48 8.05 0.04 11.57 3.67 13.24 0.11 17.01 245.99 38.64 28.58 799.33

4302N 55 3.48 8.05 0.04 11.57 3.67 13.24 0.11 17.01 245.99 38.64 28.58 799.33

4303N 56 7.25 11.80 0.07 19.12 2.63 6.24 0.05 8.91 236.70 32.71 28.03 769.23

4304N 56 7.25 11.80 0.07 19.12 2.63 6.24 0.05 8.91 236.70 32.71 28.03 769.23

4305N 57 10.63 24.22 0.10 34.95 4.60 13.78 0.13 18.51 603.07 83.31 53.46 1470.00

4306N 57 10.63 24.22 0.10 34.95 4.60 13.78 0.13 18.51 603.07 83.31 53.46 1470.00

4306P 57 10.63 24.22 0.10 34.95 4.60 13.78 0.13 18.51 603.07 83.31 53.46 1470.00

4307N 58 14.57 15.91 0.08 30.56 2.25 18.55 0.13 20.93 440.54 64.87 51.49 1421.59

4308N 58 14.57 15.91 0.08 30.56 2.25 18.55 0.13 20.93 440.54 64.87 51.49 1421.59

4308P 58 14.57 15.91 0.08 30.56 2.25 18.55 0.13 20.93 440.54 64.87 51.49 1421.59

4309N 59 13.85 27.04 0.14 41.03 3.00 18.29 0.10 21.39 424.92 74.66 62.41 1715.71

4309P 59 13.85 27.04 0.14 41.03 3.00 18.29 0.10 21.39 424.92 74.66 62.41 1715.71

5000N 60 0.14 0.54 0.00 0.68 0.00 0.11 0.00 0.12 2.74 0.92 0.80 21.61

6111N 61 8.70 0.00 0.00 8.70 15.51 42.69 0.50 58.71 402.97 91.36 67.41 1920.93

6112N 61 8.70 0.00 0.00 8.70 15.51 42.69 0.50 58.71 402.97 91.36 67.41 1920.93

6113N 62 21.43 3.50 0.02 24.95 15.91 47.00 0.18 63.08 1418.96 143.70 88.03 2482.74

6114N 62 21.43 3.50 0.02 24.95 15.91 47.00 0.18 63.08 1418.96 143.70 88.03 2482.74

6115N 63 5.15 1.92 0.04 7.10 74.38 68.36 1.10 143.84 731.91 171.58 150.94 4324.94

6116N 63 5.15 1.92 0.04 7.10 74.38 68.36 1.10 143.84 731.91 171.58 150.94 4324.94

6117N 64 11.29 3.86 0.05 15.20 19.78 67.80 0.55 88.14 300.96 105.00 103.34 2941.21

6118N 64 11.29 3.86 0.05 15.20 19.78 67.80 0.55 88.14 300.96 105.00 103.34 2941.21

6119N 65 16.23 25.65 0.13 42.01 18.12 50.81 0.37 69.30 835.12 140.53 111.31 3119.19

6127N 66 3.11 2.89 0.01 6.01 0.62 1.41 0.02 2.05 712.04 30.93 8.07 220.44

6128N 66 3.11 2.89 0.01 6.01 0.62 1.41 0.02 2.05 712.04 30.93 8.07 220.44

6129N 66 3.11 2.89 0.01 6.01 0.62 1.41 0.02 2.05 712.04 30.93 8.07 220.44

6131N 67 23.84 53.20 0.24 77.28 8.06 44.70 0.28 53.05 458.71 139.43 130.32 3598.43

6132N 68 5.32 9.27 0.05 14.64 2.94 5.16 0.04 8.15 419.13 38.32 22.79 627.02


6133N 69 3.42 5.25 0.02 8.68 6.47 2.16 0.01 8.65 713.65 44.53 17.34 481.71

6134N 70 4.64 3.53 0.00 8.17 14.45 19.24 0.19 33.88 968.20 74.97 42.06 1193.26

6135N 71 5.35 1.89 0.01 7.25 5.14 11.30 0.09 16.53 303.10 35.03 23.78 669.66

6136N 72 9.38 22.94 0.11 32.43 4.64 12.06 0.12 16.81 487.98 63.48 49.24 1353.52

6137N 73 16.63 31.69 0.14 48.45 4.06 26.51 0.16 30.73 419.64 96.10 79.18 2183.50

6138N 74 7.42 4.73 0.04 12.20 1.88 4.18 0.04 6.10 238.39 24.71 18.30 502.83

6139N 75 19.46 37.96 0.19 57.61 3.79 17.73 0.10 21.62 542.99 99.24 79.23 2167.44

6200N 76 0.77 0.89 0.00 1.66 1.04 2.16 0.02 3.22 79.40 8.22 4.88 137.10

7000N 77 2.58 1.25 0.01 3.84 4.73 1.19 0.01 5.94 142.83 16.71 9.78 273.83


Table B3. Statistics on Standing Biomass Strata.

Strata Total Tons

BAP Acres Plot Count

Degrees of Freedom

Lower Limit

Mean TONS PerAcre Upper Limit Standard

Deviation Standard

Error Coefficient of

Variation

Half Confidence

Interval

Confidence Interval % Error

1000N 6,137,400.00 965.00 964.00 5.10 6.10 7.20 20.00 0.60 326.60 1.10 17.30

2100N 1,754,614.00 262.00 261.00 0.40 0.80 1.30 4.60 0.30 545.70 0.50 55.70

2200N 4,308,428.00 677.00 676.00 4.40 5.50 6.60 16.80 0.60 305.20 1.10 19.30

2320N 984,425.00 169.00 168.00 - 0.70 1.30 5.00 0.40 756.20 0.60 96.20

4101N 30,812.00 2.00 1.00 (3.40) 7.30 18.00 2.50 1.80 34.40 10.70 146.20

4102N 30,812.00 2.00 1.00 (3.40) 7.30 18.00 2.50 1.80 34.40 10.70 146.20

4103N 30,812.00 2.00 1.00 (3.40) 7.30 18.00 2.50 1.80 34.40 10.70 146.20

4104N 30,812.00 2.00 1.00 (3.40) 7.30 18.00 2.50 1.80 34.40 10.70 146.20

4105N 19,877.00 3.00 2.00 (17.30) 47.80 112.80 38.70 22.30 81.00 65.10 136.30

4106N 19,877.00 3.00 2.00 (17.30) 47.80 112.80 38.70 22.30 81.00 65.10 136.30

4107N 23,060.00 5.00 4.00 15.70 81.20 146.80 68.70 30.70 84.60 65.50 80.70

4108N 23,060.00 5.00 4.00 15.70 81.20 146.80 68.70 30.70 84.60 65.50 80.70

4109N 452,642.00 55.00 54.00 47.70 60.00 72.20 54.40 7.30 90.70 12.30 20.50

4109P 452,642.00 55.00 54.00 47.70 60.00 72.20 54.40 7.30 90.70 12.30 20.50

4211N 938,310.00 30.00 29.00 56.50 72.30 88.10 50.80 9.30 70.30 15.80 21.80

4211P 938,310.00 30.00 29.00 56.50 72.30 88.10 50.80 9.30 70.30 15.80 21.80

4221N 4,824.00 3.00 2.00 (6.50) 13.80 34.00 12.00 6.90 87.40 20.20 147.10

4222N 4,824.00 3.00 2.00 (6.50) 13.80 34.00 12.00 6.90 87.40 20.20 147.10

4223N 29,202.00 5.00 4.00 2.90 11.80 20.80 9.40 4.20 79.10 8.90 75.50

4224N 29,202.00 5.00 4.00 2.90 11.80 20.80 9.40 4.20 79.10 8.90 75.50

4225N 44,718.00 6.00 5.00 17.40 56.30 95.20 47.30 19.30 83.90 38.90 69.00

4226N 44,718.00 6.00 5.00 17.40 56.30 95.20 47.30 19.30 83.90 38.90 69.00

4227N 44,718.00 6.00 5.00 17.40 56.30 95.20 47.30 19.30 83.90 38.90 69.00

4228N 44,718.00 6.00 5.00 17.40 56.30 95.20 47.30 19.30 83.90 38.90 69.00


4229N 161,479.00 31.00 30.00 55.70 65.40 75.00 31.60 5.70 48.30 9.60 14.70

4221P 14,112.00 2.00 1.00 (2.60) 0.50 3.70 0.70 0.50 141.40 3.20 601.00

4222P 14,112.00 2.00 1.00 (2.60) 0.50 3.70 0.70 0.50 141.40 3.20 601.00

4223P 28,600.00 4.00 3.00 (1.90) 38.20 78.30 34.10 17.10 89.30 40.10 105.10

4224P 28,600.00 4.00 3.00 (1.90) 38.20 78.30 34.10 17.10 89.30 40.10 105.10

4225P 77,916.00 20.00 19.00 72.40 84.90 97.30 32.20 7.20 38.00 12.50 14.70

4226P 77,916.00 20.00 19.00 72.40 84.90 97.30 32.20 7.20 38.00 12.50 14.70

4227P 18,392.00 5.00 4.00 17.70 74.40 131.10 59.50 26.60 80.00 56.70 76.30

4228P 18,392.00 5.00 4.00 17.70 74.40 131.10 59.50 26.60 80.00 56.70 76.30

4229P 44,630.00 5.00 4.00 30.20 71.80 113.40 43.60 19.50 60.80 41.60 57.90

4231N 22,496.00 3.00 2.00 3.70 20.80 38.00 10.20 5.90 49.00 17.20 82.40

4232N 22,496.00 3.00 2.00 3.70 20.80 38.00 10.20 5.90 49.00 17.20 82.40

4233N 67,662.00 9.00 8.00 18.40 36.40 54.40 29.00 9.70 79.70 18.00 49.40

4234N 67,662.00 9.00 8.00 18.40 36.40 54.40 29.00 9.70 79.70 18.00 49.40

4235N 47,915.00 4.00 3.00 2.90 29.30 55.80 22.50 11.20 76.60 26.40 90.20

4236N 47,915.00 4.00 3.00 2.90 29.30 55.80 22.50 11.20 76.60 26.40 90.20

4237N 43,395.00 9.00 8.00 15.60 40.20 64.70 39.60 13.20 98.60 24.50 61.10

4238N 43,395.00 9.00 8.00 15.60 40.20 64.70 39.60 13.20 98.60 24.50 61.10

4239N 737,016.00 123.00 122.00 68.30 76.50 84.60 54.40 4.90 71.10 8.10 10.60

4231P - 34.00 33.00 58.70 71.10 83.60 42.80 7.30 60.10 12.40 17.40

4232P 521,274.00 82.00 81.00 12.10 17.30 22.40 27.90 3.10 161.80 5.10 29.70

4233P 550,035.00 85.00 84.00 23.20 28.00 32.80 26.70 2.90 95.40 4.80 17.20

4234P 682,410.00 115.00 114.00 46.20 51.40 56.60 33.80 3.10 65.60 5.20 10.20

4235P 436,394.00 73.00 72.00 48.80 56.10 63.30 37.00 4.30 65.90 7.20 12.90

4236P 281,376.00 48.00 47.00 59.80 72.80 85.80 53.60 7.70 73.70 13.00 17.80

4237P 185,136.00 24.00 23.00 54.50 73.50 92.40 54.10 11.10 73.70 18.90 25.80

4238P 140,560.00 20.00 19.00 73.60 94.10 114.60 53.00 11.90 56.40 20.50 21.80

4239P 1,470,628.00 218.00 217.00 61.70 67.50 73.20 51.20 3.50 76.00 5.70 8.50

4241N 18,735.00 6.00 5.00 6.30 25.60 45.00 23.50 9.60 91.70 19.30 75.40

4242N 18,735.00 6.00 5.00 6.30 25.60 45.00 23.50 9.60 91.70 19.30 75.40


4243N 29,475.00 3.00 2.00 (28.30) 74.60 177.50 61.20 35.30 82.10 102.90 138.00

4244N 29,475.00 3.00 2.00 (28.30) 74.60 177.50 61.20 35.30 82.10 102.90 138.00

4245N 29,800.00 4.00 3.00 2.90 20.90 39.00 15.40 7.70 73.30 18.10 86.20

4246N 29,800.00 4.00 3.00 2.90 20.90 39.00 15.40 7.70 73.30 18.10 86.20

4247N 70,945.00 11.00 10.00 32.20 50.50 68.80 33.50 10.10 66.40 18.30 36.30

4248N 70,945.00 11.00 10.00 32.20 50.50 68.80 33.50 10.10 66.40 18.30 36.30

4249N 516,150.00 93.00 92.00 51.90 60.60 69.20 50.20 5.20 82.90 8.70 14.30

4241P 24,300.00 6.00 5.00 39.00 61.50 84.00 27.30 11.20 44.50 22.50 36.60

4242P 24,300.00 6.00 5.00 39.00 61.50 84.00 27.30 11.20 44.50 22.50 36.60

4243P 24,300.00 6.00 5.00 39.00 61.50 84.00 27.30 11.20 44.50 22.50 36.60

4244P 24,300.00 6.00 5.00 39.00 61.50 84.00 27.30 11.20 44.50 22.50 36.60

4245P 22,212.00 3.00 2.00 (34.30) 50.50 135.30 50.40 29.10 99.90 84.80 168.00

4246P 22,212.00 3.00 2.00 (34.30) 50.50 135.30 50.40 29.10 99.90 84.80 168.00

4247P 22,212.00 3.00 2.00 (34.30) 50.50 135.30 50.40 29.10 99.90 84.80 168.00

4248P 22,212.00 3.00 2.00 (34.30) 50.50 135.30 50.40 29.10 99.90 84.80 168.00

4249P 35,140.00 4.00 3.00 9.60 58.80 108.00 41.80 20.90 71.20 49.20 83.70

4251N 1,719.00 3.00 2.00 5.60 85.00 164.50 47.20 27.30 55.50 79.40 93.40

4252N 1,719.00 3.00 2.00 5.60 85.00 164.50 47.20 27.30 55.50 79.40 93.40

4253N 39,388.00 4.00 3.00 (14.50) 24.50 63.50 33.20 16.60 135.40 39.00 159.30

4254N 39,388.00 4.00 3.00 (14.50) 24.50 63.50 33.20 16.60 135.40 39.00 159.30

4255N 11,762.00 2.00 1.00 (0.40) 12.40 25.20 3.00 2.10 24.40 12.80 103.50

4256N 11,762.00 2.00 1.00 (0.40) 12.40 25.20 3.00 2.10 24.40 12.80 103.50

4257N 15,789.00 3.00 2.00 (10.50) 21.90 54.40 19.30 11.10 88.00 32.50 148.00

4258N 15,789.00 3.00 2.00 (10.50) 21.90 54.40 19.30 11.10 88.00 32.50 148.00

4259N 430,518.00 66.00 65.00 36.40 44.60 52.70 39.90 4.90 89.50 8.20 18.40

4251P 17,092.00 5.00 4.00 (2.70) 36.20 75.00 40.70 18.20 112.60 38.80 107.40

4252P 17,092.00 5.00 4.00 (2.70) 36.20 75.00 40.70 18.20 112.60 38.80 107.40

4253P 33,432.00 7.00 6.00 2.30 20.90 39.40 25.20 9.50 120.90 18.50 88.80

4254P 33,432.00 7.00 6.00 2.30 20.90 39.40 25.20 9.50 120.90 18.50 88.80

4255P 33,432.00 7.00 6.00 2.30 20.90 39.40 25.20 9.50 120.90 18.50 88.80


4256P 33,432.00 7.00 6.00 2.30 20.90 39.40 25.20 9.50 120.90 18.50 88.80

4257P 49,126.00 7.00 6.00 6.20 30.30 54.40 32.80 12.40 108.20 24.10 79.50

4258P 49,126.00 7.00 6.00 6.20 30.30 54.40 32.80 12.40 108.20 24.10 79.50

4259P 49,126.00 7.00 6.00 6.20 30.30 54.40 32.80 12.40 108.20 24.10 79.50

4301N 67,157.00 11.00 10.00 12.90 29.00 45.10 29.50 8.90 101.40 16.10 55.50

4302N 67,157.00 11.00 10.00 12.90 29.00 45.10 29.50 8.90 101.40 16.10 55.50

4303N 88,232.00 14.00 13.00 11.70 27.50 43.40 33.40 8.90 121.50 15.80 57.50

4304N 88,232.00 14.00 13.00 11.70 27.50 43.40 33.40 8.90 121.50 15.80 57.50

4305N 64,580.00 11.00 10.00 38.20 53.00 67.70 26.90 8.10 50.90 14.70 27.80

4306N 64,580.00 11.00 10.00 38.20 53.00 67.70 26.90 8.10 50.90 14.70 27.80

4306P 64,580.00 11.00 10.00 38.20 53.00 67.70 26.90 8.10 50.90 14.70 27.80

4307N 93,818.00 18.00 17.00 33.80 50.80 67.80 41.50 9.80 81.80 17.00 33.50

4308N 93,818.00 18.00 17.00 33.80 50.80 67.80 41.50 9.80 81.80 17.00 33.50

4308P 93,818.00 18.00 17.00 33.80 50.80 67.80 41.50 9.80 81.80 17.00 33.50

4309N 1,120,362.00 179.00 178.00 55.50 61.20 66.90 46.20 3.50 75.60 5.70 9.30

4309P 1,120,362.00 179.00 178.00 55.50 61.20 66.90 46.20 3.50 75.60 5.70 9.30

5000N 6,403,800.00 975.00 974.00 0.10 0.70 1.30 11.40 0.40 1,541.40 0.60 81.30

6111N 4,264.00 2.00 1.00 (130.00) 71.10 272.20 47.30 33.50 66.50 201.10 282.70

6112N 4,264.00 2.00 1.00 (130.00) 71.10 272.20 47.30 33.50 66.50 201.10 282.70

6113N 5,826.00 2.00 1.00 (453.20) 90.50 634.20 127.90 90.50 141.40 543.70 601.00

6114N 5,826.00 2.00 1.00 (453.20) 90.50 634.20 127.90 90.50 141.40 543.70 601.00

6115N 6,794.00 2.00 1.00 (80.10) 163.90 407.90 57.40 40.60 35.00 244.00 148.90

6116N 6,794.00 2.00 1.00 (80.10) 163.90 407.90 57.40 40.60 35.00 244.00 148.90

6117N 7,512.00 2.00 1.00 (258.00) 106.80 471.60 85.80 60.70 80.40 364.80 341.50

6118N 921,393.00 123.00 122.00 97.00 110.60 124.20 91.10 8.20 82.40 13.60 12.30

6119N 921,393.00 123.00 122.00 97.00 110.60 124.20 91.10 8.20 82.40 13.60 12.30

6127N 759,111.00 47.00 46.00 2.40 7.70 13.00 21.60 3.20 279.80 5.30 68.50

6128N 759,111.00 47.00 46.00 2.40 7.70 13.00 21.60 3.20 279.80 5.30 68.50

6129N 759,111.00 47.00 46.00 2.40 7.70 13.00 21.60 3.20 279.80 5.30 68.50

6131N 106,048.00 8.00 7.00 62.40 125.20 188.00 93.80 33.20 74.90 62.80 50.20


6132N 61,040.00 14.00 13.00 4.60 22.60 40.50 37.90 10.10 168.20 18.00 79.60

6133N 111,006.00 18.00 17.00 10.80 17.80 24.80 17.00 4.00 95.40 7.00 39.10

6134N 93,618.00 9.00 8.00 19.90 44.20 68.60 39.30 13.10 88.80 24.30 55.10

6135N 81,666.00 9.00 8.00 13.40 25.10 36.80 18.90 6.30 75.20 11.70 46.60

6136N 50,580.00 6.00 5.00 15.30 49.10 82.90 41.00 16.80 83.60 33.80 68.80

6137N 71,076.00 12.00 11.00 49.90 77.70 105.50 53.60 15.50 69.00 27.80 35.80

6138N 34,910.00 5.00 4.00 (6.70) 18.00 42.80 26.00 11.60 144.10 24.70 137.40

6139N 4,547,037.00 681.00 680.00 72.20 76.80 81.40 72.80 2.80 94.80 4.60 6.00

6200N 4,452,120.00 664.00 663.00 3.50 5.00 6.50 23.50 0.90 468.10 1.50 29.90

7000N 162,480.00 24.00 23.00 1.00 10.40 19.70 26.80 5.50 258.90 9.40 90.60


Table B4. Annual growth rates for each stratum. Tons Hardwood Tons Softwood Totals

BAP Region Sawlog Pulpwood Total Sawlog Pulpwood Total Total CuFt/Ac Total Tons/Ac 1000N North 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2100N North 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2200N North 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2320N North 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 6200N North 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 7000N North 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 4101N North 0.078957 0.257213 0.336170 0.142886 0.326318 0.469204 22.507659 0.805374 4102N North 0.078957 0.257213 0.336170 0.142886 0.326318 0.469204 22.507659 0.805374 4103N North 0.078957 0.257213 0.336170 0.142886 0.326318 0.469204 22.507659 0.805374 4104N North 0.078957 0.257213 0.336170 0.142886 0.326318 0.469204 22.507659 0.805374 4105N North 0.459088 0.563327 1.022415 0.176868 0.194156 0.371023 38.101879 1.393438 4106N North 0.459088 0.563327 1.022415 0.176868 0.194156 0.371023 38.101879 1.393438 4107N North 0.459088 0.563327 1.022415 0.176868 0.194156 0.371023 38.101879 1.393438 4108N North 0.459088 0.563327 1.022415 0.176868 0.194156 0.371023 38.101879 1.393438 4109N North 1.051995 0.315958 1.367954 0.082310 0.024268 0.106578 39.773970 1.474532 4109P North 3.630778 1.090474 4.721252 0.284080 0.083756 0.367836 137.272889 5.089089 4211N North 0.008318 0.043214 0.051532 0.051177 0.388015 0.439192 14.006430 0.490724 4211P North 0.016928 0.087941 0.104869 0.104146 0.789620 0.893765 28.503420 0.998634 4221N North 0.045232 0.003684 0.048916 0.005515 0.859822 0.865337 26.184759 0.914253 4221P North 0.116199 0.009465 0.125665 0.014169 2.208855 2.223024 67.267852 2.348689 4222N North 0.045232 0.003684 0.048916 0.005515 0.859822 0.865337 26.184759 0.914253 4222P North 0.116199 0.009465 0.125665 0.014169 2.208855 2.223024 67.267852 2.348689 4223N North 0.045232 0.003684 0.048916 0.005515 0.859822 0.865337 26.184759 0.914253 4223P North 0.116199 0.009465 0.125665 0.014169 2.208855 2.223024 67.267852 2.348689 4224N North 0.045232 0.003684 0.048916 0.005515 0.859822 0.865337 26.184759 0.914253 4224P North 0.116199 0.009465 0.125665 0.014169 2.208855 2.223024 67.267852 2.348689


4225N North 0.008175 0.012704 0.020880 0.146515 0.752806 0.899321 26.409154 0.920201 4225P North 0.032115 0.049905 0.082021 0.575551 2.957228 3.532780 103.742377 3.614800 4226N North 0.008175 0.012704 0.020880 0.146515 0.752806 0.899321 26.409154 0.920201 4226P North 0.032115 0.049905 0.082021 0.575551 2.957228 3.532780 103.742377 3.614800 4227N North 0.008175 0.012704 0.020880 0.146515 0.752806 0.899321 26.409154 0.920201 4227P North 0.032115 0.049905 0.082021 0.575551 2.957228 3.532780 103.742377 3.614800 4228N North 0.008175 0.012704 0.020880 0.146515 0.752806 0.899321 26.409154 0.920201 4228P North 0.032115 0.049905 0.082021 0.575551 2.957228 3.532780 103.742377 3.614800 4229N North 0.009550 0.020587 0.030137 -0.258979 0.018283 -0.240696 -6.109472 -0.210559 4229P North 0.037516 0.080870 0.118387 -1.017342 0.071821 -0.945521 -23.999678 -0.827134 4231N North 0.001508 0.061960 0.063468 0.439009 2.494087 2.933096 86.008016 2.996563 4231P North 0.001411 0.057979 0.059390 0.410805 2.333854 2.744658 80.482414 2.804049 4232N North 0.001508 0.061960 0.063468 0.439009 2.494087 2.933096 86.008016 2.996563 4232P North 0.001411 0.057979 0.059390 0.410805 2.333854 2.744658 80.482414 2.804049 4233N North 0.001508 0.061960 0.063468 0.439009 2.494087 2.933096 86.008016 2.996563 4233P North 0.001411 0.057979 0.059390 0.410805 2.333854 2.744658 80.482414 2.804049 4234N North 0.001508 0.061960 0.063468 0.439009 2.494087 2.933096 86.008016 2.996563 4234P North 0.001411 0.057979 0.059390 0.410805 2.333854 2.744658 80.482414 2.804049 4235N North -0.014836 0.080319 0.065482 1.090318 1.285606 2.375923 70.047503 2.441406 4235P North -0.031927 0.172844 0.140917 2.346343 2.766600 5.112943 150.740937 5.253860 4236N North -0.014836 0.080319 0.065482 1.090318 1.285606 2.375923 70.047503 2.441406 4236P North -0.031927 0.172844 0.140917 2.346343 2.766600 5.112943 150.740937 5.253860 4237N North -0.014836 0.080319 0.065482 1.090318 1.285606 2.375923 70.047503 2.441406 4237P North -0.031927 0.172844 0.140917 2.346343 2.766600 5.112943 150.740937 5.253860 4238N North -0.014836 0.080319 0.065482 1.090318 1.285606 2.375923 70.047503 2.441406 4238P North -0.031927 0.172844 0.140917 2.346343 2.766600 5.112943 150.740937 5.253860 4239N North -0.008972 0.065456 0.056484 0.670094 0.173344 0.843438 25.758386 0.899921 4239P North -0.019308 0.140859 0.121552 1.442029 0.373033 1.815062 55.431572 1.936614 4241N North 0.000000 0.065568 0.065568 0.020987 1.104358 1.125344 34.104924 1.190913 4241P North 0.000000 0.039857 0.039857 0.012757 0.671304 0.684061 20.731289 0.723918


4242N North 0.000000 0.065568 0.065568 0.020987 1.104358 1.125344 34.104924 1.190913 4242P North 0.000000 0.039857 0.039857 0.012757 0.671304 0.684061 20.731289 0.723918 4243N North 0.000000 0.065568 0.065568 0.020987 1.104358 1.125344 34.104924 1.190913 4243P North 0.000000 0.039857 0.039857 0.012757 0.671304 0.684061 20.731289 0.723918 4244N North 0.000000 0.065568 0.065568 0.020987 1.104358 1.125344 34.104924 1.190913 4244P North 0.000000 0.039857 0.039857 0.012757 0.671304 0.684061 20.731289 0.723918 4245N North 0.013666 0.131123 0.144789 0.408793 0.655973 1.064766 34.489711 1.209555 4245P North 0.041030 0.393684 0.434714 1.227362 1.969496 3.196858 103.552059 3.631572 4246N North 0.013666 0.131123 0.144789 0.408793 0.655973 1.064766 34.489711 1.209555 4246P North 0.041030 0.393684 0.434714 1.227362 1.969496 3.196858 103.552059 3.631572 4247N North 0.013666 0.131123 0.144789 0.408793 0.655973 1.064766 34.489711 1.209555 4247P North 0.041030 0.393684 0.434714 1.227362 1.969496 3.196858 103.552059 3.631572 4248N North 0.013666 0.131123 0.144789 0.408793 0.655973 1.064766 34.489711 1.209555 4248P North 0.041030 0.393684 0.434714 1.227362 1.969496 3.196858 103.552059 3.631572 4249N North -0.021070 0.028673 0.007603 0.361747 0.176191 0.537938 15.665779 0.545541 4249P North -0.063259 0.086087 0.022828 1.086111 0.528997 1.615108 47.035003 1.637936 4301N North 0.018294 0.133608 0.151902 0.235401 0.566191 0.801592 27.116303 0.953494 4302N North 0.018294 0.133608 0.151902 0.235401 0.566191 0.801592 27.116303 0.953494 4303N North 0.018294 0.133608 0.151902 0.235401 0.566191 0.801592 27.116303 0.953494 4304N North 0.018294 0.133608 0.151902 0.235401 0.566191 0.801592 27.116303 0.953494 4305N North 0.100658 0.491013 0.591671 0.530700 0.490181 1.020881 45.220969 1.612553 4306N North 0.100658 0.491013 0.591671 0.530700 0.490181 1.020881 45.220969 1.612553 4306P North 0.158881 0.775024 0.933905 0.837667 0.773711 1.611379 71.377655 2.545284 4307N North 0.100658 0.491013 0.591671 0.530700 0.490181 1.020881 45.220969 1.612553 4308N North 0.100658 0.491013 0.591671 0.530700 0.490181 1.020881 45.220969 1.612553 4308P North 0.158881 0.775024 0.933905 0.837667 0.773711 1.611379 71.377655 2.545284 4309N North 0.264366 0.370781 0.635147 0.746446 0.155177 0.901622 42.959890 1.536770 4309P North 0.417281 0.585248 1.002529 1.178205 0.244934 1.423138 67.808724 2.425667 5000N North 0.288224 0.404242 0.692466 0.813809 0.169181 0.982989 46.836809 1.675456 6111N North -0.011592 0.164349 0.152757 0.264686 0.340117 0.604804 21.483044 0.757561


6112N North -0.011592 0.164349 0.152757 0.264686 0.340117 0.604804 21.483044 0.757561 6113N North -0.011592 0.164349 0.152757 0.264686 0.340117 0.604804 21.483044 0.757561 6114N North -0.011592 0.164349 0.152757 0.264686 0.340117 0.604804 21.483044 0.757561 6115N North 0.199452 0.365264 0.564716 1.348432 0.560164 1.908596 70.012827 2.473312 6116N North 0.199452 0.365264 0.564716 1.348432 0.560164 1.908596 70.012827 2.473312 6117N North 0.199452 0.365264 0.564716 1.348432 0.560164 1.908596 70.012827 2.473312 6118N North 0.199452 0.365264 0.564716 1.348432 0.560164 1.908596 70.012827 2.473312 6119N North 0.696828 0.409585 1.106413 0.939186 0.302942 1.242127 65.393863 2.348541 6127N North 0.280275 0.029692 0.309967 0.000000 0.000000 0.000000 8.318325 0.309967 6128N North 0.280275 0.029692 0.309967 0.000000 0.000000 0.000000 8.318325 0.309967 6129N North 0.280275 0.029692 0.309967 0.000000 0.000000 0.000000 8.318325 0.309967 6131N North 0.116950 0.324216 0.441166 0.136173 0.202391 0.338564 21.570422 0.779730 6132N North 0.116950 0.324216 0.441166 0.136173 0.202391 0.338564 21.570422 0.779730 6133N North 0.116950 0.324216 0.441166 0.136173 0.202391 0.338564 21.570422 0.779730 6134N North 0.116950 0.324216 0.441166 0.136173 0.202391 0.338564 21.570422 0.779730 6135N North 0.347452 0.868495 1.215947 0.120768 0.169913 0.290681 40.986299 1.506629 6136N North 0.347452 0.868495 1.215947 0.120768 0.169913 0.290681 40.986299 1.506629 6137N North 0.347452 0.868495 1.215947 0.120768 0.169913 0.290681 40.986299 1.506629 6138N North 0.347452 0.868495 1.215947 0.120768 0.169913 0.290681 40.986299 1.506629 6139N North 0.769065 0.832453 1.601518 0.107234 0.075646 0.182879 48.235034 1.784397 1000N South 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2100N South 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2200N South 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2320N South 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 6200N South 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 7000N South 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 4101N South 0.066294 0.306742 0.373036 -0.194640 0.077717 -0.116923 6.650185 0.256113 4102N South 0.066294 0.306742 0.373036 -0.194640 0.077717 -0.116923 6.650185 0.256113 4103N South 0.066294 0.306742 0.373036 -0.194640 0.077717 -0.116923 6.650185 0.256113 4104N South 0.066294 0.306742 0.373036 -0.194640 0.077717 -0.116923 6.650185 0.256113


4105N South 0.289774 0.365460 0.655234 -0.052257 0.027753 -0.024504 16.879657 0.630730 4106N South 0.289774 0.365460 0.655234 -0.052257 0.027753 -0.024504 16.879657 0.630730 4107N South 0.289774 0.365460 0.655234 -0.052257 0.027753 -0.024504 16.879657 0.630730 4108N South 0.289774 0.365460 0.655234 -0.052257 0.027753 -0.024504 16.879657 0.630730 4109N South 0.514337 0.206042 0.720379 0.048378 -0.011969 0.036410 20.378715 0.756788 4109P South 1.862221 0.746001 2.608222 0.175159 -0.043334 0.131825 73.783687 2.740047 4211N South 0.003188 0.015755 0.018943 -0.197644 -0.031313 -0.228958 -6.072469 -0.210014 4211P South 0.006488 0.032062 0.038550 -0.402211 -0.063723 -0.465934 -12.357619 -0.427384 4221N South 0.367420 0.156393 0.523813 0.391266 0.895305 1.286571 51.036515 1.810384 4221P South 0.943890 0.401770 1.345660 1.005150 2.300011 3.305161 131.111260 4.650821 4222N South 0.367420 0.156393 0.523813 0.391266 0.895305 1.286571 51.036515 1.810384 4222P South 0.943890 0.401770 1.345660 1.005150 2.300011 3.305161 131.111260 4.650821 4223N South 0.367420 0.156393 0.523813 0.391266 0.895305 1.286571 51.036515 1.810384 4223P South 0.943890 0.401770 1.345660 1.005150 2.300011 3.305161 131.111260 4.650821 4224N South 0.367420 0.156393 0.523813 0.391266 0.895305 1.286571 51.036515 1.810384 4224P South 0.943890 0.401770 1.345660 1.005150 2.300011 3.305161 131.111260 4.650821 4225N South 0.000000 -0.022928 -0.022928 0.256228 0.228159 0.484387 13.307241 0.461459 4225P South 0.000000 -0.090066 -0.090066 1.006534 0.896270 1.902804 52.274480 1.812737 4226N South 0.000000 -0.022928 -0.022928 0.256228 0.228159 0.484387 13.307241 0.461459 4226P South 0.000000 -0.090066 -0.090066 1.006534 0.896270 1.902804 52.274480 1.812737 4227N South 0.000000 -0.022928 -0.022928 0.256228 0.228159 0.484387 13.307241 0.461459 4227P South 0.000000 -0.090066 -0.090066 1.006534 0.896270 1.902804 52.274480 1.812737 4228N South 0.000000 -0.022928 -0.022928 0.256228 0.228159 0.484387 13.307241 0.461459 4228P South 0.000000 -0.090066 -0.090066 1.006534 0.896270 1.902804 52.274480 1.812737 4229N South 0.000000 -0.022928 -0.022928 0.256228 0.228159 0.484387 13.307241 0.461459 4229P South 0.000000 -0.090066 -0.090066 1.006534 0.896270 1.902804 52.274480 1.812737 4241N South 0.031144 0.138431 0.169575 0.341616 0.743576 1.085192 35.741980 1.254767 4241P South 0.031144 0.138431 0.169575 0.341616 0.743576 1.085192 35.741980 1.254767 4242N South 0.031144 0.138431 0.169575 0.341616 0.743576 1.085192 35.741980 1.254767 4242P South 0.031144 0.138431 0.169575 0.341616 0.743576 1.085192 35.741980 1.254767


4243N South 0.031144 0.138431 0.169575 0.341616 0.743576 1.085192 35.741980 1.254767 4243P South 0.031144 0.138431 0.169575 0.341616 0.743576 1.085192 35.741980 1.254767 4244N South 0.031144 0.138431 0.169575 0.341616 0.743576 1.085192 35.741980 1.254767 4244P South 0.031144 0.138431 0.169575 0.341616 0.743576 1.085192 35.741980 1.254767 4245N South 0.049401 0.042425 0.091826 0.758675 0.648623 1.407298 42.913659 1.499124 4245P South 0.049401 0.042425 0.091826 0.758675 0.648623 1.407298 42.913659 1.499124 4246N South 0.049401 0.042425 0.091826 0.758675 0.648623 1.407298 42.913659 1.499124 4246P South 0.049401 0.042425 0.091826 0.758675 0.648623 1.407298 42.913659 1.499124 4247N South 0.049401 0.042425 0.091826 0.758675 0.648623 1.407298 42.913659 1.499124 4247P South 0.049401 0.042425 0.091826 0.758675 0.648623 1.407298 42.913659 1.499124 4248N South 0.049401 0.042425 0.091826 0.758675 0.648623 1.407298 42.913659 1.499124 4248P South 0.049401 0.042425 0.091826 0.758675 0.648623 1.407298 42.913659 1.499124 4249N South 0.075102 0.096944 0.172046 0.722824 0.286768 1.009592 33.635357 1.181638 4249P South 0.075102 0.096944 0.172046 0.722824 0.286768 1.009592 33.635357 1.181638 4251N South 0.033575 0.149238 0.182813 0.368285 0.801625 1.169910 38.532240 1.352723 4251P South 0.030805 0.136923 0.167728 0.337895 0.735477 1.073373 35.352691 1.241101 4252N South 0.033575 0.149238 0.182813 0.368285 0.801625 1.169910 38.532240 1.352723 4252P South 0.030805 0.136923 0.167728 0.337895 0.735477 1.073373 35.352691 1.241101 4253N South 0.033575 0.149238 0.182813 0.368285 0.801625 1.169910 38.532240 1.352723 4253P South 0.030805 0.136923 0.167728 0.337895 0.735477 1.073373 35.352691 1.241101 4254N South 0.033575 0.149238 0.182813 0.368285 0.801625 1.169910 38.532240 1.352723 4254P South 0.030805 0.136923 0.167728 0.337895 0.735477 1.073373 35.352691 1.241101 4255N South 0.027350 0.023488 0.050838 0.420033 0.359104 0.779136 23.758708 0.829975 4255P South 0.065687 0.056411 0.122098 1.008788 0.862455 1.871243 57.061035 1.993341 4256N South 0.027350 0.023488 0.050838 0.420033 0.359104 0.779136 23.758708 0.829975 4256P South 0.065687 0.056411 0.122098 1.008788 0.862455 1.871243 57.061035 1.993341 4257N South 0.027350 0.023488 0.050838 0.420033 0.359104 0.779136 23.758708 0.829975 4257P South 0.065687 0.056411 0.122098 1.008788 0.862455 1.871243 57.061035 1.993341 4258N South 0.027350 0.023488 0.050838 0.420033 0.359104 0.779136 23.758708 0.829975 4258P South 0.065687 0.056411 0.122098 1.008788 0.862455 1.871243 57.061035 1.993341


4259N South 0.041580 0.053672 0.095252 0.400184 0.158766 0.558950 18.621871 0.654202 4259P South 0.099861 0.128903 0.228765 0.961118 0.381307 1.342425 44.723948 1.571190 4301N South 0.000000 0.000000 0.000000 0.000000 0.308970 0.308970 8.880610 0.308970 4302N South 0.000000 0.000000 0.000000 0.000000 0.308970 0.308970 8.880610 0.308970 4303N South 0.000000 0.000000 0.000000 0.000000 0.308970 0.308970 8.880610 0.308970 4304N South 0.000000 0.000000 0.000000 0.000000 0.308970 0.308970 8.880610 0.308970 4305N South -0.052269 0.511434 0.459164 0.451571 0.327038 0.778609 34.701456 1.237773 4306N South -0.052269 0.511434 0.459164 0.451571 0.327038 0.778609 34.701456 1.237773 4306P South -0.052269 0.511434 0.459164 0.451571 0.327038 0.778609 34.701456 1.237773 4307N South -0.052269 0.511434 0.459164 0.451571 0.327038 0.778609 34.701456 1.237773 4308N South -0.052269 0.511434 0.459164 0.451571 0.327038 0.778609 34.701456 1.237773 4308P South -0.082503 0.807257 0.724754 0.712768 0.516204 1.228972 54.773451 1.953725 4309N South 0.099258 0.328391 0.427648 0.288813 0.071786 0.360599 21.840990 0.788247 4309P South 0.156670 0.518338 0.675008 0.455868 0.113308 0.569176 34.474242 1.244185 5000N South 0.108215 0.358026 0.466241 0.314877 0.078264 0.393141 23.812032 0.859383 6111N South -0.092959 -0.020319 -0.113278 -1.096671 -0.268075 -1.364746 -42.266299 -1.478024 6112N South -0.092959 -0.020319 -0.113278 -1.096671 -0.268075 -1.364746 -42.266299 -1.478024 6113N South -0.092959 -0.020319 -0.113278 -1.096671 -0.268075 -1.364746 -42.266299 -1.478024 6114N South -0.092959 -0.020319 -0.113278 -1.096671 -0.268075 -1.364746 -42.266299 -1.478024 6115N South 0.039976 0.045629 0.085605 0.161423 0.636088 0.797511 25.219849 0.883116 6116N South 0.039976 0.045629 0.085605 0.161423 0.636088 0.797511 25.219849 0.883116 6117N South 0.039976 0.045629 0.085605 0.161423 0.636088 0.797511 25.219849 0.883116 6118N South 0.039976 0.045629 0.085605 0.161423 0.636088 0.797511 25.219849 0.883116 6119N South 0.005287 0.010272 0.015559 0.409750 0.406945 0.816695 23.891493 0.832254 6127N South 0.280275 0.029692 0.309967 0.000000 0.000000 0.000000 8.318325 0.309967 6128N South 0.280275 0.029692 0.309967 0.000000 0.000000 0.000000 8.318325 0.309967 6129N South 0.280275 0.029692 0.309967 0.000000 0.000000 0.000000 8.318325 0.309967 6131N South 0.051062 0.165528 0.216589 0.011250 -0.184271 -0.173021 0.839353 0.043568 6132N South 0.051062 0.165528 0.216589 0.011250 -0.184271 -0.173021 0.839353 0.043568 6133N South 0.051062 0.165528 0.216589 0.011250 -0.184271 -0.173021 0.839353 0.043568


6134N South 0.051062 0.165528 0.216589 0.011250 -0.184271 -0.173021 0.839353 0.043568 6135N South 0.273581 0.784602 1.058183 0.052324 0.105631 0.157955 32.937628 1.216139 6136N South 0.273581 0.784602 1.058183 0.052324 0.105631 0.157955 32.937628 1.216139 6137N South 0.273581 0.784602 1.058183 0.052324 0.105631 0.157955 32.937628 1.216139 6138N South 0.273581 0.784602 1.058183 0.052324 0.105631 0.157955 32.937628 1.216139 6139N South -0.119381 0.314075 0.194695 -0.422307 0.029759 -0.392547 -6.057972 -0.197853

comprehensive statewide forest inventory analysis and ...€¦ · the methods used for the project...

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