comprehensive statewide forest inventory analysis and ... · 1 executive summary the comprehensive...

Comprehensive Statewide Forest Inventory Analysis and Study (CSFIAS)

Technical Report

Prepared for:

Florida Forest Service

Update 2015

Prepared by:

Andrew Brenner, Chad Lopez, and Mark Yoders

John Cothrun, F4 Tech

and Brian Condon, BRM Inc.

6/15/2016

2 Florida Forest Service

Table of Contents

1 EXECUTIVE SUMMARY .................................................................................................... 3

2 INTRODUCTION ................................................................................................................ 5

3 OVERVIEW OF PROJECT ................................................................................................ 6

3.1 ORIGIN AND DESCRIPTION OF MAIN SOURCE DATASETS USED IN THIS PROJECT .............. 7 3.2 GEOSPATIAL DATASETS CONSTRUCTED FOR THIS PROJECT ........................................... 7

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

4.1 FOREST COVER 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 ....................................................................................................14

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 4.3.6 2014 Update ..........................................................................................................21

4.4 STATEWIDE TIMBER STAND AGE CLASSES DATA LAYER ................................................21 4.4.1 Change Detection 2013-2014 ................................................................................21

4.5 STATEWIDE ORIGIN OF THE FORESTS DATA LAYER .......................................................21 4.5.1 Creation of 2011 Plantation Mask ..........................................................................21 4.5.2 2014 Plantation Mask ............................................................................................22

4.6 STATEWIDE TIMBER PRODUCT CLASSES DATA LAYER ...................................................22 4.7 STATEWIDE OWNERSHIP OF FORESTLAND DATA LAYER .................................................22

4.7.1 Data Source and Development ..............................................................................22 4.7.2 Accuracy Assessment............................................................................................24

4.8 STATEWIDE PRIMARY WOOD-USING PLANTS DATA LAYER .............................................26 4.8.1 Documenting Wood Using Plants in Florida ...........................................................26 4.8.2 Estimating Timber Removal, Sustainability, and Availability ...................................26

4.9 TIMBER RESOURCES DISTRIBUTION IN GREEN TONS .....................................................32 4.9.1 Calculation of Standing Timber Biomass................................................................32 4.9.2 Equations, Processes and Programs used for Stratified Inventory .........................35 4.9.3 Stratified Inventory Process ...................................................................................35

5 CHANGES MADE IN 2015 STUDY...................................................................................38

6 APPENDIX A: CROSSWALKS .........................................................................................38

7 APPENDIX B: STRATA TABLES .....................................................................................45


1 Executive Summary

The Comprehensive Statewide Forest Inventory Analysis and Study (CSFIAS), authorized by the 2012 House Bill HB7117, provides comprehensive statewide assessment of timber biomass resources on forestlands for the State of Florida by using a stratified inventory approach. This approach provides an overall estimate of timber volume, net growth, timber removal levels, and their spatial distribution across the state. In addition, the project identifies and leverages forest ownership to improve timber estimates by considering areas that are non-harvestable forestlands because of legal or policy reasons. Ownership influence on timber harvest is one example where the 2014 and 2015 analyses were modified. Other areas of the analysis were modified based on stakeholder input. The following report details the original analysis and its modification for the 2014 and 2015 updates.

The methods used for the project are presented in this Technical Report (update 2015) and the results from the study are located in the Executive Report (update 2015) and Map Book (update 2015). This report details the approach which used 2013/14 and 2015 Landsat OLI TIRS and legacy Landsat TM imagery (30 m spatial resolution) in combination with the Florida land cover dataset created in 2013 for the original project to update the forestland cover, age, and origin data layers. The primary plot information used for the assessment was from the United States Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program. The 2011 forestland cover layer was predominantly created by combining 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) and updating it to 2014/5 conditions and the classification system required by the project. Other datasets, such as soil type and habitat land cover, were also used to support the analyses and updates for 2011. To build the 2014 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. This was updated in 2015 by assessing the change of land cover using Landsat imagery and by increasing the age of 2011 stands by three years. Areas of cleared forestland which were not converted to urban or agricultural uses were considered to still be forestland. The 2011 and updates to the 2014 stand origin data layer was created using FLUCCS, state lands data obtained from FFS, volunteered private information, and photo interpretation to identify areas planted with trees versus areas with naturally regenerating vegetation. Forestland ownership datasets for the entire state were also developed and categorized by federal, state, local government, and private ownerships with sub-categories. These datasets were derived from 2012 through 2014 Florida Department of Revenue (DOR) parcel data overlain on the 2014 forestland cover dataset. A single layer that combined forestland cover (B), age (A) and origin (P) data layers, singularly the BAP layer, was created and used for the stratification of FIA plots. The FIA plots were grouped together based on their BAP labels that were derived from plot data and were


averaged into 0 – 20 years, 20 – 40 years and > 40 years age classes. Strata tables for each BAP class were calculated. These strata tables were then linked to the BAP map using an alphanumeric join. These data were then used to estimate standing timber for three timber product classes: sub-merchantable, pulpwood and sawtimber. Note: In 2013 the analysis grouped cypress with pine; however the 2014/15 update grouped cypress with hardwoods. The net timber growth data were also broken into timber product classes and pine and hardwood & cypress groups. 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 origin by FIA unit. Florida contains four FIA Units which were aggregated into Northern Region, comprised of Units 1 and 2, and Southern Region, comprised of Units 3 and 4. The resulting data were linked to the BAP strata map and estimates of net timber growth were calculated and summarized by county and FIA unit for the state of Florida. Removals of forest products were estimated using known mill locations, mill capacities and mill surveys. These layers were summed by county for the known mills. The sustainability indices (net timber growth to timber removal ratios) and the timber availability estimates (net timber growth minus timber removal) for pulpwood and sawtimber timber product types in each pine, and hardwood & cypress species groups were developed for each Florida county and FIA unit. The Section 5 of this report describes improvements made to the approach and strategies for future updates, and recommends future 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 removal 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 removal on timber biomass from established forest product industries and the ability of the ecosystem to sustainably support timber removal. The risk of over-utilization has been identified by the State of Florida as a concern. 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 timber removals and 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 plot data used for the assessment were 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 data source was Landsat Thematic Mapper (TM) imagery mainly from Landsat 5 and 8 which have a spatial resolution of 30 m. The Landsat sensors adequately detect changes over large landscapes but are not optimal for detecting changes over small 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 estimated removals of timber resources by mills and identified forest ownerships across the state. Removal of timber resources is driven by the activities of 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 removal and calculating sustainability index and timber availability. Not all timber biomass was available for harvest as there are large areas of the state that are protected/ preserved. Forest ownership was identified across the state and mapped into federal, state, local government and private categories to support the current analyses, and the reserved status of the timber was considered when calculating the sustainability indices. 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 the Florida Forest Service:

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.


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 for the 2013 study to 2011 conditions. In the 2014 study, these data were updated to 2013 conditions and in the 2015 study these conditions were updated to 2014 conditions. 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 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 removal, 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 2011: 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.

a) Update to 2013 conditions: 2013 Forestland cover update involved the assessment of change using Landsat 8 imagery collected in 2013. Using automated change detection methods, change areas were identified and reclassified into either forestland or other land covers based on the area’s classification. All change areas were reviewed using high resolution imagery to verify the classification. In addition, some forest land owners provided the team their land cover maps that were used to correct errors in the 2011 map. As a result, the 2011 dataset was first updated using these ancillary datasets and then the 2011 map was updated to the 2013 date using the imagery change detection approaches.

b) Update to 2014 conditions: 2014 Forestland cover update involved the assessment of change using Landsat 8 imagery collected in early 2015. Using automated change detection methods, change areas were identified and reclassified into either forestland or other land covers based on the area’s classification. All change areas were reviewed using high resolution imagery to verify the classification. The 2013 dataset was updated to the 2015 date using the imagery change detection approaches.

2. Forest Stand Age 2011: 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.

a) Update to 2013 conditions: The temporal interval of imagery was shorter (2 years) than the stand age interval (5 years) forest age was updated in two ways. First, any areas that were harvested and remaining in forest the stand age class was reset to 0 – 5 years. Second, 2011 stands had 2/5 of their area increased into the next age class. The remaining 3/5 of the area stayed at the original 2011 age. These adjusted areas and adjusted age classes were then tabulated and used for summary table calculations.

b) Update to 2014 conditions: The methods used were similar to the 2013 update First, any areas that were harvested and remaining in forest the stand age class was reset to 0 – 5 years. Second, 2011 stands had 3/5 of their area


increased into the next age class. The remaining 2/5 of the area stayed at the original 2011 age. These adjusted areas and adjusted age classes were then tabulated and used for summary table calculations.

3. Timber Product Class 2013: The timber product dataset was created by linking the FIA derived data tables to the appropriate forestland cover type (B), age (A) and origin (P) classes. This combination dataset (BAP) had a pixel resolution of 30 m. From this dataset timber product values based on the diameter and species of the inventoried trees were calculated for each pixel. The strata tables included four timber product types: pine pulpwood, pine chip-n-saw and sawtimber, hardwood & cypress pulpwood and hardwood & cypress sawtimber. Many BAP strata tables have more than one timber product, the summary by county of each product is provided in the summary tables.

4. Origin 2011: 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.

a) Update to 2013 conditions: The updated dataset utilized information provided by forest landowners to first bring up to date the 2011 dataset with more accurate ancillary information. Then areas where forests had been harvested and were assumed to return to pine plantation unless there was other information to indicate otherwise.

b) Update to 2014 conditions: Areas where forests had been harvested and were returning to forest conditions were assumed to return to pine plantation unless there was other information to indicate otherwise.

5. Ownership 2014: Forest ownership was determined by examination of statewide parcel shapefiles and databases maintained by the Florida Department of Revenue (DOR). Parcels containing forest were identified and classified at a primary and sub-category levels. The primary ownership category identified federal, state, 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 2014: The FFS primary wood-using mills list was the starting point for the analysis of timber removal. The type and quantity of products generated by each mill were identified along with the timber species and size class employed as raw material. A survey was conducted for all known mills to provide information on product and county where the product came from. For mills that did not respond to the survey conversion factors were utilized to move from finished product volume to timber product volume, a representation of each mill’s removal 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 removal for timber was then distributed across its woodshed, a geographic area defined by distances from 75 to 115 road miles from the mill location. This distribution of removal was applied in further analyses of timber resource sustainability and availability. Details of this approach are covered later in this report.

7. Timber Resources 2014: 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 removal, sustainability index, and timber availability. These values were calculated for each of the four timber product types: pine pulpwood and pine chip-n-saw and sawtimber, and hardwood & cypress pulpwood and hardwood & cypress 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 Forest Cover Classification Scheme The first element of any land cover map is its 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 Statewide ownership of forestland data layer South Florida Slash, then Longleaf/South Florida Slash Else if in South Florida Loblolly and North Florida Slash (will include Pond, Shortleaf etc.)

Else if in North Florida and > 75% of the canopy is Longleaf, then Longleaf Else Loblolly and North 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

4.2.1.1 Years 2013 – 2014 Change Classification The scenes used for the 2013 to 2014 change detection and the 2014 cover classification are detailed in Table 2 below. All of the late-date scenes are from early 2015 and so represent 2015 conditions. Table 2. Landsat imagery used for 2013-2014 change detection and classification.

Path/Row 2013-14 Imagery Date 2015 Imagery Date p15r41 11 Jan 2014 21 Jan 2015 p15r42 23 Mar 2014 6 Feb 2015 p15r43 24 April 2014 6 Feb 2015 p16r39 30 Mar 2014 17 Mar 2015 p16r40 30 Mar 2014 13 Feb 2015 p16r41 30 Mar 2014 13 Feb 2015 p16r42 14 May 2013 13 Feb 2015 p17r39 16 Jan 2014 19 Jan 2015 p17r40 16 Jan 2014 20 Feb 2015 p17r41 14 Feb 2014 20 Feb 2015 p18r39 26 April 2014 11 Feb 2015 p19r39 10 Oct 2013 18 Feb 2015 p20r39 21 Jan 2014 24 Jan 2015

4.2.1.2 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 Coastal Change Analysis Program (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.

4.2.2.2 Florida Land Use, Cover and forms Classification System (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/HARN1, converted to 30 m resolution raster format, and mosaicked into a single raster layer covering the state.

4.2.2.3 Florida Forest Services - Florida Risk Assessment Canopy Inventory Project (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, USFS, and Forest Landowner 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

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


pine. The data also contained stand age and plantation/non-plantation descriptions that not only supported quality control (QC) of the dataset and accuracy assessment sites, but also allowed for integration of forest cover type, age, and origin maps. Private forest landowners provided similar data that was crosswalked into the 2014 update as well.

4.2.2.5 Cooperative Research in Forest Fertilization Program (CRIFF) Gridded Soil Survey Geographic Database (SSURGO) soil data were downloaded from the Natural Resources Conservation Service (NRCS) website. 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 Florida Fish and Wildlife Commission (FWC) Habitat and Land Cover Florida Fish and Wildlife Conservation Commission (FWC) Habitat and Land Cover 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 Florida Natural Areas Inventory 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. 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.


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.

4.3 Statewide Forestland Cover Data Layer

4.3.1 Image Preprocessing All 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). 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.


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 would have been extremely difficult to identify from current Landsat imagery.

4.3.4.3 Water Mask 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 classified as water. Also, single isolated pixels were removed, although they may have represented small water bodies.

4.3.4.4 Other Mask 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 Mask 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 Mask 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, remote sensing approaches were applied 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 Forests and National Forests and visually inspected.

4.3.4.7 Hardwoods Mask 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 Mask 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.


4.3.6 2014 Update Using the 2013 forestland map as a base, the 2014 forestland cover mask was built via the change detection process. The change detection process located probable harvest sites. These updates covered all appropriate changes to the 2013 forestland map to create the final forestland map which was used to create the updated 2014 forest cover, age, and plantation masks. Observed changes from the change detection were incorporated to move the map from 2013 to 2014 vintage.

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 2013-2014 The change detection approach for updating the 2013 dataset to 2014 used the approved USGS and NOAA multi-index integrated change analysis system (MIICA) methodology. This methodology has been used to update the National Land Cover Dataset. The imagery used covers the latest available high quality imagery available for each Landsat path-row and generally represents the conditions on the ground at the beginning of 2015. Imagery used is provided in Table 2. For additional details of the method please see the Technical Report Update 2013. Although the movement of areas between classes is not possible in the map because the changes need to be at least five years to move an area between classes. It was accommodated in the tabular data by incrementing 3/5 of the area of an age class into the older age class unless it was harvested.

4.5 Statewide Origin of the Forests Data Layer

4.5.1 Creation of 2011 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.


4.5.2 2014 Plantation Mask The 2014 Plantation mask was created using the 2014 BAP map. The 2014 BAP map was created by using the original 2011 BAP map and updating it with 2011-2014 harvests and the most up to date ancillary data from FFS, USFS Forest Service and private landowners.

4.6 Statewide Timber Product Classes Data Layer The statewide timber classes were produced from analysis of the 2014 FIA data where plots were crosswalked to combined layer of Forestland Cover (B), Age (A) and Origin (P) layer. The analysis 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. The BAP strata tables used for this map are in Appendix B. The accuracy of these classes was assessed for the 2011 maps and these results are reported in the original 2011 Technical Report and the 2013 Technical Report and are not repeated here.

4.7 Statewide Ownership of Forestland Data Layer

4.7.1 Data Source and Development FFS provided land ownership data as individual county polygon shapefiles obtained from the DOR current to 2014. The data provided included the NAL (name, address, legal) file compiled for each county joined to individual parcel polygons. Changes in ownership resulting from name changes or parcel subdivision were identified by comparing the 2014 DOR data set to the ownership layer utilized in the original CSFIAS work completed last year. Parcels where change was detected were then classified in the same manner as previous work, described below. Newly classified parcels along with the unchanged ownerships were then compiled within the same data layer using the 2014 parcels. 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 3. 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 2014 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 3. 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

4.7.2 Accuracy Assessment

4.7.2.1 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.


4.7.2.2 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 varying levels of errors were observed 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. The former type of error was not corrected, although when detected the parcel was drawn but the ownership was not determined. 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.

4.7.2.3 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

4.7.2.4 Results A total of 3,264 ownership parcels were drawn and independently evaluated for the accuracy analysis. Ownership classification at the primary level was in agreement for 99.4% of all parcels, while the sub-category classification was in agreement for 93.6% of all drawn parcels.


4.8 Statewide Primary Wood-Using Plants Data Layer

4.8.1 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 outreach to the mills themselves. Some additional mills were identified through additional research.

4.8.2 Estimating Timber Removal, Sustainability, and Availability The removal analysis matched sustainable levels of timber supply with existing mill removal for raw materials to generate timber sustainability and availability maps for four product categories (Pine and Hardwood & Cypress 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.

4.8.2.1 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: pine and hardwood & cypress, pulpwood and sawtimber size classes. The starting point for timber supply was the statewide BAP 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.

4.8.2.2 Reserved and non-reserved timber With timber resources not always on the market due to ownership policies and/or restrictions, reserved and non-reserved timber estimates were established. Non-reserved timber is timber that is available for purchase on the market; reserved timber is timber that is locked out of market, for example the National Park Service or Babcock Ranch, (Table 4). A score of 100 indicates the percentage of timber that is non-reserved, or in other words, available for purchase on the market. Table 4: Non-reserved timber percentages by forest cover and ownership subcategory

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

611 Cypress

612 Mangroves

613 Other Forested Wetlands

Federal, BIA and other

Indian lands 0 100 100 100 100 100 100 100 0 100

Federal, other or

unknown agency

0 100 100 100 100 100 100 100 0 100

Federal, USDA

National Forest

0 94 94 94 94 94 94 94 0 94

Federal, USDI Bureau

of Land Management

0 100 100 100 100 100 100 100 0 100


Federal, USDI Fish

and Wildlife Service

0 100 100 100 100 100 100 100 0 100

Federal, USDI

National Park Service

0 0 0 0 0 0 0 0 0 0

Federal, USDOD 0 100 100 100 100 100 100 100 0 100

Local, county 0 100 100 100 100 100 100 100 0 100

Local, municipal 0 100 100 100 100 100 100 100 0 100

Private

nonindustrial, corporate

other

0 100 100 100 100 100 100 100 0 100

Private nonindustrial,

corporate REIT

0 100 100 100 100 100 100 100 0 100


corporate TIMO

0 100 100 100 100 100 100 100 0 100


non-corporate (individual and family)

0 100 100 100 100 100 100 100 0 100

Private, conservation

land 0 100 100 100 100 100 100 100 0 100

Private, forest

products industry

0 100 100 100 100 100 100 100 0 100

Private, other 0 100 100 100 100 100 100 100 0 100

State, Babcock Ranch

(Babcock Ranch

Management, LLC)

0 0 0 100 100 50 100 10 0 0

State, Department

of Corrections

(PRIDE)

0 100 100 100 100 100 100 100 0 100

State, Department of Military

Affairs

0 100 100 100 100 100 100 100 0 100

State, FDACS Florida Forest Service

0 100 100 100 100 100 100 100 0 100


State, FDEP Bureau of

Mine Reclamation

0 100 100 100 100 100 100 100 0 100

State, FDEP Division of Recreation and Parks

0 100 100 100 100 100 100 100 0 100

State, FDEP Division of

State Lands 0 100 100 100 100 100 100 100 0 100

State, FDEP Office of

Coastal and Aquatic

Managed Areas

0 100 100 100 100 100 100 100 0 100

State, FDEP Office of

Greenways and Trails

0 100 100 100 100 100 100 100 0 100

State, Fish and Wildlife

Conservation Commission

0 100 100 100 100 100 100 100 0 100

State, other including

Undesignated State Land or unknown

agency

0 100 100 100 100 100 100 100 0 100

State, universities

and colleges 0 100 100 100 100 100 100 100 0 100

State, Water Management

Districts 0 100 85 85 85 85 85 85 0 85


Districts 0 100 85 85 85 85 85 85 0 85


Districts 0 100 85 85 85 85 85 85 0 85

4.8.2.3 Timber removal Mill removal for each of the four product categories was estimated by the study starting with the primary wood using mills database. The volume removed by mills and its geographic distribution by the state’s mills was determined by two methods: (1) a survey delivered to each mill in the mills database and (2) estimation utilizing a similar model to that employed in the previous year for non-responsive mills. (1) Mill removal survey. The mill survey was conducted as follows:

1. Email and paper copy survey and non-disclosure agreement to mill owners and operators in May 2015.


a. FFS reviewed list of active and inactive sawtimber, chip-n-saw, and pulpwood mills in Florida and nearby states.

b. Survey questions pertained to green tonnage processed by county in 2014. 2. Data collected and aggregated July-August 2014.

Response rate to the survey was relatively low, 10 of 76 active mills in the database replied to the survey. Survey respondents were disproportionately larger facilities and 46% of all product volume removal was determined from survey responses. (2) Mill removal model. Raw material removal from responsive facilities was assigned to the counties as indicated in the mill’s survey response. Removal from facilities that did not respond to the survey was determined through a removal model that used end product types, mill capacity, and geographic location to estimate the mill’s raw material removal and the geographic distribution of that removal. The model employed here is the same as that used the previous year. Raw material removal was distributed across a woodshed determined for each individual mill. Removal 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 removal 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 removal for each product estimated.

3. Mills were classified based on their product type, capacity and geographic location. 4. Mill woodshed boundaries were delineated according to their classification. 5. Mill raw material removal was distributed into 2, 3 or 4 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 or mill closures. Mill product types and raw material removal. 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 removal. 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 removal. Sources for conversion factors were similar to those utilized for mill capacity. 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 removal 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 removal 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 five categories based on individual capacity and product type: small, medium, large/specialty mills, large interior pulpwood-using mills, and large coastal pulpwood-using mills. 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 removal 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. The coastal location of large pulpwood consumers extended the reach of their woodsheds to 115 miles. Mill raw material removal 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 removal, 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 removal across its woodshed. This analysis partitioned each mill’s removal into woodshed zones reflecting varying freight distances from the mill (measured in road miles from the mill location) using the distribution in Tables 5-9. The right hand column indicates the share of the overall mill removal that is estimated to originate from each woodshed zone described in the left hand column. The distributed removal was plotted along with the outer woodshed boundaries in the Mill Woodshed and Removal Level map series. Removal was summed for overlapping woodsheds to indicate the total estimated removal for any given location. Mill removal was then aggregated at the county level to generate the Timber Removal map series for Pine and Hardwood & Cypress species groups each for sawtimber and pulpwood product types.


Mills were divided into pulpwood and non-pulpwood, if a mill was physically on the coast it was considered coastal otherwise interior, the specialty mills were pole or plywood etc., these mills draw up on a wider area for their products than the pulp mills. The classification of small medium and large was based on capacity of the mill, the definitions were based on the Florida Forest Service definitions that are based on the amount of roundwood received at the mill for processing in units of million board feet of timber.

• Small Mills < 1.0 MMBF • Medium Mills 1.1 to 10.0 MMBF • Large Mills > 10.1 MMBF

All pulp mills and chip mills are classified as Large Mills Table 5. Percentage of raw material furnished in two concentric zones extending from small mill types.

Table 6. Percentage of raw material furnished in three concentric zones extending from medium mill types.

Table 7. Percentage of raw material furnished in three concentric zones extending from large and specialty mill types.

Table 8. Percentage of raw material furnished in three concentric zones extending from large interior pulpwood-using mill types.

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

47.4 estimated average mileage

MEDIUMRadius (miles) % raw material furnish0-40 35%41-65 40%66-90 25%


LARGE/SPECIALTY MILLRadius (miles) % raw material furnish0-40 55%41-65 35%66-90 10%



Table 9. Percentage of raw material furnished in three concentric zones extending from large coastal pulpwood-using mill types.

4.8.2.4 Sustainability Index and Availability Maps The Timber Sustainability Index map evaluated the ratio of net growth to estimated timber removal at the county level for each of the four product categories: pine and hardwood & cypress, for sawtimber and pulpwood. Sustainability indices with values greater than 1.0 indicated that net growth exceeded estimated removal for that species group and product type in that county. Conversely, sustainability indices less than 1.0 indicated greater removal than net growth of 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 removal 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 removal for that county for each species group (pine or hardwood & cypress) 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 removal, and which counties were in timber deficit for any species group product type combination. In Southern Florida, net timber growth and/or timber removal 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 removal. Generally, if the timber removal was less than 100 tons per year, the timber sustainability index and timber availability was not estimated. Similarly, where this low timber removal was accompanied by negative timber growth, these situations were indicated on the maps with different symbols. Finally, sustainability ratios larger than 5.0 were grouped symbols in the 2014 maps.

4.9 Timber Resources Distribution in Green Tons

4.9.1 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

INTERIOR LARGE PULPWOOD-USING MILLRadius (miles) % raw material furnish0-40 55%41-65 30%66-90 15%


COASTAL LARGE PULPWOOD-USING MILLRadius (miles) % raw material furnish0-40 30%41-65 25%66-90 25%91-115 20%



table. The BAP stratification layer was a combination of the statewide forestland cover data layer (described in section 4.3), 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 4.5). The stratified yield lookup table was generated from data collected by the USFS FIA Program and its synthesis is detailed in this section. Each polygon 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 polygon 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 Pine species and Hardwood & Cypress species using the Forest Inventory and Analysis species list in Table 10 (O’Connell et al. 2012) 5. 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 11. Table 10. FIA Species Group Codes used for CFIAS categories (including species not growing in Florida). Species Group Species Code Pine species group Longleaf and slash pines 1 Loblolly and shortleaf pines 2 Other yellow pines 3 Hardwood & cypress 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

5 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.


Other eastern hard hardwoods 42 Eastern noncommercial hardwoods 43 Cypress 8


Table 11. Minimum DBH values for product classes in general species groups.

Product Class Pine Hardwood Cypress

Minimum DBH

Maximum DBH

Minimum DBH

Maximum DBH

Minimum DBH

Maximum DBH

Sub-merchantable 1 4.9 1 4.9 1 4.9

Pulpwood 5 8.9 5 10.9 5 8.9 Sawtimber 9 -- 11 -- 9 --

4.9.2 Equations, Processes and Programs used for Stratified Inventory

The following section details the process 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 primarily by aggregating data retrieved from the Forest Inventory Analysis online reporting tools (FIDO and EVALidator).

4.9.2.1 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, these plots were used 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 standing volume and growth rates. The Forest Inventory Database Online (FIDO) and EVALidator tools were used to generate tables on volume, annual growth rates and acreage which were used to calculate volume growth per year by BAP strata levels.

4.9.3 Stratified Inventory Process

4.9.3.1 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 20142013 vintage of the imagery, the most recent inventory sub-cycle utilized was 20142013. The initial sample size was 7,2536,779 fixed radius sub-plot clusters were available for use in 20142013. 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 five inventory sub-cycles:

• 128092295010854 • 128092296010854 • 128092297010854


• 128092298010854 • 128092299010854 • 218217928020004

Standing Timber was calculated to determine the amount of biomass currently available. Standing timber was calculated using FIA reports generated by the FIA online tools, including FIDO Reports. Total standing green tons 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. Standing volume and green tons was calculated using inventory information based on all FIA plots and the FIA Program analysis available for download online. Reports were generated from the FIA online tools that gave total standing volume 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, age class in 20-year increments, stand origin and FIA region. Additional reports were created that gave the acreage that was present in each of the forest types and utilizing the same filtering.

4.9.3.2 Aggregate Growth Datasets Total standing volume in cubic feet and total acreage for each FIA forest type were cross-walked and aggregated to conform to the CFIAS forest typing convention. This newly aggregated data was organized by CFIAS BAP class which included forest type, age class, origin and region. These two sets of data were used to calculated a per acre value in cubic feet for each BAP class in each region. This value of cubic feet per acre was then converted to green tons per acre using the conversion weights established in the original statement of work. A yield lookup table was created containing per acre values for sawtimber tons per acre, sawtimber cubic feet per acre, pulpwood tons per acre and pulpwood cubic feet 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.

4.9.3.1 Calculating Net Growth Rate Timber net growth rate is calculated to determine the amount of biomass available after mills’ removal and the relative level of sustainability of an area. Growth was calculated using FIA reports generated by the FIA online tools, including FIDO Reports. 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.


5 Changes made in 2015 study Methods in the 2015 study, based on 2014 year data were the same as the methods used in 2013.

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 Land Cover crosswalk to Florida Forest Cover Classification

FWC Habitat and Land Cover 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 fourth 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 Age Class Origin Class 100: Urban 1: 0-5 yrs. P: Planted 200: Agriculture 2: 5-10 yrs. N: Natural 210: Row Crops 3: 10-15 yrs. 220: Pasture/Grassland 4: 15-20 yrs. 500: Water 5: 20-25 yrs. 600: Wetlands 6: 25-30 yrs. 610: Forested Wetlands 7: 30-35 yrs. 611: Cypress 8: 35-40 yrs. 612: Mangrove 9: 40+ yrs. 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


Table B2. Standing Timber Strata Tables. All units are in green tons per acre. Note: #101(N or P) includes the BAP classes #102, #103, and #104, and #105(N or P) includes #106, #107, and #108.

BAP Region Pine

Pulpwood Pine

Sawtimber Total

Pine

Hardwood & Cypress Pulpwood

Hardwood & Cypress Sawtimber

Total Hardwood & Cypress

4101N North 0.35 0.66 1.01 2.82 3.78 6.60 4105N North 1.06 2.51 3.57 11.82 12.71 24.53 4109N North 0.37 4.07 4.44 12.40 48.94 61.33 4101N South 0.32 0.14 0.45 2.27 1.81 4.08 4105N South 0.83 1.55 2.38 8.51 11.56 20.06 4109N South 0.40 3.37 3.77 12.73 38.76 51.49 4101P North 0.35 0.66 1.01 2.82 3.78 6.60 4105P North 1.06 2.51 3.57 11.82 12.71 24.53 4109P North 0.37 4.07 4.44 12.40 48.94 61.33 4101P South 0.32 0.14 0.45 2.27 1.81 4.08 4105P South 0.83 1.55 2.38 8.51 11.56 20.06 4109P South 0.40 3.37 3.77 12.73 38.76 51.49 4211N North 0.34 0.64 0.99 0.47 0.00 0.47 4211P North 0.52 0.20 0.72 0.02 0.00 0.02 4211N South 0.73 1.85 2.58 0.54 0.00 0.54 4211P South 0.56 0.93 1.49 0.00 -0.08 -0.08 4221N North 6.26 0.00 6.26 0.79 0.00 0.79 4225N North 27.87 9.84 37.71 3.05 -0.26 2.79 4229N North 24.19 36.01 60.20 2.16 -0.01 2.15 4221N South 6.37 0.00 6.37 0.93 0.00 0.93 4225N South 23.03 14.07 37.10 0.67 0.00 0.67 4229N South 27.13 46.63 73.76 5.61 0.00 5.61 4221P North 12.86 0.09 12.95 0.07 0.00 0.07 4225P North 51.64 10.63 62.26 0.18 0.00 0.18 4229P North 40.91 19.55 60.45 0.00 0.00 0.00 4221P South 33.06 2.88 35.93 14.42 6.28 20.70 4225P South 65.55 19.46 85.01 0.00 0.00 0.00 4229P South 60.07 0.00 60.07 0.00 0.00 0.00 4231N North 4.73 4.20 8.93 1.36 -0.26 1.10 4235N North 15.47 28.49 43.96 4.99 1.23 6.21 4239N North 11.70 52.30 64.00 10.33 2.48 12.80 4231P North 20.38 3.36 23.74 0.84 0.22 1.06 4235P North 31.35 25.24 56.58 1.91 0.17 2.07 4239P North 20.82 59.76 80.58 4.24 2.79 7.03


4241N North 4.10 1.57 5.67 0.27 0.00 0.27 4245N North 13.57 17.42 30.99 2.00 0.09 2.09 4249N North 7.50 38.04 45.54 2.52 0.48 2.99 4241N South 0.00 0.00 0.00 0.00 0.00 0.00 4245N South 0.00 0.00 0.00 0.00 0.00 0.00 4249N South 0.00 0.00 0.00 0.00 0.00 0.00 4241P North 3.94 1.41 5.35 0.27 0.00 0.27 4245P North 18.79 9.24 28.04 3.27 0.09 3.35 4249P North 6.77 40.43 47.20 3.52 0.20 3.72 4241P South 0.00 0.00 0.00 0.00 0.00 0.00 4245P South 0.00 0.00 0.00 0.00 0.00 0.00 4249P South 0.00 0.00 0.00 0.00 0.00 0.00 4251N South 6.03 5.19 11.22 1.76 0.01 1.77 4255N South 10.55 18.68 29.23 2.67 0.07 2.75 4259N South 12.68 36.21 48.89 8.23 0.54 8.77 4251P South 13.01 0.35 13.36 1.14 0.42 1.57 4255P South 31.61 20.25 51.86 4.30 2.16 6.46 4259P South 8.56 28.25 36.81 5.69 0.00 5.69 4301N North 2.40 4.44 6.83 1.41 2.86 4.27 4305N North 4.57 15.82 20.38 10.90 7.20 18.10 4309N North 3.73 31.62 35.35 15.25 22.74 37.98 4301N South 2.40 5.49 7.89 4.45 1.86 6.30 4305N South 4.99 12.19 17.18 9.37 6.60 15.98 4309N South 4.85 23.46 28.31 12.65 18.04 30.69 4301P North 2.29 1.64 3.94 1.52 4.77 6.29 4305P North 5.34 12.51 17.85 4.08 1.30 5.38 4309P North 5.72 12.73 18.46 3.85 5.93 9.78 4301P South 0.70 1.24 1.94 0.00 0.40 0.40 4305P South 0.00 15.63 15.63 0.00 0.00 0.00 4309P South 1.80 16.66 18.46 3.82 5.98 9.80 6111N North 0.04 0.52 0.56 9.02 9.45 18.48 6115N North 0.83 7.89 8.72 20.20 12.02 32.22 6119N North 1.77 1.13 2.89 35.39 98.12 133.51 6111N South 0.11 0.00 0.12 2.59 2.98 5.57 6115N South 0.81 1.03 1.84 20.78 13.57 34.35 6119N South 0.46 0.87 1.33 49.89 68.22 118.11 6111P North 0.04 0.52 0.56 9.02 9.45 18.48 6115P North 0.83 7.89 8.72 20.20 12.02 32.22 6119P North 1.77 1.13 2.89 35.39 98.12 133.51 6111P South 0.11 0.00 0.12 2.59 2.98 5.57 6115P South 0.81 1.03 1.84 20.78 13.57 34.35


6119P South 0.46 0.87 1.33 49.89 68.22 118.11 6121N North 0.00 0.00 0.00 1.19 0.00 1.19 6125N North 0.00 0.00 0.00 1.50 0.00 1.50 6129N North 0.00 0.00 0.00 15.52 0.00 15.52 6121N South 0.00 0.00 0.00 1.19 0.00 1.19 6125N South 0.00 0.00 0.00 1.50 0.00 1.50 6129N South 0.00 0.00 0.00 15.52 0.00 15.52 6131N North 0.64 0.52 1.16 2.95 2.11 5.06 6135N North 0.86 2.42 3.28 17.94 9.12 27.05 6139N North 0.55 5.63 6.17 29.86 65.88 95.74 6131N South 0.15 0.36 0.51 2.05 1.56 3.61 6135N South 0.06 0.46 0.52 11.92 15.97 27.89 6139N South 0.22 1.41 1.63 21.57 45.72 67.29 6131P North 0.64 0.52 1.16 2.95 2.11 5.06 6135P North 0.86 2.42 3.28 17.94 9.12 27.05 6139P North 0.55 5.63 6.17 29.86 65.88 95.74 6131P South 0.15 0.36 0.51 2.05 1.56 3.61 6135P South 0.06 0.46 0.52 11.92 15.97 27.89 6139P South 0.22 1.41 1.63 21.57 45.72 67.29


Table B3. Growth Rates Strata Tables. All units are in green tons per acre. Note: #101(N or P) includes the BAP classes #102, #103, and #104, and #105(N or P) includes #106, #107, and #108.

BAP Region Pine

Pulpwood Pine

Sawtimber Tota

l Pine

Hardwood & Cypress Pulpwood

Hardwood & Cypress Sawtimber

Total Hardwood & Cypress

4101N North 0.08 0.23 0.31 0.20 0.21 0.40 4105N North 0.11 0.30 0.41 0.66 0.78 1.45 4109N North 0.01 0.17 0.19 0.28 1.27 1.55 4101N South -0.09 -0.14 -0.23 0.18 0.08 0.27 4105N South -0.01 0.13 0.12 0.49 0.67 1.17 4109N South 0.01 0.03 0.04 0.32 1.09 1.40 4101P North 0.08 0.23 0.31 0.20 0.21 0.40 4105P North 0.11 0.30 0.41 0.66 0.78 1.45 4109P North 0.01 0.17 0.19 0.28 1.27 1.55 4101P South -0.09 -0.14 -0.23 0.18 0.08 0.27 4105P South -0.01 0.13 0.12 0.49 0.67 1.17 4109P South 0.01 0.03 0.04 0.32 1.09 1.40 4211N North -0.01 0.10 0.09 0.04 0.00 0.04 4211P North 0.93 0.24 1.17 0.00 0.00 0.10 4211N South 0.20 0.27 0.47 0.05 0.00 0.06 4211P South 1.03 0.23 1.26 0.00 0.00 0.09 4221N North 0.97 0.15 1.12 0.06 0.00 0.06 4225N North 1.66 0.43 2.09 0.06 -0.06 0.01 4229N North 0.05 0.04 0.09 0.07 0.01 0.08 4221N South 0.83 0.01 0.84 0.09 0.00 0.09 4225N South 1.59 0.78 2.37 0.13 0.00 0.13 4229N South 0.27 0.13 0.40 0.10 0.00 0.10 4221P North 2.75 0.01 2.76 0.01 0.00 0.01 4225P North 3.64 0.34 3.98 0.02 0.00 0.02 4229P North 2.46 1.13 3.59 0.00 0.00 0.00 4221P South 3.92 0.39 4.31 1.01 -0.55 0.47 4225P South 5.33 1.56 6.89 0.00 0.00 0.00 4229P South 4.99 0.00 4.99 0.00 0.00 0.00 4231N North 0.61 0.41 1.02 0.08 -0.01 0.07 4235N North 0.90 1.70 2.60 0.24 0.06 0.30 4239N North 0.26 1.15 1.41 0.18 0.11 0.29 4231P North 3.24 0.48 3.72 0.06 0.02 0.08 4235P North 2.42 1.93 4.35 0.09 0.01 0.10 4239P North 0.85 1.15 2.00 0.06 0.12 0.17 4241N North 1.04 0.11 1.15 -0.01 -0.01 -0.02


4245N North 0.71 0.95 1.65 0.19 0.00 0.19 4249N North 0.14 0.77 0.92 0.07 0.09 0.16 4241N South 1.21 0.18 1.39 -0.02 0.02 -0.01 4245N South 0.55 0.85 1.40 0.15 0.00 0.15 4249N South 0.14 -0.08 0.06 -0.01 0.00 -0.01 4241P North 1.04 0.17 1.21 0.06 0.00 0.06 4245P North 1.84 0.86 2.71 0.23 0.00 0.24 4249P North 0.10 0.81 0.91 0.12 0.04 0.16 4241P South 1.04 0.17 1.21 0.06 0.00 0.06 4245P South 1.84 0.86 2.71 0.23 0.00 0.24 4249P South 0.10 0.81 0.91 0.12 0.04 0.16 4251N South 0.58 0.38 0.96 0.31 0.01 0.32 4255N South 0.44 0.89 1.33 0.05 -0.01 0.04 4259N South 0.33 0.96 1.29 0.13 0.04 0.18 4251P South 2.11 0.03 2.14 0.00 0.00 0.00 4255P South 1.90 1.32 3.23 0.16 0.09 0.24 4259P South 0.09 0.45 0.54 0.10 -0.01 0.09 4301N North 0.35 0.63 0.98 0.07 0.13 0.19 4305N North 0.25 0.99 1.24 0.47 0.22 0.69 4309N North 0.11 0.98 1.09 0.38 0.56 0.92 4301N South 0.26 0.45 0.71 0.36 0.27 0.61 4305N South 0.40 0.39 0.79 0.22 0.15 0.37 4309N South 0.14 0.74 0.87 0.24 0.38 0.61 4301P North 0.35 0.30 0.64 0.07 0.12 0.20 4305P North 0.43 1.42 1.85 0.33 0.13 0.47 4309P North 0.21 0.46 0.67 0.03 -0.16 -0.12 4301P South 0.11 0.19 0.30 0.00 0.00 0.00 4305P South 0.30 1.01 1.31 1.55 1.24 2.80 4309P South 0.13 1.18 1.31 1.09 1.71 2.80 6111N North -0.02 -0.31 -0.34 0.52 0.55 1.07 6115N North 0.04 0.80 0.84 0.87 0.37 1.24 6119N North 0.06 0.64 0.71 0.70 1.95 2.65 6111N South -0.21 -0.01 -0.22 -0.30 0.52 0.22 6115N South -0.12 -0.14 -0.25 0.49 0.35 0.84 6119N South 0.01 0.04 0.05 0.96 1.41 2.37 6111P North -0.02 -0.33 -0.34 0.47 0.62 1.09 6115P North 0.04 0.80 0.84 0.87 0.37 1.24 6119P North 0.06 0.64 0.71 0.70 1.95 2.65 6111P South -0.21 -0.01 -0.22 -0.30 0.52 0.22 6115P South -0.12 -0.14 -0.25 0.49 0.35 0.84 6119P South 0.01 0.04 0.05 0.96 1.41 2.37


6121N North 0.00 0.00 0.00 0.07 0.01 0.07 6125N North 0.00 0.00 0.00 0.10 0.00 0.10 6129N North 0.00 0.00 0.00 0.36 -0.03 0.34 6121N South 0.00 0.00 0.00 0.07 0.01 0.07 6125N South 0.00 0.00 0.00 0.10 0.00 0.10 6129N South 0.00 0.00 0.00 0.36 -0.03 0.34 6131N North 0.08 0.03 0.11 0.18 0.10 0.28 6135N North 0.05 0.18 0.22 0.71 0.40 1.11 6139N North 0.01 0.19 0.20 0.53 1.19 1.72 6131N South -0.08 -0.07 -0.15 0.15 0.19 0.35 6135N South 0.00 0.03 0.04 0.50 0.65 1.14 6139N South -0.02 -0.09 -0.11 0.12 0.46 0.58 6131P North 0.08 0.03 0.11 0.18 0.10 0.28 6135P North 0.05 0.18 0.22 0.71 0.40 1.11 6139P North 0.01 0.19 0.20 0.53 1.19 1.72 6131P South -0.08 -0.07 -0.15 0.15 0.19 0.35 6135P South 0.00 0.03 0.04 0.50 0.65 1.14 6139P South -0.02 -0.09 -0.11 0.12 0.46 0.58

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