GEOG385 Final Project Kimberly Mauch

Development of a Web-based Map Service for Nevada Greater Sage-Grouse Habitat Management

Project Summary

The purpose of my final project was to demonstrate how a web-based map service of the conifer extraction project and Greater Sage-Grouse management categories dataset would be useful to the project manager and land managers to quickly gain insight into the status and progress of the project as well as where suitable sage-grouse habitat is located within a specific region. Another purpose was to show how the two datasets could be utilized by land managers for habitat restoration to benefit sage-grouse.

Three dataset layers were utilized to make four geoprocessing widgets for the map service. Geoprocessing outputs were appropriately symbolized but weren’t necessarily correctly displayed in the map. While there were issues with the symbolism of the selected output and placement of the information table, the web-based map did provide correct information and still proved that it could be useful to the targeted audience.

Introduction

The U.S. Geological Survey’s (USGS) Western Ecological Research Center (WERC) field station in Dixon, California was contracted to create a high-resolution spatial data layer and map of pinyon-juniper trees to be used for analysis and identification of areas where pinyon-juniper forests are expanding into sagebrush habitat. This task is one of a larger set of ongoing research and mapping projects in progress relating to the Greater Sage-Grouse, a species proposed for federal listing as endangered. Pinyon-Juniper encroachment has been identified as one of the primary causes of Greater Sage-Grouse (Centrocercus urophasianus) decline in Nevada, as studies undertaken by WERC biologists have documented their avoidance of suitable sagebrush habitat if trees are present.

The mapping project consists of using high-resolution USDA National Agriculture Imagery Program (NAIP) imagery and Feature Analyst software to extract shapes of individual trees from the imagery, process the outputs and create a mosaiced spatial layer. The project area encompasses nearly two-thirds of the state of Nevada and adjacent areas of California, Oregon and Idaho. The project is quite extensive, with over 7,000 images that require analysis and extraction methodology to be applied. This process is slow and tedious, depending in large part to the image quality, ranging from very good to very poor, which has resulted in the project taking much longer than anticipated.

Purpose

The development of a web-based mapping service for this and other greater sage-grouse habitat projects would serve several purposes and serve two categories of end users. The project manager could at a moment’s notice (and depending on the timeliness of updates to the map service) see how and where progress toward the final product is being made, who the

analyst is, how large the area is, the tile count of the area and other pertinent information. This would allow the project manager to update the Sagebrush Ecosystem Council on a frequent basis on how the project was coming along. As the project manager travels periodically, the web-based map could be accessed remotely and allow for an update on the project no matter where he might be.

One of the main purposes of developing the conifer extraction layer in the first place is that it will be used by land managers, the second category of end users, to identify those areas of the sagebrush ecosystem that would most benefit from removal of conifers. These areas once identified become proposed Conifer Treatment Areas (CTA).

Land managers could also check on the status of the project or on a specific area at any time and gain valuable up-to-date information on the status – has it been started, has a draft been approved, or is it completed. This information would be quite useful for development of long-term planning for CTAs. For example, a land manager might choose to identify CTAs in areas first where the layer has been completed, and plan their funding accordingly.

As more datasets are added, such as greater sage-grouse habitat location and management categories, the web-based map becomes even more useful to identify CTAs. The mapping service would allow access to information such as where suitable sage-grouse habitat is within a certain region, what the habitat categories are and how much of that category exists. When cross-referenced with conifer extraction layer, it provides even more useful information to locate areas where conifer removal would benefit sage-grouse the most.

Description of Data and Web Application Development

For purposes of keeping the mapping service as simple as possible, I clipped the project boundary layer to the Nevada county layer, eliminating the need for additional layers for the other states – California, Oregon and Idaho – to which the full extent of our project area encompasses.

Manipulation and processing of the datasets was required before they would be appropriate for a simple query-based web map application. The following layers were used and any pre-processing tasks are described:

PMU dataset: Population Management Units (PMU) were initially created by the Nevada Department of Wildlife to identify distinct greater sage-grouse leks and populations. The conifer extraction project used these PMU boundaries to divide the project into feasible parts. Fields were added to this layer including tile count, analyst initials and status.

Greater Sage-Grouse Habitat with management categories: Suitable sage-grouse habitat is divided into management categories; in order of importance they are core, priority and general. Core habitat represents the best habitat for sage-grouse and incorporate the highest density of breeding populations. Priority habitat is highly suitable habitat that is not contained within the Core management area. General habitat is less suitable than priority.

I could have intersected the two files in order to perform simple queries; however, both contained fields with the same name but not in the same format, so for better success of map

querying, I used the PMU dataset to clip the Habitat layer, producing discrete polygons of habitat categories with more accurate area amounts. Each clipped PMU now had four habitat category types: core, priority, general and nonhabitat.

Nevada county dataset: no processing was done on this layer. However, the county names were all formatted the same for query success.

The Nevada County layer was also used to clip the sage-grouse Habitat layer, again providing discrete polygons of each management category within each county and providing accurate area figures.

Several models were built to automate the above-mentioned processes. ‘ClipPMU’ was created to create a clipped PMU based on PMU name – 49 of them - then add a square kilometers area field and calculate the field.

ClipPMU Model

The ClipMerge model clipped the Sage-Grouse Habitat Management Category dataset by each clipped PMU in order to achieve the management categories within the PMU and accurate calculations. I thought this would make end user queries simpler and make the geoprocessing tools in the mapping service less likely to break, since it would involve only one layer and a simple attribute and location query.

With all the layers now prepared, the geoprocessing models were now created. There were four geoprocessing tasks I wanted the web-based mapping service to provide to the end user:

Select by a specific PMU name; Select Greater Sage-Grouse Habitat by PMU; Select PMUs by County; Select Greater Sage-Grouse Habitat by County.

These are represented by the four models shown below. Each model was run with the final output designated as a parameter in order to have the final output show up in the table of contents and be uploaded as the geoprocessing tool model.

The outputs of each model were symbolized the way I wanted them to appear in the mapping service and saved. With the geoprocessing models complete, the map service was then published. The following snippet shows the main map after the map service and the geoprocessing widgets were published.

A terrain base map layer was used and the Greater Sage-Grouse habitat categories, shown in red, orange or yellow were made semi-transparent in order for the end user to still see the location information of the base map. The PMU dataset is visible, with PMU names not capitalized. The Nevada county names are also visible but capitalized to distinguish them from PMU names. The county names also appear in a medium gray and the PMU names in black, in order to help separate the two datasets.

The snippet above shows the first geoprocessing model, selecting a specific PMU. A drop-down box appears with all the PMU names listed. The snippet below shows the end results of this geoprocessing widget, showing the PMU ‘Battle Mountain’ highlighted in aqua, similar to the appearance of highlighted features in ArcMap.

The second geoprocessing widget is shown below, selecting Greater Sage-Grouse habitat management categories by PMU. The ‘Battle Mountain’ PMU is shown once again, but this time the habitat management categories are displayed with no transparency, distinguishing them from the surrounding habitat polygons.

The two geoprocessing widgets selecting by county were similarly symbolized. The first attempt at trying the four geoprocessing models resulted in the output being correctly symbolized, but unfortunately it was the only time that I could get the desired results to occur. The issues encountered after publishing the map service will now be discussed.

Discussion

As mentioned, after the first geoprocessing widget results were obtained, the map service never correctly symbolized the outputs again. Now in place of the highlighted PMU or county appeared dark boxes that covered the desired output that needed to be moved out of the way in order to see the output! However, the boxes that now appeared resulted in the display of the desired information. Results for selecting PMU by county and selecting sage-grouse habitat management categories by county are shown below.

The above snippet shows on the left the desired county, with the PMUs within the county ‘highlighted’ as a semi-transparent dark gray. A box representing each PMU appears showing various information about the PMU. In theis case, there are five PMUs and five tables. However, the PMU must be selected with cursor before the appropriate box will be highlighted. I was hoping that a summary table could be created, listing the pmus and desired information, but I never figured out how to do it.

The snippet below shows the Priority habitat management category selected for Humboldt County. The priority polygons within the county are highlighted in aqua as desired, but the table covers the area and is annoying. It does, however, give the correct area calculation for that habitat category.

I have come to the conclusion, however, that the vast majority of my classmates ran into the same type of symbolization and display issues, and that FlexBuilder is not very flexible when it comes to the display of geoprocessing output information appearance and location. I was also disappointed that all of the attribute information appeared, and thought that I had hidden the

fields that I did not want displayed through the settings of FlexBuilder, but I apparently did not do this correctly.

Concerning thoughts on project improvement that I can control, I need to return to the settings and reset the field attributes I want to appear. The other issues that I see as ‘problems’ I’m not sure I have any ability to change, such as where the table appears and the color of the highlighted feature; I think these are issues controlled by FlexBuilder.

I have learned that it is most important to have a plan before you attempt to create and/or symbolize your data that will be utilized in the map service. Know what your intent and purpose are for the map service before any dataset is manipulated. Also know what you want the end results to be for the end user. I can see where knowing html code is very useful and this experience has given me an new appreciation for web page designers and programmers!