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 longterm 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 preprocessing 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!