National Environmental Threats
Assessment Mapping (NETAM)
October 2009
United States
Department of
Agriculture
RSAC-10003-RPT1
Forest
Service
Geospatial
Management
Office
Remote Sensing
Applications Center
Western Wildland
Environmental Threat
Assessment Center
Abstract
The purpose of this project was to adapt existing geospatial products to create a set of environmental threat maps on a national
scale. We limited the analysis to forested areas on the 48 contiguous states. Available geospatial datasets to support threat
mapping include: insect & disease risk, wildland fire potential, and road density. We created a simple GIS model to map these
threats consistently and assess cumulative threat variables. The threat variables were summarized according to three geographic
accounting units: county, watershed and EMAP hexagon. The ultimate goal is to develop a multi-criteria spatial decision
support system to inform and support national and regional-level natural resource decision-making.
Key words
Forest Threats, Wildland Fire, Insect & Disease, Forest Fragmentation, Road Density, ArcGIS Modeler
Authors
Richard Warnick is a remote sensing analyst at the USDA Forest Service Remote Sensing Applications Center in Salt Lake
City, Utah.
Ken Brewer is the Remote Sensing Research Program Leader, USDA Forest Service Research and Development in Arlington,
Virginia.
Mark Finco is a senior analyst at the USDA Forest Service Remote Sensing Applications Center and a principal in RedCastle
Resources.
Jerry Beatty is the Director of the Western Wildlands Environmental Threat Assessment Center (WWETAC) at the USDA
Forest Service Pacific Northwest Research Station in Corvallis, Oregon.
Warnick, R.; Brewer, K.; Finco, M.; Beatty, J. 2009. National environmental threats assessment mapping. RSAC-10003RPT1. Salt Lake City, UT: U.S. Department of Agriculture Forest Service, Remote Sensing Applications Center. 25 p.
ii
Table of Contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Project Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Analytical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Threat Layer Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Source and Ancillary Datasets. . . . . . . . . . . . . . . . . . . . . . . . . 2
Source Datasets Selected for Threat Layers . . . . . . . . . . . . . . . 4
Accounting Unit Source Datasets . . . . . . . . . . . . . . . . . . . . 4
Ancillary Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Threat Mapping Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Appendix A: National Environmental Threat Assessment Mapping (NETAM)
Workshop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
National Environmental Threat Assessment Mapping (NETAM)
Workshop Agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
iii
Appendix B: Source Datasets Used . . . . . . . . . . . . . . . . . . . . . 12
FHTET National Insect & Disease Risk Map . . . . . . . . . . . . . . . 12
Wildland Fire Potential . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Road Density from StreetMap Pro . . . . . . . . . . . . . . . . . . . . 16
CONUS Counties . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Counties Percent Forested . . . . . . . . . . . . . . . . . . . . . . . 18
Watersheds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Watersheds Percent Forested . . . . . . . . . . . . . . . . . . . . . . 20
EMAP Hexagons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
EMAP Hexagons Percent Forested. . . . . . . . . . . . . . . . . . . 23
CONUS Forest/Non-forest Mask . . . . . . . . . . . . . . . . . . . . 24
Contiguous 48 States. . . . . . . . . . . . . . . . . . . . . . . . . . 24
Appendix C: Arc GIS Model Builder Flow Chart . . . . . . . . . . . . . . . 25
iv
Overview
In cooperation with the Western
Wildlands Environmental Threat
Assessment Center (WWETAC), the
Remote Sensing Applications Center
(RSAC) adapted existing geospatial
products to create a set of
environmental threat maps on a
national scale. For this project, we
limited the analysis to forested areas on
the 48 contiguous states.
Available geospatial datasets to support
threat mapping include: insect &
disease risk, wildland fire potential, and
road density. We explored ways to map
these threats consistently and assess
cumulative threat variables. These
variables were summarized according to
three geographic accounting units:
county, watershed and EMAP hexagon.
The ultimate goal of this project is to
develop a multi-criteria spatial decision
support system to inform and support
national and regional-level natural
resource decision-making.
An additional project objective was to
explore a simplified method of mapping
climate change effects, in cooperation
with PNW Research Station
Bioclimatologist Ron Neilson, using
data from the Mapped AtmospherePlant-Soil System (MAPSS) Project.
This effort is addressed in a separate
report: Mapping Climate Change from
MAPPS Data.
Background
An environmental threat (or risk
variable) is an indication of potential
negative environmental impact to a
specific area. Environmental threats
include all activities, events, organisms,
or materials likely to disrupt natural
ecosystems, degrade ecological
functions, or impair the flow of benefits
which derive from them.
North American forests and rangelands
are affected by a host of natural and
anthropogenic threats, including the
three addressed in this project:
Figure 1—Example of National Environmental Threats Assessment Mapping
(NETAM) map output.
 Insect & disease risk— Potential tree
mortality caused by insects and
diseases
 Uncharacteristic fire— Potential tree
mortality, watershed degradation,
and damage in wildland-urban
interface caused by wildfires
(surrogate: high and very high
wildland fire potential)
 Forest fragmentation— Humancaused forest habitat fragmentation
and disturbance of ecosystems
(surrogate: road density above 10
percent)
Other threats include watershed
degradation, invasive species, severe
weather, climate change, plus
urbanization and other conversions of
wildlands.
Mapping forest threats on a national
scale can aid in setting priorities for
further analysis at the region and
national forest level. Such mapping can
aid in developing integrated approaches
to deal with the interaction of the
1 | RSAC-10003-RPT1
multiple stresses that may affect
wildlands.
Geospatial information about forest
threats can be used in decision-support
systems to help land managers
anticipate disturbances and take action
to prevent or ameliorate effects.
The State & Private Forestry Redesign
initiative, working with Environmental
Systems Research Institute, Inc. (ESRI),
is nearing the completion of a webaccessible National Assessment (PDF)
tool similar in concept to the threat
mapping model presented in this report.
The National Assessment is geared
toward themes important for the S&PF
Redesign, and incorporates nationally
consistent datasets representing values
(e.g. forest biomass, critical wildlife
habitat) as well as threats. This tool
could be made available within the
Forest Service sometime later this year.
RSAC expects to participate in the
beta-testing of the final version.
Project Tasks
This project included the following
tasks:
1. Create insect & disease risk layer
based on FHTET National 2006
Composite Insect and Disease Risk
Map.
2. Create uncharacteristic wildfire risk
layer based on Jim Menakis’ wildfire
potential dataset.
3. Create road density/development risk
layer based on up-to-date GIS data
for roads. ESRI StreetMapPro was
recommended by Kurt Riiters.
4. Create an invasive species risk layer
based on FIA P-3 plot data (this
layer was not created because of
incomplete data).
5. Integrate the risk layers into a
composite risk map.
6. Summarize the mean composite risk
values according to three accounting
units: counties, watersheds and
EMAP hexagons.
7. Normalize the mean values for all
accounting units according to
percent forested area.
Analytical Approach
We employed a simple approach to
threat mapping, using binary (0-1) grid
layers, with a 1 km cell size. The
mapping extent was the 48 contiguous
United States. Three threat layers were
used. If additional nationally consistent
datasets become available, we can
expand the number of layers used.
forested area for each accounting unit
(figure 2).
GIS processing of the threat layers was
semi-automated using the ArcGIS
Model Builder. In the future, more
automation can be added.
Threat Layer
Creation
We developed a standard process for
making the threat layers. The most
important decision is the choice of
source datasets. These must be national
in extent (covering 48 states), consistent
and continuous, with a minimum of
data gaps. They can be vector or raster,
but source scale and spatial resolution
must correspond to a ground sample
distance of 1 kilometer or less.
In some cases, it may be necessary to
make a subset of attributes. For
example, the vector StreetMap Pro
dataset contained a lot more than road
locations, which was all we needed.
Datasets must be converted to a
1-kilometer grid format if necessary
(StreetMap Pro), and clipped to forest
areas only, using the FIA Forest Mask.
The process for creating the binary grid
layers is very flexible. The threat
thresholds can be set as desired. The
layers can be created from either
continuous (e.g. insect & disease risk)
or discrete (e.g. wildland fire potential)
source data.
The next step is to determine a
threshold that will be indicative of a
threat. The ideal source dataset is one
that can be represented in the form of a
continuous 0-100 percent grid. The
FHTET National Insect and Disease
Risk Map is a good example. Datasets
represented on a ratio scale allow for the
most flexibility in setting thresholds. In
the case of insect & disease risk,
FHTET chose a 25 percent threshold
for their published map, and we used
the same number.
We calculated a cumulative threat value
map by summing the threat layers. We
also calculated the mean cumulative
threat values by county, watershed and
EMAP hexagon, normalized by percent
In the case of wildland fire potential
(WFP), the best available dataset was on
an ordinal scale. WFP was classified as
1-5, not 0-100. The classes are: Very
Low, Low, Moderate, High, and Very
2 | RSAC-10003-RPT1
High. Our options for setting a
threshold were limited—we could have
used only the highest class, or combined
the two highest. We used two classes,
although the empirical justification for
the one-class option could also be valid.
The last step in threat layer creation is
to re-class to a binary grid, with 0
representing “non-threat” and 1
representing “threat.”
Source and Ancillary
Datasets
RSAC has reviewed and experimented
with a variety of datasets for threat
mapping. For this project, we selected
three source datasets for the threat layers
(see below).
On May 6-7, 2008, we held a workshop
and WebEx/conference call in Salt Lake
City to discuss national environmental
threat assessment mapping (NETAM)
and available data with a number of
subject-matter experts (see appendix A).
It brought together researchers and
analysts with representatives of the
Western Wildland Environmental
Threat Assessment Center, the Eastern
Forest Environmental Threat
Assessment Center, and the State and
Private Forestry organization of the
Forest Service.
Most of the workshop discussions were
about available datasets, their
limitations, and how they could be used
for threat mapping. Nearly all of the
workshop presentations are available in
PDF format on the NETAM wiki
(FSWeb). We are also using the wiki to
maintain an updated list of nationally
consistent datasets.
This project also made use of five
ancillary datasets, which were used for
summarizing the cumulative threat
values by accounting units. See
appendix B for more information about
the geoprocessing of the datasets.
Figure 2— Geoprocessing flow chart for threat mapping. As configured for this project, the threat mapping model accepts three input
grids (blue) and produces four output grids (green).
3 | RSAC-10003-RPT1
Source Datasets Selected for
Threat Layers
(see appendix B)
FHTET National Insect & Disease Risk
Map
National 2006 Composite Insect and
Disease Risk Map
Percent basal loss per acre over 15-year
period – 1 km grid
www.fs.fed.us/foresthealth/technology/
nidrm.shtml
Wildland Fire Potential
Obtained from Jim Menakis, U.S.
Forest Service Rocky Mountain
Research Station, Missoula Fire Sciences
Laboratory http://firelab.org/
WFP represents a combination of fuel,
weather and ignition potential – 1 km
grid
Road Density from StreetMap Pro
StreetMap Pro 2003 road data obtained
from ESRI
Rasterized to 30-meter grid cells
Converted to percent road density using
ERDAS Imagine
http://www.esri.com/software/arcgis/
extensions/streetmap/
Ancillary Datasets
(see appendix B):
CONUS Forest/Non-forest Mask
USDA Forest Service Remote Sensing
Applications Center (RSAC)
http://svinetfc4.fs.fed.us/rastergateway/
biomass/
Contiguous 48 States
Subset of states.shp
Environmental Systems Research
Institute (ESRI)
www.esri.com/data/
Threat Mapping
Model
After producing the binary threat layers
and the ancillary data layers, we used
ArcGIS Model Builder to construct the
threat mapping model (see appendix C).
The model does three things:
1. Integrate the risk layers into a
composite risk map.
2. Summarize the mean composite risk
values according to three accounting
units: counties, watersheds and
EMAP hexagons.
3. Normalize the mean values for all
accounting units according to
percent forested area.
Any three binary grids (ArcGIS format)
can be used as input for the model. It’s
also fairly easy to modify the model to
allow more than three input files (figure
3).
The model takes about two minutes to
run, and generates four output files:
1. Cumulative threat values
2. Normalized mean threats by county
3. Normalized mean threats by
watershed
4. Normalized mean threats by EMAP
hexagon
Cumulative threat values are calculated
by summing the binary threat layers,
using the map algebra expression
(figures 4 and 5).
Accounting Unit Source
Datasets
(see appendix B):
CONUS Counties
Subset of counties.shp
Environmental Systems Research
Institute (ESRI)
www.esri.com/data/
Watersheds
Subset of huc250k.shp
USGS 8th level hydrological units
http://water.usgs.gov/GIS/dsdl/
huc250k.e00.gz
EMAP Hexagons
Subset of ushexes_poly.shp
Environmental Monitoring and
Assessment Program (EMAP)
www.epa.gov/wed/pages/staff/white/
getgrid.htm
Figure 3—Threat mapping model input dialog.
4 | RSAC-10003-RPT1
Figure 4—Model output - cumulative threat map.
Figure 5—Close-up view of cumulative threat map.
5 | RSAC-10003-RPT1
cum_threat = insect_bin + rd_for_
bin + wfp_for_bin
Note: because the input binary threat
layers are defined as parameters, the file
names will correspond to whatever
input files are chosen.
The next step is to run zonal statistics to
calculate mean cumulative threat values
for counties, watersheds, and EMAP
hexagons. The model does this without
any additional user interaction. For
example:
easy to explain, it’s important to have
well-understood model inputs. The lack
of continuous, nationally consistent
datasets is the limiting factor. In the
future, we expect more and better data
to become available. We also expect to
learn more about the relative impact of
different threats, providing a basis for
weighting threats within the model.
In the near future, RSAC will create
interactive forest threat maps for the
web using ArcGIS Server.
The final processing step is to normalize
the mean threat values for counties,
watersheds, and EMAP hexagons
according to the percent forested area in
each of these accounting units. Example
map algebra expression:
norm_county = mean_county *
(cty_fpct / 100)
The result is three grids representing the
normalized mean cumulative forest
threat values for all three accounting
units (see model output maps, following
pages).
Close-up view of cumulative threat
map.
Conclusion
We believe that simple and flexible
methods of modeling forest threats on a
national scale are the best approach. In
order to make model outputs that are
Figure 6—Zonal statistic model example.
6 | RSAC-10003-RPT1
Reference
Ruefenacht, Bonnie, Alex Hoppus, Jule Caylor,
David Nowak, Jeff Walton, Limin Yang, Collin
Homer, and Greg Koelin. 2002. Analysis of
canopy cover and impervious surface cover of
Zone 41. RSAC-4002-RPT1. Salt Lake City, UT:
USDA Forest Service Remote Sensing
Applications Center. Available: http://fsweb.rsac.
fs.fed.us/documents/4002-RPT1.pdf
For additional information, contact:
EDS Program Leader
Enterprise Data & Services
Remote Sensing Applications Center
2222 West 2300 South
Salt Lake City, UT 84119
phone: (801) 975-3750
e-mail: Mailroom_WO_RSAC@fs.fed.us
This publication can be downloaded from the
RSAC Web sites: http://fsweb.rsac.fs.fed.us
The Forest Service, United States
Department of Agriculture (USDA), has
developed this information for the guidance
of its employees, its contractors, and its
cooperating Federal and State agencies
and is not responsible for the interpretation
or use of this information by anyone except
its own employees. The use of trade, firm,
or corporation names in this document is
for the information and convenience of
the reader. Such use does not constitute
an official evaluation, conclusion,
recommendation, endorsement, or approval
by the Department of any product or
service to the exclusion of others that
may be suitable.
Figure 7—Model output - county means.
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Figure 8—Model output - watershed means.
Figure 9—Model output - EMAP means.
7 | RSAC-10003-RPT1
8 | RSAC-10003-RPT1
Appendix A: National Environmental Threat Assessment Mapping
(NETAM) Workshop
National Environmental Threat Assessment Mapping (NETAM) Workshop Agenda
Salt Lake City, Utah
May 6-7, 2008
Tuesday, May 6, 2008
1300-1305
Everett Hinkley
1305-1315
RSAC Center
Manager
1315-1330
Jerry Beatty
1330-1345
David Weinstein
1345-1400
BREAK
1400-1430
Rich Warnick
1430-1500
Bill Rush
1500-1630
Group discussion
Facilitator introduction
Welcome
WWETAC introduction & workshop objectives and working definitions
All threats model
NETAM background & current status
S&P Forestry Redesign & National Assessment
NETAM & National Assessment including; Potential roles of the
WWETAC and EFETAC in the assessment and mapping of threats relative
to the S&P effort.
Wednesday, May 7, 2008 (Morning)
0800-0830
Jim Menakis
Fire Lab wildland fire potential mapping
0830-0900
Boris Tkacz
FHM P-3 plot data & invasive plants
0900-0930
Eric Smith
FHTET Insect & Disease Risk Map
0930-1000
Ron Nielson
Near-term fire predictions & other applications
BREAK
1030-1100
Roger Hammer
Housing density & wildland-urban interface
1100-1130
Kurt Riitters
Forest fragmentation via WebEx
1130-1200
Rich Warnick
Road density from distance to nearest road
1200-1300
LUNCH
9 | RSAC-10003-RPT1-Appendix A
Wednesday, May 7, 2008 (Afternoon)
1300-1320
Terry Shaw
Datasets related to threat mapping
1320-1340
Mark Finco
Woody biomass
1340-1400
Ken Brewer
Monitoring Trends in Burn Severity (MTBS)
1400-1420
Rich Warnick
T&E species critical habitat map
1420-1440
BREAK
1440-1500
Dave Merritt
Riparian systems & water quality
1500-1515
Alan Ager
WFLC national wildfire mapping process and data
1515-1530
Jerry Beatty
Refresher on workshop objectives
1530-1700
Group discussion Why, what & how of threat/risk mapping
Thursday, May 8, 2008
0800-0900
Ron Neilson
Climate change mapping
0900-1000
Group discussion Integration of climate change scenarios into threat assessments.
1020-1020
BREAK
1020-1130
Group discussion Where do we go from here?
Which projects can be best leveraged for assessing threat interactions;
gaps, data needs, problems
Which projects could benefit from a risk based approach
Niche for the Threat Centers
What will it take it take for a national data set of highly valued resources
Loss function
Opportunity for employing global mapping programs to serve up threat
data
1130-1200
Jerry Beatty,
Summary observations
Terry Shaw
1200
ADJOURN
10 | RSAC-10003-RPT1-Appendix A
NETAM Workshop
List of Participants
Alan Ager
Operations Research Analyst, Pacific Northwest Research Station
Jerry Beatty
Director, Western Wildlands Environmental Threat Assessment Center
Brian Schwind
Acting Center Manager, Remote Sensing Applications Center
Ken Brewer
IAAA Program Leader, Remote Sensing Applications Center
Mark Finco
Remote Sensing Specialist, Remote Sensing Applications Center
Roger Hammer
Assistant Professor, Department of Sociology, Oregon State University
William Hargrove
Research Ecologist, Eastern Forest Environmental Threat Assessment Center
Everett Hinkley
LSP Program Leader, Remote Sensing Applications Center
Jeff Kline
Research Forester, Pacific Northwest Research Station
Greg Kujawa
Inventory and Monitoring Coordinator, Integrated Vegetation Management Group
Jim Menakis
Forester, Rocky Mountain Research Station Fire Lab
David Merritt
Riparian Plant Ecologist, Rocky Mountain Research Station
Ron Neilson
Bioclimatologist, Pacific Northwest Research Station
Tom Quigley
Senior Advisor, Natural Resource Management and Science, METI, Inc.
Greg Reams
FIA National Program Leader
Kurt Riitters
Deputy Program Manager, Forest Health Monitoring, Southern Research Station
Bill Rush
Group Leader, WOSYS, State & Private Forestry
Terry Shaw
Chief Scientist, Western Wildlands Environmental Threat Assessment Center.
Eric L. Smith
Quantitative Analysis Program Manager, Forest Health Technology Enterprise
Team, State & Private Forestry
Susan A. Stewart
Fire Ecologist, State & Private Forestry
Borys Tkacz
National Program Manager, Forest Health Monitoring
Richard Warnick
Remote Sensing Analyst, Remote Sensing Applications Center
David Weinstein
Associate Research Scientist, Boyce Thompson Institute for Plant Research,
Cornell University
11 | RSAC-10003-RPT1-Appendix A
Appendix B: Source Datasets Used
FHTET National Insect & Disease Risk Map
National 2006 Composite Insect and Disease Risk Map
Source: The Forest Health Technology and Enterprise Team (FHTET), Information Technology, Fort Collins, CO. http://
www.fs.fed.us/foresthealth/technology/nidrm.shtml
Reference: USDA Forest Service. 2007. Mapping risk from forest insects and diseases 2006. FHTET 2007-06. Fort Collins,
CO: Forest Health Technology and Enterprise Team.
http://www.fs.fed.us/foresthealth/technology/pdfs/FHTET2007-06_RiskMap.pdf
(20 MB PDF)
Description: Outputs from 188 models which predict the reaction of 42 mortality agents acting on 57 tree species were
documented by FHTET and summarized as a 1 km grid of total basal area (BA) losses from all models over a 15-year period
starting in 2006. The National Insect and Disease Risk Map (NIDRM) is a grid of total BA losses from all models divided by
the grid of total BA, expressed as percent. All areas at greater than or equal to 25 percent are considered at risk.
Format: Raster (cell size 1 km)
Projection: Albers (NAD83)
Extent: Contiguous United States and Alaska
12 | RSAC-10003-RPT1-Appendix A
PROCESSING STEPS
1. Import into IMG format using ERDAS Imagine
2. Reproject to USA Contiguous Albers Equal Area Conic
3. Convert to binary (0-1) with thresholding at 25 percent
13 | RSAC-10003-RPT1-Appendix A
Wildland Fire Potential
Source: Obtained from Jim Menakis, U.S. Forest Service Rocky Mountain Research Station, Missoula Fire Sciences
Laboratory http://firelab.org/
Reference: Menakis, James. 2008. Mapping Wildland Fire Potential for the Conterminous United States. Proceedings,
Twelfth Biennial USDA Forest Service Remote Sensing Applications Conference, Salt Lake City, Utah April 15-17, 2008.
http://fsweb.gac.fs.fed.us/RS2008/j_menakis/index.htm
Description: Jim Menakis mapped wildland fire potential for the conterminous United States by combining national spatial
layers that delineate fire behavior and fire probability under extreme conditions. Fire behavior included both crown fire
potential and surface fire potential. Crown fire potential was based on assigning relative classes (very low – extreme) to forest
cover types and range cover type layers. Surface fire potential was also based assigning relative classes to surface fire potential
rate of spread and flame length, which were developed as part the Fuels Characteristic Classification project out of the Pacific
Northwest Research Station.
Fire probability was represented by both fire weather and fire occurrence. Fire weather included problem fire days and length
of fire seasons. Problem fire days was based on average number of days a year that experience extreme fire weather based on
thresholds of temperature, wind, and humidity from 1982 to 1997. Fire seasons was based on the average number of days per
year relative energy release component (RERC) is above 95 percent based on daily RERC maps from 1980 to 2005. Fire
occurrence was based on small fires and largest occurrence. Small fires were defined as any fire greater than a tenth of an acre
and large fires were any fire greater than 500 acres from 1980 to 2003.
14 | RSAC-10003-RPT1-Appendix A
Format: Raster (cell size 1 km)
Projection: Albers (NAD83)
Extent: Contiguous United States
PROCESSING STEPS
1. Reproject to USA Contiguous Albers Equal Area Conic
2. Recode from 1-9 (WFP 1-5 plus other cover types) to binary (0-1) by selecting fire potential of High (4) and Very High (5)
to represent wildfire threat.
3. Clip to forest mask
15 | RSAC-10003-RPT1-Appendix A
Percent Road Density – Contiguous 48 States
Road Density from StreetMap Pro
Source: ESRI StreetMap Pro, ESRI, Redlands, CA. DVD set.
References: ESRI: http://www.esri.com/software/arcgis/extensions/streetmap/
TeleAtlas: www.teleatlas.com
Description: StreetMap Pro provides detailed streets for the entire United States and Canada. The dataset originated from the
July 2003 Tele Atlas Dynamap Transportation version 5.2 product. According to Tele Atlas, data sources include: “satellite
and aerial imagery, public and government sources, input from utility, fleet and postal drivers, and our proprietary mobile
mapping vans.” The dataset includes artifacts that probably occur due to varied source data; for example, roads end abruptly at
the Wyoming-Utah boundary.
Format: Vector
Projection: Geographic (NAD83)
Extent: North America except for Mexico
16 | RSAC-10003-RPT1-Appendix A
PROCESSING STEPS
1. Subset to contiguous 48 states (each state separately due to file size)
2. Reproject to USA Contiguous Albers Equal Area Conic
3. Convert polyline to raster (spatial resolution 30 meters)
4. Merge individual state grids together
5. Import into Imagine as .img format and enter projection parameters
6. Convert to percent road density using Calculate Percent Cover tool (Ruefenacht and others, 2002)
7. Resample to 1 km grid
8. Clip to forested areas
9. Convert to binary (0-1) with thresholding at 10 percent
17 | RSAC-10003-RPT1-Appendix A
CONUS Counties
Source: Environmental Systems Research Institute (ESRI)
Reference: www.esri.com/data/
Description: Coarse-scale (1:2,000,000) county boundaries for the contiguous 48 states.
Format: Vector
Projection: Geographic (NAD83)
Extent: United States
PROCESSING STEPS
1. Reproject to USA Contiguous Albers Equal Area Conic
2. Subset to Contiguous United States
Counties Percent Forested
Sources: Counties & CONUS Forest/Non-forest Mask
Reference: None
18 | RSAC-10003-RPT1-Appendix A
Description: Percent forested area by county, calculated from ESRI Counties and FIA Forest/Non-forest Mask.
Format: Raster (cell size 1 km)
Projection: USA Contiguous Albers Equal Area Conic
Extent: Contiguous United States
PROCESSING STEPS
1. Calculate total areas for counties in km2 and output to grid
2. Calculate forest areas for counties in km2 and output to grid
3. Calculate percentage grid: forest percent = ([forest area] ( 100) / [total area]
19 | RSAC-10003-RPT1-Appendix A
Watersheds
Source: U.S. Geological Survey
Reference: http://water.usgs.gov/GIS/dsdl/huc250k.e00.gz
Description: USGS 8th level hydrological units. Source scale: 1:250,000.
Format: Vector
Projection: Geographic (NAD83)
Extent: United States
PROCESSING STEPS
1. Reproject to USA Contiguous Albers Equal Area Conic
2. Subset to Contiguous United States
Watersheds Percent Forested
Sources: Watersheds & CONUS Forest/Non-forest Mask
Reference: None
20 | RSAC-10003-RPT1-Appendix A
Description: Percent forested area by watershed, calculated from USGS Watersheds and FIA Forest/Non-forest Mask.
Format: Raster (cell size 1 km)
Projection: USA Contiguous Albers Equal Area Conic
Extent: Contiguous United States
PROCESSING STEPS
1. Calculate total areas for watersheds in km2 and output to grid
2. Calculate forest areas for watersheds in km2 and output to grid
3. Calculate percentage grid: forest percent = ([forest area] ( 100) / [total area]
21 | RSAC-10003-RPT1-Appendix A
EMAP Hexagons
Source: U.S. EPA, Environmental Monitoring and Assessment Program (EMAP)
Reference: www.epa.gov/wed/pages/staff/white/getgrid.htm
Description: Original EMAP grid 648 square kilometer hexagons. These equal area hexagons are used in the USGS-BRD
GAP program and related applications. They are similar to, but not the same as, the hexagons used by USDA Forest Service
Forest Inventory and Analysis (FIA).
Format: Vector
Projection: Geographic (Clarke 1866)
Extent: Contiguous United States
PROCESSING STEP
1. Reproject to USA Contiguous Albers Equal Area Conic
22 | RSAC-10003-RPT1-Appendix A
EMAP Hexagons Percent Forested
Sources: EMAP Hexagons & CONUS Forest/Non-forest Mask
Reference: None
Description: Percent forested area by EMAP hexagon, calculated from EMAP Hexagons and FIA Forest/Non-forest Mask.
Format: Raster (cell size 1 km)
Projection: USA Contiguous Albers Equal Area Conic
Extent: Contiguous United States
PROCESSING STEPS
1. Calculate forest areas for hexagons in km2 and output to grid
2. Calculate percentage grid: forest percent = ([forest area] ( 100) / 648
23 | RSAC-10003-RPT1-Appendix A
CONUS Forest/Non-forest Mask
Source: USDA Forest Service Remote Sensing Applications Center (RSAC)
Reference: http://svinetfc4.fs.fed.us/rastergateway/biomass/
Description: Forest Inventory and Analysis (FIA) scientists from each FIA unit produced a forest / non-forest mask. RSAC
compiled these into a single dataset.
Format: Raster (cell size 1 km)
Projection: Albers (NAD83)
Extent: Contiguous United States
PROCESSING STEP
1. Reproject to USA Contiguous Albers Equal Area Conic
Contiguous 48 States
Subset of states.shp
Source: Environmental Systems Research Institute (ESRI)
www.esri.com/data/
\
24 | RSAC-10003-RPT1-Appendix A
25 | RSAC-10003-RPT1-Appendix A
Blue = input data layers
P = parameter (user-selected input file)
Yellow = processes
Green = output data layers
Legend
Appendix C: Arc GIS Model Builder Flow Chart