GIS FAQs:
Wildfire Risk Assessment: Piedmont
piedmont
Metadata also available as
Frequently-anticipated questions:
What does this data set describe?
- Title: piedmont
- Abstract: The Virginia Department of Forestry (VDOF) used GIS
to develop a statewide spatial Wildfire Risk Assessment model that aims to:
(1) identify areas where conditions are more conducive and favorable to wildfire
occurrence and wildfire advancement; (2) identify areas that require closer
scrutiny at larger scales; and (3) examine the spatial relationships between
areas of relatively high risk and other geographic features of concern such
as woodland home communities, fire stations and fire hydrants. This model
incorporates data from several other state and federal agencies including
land cover, demographics, transportation corridors and topography. Differences
in the relative importance of model variables necessitated the use of three
individual analyses broken along Virginia's mountain, piedmont and coastal
plain physiographical regions. The three model results were merged to produce
the statewide Wildfire Risk Assessment.
- How should this data set be cited?
- What geographic area does
the data set cover?
- West_Bounding_Coordinate: -78.906949
- East_Bounding_Coordinate: -77.635334
- North_Bounding_Coordinate: 37.799719
- South_Bounding_Coordinate: 36.687200
- What does it look like?
- Does the data set describe conditions during
a particular time period?
- Calendar_Date: Based on input layers with varying source dates
- Currentness_Reference: Based on input layers with varying
source dates
- What is the general form of this data set?
- Geospatial_Data_Presentation_Form: vector digital data
- How does the data set represent geographic
features?
- How are geographic features stored in
the data set?
- This is a Vector data set. It contains the following vector data
types (SDTS terminology):
- What coordinate system is used to represent
geographic features?
- The map projection used is Lambert Conformal Conic.
- Projection parameters:
- Standard_Parallel: 37.000000
- Standard_Parallel: 39.500000
- Longitude_of_Central_Meridian: -79.500000
- Latitude_of_Projection_Origin: 36.000000
- False_Easting: 0.000000
- False_Northing: 0.000000
- Planar coordinates are encoded using coordinate pair
- Abscissae (x-coordinates) are specified to the nearest
0.000256
- Ordinates (y-coordinates) are specified to the nearest
0.000256
- Planar coordinates are specified in meters
- The horizontal datum used is North American Datum of 1983.
- The ellipsoid used is Geodetic Reference System 80.
- The semi-major axis of the ellipsoid used is 6378137.000000.
- The flattening of the ellipsoid used is 1/298.257222.
- How does the data set describe geographic
features?
- FID Internal feature number. (Source: ESRI) Sequential
unique whole numbers that are automatically generated.
- Shape Feature geometry. (Source: ESRI) Coordinates
defining the features.
- ID
- GRIDCODE
- 0: None (water)
- 1: Low
- 2: Moderate
- 3: High
Who produced the data set?
- Who are the originators of the data set?(may
include formal authors, digital compilers, and editors)
- Virginia Department of Forestry
- Who also contributed to the data set?
- Virginia Department of Forestry
- To whom should users address questions about
the data?
- Jason Braunstein
Virginia Department of Forestry
GIS Manager
900 Natural Resources Drive, Suite 800
Charlottesville, VA 22903
434.977.6555 (voice)
434.296.2369 (FAX)
Why was the data set created?
(See abstract)
How was the data set created?
- From what previous works were the data drawn?
- (source 1 of 1)
- Source_Contribution:
- The Wildfire Risk Assessment was derived from a GIS analysis
of multiple input layers (see below).
- How were the data generated, processed, and
modified?
- (process 1 of 3)
- This section describes the data used as input in this model, the
organizations from which the various input datasets originated and
how these data were prepared for the modeling process.
In 2002 and 2003, VDOF examined which factors influence the occurrence
and advancement of wildfires and how these factors could be represented
in a GIS model. VDOF determined that historical fire incidents, land
cover (fuels surrogate), topographic characteristics, population
density, and distance to roads were critical variables in a wildfire
risk analysis. DOF gathered these data layers, sometimes creating
them, and used them in a raster-based weighted aggregate model.
The weights assigned to input variables (specifically topographic
variables) differ depending on the physiographic zone being represented
because the topographic characteristics of the landscape change dramatically
across Virginia. The Coastal Plain, Piedmont and Mountains physiographic
zones were used to clip the input layers during analysis. Each of
the physiographical zones was buffered 2 miles beyond their boundaries
to reduce edge effects during the merge process.
The resolution of the model is defined by the coarsest resolution
of input data. The National Land Cover Dataset and National Elevation
Dataset both have a spatial resolution of 30-meter pixels, therefore
all other layers were created or resampled to this resolution. Each
input layer is normalized on an interval scale from 0 to 10 with
10 representing the characteristics of each layer that have the highest
wildfire risk.
Model Inputs
DENSITY OF HISTORICAL WILDFIRES
- Premise: Wildfire density was mapped to identify areas where
wildfires have historically been relatively prevalent and relatively
absent. It is assumed that these spatial patterns will remain
similar in the future.
- Data Preparation: Point locations for wildfires occurring in
the years 1995 - 2001 inclusive were obtained from the George
Washington and Jefferson National Forests and Shenandoah National
Park. They were merged with the point wildfire locations documented
by VDOF. Generally, VDOF does not document fires occurring on
federal lands and unsuccessful attempts were made to obtain wildfire
GIS data from most of the remaining federal agencies that manage
land in the Commonwealth. Using ESRI's Spatial Analyst for ArcView
8.2, a Kernel density function was applied to the point data
using a search radius of 5000 meters. The output grid was reclassified
into ten classes using the natural breaks classification method
and then assigned an interval value from 1 to 10.
Straddling the border of Lee and Scott Counties is an area of extreme
wildfire density that significantly skewed the remaining data in
the mountain physiographical zone. The upper bound values of the
density layer were excluded at increasing intervals until the effects
caused by the skewed data were reduced to a level that visually appeared
more balanced. The remaining values were classified into 10 natural
breaks classes and the cells that were previously excluded were then
added to the class with the highest value (10).
LAND COVER
- Premise: Land Cover data reveal the type of wildfire fuels
that are likely to be found in different areas. The USGS Multi-Resolution
Land Cover data were used in this model to identify areas of
the state where there are fuel types that ignite more easily,
burn with greater intensity and facilitate a greater rate of
wildfire advancement. Fuels data of this resolution and scale
have their limitations and the lack of detailed fuel models is
commonly recognized as the most prominent limitation in the various
types of wildfire risk modeling. Although some advanced processing
of remotely sensed data can be used to estimate canopy crown
closure and moisture content, data of these types can rarely
divulge the degree of fuel loading within a pixel.
- Data Preparation: Each fuel type identified by the MRLC data
was rated on a 0 to 10 interval scale as follows: Water: NoData*
Low-Intensity Development: 3 High-Intensity Development: 2 Hay,
Pasture, Grass: 6 Row Crops: 2 Probable Row Crops: 3 Conifer
(Evergreen) Forest: 10 Mixed Forest: 9 Deciduous Forest: 8 Woody
Wetlands: 2 Emergent Wetlands: 1 Barren (Quarry, Coal, Beach):
0 Barren Transitional (includes clear-cut): 2
- Water was classified as NoData due to the undesirable effect
a value of zero would have on the final output. Because land
cover is weighted relatively high, the initial out put would
contain very low values over water bodies if the water class
was assigned a value of zero. This effect seems appropriate,
but these low values would have a profound and undesired
effect on the surrounding areas when the neighborhood function
was executed. Hence, the water class was initially classified
as NoData.
PERCENT SLOPE
- Premise: Through convective pre-heating, wildfires generally
advance up-hill. Generally, steeper slopes cause greater the
pre-heating and ease of ignition. As a result, steeper slopes
were assigned higher values to reflect this effect.
- Data Preparation: Percent slope was calculated from the U.S.
Geological Survey's National Elevation Dataset (30m resolution)
using the slope command in command-line ArcInfo. The resulting
slope grid was classified into three classes: 0 - 5%, 6 - 25%
and > 25%. These classes were then assigned values of 1, 5
and 10 respectively.
SLOPE ORIENTATION/ASPECT
- Premise: Slopes that generally face south receive more direct
sunlight than those generally facing north. Direct sunlight in
turn dries fuels and thereby creates conditions that are more
conducive to wildfire ignition. Additionally, drier fuels generally
increase the intensity of a wildfire and facilitate faster fire
advancement.
- Data Preparation: Slope aspect was derived from U.S. Geological
Survey's National Elevation Dataset using the Aspect command
in command-line ArcInfo. Areas where the slope is less than 5%
were assigned values of zero; slopes facing N, NE, and E (0 -
112.5 degrees) were assigned a value of 1; Slopes facing W and
NW (247.5 - 337.5 degrees) were assigned a value of 5. The remaining
slopes, S, SE, and SW (112.5 - 247.5 degrees), were assigned
values of 10.
POPULATION DENSITY
- Premise: Because an overwhelming majority of the wildfires
in the Commonwealth are ignited intentionally or unintentionally
by humans , population density was included in this model to
capture this causal relationship. The general premise is that
as population density increases, more opportunities for wildfire
ignition will exist. But once the density reaches a threshold,
the resulting urbanization decreases the presence of wildland
fuels. This relative absence of fuels generally produces a negative
impact on the wildfire risk. In order to determine this threshold,
a kernel population density grid was produced and visually examined
over urbanized areas with which VDOF staff were familiar. Road
layers from VDOT and the US Census Bureau's TIGER datasets were
also examined in this threshold determination process. Then the
upper bound population density figures were repeatedly removed
in small intervals, producing an expanding no-data "hole" over
the urbanized area. This process was repeated until the hole
reasonably encompassed the urbanized areas.
- Data Preparation: Centroid points were derived from the 2000
US Census Bureau Blocks and the density function of Spatial Analyst
was employed to create a kernel population density grid using
a 2000 meter search radius and the total population as the population
field. Values in the output grid that were greater than 1500
people per square mile were assigned a value of zero and the
remaining cells were then reclassified into ten interval classes
ranging in values from 1 to 10 using the quantile classification
method.
DISTANCE TO ROADS
- Premise: A distance to roads layers was also included to further
capture the human/wildfire causal relationship. Travel corridors
increase the probability of human presence which could in turn
result in wildfire ignition. Hence, areas closer to roads will
attain a higher ignition probability and these areas were assigned
higher values to reflect this increased risk.
- Data Preparation: Roads features from the US Census Bureau's
TIGER data were merged with those provided by the USFS and Shenandoah
National Park. The metadata provided by the USFS did contain
attribute information regarding the closure status of the some
roads in the land they manage. However, the roads classified
as "closed" were not omitted because the closure devices
on publicly managed lands are not always effective, especially
with respect to ATVs and motorbikes. The "Straight Line" distance
function was applied to this merged roads layer. The output grid
was then reclassified into 10 interval classes from 1 to 10 using
the quantile classification method, with 10 representing areas
in closest proximity to roads and 1 representing areas furthest
from roads.
RAILROAD BUFFER
- Premise: Railroad operations can produce sparks that may ignite
a wildfire. However, about 2% of the wildfires occurring in the
Commonwealth were reported to have been ignited from railroad
use. As a result, a quarter-mile buffer of Virginia railroads
was included, but was weighted low.
- Data preparation: Using railroad line features from data produced
by the Virginia Department of Transportation, a quarter-mile
buffer of railroads was generated. The resulting buffer polygon
was directly rasterized and all cells were assigned a value of
five.
Below is the weight applied to each input layer in the Mountain,
Piedmont and Coastal Plain physiographic zones, respectively: Fire
Density: 24, 24, 23 Landcover: 32, 40, 21 Slope: 9, 4, 13 Aspect
(Slope Orientation): 10, 2, 13 Population Density: 14, 16, 14 Distance
to Roads: 7, 8, 11 Railroad Buffer: 4, 6, 5
The output grids retained their buffers where they overlapped one
another and were mosaiced together using the Feather Mosaic function
of ERDAS Imagine. To make this process possible, the NoData values
were reclassified as zero (no values of zero were present in the
individual grids) using a conditional statement in Raster Calculator.
Then the grids were converted to Image (.img) files in ERDAS. Using
the physiographical zone boundaries as the "cut line," the
Feather Mosaic function assigned the output values of the overlapping
input cells using the following algorithm: "The overlap area
is replaced by a linear interpolation of the pixels in the overlap.
A pixel in the middle of the overlap area [where the cutline was
established] is 50% of each of the corresponding pixels in the overlapping
images. A pixel 1/10 of the overlap from an edge would be 90% one
image and 10% the other." --From ERDAS IMAGINE On-Line Help
Copyright (c) 1982-1999 ERDAS, Inc. This process virtually eliminates
the abrupt transition that would have otherwise occurred where the
three physiographical zones meet. It also establishes an appropriate
value based on the overlapping cells' distance from the zone boundaries.
The mosaiced output was then converted back to a grid using ERDAS
and all values of zero were calculated back to NoData in Raster Calculator.
Wildfires are not isolated events and fire potential is not solely
determined by the conditions found at a single point, but also by
the surrounding conditions. To apply this notion to the raw, mosaiced
output, a mean neighborhood function was employed using a circular
area with a radius of 250m in Spatial Analyst for ArcView 8.2. Doing
so also generalized the output grid so that it would be more administratively
usable.
ROAD DENSITY AND DEVELOPED AREAS
- Premise: Areas that contain high road densities and a large
percentage of developed land generally feature low amounts of
wildland fuels. Furthermore, the wildland fuels that are present
are typically fragmented to such a degree that the resulting
fire risk is drastically reduced.
- Data Preparation: A kernel density function was executed on
TIGER roads from the US Census Bureau and cells of the resulting
grid with high values were classified into a new raster. If greater
than 50% of the cells in these high density areas contained cells
classified as "developed" in the USGS MRLC dataset
(classes 21, 22 and 23 from above), the value of corresponding
cells in the output of the neighborhood function were reduced
by 50%.
Road Density and Developed Areas
- Areas that contain high road densities and a large percentage
of developed land generally feature low amounts of wildland fuels.
Furthermore, the wildland fuels that are present are typically
fragmented to such a degree that the resulting fire risk is drastically
reduced.
- Data Preparation: A kernel density function was executed on
TIGER roads from the US Census Bureau and cells of the resulting
grid with high values were classified into a new raster. If greater
than 50% of the cells in these high density areas contained cells
classified as "developed" in the USGS MRLC dataset
(classes 21, 22 and 23 from above), the value of corresponding
cells in the output of the neighborhood function were reduced
by 50%.
The final grid was then reclassified into six Jenks/Natural-Breaks
classes. These six classes were then "clumped" into three
classes and assigned values of 1 (low), 2 (moderate) and 3 (high).
Finally, this reclassified raster was converted to the shapefile
vector format and clipped to each PDC boundary.
- (process 2 of 3)
- Metadata imported.
- Data sources used in this process:
- (process 3 of 3)
- Metadata imported.
- Data sources used in this process:
How reliable are the data; what problems remain in the data set?
- How well have the observations been checked?
- Maps of the model output were sent to each DOF field office for verification.
Changes were made to the model weights to better reflect the conditions
at the local scale
- How accurate are the geographic locations?
- This Wildfire Risk Assessment is meant to be used at county or regional
scales; it is not as reliable at the site scale.
- How accurate are the heights or depths? N/A
- Where are the gaps in the data? What is
missing?
- How consistent are the relationships among
the observations, including topology?
How can someone get a copy of the data set?
Are there legal restrictions on access or use of the data?
- Access_Constraints: This dataset is to be acquired directly from
the Virginia Dept. of Foresty only. Distributing it to third parties is not
permitted.
- Use_Constraints: Use is permitted only after having accepted the
Virginia Dept. of Forestry data use agreement and disclaimer for this dataset.
- Who distributes the data set?
- Jason Braunstein
Virginia Department of Forestry
GIS Manager
900 Natural Resources Drive, Suite 800
Charlottesville, VA 22903
434.977.6555 (voice)
434.296.2369 (FAX)
- What's the catalog number I need to order
this data set?
- What legal disclaimers am I supposed to
read?
- This information is provided with the understanding that it is not
guaranteed to be correct or complete and conclusions drawn from such
information are the sole responsibility of the user. While The Virginia
Department of Forestry (DOF) has attempted to ensure that this documentation
is accurate and reliable, DOF does not assume liability for any damages
caused by inaccuracies in these data or documentation, or as a result
of the failure of the data or software to function in a particular manner.
DOF makes no warranty, expressed or implied, as to the accuracy, completeness,
or utility of this information, nor does the fact of distribution constitute
a warranty.
- How can I download or order the data?
- Availability in digital form:
- Data format
- Network links:
- http://www.dof.virginia.gov/gis/dwnload/index.htm
- Cost to order the data:
- What hardware or software do I need in order to use the data set?
- Data can be viewed using any GIS software capable of reading ESRI shapefile
vector data.
Who wrote the metadata?
- Dates:
- Last modified: 10-Jul-2003
- Metadata author:
- Jason Braunstein
Virginia Department of Forestry
GIS Manager
900 Natural Resources Drive, Suite 800
Charlottesville, VA 22903
434.977.6555 (voice)
434.296.2369 (FAX)
- Metadata standard:
- FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)
- Metadata extensions used:
- http://www.esri.com/metadata/esriprof80.html
nerated by http://geology.usgs.gov/tools/metadata/tools/doc/mp.html version
2.7.3 on Thu Jul 10 18:20:33 2003