Forest Stewardship Spatial Analysis Project
Indiana Methodology
March, 2006
Project Summary
One purpose of the Spatial Analysis Project (SAP) is to create a data layer for a state that
represents levels of potential benefit from—or suitability for inclusion in—the Forest
Stewardship Program as delivered by state forestry agencies and the U.S. Forest Service.
Private land program and GIS staff from the four states involved in the pilot SAP effort
(Connecticut, Maryland, Massachusetts and Missouri), along with Forest Service
program and GIS staff identified 12 factors which help identify the “Stewardship
potential” of a given piece of land. The factors were differentiated into two groups:
resource potential and resource threats.
The resource potential factors include:
ƒ Riparian Zones
ƒ Priority Watersheds
ƒ Forest Patch Size
ƒ Natural Heritage Data
ƒ Public Drinking Water Supply Sources
ƒ Private Forest Lands
ƒ Proximity to Public Lands
ƒ Wetlands
ƒ Topographic Slope
The resource threat factors include:
ƒ Forest Health
ƒ Development Level
ƒ Wildfire Assessment
Certain lands within any state are not eligible for inclusion in the Forest Stewardship
program. Land use / land cover factors which identify these areas are open water, urban
areas and publicly owned lands. Additionally, lands already enrolled in the stewardship
program were removed since they cannot be enrolled twice. A mask was created to
exclude these areas from the analysis.
Once the 12 factors were identified, each state determined the relative importance of each
of the criteria based on their state-specific conditions. The Indiana Forest Stewardship
Committee and employees of the Indiana Division of Forestry Cooperative Forest
Management Section voted (in two separate voting sessions) on the relative importance
of the above 12 layers. The percentages for each layer were then averaged between the
two sets of results to yield our final weighting scheme.
The 12 layers were then combined in a GIS overlay analysis which took into account the
weight for each factor. The final product was a single data layer which represents the
suitability of the land for inclusion in the Forest Stewardship Program. Values from this
analysis range from 0 to 1, with a value of 1 representing the highest level of suitability.
A natural breaks classification algorithm was used to break the values into low, medium
and high classes. The result is shown below.
Summary statistics were calculated and a series of maps was then created to display the
data.
While the process outlined above was taking place a parallel effort was occurring. In
order to understand where the Forest Stewardship Program has been previously
implemented, the property boundaries for ownerships with a Stewardship plan were
digitized.
Stewardship plan polygons were then overlaid on the Stewardship potential layer to
assess Stewardship efforts to date.
Indiana Composite Forest Stewardship Potential
Stewardship Potential
Low
Medium
High
Masked out areas
The 12 Data Layers
Riparian Corridors: Riparian corridors were
created by buffering perennial streams and lakes
in Indiana from the USGS 1:24,000 DLG files.
The buffers were 100 meters on each side and are
shown in blue in the map on the right.
Priority Watersheds: These watersheds were
designated as non-EPA compliant (categories 5A5B). The Indiana Department of Environment
Management tested water bodies across the state
in 2004. The resultant non-compliant bodies were
then linked to their respective 14 digits
watersheds (digitized at 1:24,000 scale), which
are shown at the right in blue.
Wetlands: Wetlands were derived from the
1:24,000 National Wetlands Inventory conducted
by the U.S. Fish and Wildlife Service. We then
extracted all forested and scrub/shrub wetlands so
that we were only left with wetlands suitable for
tree cover.
Public Water Supply Areas: This layer is derived
from USGS and IDEM layers showing public and
community wells as well as surface water supply
watersheds. All three data layers are originally
1:24,000. The two well layers were each buffered
by one mile, then the three layers were combined.
Public water supply areas are shown at the right in
light blue.
Private Forest: Private forest lands were derived
from the 1992 NLCD dataset by combining
classes 41,42,43,51 and 91, which are deciduous
forest, coniferous forest, mixed forest and
woodland, and woody wetlands. The NLCD data
was derived from Landsat TM data, with a
resolution of 30 meters. Since public lands were
removed from the composite map as part of the
Analysis Mask, we did not remove public lands
from this layer. Thus, this map actually shows all
forests in Indiana. However, this proved useful
when it came time to make the Forest Patches
layer, as will be discussed below. Forestland in
Indiana is shown at the right in light green.
Forest Patches: Forest patches were derived from
Private Forest. A professor of Forestry at Purdue
University determined that forest patches greater
than 50 acres would be large enough to function
as wildlife habitat for most species. All forest
patches 50 acres or larger were selected. Then all
state and U.S. roads were selected and buffered by
15 meters on a side. It was felt that U.S. and State
highways would serve as a barrier between forests
on either side of the road, while county and local
roads would be less of a barrier. Additionally,
trying to run an erase function with all the roads
in
Indiana would have been nearly impossible on a
normal desktop computer. These roads were then
erased from the initial selection of forest patches.
We then reselected forest patches 50 acres or
larger. As mentioned above, this layer also
includes public forests. However, we find this to be beneficial, because we would like to
enroll private forest land that adjoins public forest, particularly if it will ensure a large,
contiguous patch of forestland. Thus, even when we remove public lands as part of the
analysis mask, private forests that are less than 50 acres will still get the additional
weighting associated with this layer if they combine with public forest to form a patch
larger than 50 acres. All forest patches ≥50 acres are shown above in dark green.
Proximity to Public Land: This layer was
created by selecting all public lands in Indiana
from our Managed Lands dataset. We then
buffered these lands by ¼ mile. There are many
different sources for the Managed Lands dataset,
but the worst resolution is 1:24,000. All private
lands within ¼ mile of public lands are shown at
right in purple.
Natural Heritage Sites (T&E species): This layer
was created from Indiana’s Natural Heritage
Database. This database contains data on
Indiana’s rare and otherwise significant natural
features, including plant and animal species,
natural communities, and animal aggregations.
This includes state and federal endangered
species. Then, using the expertise of several
IDNR employees, we removed all habitats and
species that would be negatively impacted if an
area was converted to forest. The remaining
points were then buffered by ½ mile, and are
shown at right in red.
Slope: Slope was derived from the USGS
National Elevation Dataset 30 meter statewide
raster. We then selected all areas of the state with
slopes >6-30%. Areas with slope 6% or lower are
prime agricultural areas, and therefore considered
ill-suited for conversion to forest. Slopes >30%
are considered too steep for any landuse other
than forest, and very unlikely to be converted to a
non-forest landuse. Slopes >6-30% are shown at
right in brown.
Forest Health: This layer is a combination of four
insect pests in Indiana; gypsy moth, forest tent
caterpillar, looper, and Emerald Ash Borer. The
gypsy moth data comes from trap data which is
then fed into an algorithm showing moth lines
and kriged surfaces. For the northeast part of the
state, the 2004 1 moth line (traps which had 1 or
more moths present) was buffered by 35 miles to
show risk of spread in the next five years. In the
southeast part of Indiana, the 2005 kriged surface
was used and buffered by 20 miles around the
isolated find in Scott County. Looper and FTC
ranges were based on aerial surveys conducted in
2003 and 2004. Emerald Ash borer data was
based on trees with confirmed cases of Emerald
Ash Borer which were then buffered by ½ mile.
All of the layers were produced under the supervision of Phil Marshall, the IDNR Forest
Entomologist. The layers were then combined into one layer. Areas at risk from these
insects are shown above in brown.
Wildfire Assessment: Wildfire risk areas were
derived from Wildland-Urban interface data as
developed by the University of Wisconsin. They
developed layers showing fuel hazard,
population density, and volunteer fire department
locations. The University of Indiana then
combined those layers into a fire risk layer with
the categories low, moderate, moderately high,
high, and water. We selected all areas that fell
into moderately high and high. The pixels were
originally 90 meters and were resampled to 30
meters to align with our other raster datasets.
The areas at risk for wildfires are shown at right
in red.
Risk of Development: Risk of Development was
derived from data developed by the USFS. Two
data sets were used; the first showed population
density in 2000, the second showed expected
population density in 2030. We combined the
two layers to find areas that were going to go
from 0-16 housing units per square mile to 16-64
housing units per square mile. Densities of
housing above 64/mile² were considered urban
and unsuitable for forests larger than 10 acres
(the cutoff to enroll in our stewardship program).
Areas at risk of development are shown at right
in orange.
Analysis Mask: This layer shows all areas
ineligible for enrollment into the forest
stewardship program. It consists of Urban and
Open Water areas as specified in the 1992 NLCD
layer, public lands as shown in our Managed
Lands dataset, and existing stewardship plans.
Areas ineligible to be enrolled are shown at right
in grey.
Existing Stewardship Plans: Existing
stewardship plans were digitized in from
legal descriptions which were usually based
on aerial photography. 8406 tracts, enrolled
between 1921 and 2002 are shown at right
in green.
Weighting
Once we had our 12 layers, we needed to determine their relative importance to
establishing an overall state map showing stewardship potential. We had two groups vote
on the importance of the 12 layers, the Forest Stewardship Committee, which had 27
people vote, and the Cooperative Forest Management section of the Division of Forestry,
which had 18 people vote. Each person was given 24 points to allocate among the 12
layers, with each person allowed to put a maximum of 5 points towards any one layer.
We then combined the point totals for each layer from the two voting sessions, and
divided by the total number of points possible. The results are shown below.
Layer
Private Forest
Weight (%)
16.32
Riparian Corridors
12.79
Forest Patches
12.53
Risk of Development
9.01
Forest Health (pests)
8.75
Proximity to Public Lands
7.31
Public Water Supply
7.18
Wetlands
6.79
TES
6.53
Slope
6.27
Impaired Watersheds/ Priority Watersheds
5.74
Fire Risk
.78
GIS Analysis
Each of the 12 layers used in the analysis had a grid cell size of 30 meters, and they were
all aligned to the slope layer, since it was the only layer to start as a 30 meter raster in
UTM 16N NAD 83. The 12 layers were all coded to a 0,1 format, where cells not
representing the layer in question were 0, and all cells that did represent the layer were 1.
For example, in the slope layer, all pixels with a slope >6-30% were recoded to 1, all
other pixels were recoded to 0.
Once this was done, each raster layer was multiplied by its weight so that each pixel took
the value of its weighting. To reduce computation time, actual weights were multiplied
by 10,000 so that all weights were integers. The 12 layers were then added together, and
the resultant raster was multiplied by .0001 so that we ended up with a raster ranging
from 0 to 1. A value of 1 was the highest possible stewardship potential score.
The next step was to remove lands ineligible for stewardship, as represented by the
Analysis Mask shown above. To do this, the analysis mask was recoded to values of
NoData and 0, with NoData areas ineligible for stewardship. This raster was then added
to the stewardship potential raster, with the result being that NoData areas in the Analysis
Mask became NoData pixels in the stewardship potential raster.
Once we had a stewardship potential mask for areas eligible for stewardship, we used the
Natural Breaks classifier in ArcMap to break Indiana into Low, Medium, and High
stewardship potential classes. The ranges are shown below. We then recoded the raster
so that 1=low potential, 2=medium potential, and 3=high potential. The resultant image
is the first map in this write-up.
Low:
Medium:
High:
0 - .0979
.0979 - .3082
.3082 – 1
Digitizing Existing Stewardship Plans
The second part of the SAP was the creation of a shapefile showing existing stewardship
plans in each state. For Indiana, this was a particularly daunting task since we have the
oldest stewardship plan in the country, and over 9000 pieces of forestland enrolled. For
the purposes of the SAP project, we digitized plans through the end of 2002, which
included 8406 tracts of land. These tracts were digitized by entering legal descriptions
through the ArcMap add-on program IcoMap. Using bearing and distance, property
boundaries were drawn. It should be noted that the quality of surveys varied widely, and
that some surveys were based on unrectified aerial photography which led to errors when
the tract was digitized. Over 99% of all tracts through 2002 were digitized, with the
remaining tracts having some insurmountable problem. These included files that had
been lost due to courthouse fires (or two, in the case of one county), conflicting deeds
with neighboring property, and other problems that invariably arise in record keeping.