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The Forest CoverType dataset
1. Title of Database:
Forest Covertype data
2. Sources:
(a) Original owners of database:
Remote Sensing and GIS Program
Department of Forest Sciences
College of Natural Resources
Colorado State University
Fort Collins, CO 80523
(contact Jock A. Blackard, jblackard 'at' fs.fed.us
or Dr. Denis J. Dean, denis.dean 'at' utdallas.edu)
NOTE: Reuse of this database is unlimited with retention of
copyright notice for Jock A. Blackard and Colorado
State University.
(b) Donors of database:
Jock A. Blackard (jblackard 'at' fs.fed.us)
GIS Coordinator
USFS - Forest Inventory & Analysis
Rocky Mountain Research Station
507 25th Street
Ogden, UT 84401
Dr. Denis J. Dean (denis.dean 'at' utdallas.edu)
Professor
Program in Geography and Geospatial Sciences
School of Economic, Political and Policy Sciences
800 West Campbell Rd
Richardson, TX 75080-3021
Dr. Charles W. Anderson (anderson 'at' cs.colostate.edu)
Associate Professor
Department of Computer Science
Colorado State University
Fort Collins, CO 80523 USA
(c) Date donated: August 1998
3. Past Usage:
Blackard, Jock A. and Denis J. Dean. 2000. "Comparative
Accuracies of Artificial Neural Networks and Discriminant
Analysis in Predicting Forest Cover Types from Cartographic
Variables." Computers and Electronics in Agriculture
24(3):131-151.
Blackard, Jock A. and Denis J. Dean. 1998. "Comparative
Accuracies of Neural Networks and Discriminant Analysis
in Predicting Forest Cover Types from Cartographic
Variables." Second Southern Forestry GIS Conference.
University of Georgia. Athens, GA. Pages 189-199.
Blackard, Jock A. 1998. "Comparison of Neural Networks and
Discriminant Analysis in Predicting Forest Cover Types."
Ph.D. dissertation. Department of Forest Sciences.
Colorado State University. Fort Collins, Colorado.
165 pages.
Abstract of dissertation:
Natural resource managers responsible for developing
ecosystem management strategies require basic descriptive
information including inventory data for forested lands to
support their decision-making processes. However, managers
generally do not have this type of data for inholdings or
neighboring lands that are outside their immediate
jurisdiction. One method of obtaining this information is
through the use of predictive models.
Two predictive models were examined in this study, a
feedforward neural network model and a more traditional
statistical model based on discriminant analysis. The overall
objectives of this research were to first construct these two
predictive models, and second to compare and evaluate their
respective classification accuracies when predicting forest
cover types in undisturbed forests.
The study area included four wilderness areas found in
the Roosevelt National Forest of northern Colorado. A total
of twelve cartographic measures were utilized as independent
variables in the predictive models, while seven major forest
cover types were used as dependent variables. Several subsets
of these variables were examined to determine the best overall
predictive model.
For each subset of cartographic variables examined in
this study, relative classification accuracies indicate the
neural network approach outperformed the traditional
discriminant analysis method in predicting forest cover types.
The final neural network model had a higher absolute
classification accuracy (70.58%) than the final corresponding
linear discriminant analysis model(58.38%). In support of these
classification results, thirty additional networks with randomly
selected initial weights were derived. From these networks, the
overall mean absolute classification accuracy for the neural
network method was 70.52%, with a 95% confidence interval of
70.26% to 70.80%. Consequently, natural resource managers may
utilize an alternative method of predicting forest cover types
that is both superior to the traditional statistical methods and
adequate to support their decision-making processes for
developing ecosystem management strategies.
-- Classification performance
-- first 11,340 records used for training data subset
-- next 3,780 records used for validation data subset
-- last 565,892 records used for testing data subset
-- 70% Neural Network (backpropagation)
-- 58% Linear Discriminant Analysis
4. Relevant Information Paragraph:
Predicting forest cover type from cartographic variables only
(no remotely sensed data). The actual forest cover type for
a given observation (30 x 30 meter cell) was determined from
US Forest Service (USFS) Region 2 Resource Information System
(RIS) data. Independent variables were derived from data
originally obtained from US Geological Survey (USGS) and
USFS data. Data is in raw form (not scaled) and contains
binary (0 or 1) columns of data for qualitative independent
variables (wilderness areas and soil types).
This study area includes four wilderness areas located in the
Roosevelt National Forest of northern Colorado. These areas
represent forests with minimal human-caused disturbances,
so that existing forest cover types are more a result of
ecological processes rather than forest management practices.
Some background information for these four wilderness areas:
Neota (area 2) probably has the highest mean elevational value of
the 4 wilderness areas. Rawah (area 1) and Comanche Peak (area 3)
would have a lower mean elevational value, while Cache la Poudre
(area 4) would have the lowest mean elevational value.
As for primary major tree species in these areas, Neota would have
spruce/fir (type 1), while Rawah and Comanche Peak would probably
have lodgepole pine (type 2) as their primary species, followed by
spruce/fir and aspen (type 5). Cache la Poudre would tend to have
Ponderosa pine (type 3), Douglas-fir (type 6), and
cottonwood/willow (type 4).
The Rawah and Comanche Peak areas would tend to be more typical of
the overall dataset than either the Neota or Cache la Poudre, due
to their assortment of tree species and range of predictive
variable values (elevation, etc.) Cache la Poudre would probably
be more unique than the others, due to its relatively low
elevation range and species composition.
5. Number of instances (observations): 581,012
6. Number of Attributes: 12 measures, but 54 columns of data
(10 quantitative variables, 4 binary
wilderness areas and 40 binary
soil type variables)
7. Attribute information:
Given is the attribute name, attribute type, the measurement unit and
a brief description. The forest cover type is the classification
problem. The order of this listing corresponds to the order of
numerals along the rows of the database.
Name Data Type Measurement Description
Elevation quantitative meters Elevation in meters
Aspect quantitative azimuth Aspect in degrees azimuth
Slope quantitative degrees Slope in degrees
Horizontal_Distance_To_Hydrology quantitative meters Horz Dist to nearest surface water features
Vertical_Distance_To_Hydrology quantitative meters Vert Dist to nearest surface water features
Horizontal_Distance_To_Roadways quantitative meters Horz Dist to nearest roadway
Hillshade_9am quantitative 0 to 255 index Hillshade index at 9am, summer solstice
Hillshade_Noon quantitative 0 to 255 index Hillshade index at noon, summer soltice
Hillshade_3pm quantitative 0 to 255 index Hillshade index at 3pm, summer solstice
Horizontal_Distance_To_Fire_Points quantitative meters Horz Dist to nearest wildfire ignition points
Wilderness_Area (4 binary columns) qualitative 0 (absence) or 1 (presence) Wilderness area designation
Soil_Type (40 binary columns) qualitative 0 (absence) or 1 (presence) Soil Type designation
Cover_Type (7 types) integer 1 to 7 Forest Cover Type designation
Code Designations:
Wilderness Areas: 1 -- Rawah Wilderness Area
2 -- Neota Wilderness Area
3 -- Comanche Peak Wilderness Area
4 -- Cache la Poudre Wilderness Area
Soil Types: 1 to 40 : based on the USFS Ecological
Landtype Units (ELUs) for this study area:
Study Code USFS ELU Code Description
1 2702 Cathedral family - Rock outcrop complex, extremely stony.
2 2703 Vanet - Ratake families complex, very stony.
3 2704 Haploborolis - Rock outcrop complex, rubbly.
4 2705 Ratake family - Rock outcrop complex, rubbly.
5 2706 Vanet family - Rock outcrop complex complex, rubbly.
6 2717 Vanet - Wetmore families - Rock outcrop complex, stony.
7 3501 Gothic family.
8 3502 Supervisor - Limber families complex.
9 4201 Troutville family, very stony.
10 4703 Bullwark - Catamount families - Rock outcrop complex, rubbly.
11 4704 Bullwark - Catamount families - Rock land complex, rubbly.
12 4744 Legault family - Rock land complex, stony.
13 4758 Catamount family - Rock land - Bullwark family complex, rubbly.
14 5101 Pachic Argiborolis - Aquolis complex.
15 5151 unspecified in the USFS Soil and ELU Survey.
16 6101 Cryaquolis - Cryoborolis complex.
17 6102 Gateview family - Cryaquolis complex.
18 6731 Rogert family, very stony.
19 7101 Typic Cryaquolis - Borohemists complex.
20 7102 Typic Cryaquepts - Typic Cryaquolls complex.
21 7103 Typic Cryaquolls - Leighcan family, till substratum complex.
22 7201 Leighcan family, till substratum, extremely bouldery.
23 7202 Leighcan family, till substratum - Typic Cryaquolls complex.
24 7700 Leighcan family, extremely stony.
25 7701 Leighcan family, warm, extremely stony.
26 7702 Granile - Catamount families complex, very stony.
27 7709 Leighcan family, warm - Rock outcrop complex, extremely stony.
28 7710 Leighcan family - Rock outcrop complex, extremely stony.
29 7745 Como - Legault families complex, extremely stony.
30 7746 Como family - Rock land - Legault family complex, extremely stony.
31 7755 Leighcan - Catamount families complex, extremely stony.
32 7756 Catamount family - Rock outcrop - Leighcan family complex, extremely stony.
33 7757 Leighcan - Catamount families - Rock outcrop complex, extremely stony.
34 7790 Cryorthents - Rock land complex, extremely stony.
35 8703 Cryumbrepts - Rock outcrop - Cryaquepts complex.
36 8707 Bross family - Rock land - Cryumbrepts complex, extremely stony.
37 8708 Rock outcrop - Cryumbrepts - Cryorthents complex, extremely stony.
38 8771 Leighcan - Moran families - Cryaquolls complex, extremely stony.
39 8772 Moran family - Cryorthents - Leighcan family complex, extremely stony.
40 8776 Moran family - Cryorthents - Rock land complex, extremely stony.
Note: First digit: climatic zone Second digit: geologic zones
1. lower montane dry 1. alluvium
2. lower montane 2. glacial
3. montane dry 3. shale
4. montane 4. sandstone
5. montane dry and montane 5. mixed sedimentary
6. montane and subalpine 6. unspecified in the USFS ELU Survey
7. subalpine 7. igneous and metamorphic
8. alpine 8. volcanic
The third and fourth ELU digits are unique to the mapping unit
and have no special meaning to the climatic or geologic zones.
Forest Cover Type Classes: 1 -- Spruce/Fir
2 -- Lodgepole Pine
3 -- Ponderosa Pine
4 -- Cottonwood/Willow
5 -- Aspen
6 -- Douglas-fir
7 -- Krummholz
8. Basic Summary Statistics for quantitative variables only
(whole dataset -- thanks to Phil Rennert for the summary values):
Name Units Mean Std Dev
Elevation meters 2959.36 279.98
Aspect azimuth 155.65 111.91
Slope degrees 14.10 7.49
Horizontal_Distance_To_Hydrology meters 269.43 212.55
Vertical_Distance_To_Hydrology meters 46.42 58.30
Horizontal_Distance_To_Roadways meters 2350.15 1559.25
Hillshade_9am 0 to 255 index 212.15 26.77
Hillshade_Noon 0 to 255 index 223.32 19.77
Hillshade_3pm 0 to 255 index 142.53 38.27
Horizontal_Distance_To_Fire_Points meters 1980.29 1324.19
9. Missing Attribute Values: None.
10. Class distribution:
Number of records of Spruce-Fir: 211840
Number of records of Lodgepole Pine: 283301
Number of records of Ponderosa Pine: 35754
Number of records of Cottonwood/Willow: 2747
Number of records of Aspen: 9493
Number of records of Douglas-fir: 17367
Number of records of Krummholz: 20510
Number of records of other: 0
Total records: 581012
=====================================================================
Jock A. Blackard
08/28/1998 -- original text
12/07/1999 -- updated mailing address, citations, background info
for study area, added summary statistics.
=====================================================================