# 1DL034 Project VT24 — Data 来源：https://www.kaggle.com/competitions/1dl034-project-vt24/data 采集时间：2026-10-19T13:35:58Z # 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: | Number | Wilderness Area | |--------|----------------------------------| | 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.| Zone | 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: | Category | Tree Species | |--------|-------------------| | 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: | Category | Number of Records | |--------------------------------------|---------------------| | Spruce-Fir | 211,840 | | Lodgepole Pine | 283,301 | | Ponderosa Pine | 35,754 | | Cottonwood/Willow | 2,747 | | Aspen | 9,493 | | Douglas-fir | 17,367 | | Krummholz | 20,510 | | Other | 0 | | **Total Records** | **581,012** | --- Jock A. Blackard 08/28/1998 -- original text 12/07/1999 -- updated mailing address, citations, background info for study area, added summary statistics. ---