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