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.
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
The evaluation of this dataset is done using Area Under the ROC curve (AUC).
Please refer to : https://archive.ics.uci.edu/ml/citation_policy.html
Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.