The Forest CoverType dataset
============================

Notes
-----
Data Set Characteristics:
    :Number of instances (observations): 581,012

    :Number of Attributes: 12 measures, but 54 columns of data
        (10 quantitative variables, 4 binary wilderness areas and 40 binary
		soil type variables)

    :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


    :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
        ==================================     ===============  ======= ========

    :Missing Attribute Values:  None.


    :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


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


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.

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.
