Forest Type Prediction

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In this challenge, I am trying to predict the forest cover type (the predominant kind of tree cover) from strictly cartographic variables (as opposed to remotely sensed data). The actual forest cover type for a given 30 x 30 meter cell was determined from US Forest Service (USFS) Region 2 Resource Information System data. Independent variables were then derived from data obtained from the US Geological Survey and USFS. The data is in raw form (not scaled) and contains binary columns of data for qualitative independent variables such as wilderness areas and soil type.

The GitHub of the project can be found here :

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.

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.

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.

The data

File descriptions :

  • train-set.csv : the training set
  • test-set.csv :the test set
  • submission-example.csv : a sample submission file in the correct format

Data descriptions

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.

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

  • 2702 Cathedral family - Rock outcrop complex, extremely stony.
  • 2703 Vanet - Ratake families complex, very stony.
  • 2704 Haploborolis - Rock outcrop complex, rubbly.
  • 2705 Ratake family - Rock outcrop complex, rubbly.
  • 2706 Vanet family - Rock outcrop complex complex, rubbly.
  • 2717 Vanet - Wetmore families - Rock outcrop complex, stony.
  • 3501 Gothic family.
  • 3502 Supervisor - Limber families complex.
  • 4201 Troutville family, very stony.
  • 4703 Bullwark - Catamount families - Rock outcrop complex, rubbly.
  • 4704 Bullwark - Catamount families - Rock land complex, rubbly.
  • 4744 Legault family - Rock land complex, stony.
  • 4758 Catamount family - Rock land - Bullwark family complex, rubbly.
  • 5101 Pachic Argiborolis - Aquolis complex.
  • 5151 unspecified in the USFS Soil and ELU Survey.
  • 6101 Cryaquolis - Cryoborolis complex.
  • 6102 Gateview family - Cryaquolis complex.
  • 6731 Rogert family, very stony.
  • 7101 Typic Cryaquolis - Borohemists complex.
  • 7102 Typic Cryaquepts - Typic Cryaquolls complex.
  • 7103 Typic Cryaquolls - Leighcan family, till substratum complex.
  • 7201 Leighcan family, till substratum, extremely bouldery.
  • 7202 Leighcan family, till substratum - Typic Cryaquolls complex.
  • 7700 Leighcan family, extremely stony.
  • 7701 Leighcan family, warm, extremely stony.
  • 7702 Granile - Catamount families complex, very stony.
  • 7709 Leighcan family, warm - Rock outcrop complex, extremely stony.
  • 7710 Leighcan family - Rock outcrop complex, extremely stony.
  • 7745 Como - Legault families complex, extremely stony.
  • 7746 Como family - Rock land - Legault family complex, extremely stony.
  • 7755 Leighcan - Catamount families complex, extremely stony.
  • 7756 Catamount family - Rock outcrop - Leighcan family complex, extremely stony.
  • 7757 Leighcan - Catamount families - Rock outcrop complex, extremely stony.
  • 7790 Cryorthents - Rock land complex, extremely stony.
  • 8703 Cryumbrepts - Rock outcrop - Cryaquepts complex.
  • 8707 Bross family - Rock land - Cryumbrepts complex, extremely stony.
  • 8708 Rock outcrop - Cryumbrepts - Cryorthents complex, extremely stony.
  • 8771 Leighcan - Moran families - Cryaquolls complex, extremely stony.
  • 8772 Moran family - Cryorthents - Leighcan family complex, extremely stony.
  • 8776 Moran family - Cryorthents - Rock land complex, extremely stony.

Note:

  • First digit: climatic zone
  • Second digit: geologic zones
  • lower montane dry 1. alluvium
  • lower montane 2. glacial
  • montane dry 3. shale
  • montane 4. sandstone
  • montane dry and montane 5. mixed sedimentary
  • montane and subalpine 6. unspecified in the USFS ELU Survey
  • subalpine 7. igneous and metamorphic
  • 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

For further information: https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.info

Notebook

The Jupyter notenook is the file : Forest Cover Type Prediction.ipynb

Outcome

This challenge was part of a private in-class Kaggle Challenge. I have reached an accuracy of 0.95943 which allowed me to rank 7 / 64.

The Kaggle can be found here


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