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Using Statistics and Spatial Data Mining to Study Land Cover in Wyoming :Can We Predict Vegetation Types from Environmental Variables?

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2 Author(s)

Factor analysis, classification and regression tree analysis (CART), discriminant analysis and classification analysis were applied to study land cover in Wyoming, to explore: 1) how environmental variables are related to one another; 2) whether land cover types (forest, grass, shrub and un-vegetated) are differentiated from one another in term of abiotic conditions; and 3) how to predicate vegetation covers based on environmental variables. A factor analysis indicated that environmental variables were characterized by three dominant conditions: 1) harsh condition for survival, 2) suitable condition during growing season, and 3) harsh condition caused by high altitude. A discriminant analysis indicated that: 1) forests and grasses required wet climate with relatively cool summers, while shrub could bear drought; 2) quite different to forests, grasses required dry winters and relatively wet and warm summers. CART and classification analysis both indicated that, by using Precipitation in July, Maximum Annual Temperature and Average Annual Precipitation, we had a fair accuracy in predicting vegetation covers in Wyoming. Keywords: Factor analysis, discriminant analysis, classification and regression tree, land cover, Wyoming .

Published in:

Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on

Date of Conference:

28-31 Oct. 2007

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