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

Content Provider	IEEE Xplore Digital Library
Author	Shang, ZongBo Hamerlinck, Jeffery D.
Copyright Year	2007
Abstract	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 .
Starting Page	661
Ending Page	666
File Size	676996
Page Count	6
File Format	PDF
ISBN	9780769530192
DOI	10.1109/ICDMW.2007.87
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Geography Information science Geographic Information Systems Vegetation Land surface temperature Data mining Statistics Regression tree analysis Classification tree analysis Information analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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