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Proceedings Paper

Generalized linear models for mapping land cover using satellite measurement and digital terrain data
Author(s): Xiong Rao; Jinping Zhang; Brian M. Steele; Roland L. Redmond
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Paper Abstract

This paper explores an approach for predicting land cover types of central Montana, USA based on satellite measurement and digital terrain data. We assume a non-linear inherent relationship existing between land cover types and Landsat TM reflectance and terrain variables. To measure this relationship we use Generalize Linear Models (GLMs), which are mathematical extensions of ordinary least-square regression models. Specifically, stepwise logistic regression technique is applied to optimize the predictive model. For the analysis of the significance of dropping or adding terms, the Akaike information criterion (AIC) is used. Likelihood Ratio Test (LRT) is applied to test the validity of explanative potential of predictor variables. We use cross-validation method to evaluate the predicative accuracy of land cover mapping using GLMs. Finally we table the relative risk ratios of GLMs. Since relative risk ratios explicitly represent the explanative efficiency of predictor variables, their ranking can pick up the variables with significant explanatory potential in discriminating land cover types, which will be significative for simplifying the predictive models. It is anticipated that GLMs will be valuable extension to semi-automatic classification of remotely sensed imagery, and an effective tool for land cover mapping.

Paper Details

Date Published: 1 August 2007
PDF: 11 pages
Proc. SPIE 6751, Geoinformatics 2007: Cartographic Theory and Models, 675108 (1 August 2007); doi: 10.1117/12.759481
Show Author Affiliations
Xiong Rao, Wuhan Univ. (China)
Jinping Zhang, Wuhan Univ. (China)
Brian M. Steele, Univ. of Montana (United States)
Roland L. Redmond, Univ. of Montana (United States)


Published in SPIE Proceedings Vol. 6751:
Geoinformatics 2007: Cartographic Theory and Models

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