Using logit models to classify land cover and land‐cover change from Landsat Thematic Mapper

Abstract

In this paper, we use logit models to classify data from Landsat Thematic Mapper (TM) among 23 land‐cover and land‐cover change classes. The logit model is a simple statistical technique that is designed to analyse categorical data. Diagnostic statistics indicate that the logit model can classify remotely sensed data in a statistically significant fashion. User accuracies for individual land‐cover classes range between 50 and 92%, with an overall accuracy of 79%. To assess these accuracies, we compare them to those generated by a Bayesian maximum likelihood classifier. While the overall accuracies are similar, the accuracies for individual land‐cover categories differ. These differences may be associated with the size of the training data for each land‐cover class. There is some evidence that the logit models generate higher accuracies for land‐cover categories for which relatively few training pixels are available. Finally, a comparison of classification results using a 12‐band composite of the six reflective TM bands and their change vectors versus a six‐band composite of the three Tasselled Cap bands and their change vectors indicates that the latter reduces classification accuracies.

Acknowledgements

This research was supported by NASA Land Cover/Land Use Change Program grant NAG5‐6214 and NASA New Investigator Program grant NAG5‐10 534. The authors would like to thank Alan Strahler for comments on an earlier draft.

Notes

Test statistic is distributed as a chi‐square with 30 degrees of freedom.

Values that exceeded the 0.01 threshold are given in bold; values that exceeded the 0.1 threshold are given in italics.