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Forest Mapping with a Generalized Classifier and Landsat TM Data

Citation

Pax-Lenney, Mary; Woodcock, Curtis E.; Macomber, Scott A.; Gopal, Sucharita; & Song, Conghe H. (2001). Forest Mapping with a Generalized Classifier and Landsat TM Data. Remote Sensing of Environment, 77(3), 241-50.

Abstract

Monitoring landcover and landcover change at regional and global scales often requires Landsat data to identify and map landscape features and patterns with sufficient detail. Analytical methods based on image-by-image interpretation are too time-consuming and labor-intensive for studies of large areas to be undertaken with any degree of frequency. One potential solution is to develop algorithms or classifiers that can be generalized beyond the arena of the initial training to new images from different spatial, temporal or sensor domains. Building upon earlier success with a generalized classifier to monitor forest change, we now address the question of generalization for classifications of stable landcovers. We evaluate the ability of a supervised neural network, Fuzzy ARTMAP, to identify conifer forest across time and space with Landsat Thematic Mapper (TM) images for a region in northwest Oregon. We also assess the effects of atmospheric corrections on generalized classification accuracies. Using midsummer images atmospherically corrected with a simple dark-object-subtraction (DOS) method, there is no statistically significant loss of accuracy as the classification is extended from the initial training image to other images from the same scene (path and row): temporal generalization is successful. Extending the classifier across space and time to nearby scenes results in a mean decline of 8-13% accuracy depending on the atmospheric correction used. Obvious sources of error, such as seasonality solar angle variation, and complexity of landcover identification, do not explain the decline in error. Additionally, the patterns in generalization accuracies are complex, and the relationship between pairs of training and testing images is not necessarily reciprocal, i.e., good training data are not necessarily good testing data. Simple DOS atmospheric corrections produce classifications with comparable accuracies as classifications from the more complex radiative transfer corrections. These findings are based on over 200 classifications. A high degree of variability in the classification accuracies underscores the importance of extensive, in-depth analysis of remote sensing techniques and applications, and highlights the potential problem for misleading results based on just a few tests. Generalization is well suited for multitemporal classifications of one Landsat scene. Using simple DOS and midsummer images, generalization offers the opportunity for frequent landcover mapping of a Landsat scene without having to retrain the classifier for each time period of interest. However, at this point, the utility of regional landcover mapping with a generalized classifier remains limited. (C) 2001 Elsevier Science Inc. Alt rights reserved.

URL

http://dx.doi.org//10.1016/s0034-4257(01)00208-5

Reference Type

Journal Article

Journal Title

Remote Sensing of Environment

Author(s)

Pax-Lenney, Mary
Woodcock, Curtis E.
Macomber, Scott A.
Gopal, Sucharita
Song, Conghe H.

Year Published

2001

Volume Number

77

Issue Number

3

Pages

241-50

Reference ID

12065