Geography

Mapping land-cover modifications over large areas: A comparison of machine learning algorithms

Document Type

Article

Abstract

Large area land-cover monitoring scenarios, involving large volumes of data, are becoming more prevalent in remote sensing applications. Thus, there is a pressing need for increased automation in the change mapping process. The objective of this research is to compare the performance of three machine learning algorithms (MLAs); two classification tree software routines (S-plus and C4.5) and an artificial neural network (ARTMAP), in the context of mapping land-cover modifications in northern and southern California study sites between 1990/91 and 1996. Comparisons were based on several criteria: overall accuracy, sensitivity to data set size and variation, and noise. ARTMAP produced the most accurate maps overall (∼ 84%), for two study areas - in southern and northern California, and was most resistant to training data deficiencies. The change map generated using ARTMAP has similar accuracies to a human-interpreted map produced by the U.S. Forest Service in the southern study area. ARTMAP appears to be robust and accurate for automated, large area change monitoring as it performed equally well across the diverse study areas with minimal human intervention in the classification process. © 2007 Elsevier Inc. All rights reserved.

Publication Title

Remote Sensing of Environment

Publication Date

2008

Volume

112

Issue

5

First Page

2272

Last Page

2283

ISSN

0034-4257

DOI

10.1016/j.rse.2007.10.004

Keywords

land-cover change, large area monitoring, machine learning

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