Land degradation monitoring using multi‐temporal Landsat TM/ETM data in a transition zone between grassland and cropland of northeast China

Abstract

Land degradation is one of the most pressing problems of environments. This research presents a methodology to monitor land degradation in a transition zone between grassland and cropland of northeast China, where soil salinization and grassland degradation, even desertification, have been observed in the past few decades. Landsat TM/ETM data in 1988, 1996 and 2001 were selected to determine the rate and status of grassland degradation and soil salinization together based on both decision tree (DT) classifier and the field investigation. The thermal radiance values of TM/ETM 6 data, the Normalized Difference Vegetation Index (NDVI), and new variables (brightness, greenness, and wetness) generated by the Kauth–homas Transforms (KT) algorithms from Landsat TM/ETM data served as the feature nodes of a DT classifer and contributed to improving the classification results. It showed an overall accuracy of more than 85% and a Kappa statistic of agreement of about 0.79 in 1996 and 2001 with the exception of about 0.69 in 1988. The statistical areas of land degradation in the observation periods revealed that land degradation, especially the salt‐affected soil, is accelerating. The distribution maps of land degradation in the years of 1988, 1996 and 2001 were generated respectively based on the classification results. Their change maps were created by the difference between the distribution maps from 1988 to 1996 and from 1996 to 2001 respectively. The changes of salt‐affected soil occurred near the water bodies due to variations of water sizes, and most of the degraded grassland appeared around the salt‐affected soil. Although climate variations play an important role in this region, human activities are also crucial to land degradation.

Acknowledgements

The authors wish to thank anonymous reviewers for their comments that helped improve the quality of the manuscript. The research was sponsored by the National Natural Sciences Foundation of China (NSFC) (No.40471086) and Open Fund of Remote Sensing Science (No.SK050006), State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences, and Beijing Normal University.