A study on spatial-temporal characteristics of grassland degradation using the Markov model

Abstract

Abstract: The spatial and temporal features of grassland cover conversion (GCC) serve as a useful input for understanding the desertification process and degradation of grassland caused by anthropogenic activities and extreme natural events in general. Thematic Mapper data (TM 30 m) were used to detect and map degraded grassland features both spatially and temporally. Two data sets of TM 30 m data were collected from the years 2000 to 2010. Supervised classifications were developed for each of the GCC change detection of the three cases (degradation,desertification, and salinization). To address this situation, the field data were used to test the GCC detection of change results presented in this paper. The GCC change detection methods worked reasonably well and detection accuracy of deserted and salinized output was >90% although degraded output identified only 75% of the covered pixels within the ground observed perimeter polygons. The applications presented in this paper also evaluated the transition matrix between 2000 and 2010 of each of the three change detections,and predicted dynamic characteristics of grassland using the Markov model. The results showed that for the next decade, and even for a further ten years, the grassland will develop positively with a reduced trend of degradation and desertification. The research also indicated, it is credible to use remote sensing technology combined with the Markov model in analyzing the dynamic characteristics of grassland cover changes.

CLC Number:

S812

LIU Ai-jun, WANG Bao-lin, CHEN Xi-mei, YANG Sheng-li, ZHENG Shu-hua. A study on spatial-temporal characteristics of grassland degradation using the Markov model[J]. Acta Prataculturae Sinica, 2012, 21(5): 229-236.

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