Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI3g Data

State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China

Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106, USA

State Key Laboratory of Space-Ground Integrated Information Technology, Beijing 100029, China

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State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

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Shenhua Baorixile Energy Co., Ltd., Hulunbuir 021000, China

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State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 100011, China

Author to whom correspondence should be addressed.

Remote Sens. 2019, 11(9), 1014; https://doi.org/10.3390/rs11091014

Received: 6 March 2019 / Revised: 25 April 2019 / Accepted: 25 April 2019 / Published: 29 April 2019

(This article belongs to the Special Issue Applications of Remote Sensing in Rangelands Research)

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Abstract

Grassland ecosystems in China have experienced degradation caused by natural processes and human activities. Time series segmentation and residual trend analysis (TSS-RESTREND) was applied to grasslands in eastern China. TSS-RESTREND is an extended version of the residual trend (RESTREND) methodology. It considers breakpoint detection to identify pixels with abrupt ecosystem changes which violate the assumptions of RESTREND. With TSS-RESTREND, in Xilingol (111°59′–120°00′E and 42°32′–46°41′E) and Hulunbuir (115°30′–122°E and 47°10′–51°23′N) grassland, 6% and 3% of the area experienced a decrease in greenness between 1984 and 2009, 80% and 73% had no significant change, 5% and 3% increased in greenness, and 9% and 21% were undetermined, respectively. RESTREND may underestimate the greening trend in Xilingol, but both TSS-RESTREND and RESTREND revealed no significant differences in Hulunbuir. The proposed TSS-RESTREND methodology captured both the time and magnitude of vegetation changes. View Full-Text

Keywords: grassland; NDVI; RESTREND; BFAST; land degradation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

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MDPI and ACS Style

Liu, C.; Melack, J.; Tian, Y.; Huang, H.; Jiang, J.; Fu, X.; Zhang, Z. Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI_3g Data. Remote Sens. 2019, 11, 1014. https://doi.org/10.3390/rs11091014

AMA Style

Liu C, Melack J, Tian Y, Huang H, Jiang J, Fu X, Zhang Z. Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI_3g Data. Remote Sensing. 2019; 11(9):1014. https://doi.org/10.3390/rs11091014

Chicago/Turabian Style

Liu, Caixia, John Melack, Ye Tian, Huabing Huang, Jinxiong Jiang, Xiao Fu, and Zhouai Zhang. 2019. "Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI_3g Data" Remote Sensing 11, no. 9: 1014. https://doi.org/10.3390/rs11091014

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Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI_3g Data

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