Human induced dryland degradation in Ordos Plateau, China, revealed by multilevel statistical modeling of normalized difference vegetation index and rainfall time-series
1 College of Life Sciences, Inner Mongolia University, Hohhot 010021, China;
2 College of Environment and Resources, Dalian Nationalities University, Dalian 116600, China;
3 Sino-US Center for Conservation, Energy, and Sustainability Science, Inner Mongolia University, Hohhot 010021, China;
4 Faculty of Resources and Environment, Baotou Teachers College, Baotou 014030, China;
5 Department of Botany and Plant Sciences, University of California, Riverside CA 92521, USA
Human induced dryland degradation in Ordos Plateau, China, revealed by multilevel statistical modeling of normalized difference vegetation index and rainfall time-series
1 College of Life Sciences, Inner Mongolia University, Hohhot 010021, China;
2 College of Environment and Resources, Dalian Nationalities University, Dalian 116600, China;
3 Sino-US Center for Conservation, Energy, and Sustainability Science, Inner Mongolia University, Hohhot 010021, China;
4 Faculty of Resources and Environment, Baotou Teachers College, Baotou 014030, China;
5 Department of Botany and Plant Sciences, University of California, Riverside CA 92521, USA
摘要 Land degradation causes serious environmental problems in many regions of the world, and although it can be effectively assessed and monitored using a time series of rainfall and a normalized difference vegetation index (NDVI) from remotely-sensed imagery, dividing human-induced land degradation from vegetation dynamics due to climate change is not a trivial task. This paper presented a multilevel statistical modeling of the NDVI-rainfall relationship to detect human-induced land degradation at local and landscape scales in the Ordos Plateau of Inner Mongolia, China, and recognized that anthropogenic activities result in either positive (land restoration and re-vegetation) or negative (degradation) trends. Linear regressions were used to assess the accuracy of the multilevel statistical model. The results show that: (1) land restoration was the dominant process in the Ordos Plateau between 1998 and 2012; (2) the effect of the statistical removal of precipitation revealed areas of human-induced land degradation and improvement, the latter reflecting successful restoration projects and changes in land management in many parts of the Ordos; (3) compared to a simple linear regression, multilevel statistical modeling
could be used to analyze the relationship between the NDVI and rainfall and improve the accuracy of detecting the effect of human activities. Additional factors should be included when analyzing the NDVI-rainfall relationship and detecting human-induced loss of vegetation cover in drylands to improve the accuracy of the approach and eliminate some observed non-significant residual trends.
Abstract: Land degradation causes serious environmental problems in many regions of the world, and although it can be effectively assessed and monitored using a time series of rainfall and a normalized difference vegetation index (NDVI) from remotely-sensed imagery, dividing human-induced land degradation from vegetation dynamics due to climate change is not a trivial task. This paper presented a multilevel statistical modeling of the NDVI-rainfall relationship to detect human-induced land degradation at local and landscape scales in the Ordos Plateau of Inner Mongolia, China, and recognized that anthropogenic activities result in either positive (land restoration and re-vegetation) or negative (degradation) trends. Linear regressions were used to assess the accuracy of the multilevel statistical model. The results show that: (1) land restoration was the dominant process in the Ordos Plateau between 1998 and 2012; (2) the effect of the statistical removal of precipitation revealed areas of human-induced land degradation and improvement, the latter reflecting successful restoration projects and changes in land management in many parts of the Ordos; (3) compared to a simple linear regression, multilevel statistical modeling
could be used to analyze the relationship between the NDVI and rainfall and improve the accuracy of detecting the effect of human activities. Additional factors should be included when analyzing the NDVI-rainfall relationship and detecting human-induced loss of vegetation cover in drylands to improve the accuracy of the approach and eliminate some observed non-significant residual trends.
This study was supported by the National Basic Research Program of China (2012CB722201), the National Natural Science Foundation of China (30970504, 31060320), and the National Science and Technology Support Program (2011BAC07B01).
通讯作者:
JianMing NIU
E-mail: jmniu2005@163.com
引用本文:
Jing ZHANG, JianMing NIU, Tongliga BAO, Alexander BUYANTUYEV, Qing ZHANG, JianJu. Human induced dryland degradation in Ordos Plateau, China, revealed by multilevel statistical modeling of normalized difference vegetation index and rainfall time-series[J]. 干旱区科学, 2014, 6(2): 219-229.
Jing ZHANG, JianMing NIU, Tongliga BAO, Alexander BUYANTUYEV, Qing ZHANG, JianJun DONG, XueFeng ZHANG. Human induced dryland degradation in Ordos Plateau, China, revealed by multilevel statistical modeling of normalized difference vegetation index and rainfall time-series. Journal of Arid Land, 2014, 6(2): 219-229.
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2014, Vol. 6 
