Paper
24 September 2013 Assessment of desertification in the agro-pastoral transitional zone in Northern China (1982-2006) using GIMMS NDVI data
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Abstract
The Agro-Pastoral Transitional Zone in Northern China (hereafter APTZNC) is situated in an arid/semi-arid area, and is one of the most vulnerable areas in the world subject to climate change. Annual integrated the NASA Global Inventory Modeling and Mapping Studies (hereafter GIMMS) Normalized Difference Vegetation Index (hereafter ΣNDVI) and annual rainfall were used in this study. Meanwhile, the dynamics of ΣNDVI and rain-use efficiency (hereafter RUE) were predicted during the period, through the use of the Mann-Kendall nonparametric test and linear regression temporal trend analysis. The tendency of desertification under different precipitation scenarios was also analyzed. The results showed that annual ΣNDVI and rainfall were not significantly correlated in most sections of the study area, yet opposite results were observed for a smaller percentage of the study area (p<0.01). Changes in vegetation productivity may increase, whereas a significant decrease in a small pixel proportion was observed. The northeast and central sections of the study area are characterized by positive trends in RUE slope values, contrary to what was observed in the southwestern sections of the study area. The results fit well with the findings through ΣNDVI and RUE. Rainfall in the range of 200-500 mm can be seen as a threshold value as the desertification trend decreases and vegetation restoration capacity is enhanced with increasing rainfall.
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Youzhi An, Wei Gao, Zhiqiang Gao, Chaoshun Liu, and Runhe Shi "Assessment of desertification in the agro-pastoral transitional zone in Northern China (1982-2006) using GIMMS NDVI data", Proc. SPIE 8869, Remote Sensing and Modeling of Ecosystems for Sustainability X, 886913 (24 September 2013); https://doi.org/10.1117/12.2021857
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Cited by 2 scholarly publications.
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KEYWORDS
Vegetation

Climate change

Data centers

Infrared sensors

Satellites

Sensors

Clouds

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