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Characterization of locations and extents of afforestation from the 
Grain for Green Project in China

 

Wenping Yuan^ab, Xianglan Li^c, Shunlin Liang^cd, Xuefeng Cui^ac, Wenjie Dong^a, Shuguang Liu^ef, Jiangzhou Xia^a, Yang Chen^a, Dan Liu^a & Wenquan Zhu^ag

 

a State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China

b State Key Laboratory of Cryospheric Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, The Chinese Academy of Sciences, Lanzhou, Gansu, China

c College of Global Change and Earth System Science, Beijing Normal University, Beijing, China

d Department of Geography, University of Maryland, College Park, MD, USA

e United States Geological Survey Earth Resources Observation and Science Center, Sioux Falls, SD, USA

f State Engineering Laboratory of Southern Forestry Applied Ecology and Technology, Central South University of Forestry and Technology, Changsha, Hunan, China

g College of Resources Science and Technology, Beijing Normal

 

ABSTRACT

The Chinese government started implementation of the Grain for Green Project (GGP) in 1999, aiming to convert cropland to forestland to mitigate soil erosion problems in areas across the country. Although the project has generated substantial environmental benefits, such as erosion reduction, carbon sequestration and water quality improvements, the magnitude of these benefits has not yet been well quantified due to the lack of locationspecific data describing the afforestation efforts. Remote sensing is well suited to detect afforestation locations, a prerequisite for estimating the impacts of the project. In this study, we first examined the practicability of using the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product to detect afforestation locations; however, the results showed that the MODIS product failed to distinguish the afforestation areas of GGP. Then, we used a normalized difference vegetation index (NDVI) time series analysis approach for detecting afforestation locations, applying statistical data to determine the NDVI threshold of converted croplands. The technique provided the necessary information for location of afforestation implemented under GGP, explaining 85% of conversion from cropland to forestlands across all provinces. The coefficients of determination between detected afforestation and statistical areas at the most provinces were more than 0.7 which indicated the high performance. Moreover, more than 60% of GGP locations identified in all the provinces had a slope of over 25°, which was consistent with the main criterion of GGP. These results should enable wide application of the method to evaluate the impacts of the project on regional carbon budgets, water yield and soil erosion.

 

PUBLISHED BY: REMOTE SENSING LETTERS, 2014.5 (3): 221-229

 

SOURCE: http://www.tandfonline.com/doi/abs/10.1080/2150704X.2014.894655