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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 location-specific 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.
Author contributions
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Wenping Yuan and Xianglan Li contributed equally.
Acknowledgement
We thank Prof. Shixiong Cao of Beijing Forestry University for the valuable suggestions and data about the GGP.
Funding
This study was supported by the National Basic Research Program of China [grant number 2011CB952001]; National Science Foundation for Excellent Young Scholars of China [grant number 41322005]; Program for New Century Excellent Talents in University [grant number NCET-12-0060] and Fundamental Research Funds for the Central Universities.