Tracking Reforestation in the Loess Plateau, China after the \"Grain for Green\" Project through Integrating PALSAR and Landsat Imagery  
[PERSON]  
1 School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; [PERSON]ADDRESS]  
[PERSON]  
1 School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; [PERSON]ADDRESS]  
[PERSON]  
2 Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (J.D. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (G.Z.) [PERSON]  
2 Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (J.D. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (G.Z.) [PERSON]  
2 Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (J.D. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (G.Z.) [PERSON]  
3 Satellite Environment Center, Ministry of Ecology and Environment, Beijing 100094, China; [EMAIL_ADDRESS] (J.D. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (G.Z.) [PERSON]  
4 Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019-0390, USA; [EMAIL_ADDRESS] (Y.Q. ); [EMAIL_ADDRESS] (X.X.) [PERSON]  
4 Correspondence: [EMAIL_ADDRESS]; Tel. : +86-010-623362304444445  
Project (GGP), was initiated by the Chinese government in 1999. The project returned the marginal croplands to forests, for alleviating soil and water loss, and preventing land degradation through afforestation and reforestation [5]. The project has achieved remarkable effectiveness such as increasing forest cover, enhancing carbon sequestration, controlling soil and water loss, and reducing dust to other regions and countries. However, there have been limited efforts on generating spatially explicit maps of forest changes during the last decade. Remote sensing has been an essential tool for assessing forest cover dynamics at regional to global scales. Such applications are quite valuable in those regions that are remote with limited human access. Previous studies have generated coarse resolution forest maps primarily from optical sensors like Advanced Very High Resolution Radiometer (AVHRR) [6; 7], and Moderate Resolution Imaging Spectroradiometer (MODIS) [8; 9; 10]. Despite the coarse resolution, those forest maps can adequately describe the general patterns of the spatial distribution of forests and can track the annual dynamics of large intact forests. However, those coarse resolution forest maps have a considerable uncertainty in fragmented landscapes like Loess Plateau [11]. The open access of the archived Landsat imagery enabled the extensive application for forest monitoring [6; 12; 13; 14; 15]. These 30-m forest products [6; 12] can capture more detailed information in the spatial distribution and dynamic changes of forests than very coarse resolution imagery like AVHRR and MODIS. [PERSON] et al. [8; 9; 12] used the Landsat data source for global forest change monitoring. However, there are two main problems in optical remote sensing images. First, the utility of optical remote sensing images is often limited by the quality of observations and restricted by persistent clouds and cloud shadows [16]. Although images from multiple years can increase the quality of observations, it will need high monetary and computational costs. Second, the canopy level characteristics cause the accuracy of the Landsat data to have a considerable commission error because of the similar structural and spectral attributes in shrubs or vegetation with high biomass [17].