## 1 Introduction  
Forest degradation could be defined as a loss of carbon stocks from forestland without land cover change caused by either natural or anthropogenic activities with relating to climate change [1, 2]. Deforestation and forest degradation have become an important issue concerning climate change, and have been the second leading cause of anthropogenic greenhouse emissions [3, 4]. Studies have shown that global estimates of the carbon emissions from degradation range from 10% to 40% of the total global greenhouse gas emissions [5, 6, 2, 7]. The uncertainty is mainly due to the difficulties in monitoring degradation over large areas as the forest kinds, climatic characteristics, forestry planning and management vary. Therefore, developing efficient methods for forest degradation is of great significance not only in revealing the process of forest quality deceased, stand structure destructed and permanent loss of forest but also giving a better understanding of regional or global carbon emissions and its relationship with climate change. Recent advances in time series remote sensing data processing suggested that yearly vegetation indices derived from MODerate Resolution Imaging Spectroradiometer (MODIS) products could play a significant role in regional forest disturbance and degradation assessment [8; 9]. Forest harvest, clearing and degradation could be detected using MODIS multispectral time series data with spatial resolution from 250 m to 500 m [10]. Studies also shown that the MODIS products were too coarse to detect the subtle changes in the density or structure of individual tree. To solve the problem, high resolution satellite data such as Landsat Enhanced Thematic Mapper Plus(ETM+)/ Operational Land Image (OLI) [2; 3; 11; 12], IKONOS [13; 14], Sentinel-2 images [15] which gained the ability to detect stresses and diseases at a crown level were adopted to classify forest health levels [16]. But the long revisit cycle and cloud contamination of high resolution satellite data have long limited their use in studying regional and global forest degradation. In addition, current studies were either applied on single sensor images or on time series data from multiple satellite sensors which have similar characteristics, it is still not easy to integrate time series data from different satellite sensors to apply in a large scale forest degradation detecting [16]. In this condition, advances and new methods are urgent to exploit multi-scale data streams for combing satellite data in multiple resolution to monitor forest degradation. Some studies tried to detect forest degradation from the perspective of carbon reduction and the canopy fraction cover decreasing. Time series of multispectral Vegetation Index (VI), such as Normalized Difference Vegetation Index (NDVI) [17; 18], Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) [19], Enhanced Wetness Difference Index (EWDI) [20], which described the vegetation phenology, have been adopted to analyze the forest health and forest degradation level. Some other indicators to describe the forest productivity, such as Gross Primary Production (GPP)/Net Primary Production (NPP) and Above Ground Biomass (AGB) [12], and indicators to reflect the biodiversity of the forest, such as Canopy Fraction Cover (CFC) [21], were also used to assess the degradation level in regional areas. But it is noteworthy that accuracy and uncertainly varied when using different criteria and indicators [1; 2; 12; 13; 15], and the influence of multiscale remote sensing indicators on the effectiveness and sensibility of forest degradation is still unknown. In this case, studying regional forest degradation from multiple indicators based on satellite observations data, and analyzing the impact of indicators on the accuracy of degraded forest detection is of great significance. As for the method to detect forest degradation, developing a robust approach to map the forest degraded level from satellite or airborne remote sensing imagery is still a challenge.