## 2 Materials and Methods
### Methods
#### 2.3.2 Methods to Detect Forest Degradation from Remote Sensing Data  
To detect the forest degradation in multiple scales, a method based on MODIS and Landsat ETM+ data was adopted in this paper, as shown in Figure 2. First, simple linear regression models were adopted to analyze the trend using time series indicators (NPP, NDVI, EVI, RVI, LAI, FPAR) from MODIS products from 2000 to 2010. Then a classification model was established to analyze the degree of degradation based on the trend of indicators. To minimize the influence of the mixed pixels in a MODIS pixel, the Landsat ETM+ data were used to analyze the situation in the degraded areas from MODIS datasets. By analyzing the degradation rate from 2000 to 2010, and adopting the same classification strategy as the degradation from MODIS products, a forest degradation map with a high resolution of 30 m was obtained. Finally, to assess the performance of the method, the results were validated with the ground-based survey data, and were compared with the forest degradation derived from Landsat ETM+ data directly. * Analysis of the Time Series Indicators from MODIS Products First, the average values of the indictors (NPP, NDVI, EVI, RVI, LAI, FPAR) derived from MODIS products in the growing season (from June to August) from 2000 to 2010 were  
Figure 2: Flowchart of detecting forest degradation from MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) data. Regression models were adopted to analyze the trend from MODIS Products, then a classification model was established to analyze the degradation level, ETM+ data were used to obtain details in the degraded MODIS pixels. acquired in this paper. By reviewing the quality flags in the MODIS products, the missing values and negative values were replaced with corresponding mean values before and after the specific time. To make the trends of different indicators comparable, the indicators were normalized. Then some linear regression models were established using the time series indicators (NDVI, EVI, RVI, LAI, FPAR, NPP) from MODIS data, and the regression slopes and standard deviations were obtained for each MODIS pixel. Considering that forest ecosystems were sensitive to climate, its structure, function and degradation were closely related to the climate and climate changes, a decision tree classification model was established based on an improved Koppen-Geiger climate classification map to categorize the pixels into four classes according to the regression slopes: No degradation, slight degradation, moderate degradation and severe degradation. Noting that each classification model was built on a sliding window (150 km \(\times\) 150 km). In the sliding window, MODIS indicators (500 m) were counted according to the Koppen-Geiger climate classification map (1 km) to generate the histograms of the indicators (See Figures S1 and S2 in Supplementary Materials). Then the classification strategy was built according to the histogram of the indicators [19]. Specifically, No degradation: \(k\geq 0\); slight degradation: \(-\delta\leq k<0\); moderate degradation: \(-2\delta\leq k<-\delta\); severe degradation: \(k<-2\delta\). \(k\) is the regression slope, and \(\delta\) is the standard deviation in the sliding window. Once the degradation trend was found in at least one indicator, the pixel was classified as degradation. And the degradation degree was determined according to the mode of degradation level from the six kinds of indicators. * Degradation Rate from Landsat Data As the influence of heterogeneity of the land surface and mixed pixels in the MODIS pixels (500 m), the degradation map from MODIS products may have a low accuracy. To detecting the finer forest degradation in a MODIS pixel, the Landsat ETM+ data were used to study the degradation areas in the MODIS pixels which was identified as the degraded areas. Firstly, from the Landsat ETM+ data, the NDVI, EVI, RVI were obtained in the degradation MODIS pixel.