## 1 Introduction  
Forest disturbances are discrete events that cause tree mortality and destruction of plant biomass. An effective method to detect the temporal and spatial distribution of forest disturbance in a large area is to use remote sensing time series data. In many ecological models, accurate monitoring of forest disturbances has a crucial role in Gross Primary Production (GPP) and Net Primary Production (NPP) estimation accuracy and aerosol and biomass estimation [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. The mapping of precise forest disturbances in large areas provides basic data for wildlife protection and strong support for large-scale vegetation biophysical and structural change monitoring [11]. Based on the needs of various applications, the use of remote sensing data for detecting forest disturbances in a large area has been carried out widely. Many efficient methods have been proposed to detect changes from image time series. The methods include detecting forest disturbance and recovery [3; 12; 13], detecting trend and seasonal changes [14; 15; 16], and extracting seasonality metrics from satellite time series [17; 18]. Current remote sensing approaches in monitoring forest disturbance detection are mainly based on vegetation indices (VI) and other vegetation parameters [19; 20; 21; 22; 23; 24], such as the normalized difference vegetation index (NDVI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) [11], to determine the disturbance by analyzing the changes in a long time series. VI is a common indicator of vegetation disturbance monitoring. Vegetation is relatively fragile in terrestrial ecosystems, and it is also sensitive to the occurrence of disturbances. It is possible to detect the disturbance events effectively by observing the change of vegetation using remote sensing data. NDVI is the most widely used VI\({}_{2}\) it can show the growth curve of vegetation over time. It is suitable for medium and long-term vegetation growth monitoring, phenophase monitoring, and crop yield estimation. The hotspot and NDVI differencing synergy (HANDS) [19] algorithm was applied to Canadian forests during the 1995-2000 fire seasons using the annual hotspot masks and differencing of the anniversary date of September NDVI composites. HANDS is designed to produce annual maps of burned forests by combining the active fire detection product with NDVI differencing, a common change detection technique. The HANDS method substantially reduces noise levels by requiring that burned areas identified through NDVI differencing be co-located with hotspots. In 2009, Mildrexler used the Moderate Resolution Imaging Spectroradiometer (MODIS) global disturbance index (MGDI) [25] with satellite data to operationally detect large-scale ecosystem disturbances at 1-km resolution. The MGDI algorithm was designed to contrast annual changes in vegetation density and land surface temperature (LST) following disturbance by enhancing the signal to effectively detect the location and intensity of disturbances. Disturbances from agents such as fire, insect damage, or strong winds are common throughout the world's forests. Fire is the dominant driver of forest disturbance at the global scale, and fire is one of the most dominant disturbance agents in Northeast China. Currently, the most widely used burned area products are three MODIS data products and three products developed within the fire disturbance project (fire_cci) [26; 27; 28; 29; 30; 31]. There is a significant difference between the detection results of different products; through a comparison of a variety of products, statistical tests identified that MCD64 was the most accurate, followed by MCD45 [32]. Biomass density varies through time as a result of disturbances; accurate estimates of biomass emissions focuses on identifying disturbances (whether anthropogenic or natural), but it is difficult to meet the application requirements with most of the forest disturbance products.