## 2 Materials and Methods
### Methodology
#### 2.3.4 Change Detection Analysis and Land Cover Updating  
The land cover information for 1980 and 1990 is provided by the CNLUCC datasets. Change areas for this period were then derived based on the land cover maps from CNLUCC, which include spatial locations and \"from-to\" types. Using the 1980 and 1990 land cover maps, change pixels were identified, such as cropland (C) converted to built-up land (U) as shown in the top left corner of the magnified nine patch in Figure 9. Unchanged pixels were masked in the change map from 1980 to 1990, resulting in the derivation of change maps for this period for use in the subsequent change detection step. In this study, the focus was on the six primary classes of land cover change, such as grassland to water and forest to cropland, rather than including changes at the subclass level, for instance, river  
Figure 8: Minibatch statistic layer. to lake in the water class and desert to Gobi in the unused land. The decision to concentrate on primary classes was made due to the lower classification accuracy of second-level land cover categories than that of the first level [7]. In the subsequent process, statistical based change-detection analysis was combined to reduce the inter-annual inconsistency in annual classification and mitigate the huge workload in collecting training data. The CNLUCC datasets were expanded to annual land cover datasets by finding the exact change year among the intervals, using the change maps and \"from-to\" types for the period. Change-detection analyses were then conducted in the pre-processed time series profiles (upscaled AVHRR NDVI at 30 m resolution) from 1980-1990 for every change pixel to determine the change year. Here, we resampled the super-resolved 250 m NDVI time series data to match the resolution of the 30 m change map using the nearest neighbor interpolation method. It is noteworthy that, in addition to the primary models, we also trained a super-resolution model with an 8\(\times\) magnification from 240 m MODIS to 30 m Landsat, as well as a model with a 4\(\times\) magnification from 240 m MODIS to 60 m Landsat. However, validation and visual interpretation of the results revealed that when the multi-stage super-resolution magnification is too high (such as \(\times\)32 or \(\times\)64), the generated images exhibit significant structural distortion, blurriness, noise, and artifacts. Compared to resampling, these models can introduce substantial errors into subsequent change detection algorithms. The rationality and validity of 30 m resampled data are validated and explained in Section 3.2.1. Change detection algorithms have been widely used to detect abrupt and subtle forest changes using various methodologies including differencing with thresholds [39; 40], temporal segmentation [41], and statistical boundary analysis [42; 43]. Considering that the target land cover changes occur in six general LULC types with significant spectral variation, we employed the BFAST algorithm in this study, which accounts for the seasonality and noise of the NDVI time series to identify the change time. Originally developed for monitoring forest disturbances using time series MODIS NDVI data [42; 44], BFAST algorithm has undergone adaptations and has been implemented across all biomes globally and regionally, leveraging various sensors including AVHRR, MODIS, and Landsat [19; 44; 45; 46]. Since a comprehensive explanation of the algorithms can be found in [42], we provide a brief overview here. The algorithm incorporates a decomposition model that decomposes the time series into trend, seasonality, and residual components, combined with an iterative process for detecting breakpoints through structural change methods [47; 48].