## 3 Research Method
### Model Development
#### 3.2.1 Monitoring Scenario 1: Identification of Disturbance Boundaries  
During the spoil accumulation process, it is inevitable that the land resources within the SDAs will be encroached upon, and potential soil erosion issues may further exacerbate this encroachment. Therefore, it is necessary to regularly identify the disturbance boundaries of SDAs to ensure that the spoil piling process is being carried out according to protocol and to promptly detect any illegal land occupation. The process of identifying disturbance boundaries in SDAs primarily involves techniques and methods such as imagery fusion and cropping, unsupervised classification, manual verification, and spatial analysis, which are executed in four main steps. Step 1: Imagery fusion and cropping  
This study utilized the band information from the GF-1 panchromatic multispectral camera, with the panchromatic band offering a spatial resolution of 2 m and the multispectral bands offering a spatial resolution of 8 m. To avoid imagery blurring due to mismatched spatial resolutions during subsequent unsupervised classification, data fusion was performed. Using ENVI 5.1, the multispectral data underwent radiometric calibration, atmospheric correction, and orthorectification, while the panchromatic data underwent radiometric calibration and orthorectification. Radiometric calibration was conducted to eliminate nonlinear sensor response errors and ensure the accuracy of reflectance data. This process can be conducted by opening the \"radiometric correction/radiometric calibration\" module in the software toolbox and loading the required multispectral data. Following the step-by-step instructions, the calibration type should be set to reflectance, and an appropriate scale factor must be defined, thus completing the radiometric calibration of the multispectral dataset. For panchromatic data, the same process is applied by specifying the calibration type as reflectance and setting the scale factor accordingly. Atmospheric correction was applied to remove the effects of atmospheric conditions on the imagery, ensuring spectral consistency. This process can be conducted by opening the \"radiometric correction/atmospheric correction module/FLAASH atmospheric correction\" in the toolbox and selecting the already radiometrically corrected multispectral data. Key parameters include geolocation (latitude and longitude), sensor type, altitude, pixel size, and the time of image acquisition. An appropriate atmospheric model and aerosol model must then be chosen based on regional climate conditions, after which the atmospheric correction process is completed. Orthorectification was performed to eliminate geometric distortions caused by terrain undulations and sensor viewing angles, thereby providing accurate geospatial positioning for the imagery. This process can be conducted by opening the \"geometric correction/orthorectification/RPC orthorectification workflow\" in the toolbox, selecting the radiometrically and atmospherically corrected multispectral and panchromatic images, and setting the desired output pixel size and image resampling parameters. Upon completion, the orthorectified images offer accurate spatial information, facilitating more reliable spatial analysis and data fusion in further research. The fusion results achieved a unified spatial resolution of 2 m, preserving the spectral characteristics of the multispectral imagery while enhancing the spatial details of the imagery. Subsequently, the fused GF-1 data were processed using ArcGIS 10.8.1. An appropriate spatial extent was delineated based on the dimensions of the study area, and the corresponding spatial reference information was established. This approach aimed to minimize the influence of other land features on the disturbance boundary identification process and to reduce the workload associated with subsequent imagery processing.