## 3 Research Method
### Model Development
#### 3.2.1 Monitoring Scenario 1: Identification of Disturbance Boundaries
During the cropping process, precise spatial reference information, including geographic coordinate systems and projection parameters, was set to ensure consistency between the imagery and other geographic data. Step 2: Unsupervised classification  
To efficiently extract valuable information from GF-1 satellite data for identifying disturbance boundaries in SDAs, both supervised and unsupervised classification methods can be employed. Supervised classification achieves precise information extraction from GF-1 satellite data by leveraging pre-labeled training samples. In contrast, unsupervised classification rapidly extracts imagery information through automatic clustering methods, particularly in scenarios lacking prior knowledge. Unsupervised classification is especially effective in identifying clusters or categories with similar spectral characteristics within the data, making it highly suitable for delineating disturbance boundaries of SDAs in HSR projects. Common unsupervised classification techniques include K-means and the iterative self-organizing data analysis technique (ISODATA). The K-means algorithm partitions data into a predefined number of clusters by minimizing the variance within each cluster, thereby achieving classification. However, the K-means algorithm presents certain limitations in practical applications, primarily due to the necessity of predefining the number of clusters. This requirement can be a significant constraint in unsupervised classification, as the exact number of categories in the sample data is often unknown. Additionally, K-means is sensitive to the initial placement of cluster centroids, which can result in convergence to local optima and affect the stability and accuracy of the classification results. To address these limitations, the ISODATA method enhances the K-means algorithm by incorporating \"merge\" and \"split\" operations during the classification process, thereby producing more robust and adaptable classification outcomes. Specifically, ISODATA dynamically adjusts the number of clusters based on intra-cluster variance and inter-cluster distances in each iteration, enabling the algorithm to merge similar clusters or split overly large clusters as necessary. This dynamic adjustment mechanism significantly improves the robustness and accuracy of the classification, particularly when dealing with complex terrains and diverse spectral characteristics in the dataset. Consequently, this study employs the ISODATA method for clustering the processed GF-1 imagery. The procedure involves selecting the \"classification/unsupervised classification/ISODATA classification\" tool, importing the dataset, and configuring key parameters (e.g., number of classes, maximum iterations, and change threshold) in the \"ISODATA parameters\" dialog. Through iterative testing, the optimal number of clusters and the number of iterations are determined. By grouping pixels with similar spectral, color, texture, spatial, and morphological characteristics within the GF-1 imagery, the unsupervised classification process is effectively completed, facilitating accurate identification of disturbance boundaries in SDAs. Step 3: Manual verification  
Since unsupervised classification methods categorize imagery pixels without directly assigning real-world attributes to objects, post-classification optimization is necessary. Additionally, due to variations in weather, incident angles, and shadowing, identical featuresmay appear differently in GF-1 imagery, while different features might exhibit similar color and texture characteristics. Therefore, after completing unsupervised classification, expert experience is required to optimize and adjust the classification categories, correct classification patterns, and verify specific features through multiple rounds of cross-checking and iterative refinement. In this study, expert knowledge was utilized to determine the physical attributes of each land category, merging those with identical attributes to accurately delineate SDAs and non-SDAs.