## 4 Case Study
### Result Analysis
#### 4.2.1 Identification of Disturbance Boundaries  
This study selects the SDAs (Figure 3) in the GD section of the GS-HSR (from distance kilometer 257 + 850 to 260 + 400) as an example for verification. The ISODATA method was used to perform unsupervised classification on the RS imagery of the SDAs. The preliminary results of the unsupervised classification were manually verified, followed by spatial analysis to remove \"noise\", ultimately yielding accurate disturbance boundaries of the SDAs. (1) Unsupervised classification of RS imagery from SDAs  
Unsupervised classification focuses on optimizing classification parameters, which is crucial for delineating spectral clusters in GF-1 satellite imagery. Using the ISODATA \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Sub-Project** & **Excavation Volume** & **Fill Volume** & \
\begin{tabular}{c} **Integrated** \\ **Utilization Volume** \\ \end{tabular} & **Spoil Volume** &
\begin{tabular}{c} **Proportion of** \\ **Spoil Volume** \\ \end{tabular} \\ \hline Roaded & 11,008,300 & 1,270,800 & 7,719,900 & 2,017,600 & 14.75\% \\ \hline Bridge & 5,213,100 & 1,992,900 & 2,688,200 & 532,000 & 3.89\% \\ \hline Tunnel & 17,044,600 & 222,100 & 8,308,700 & 8,513,800 & 62.23\% \\ \hline Station & 12,794,200 & 4,233,300 & 5,942,900 & 2,618,000 & 19.14\% \\ \hline Large temporary facilities & 3,039,500 & 3,039,500 & 0.00 & 0.00 & 0.00\% \\ \hline Total & 49,099,700 & 10,758,600 & 24,659,700 & 13,681,400 & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of spoil volume from different sub-projects of the GD section of GS-HSR (volume unit: 10,000 m\({}^{3}\)). Figure 3: SDAs in the GD section of GS-HSR (from distance kilometer 257 + 850 to 260 + 400). unsupervised classification method, repeated testing revealed that at least three iterations and more than thirteen classifications were optimal for accurately identifying SDAs. As shown in Figure 4, 14 categories were determined, and the SDAs exhibit clear spectral differences from the surrounding vegetation, primarily due to the contrast between vegetation and bare soil. This distinction facilitates the identification of the disturbed area within the SDAs. (2) Manual preliminary judgment of classification results  
Because the unsupervised classification method relies solely on the spectral characteristics of surface features in GF-1 satellite imagery, misclassifications may occur, necessitating manual verification of the preliminary classification outcomes to accurately identify SDAs. As illustrated in Figure 3, SDAs exhibit a distinctive gray-brown tone in the RS imagery, contrasting sharply with the surrounding vegetation. Consequently, in Figure 4, most pixels categorized under class 14 correspond to SDAs. However, a portion of non-SDA features--such as the residential area in the lower right corner of Figure 3--is also included within class 14 and must be manually removed. In addition, due to the \"same object, different spectra\" phenomenon, certain areas that are actually SDAs were not assigned to class 14, instead being misclassified into other categories, requiring further manual judgment and correction. Given that this study primarily focuses on the extraction of SDA regions, only the actual physical attributes of the SDA areas are interpreted, whereas the physical properties of other land classes are not evaluated. Specifically, through expert-based interpretation and consolidation, the initially identified 14 categories from Figure 4 are integrated and summarized into two categories: \"Manually verified SDAs\" and \"Non-SDAs\". This approach merges unsupervised classification outcomes on the basis of manual verification, conferring real-world physical meaning exclusively to the classes identified as SDAs while abstaining from interpreting the physical attributes of the remaining classes. The preliminary identification results, as shown in Figure 5, ensure high-precision SDA extraction and provide robust data support for subsequent spatial analysis and environmental management activities.