## 5 Discussion  
This study proposes an innovative monitoring framework for SDAs during the construction of HSRs by integrating 3S techniques, showcasing the novel application of 3S techniques in new monitoring scenarios. Compared to traditional methods that rely solely on manual monitoring, this approach significantly improves observation efficiency. For disturbance boundary identification of SDAs, this study employs an unsupervised classification method for processing RS imagery, which, unlike supervised classification, does not require extensive manually labeled data and can automatically identify feature connections, making it more suitable for monitoring SDAs [52]. When performing unsupervised classification, the ISODATA method enhances accuracy over the traditional K-means method. However, as unsupervised classification only classifies imagery pixels without assigningreal-world attributes, it requires repeated expert validation. Combining supervised and unsupervised classification methods could potentially yield even better results. For the extraction of soil and water conservation measures, UAV imagery is employed, effectively addressing the limitations of low resolution and slow update rates of traditional RS imagery [47]. UAV offers high-resolution capabilities, reaching centimeter or even millimeter levels, compared to the meter-level resolution of traditional RS imagery. Additionally, UAV periodicity can be flexibly adjusted based on specific needs, with minimal operational costs. However, due to the limited imagery width, UAVs are more suitable for monitoring small-scale SDAs. For larger-scale SDAs, traditional RS imagery remains more appropriate due to its broader coverage. For monitoring dynamic changes in the volume of earth and rock, using the Yagongshan SDA as an example, the D-InSAR method overestimated the elevation change by 0.36 m, equivalent to 9.5%. This error primarily stems from the coarse resolution of the SAR data. When calculating volume changes based on the disturbance boundary of the Yagongshan SDA extracted by the GF-1 satellite, the estimated volume was 92,000 m\({}^{3}\) higher than the actual data, an overestimation of 15.5%. The discrepancy is further exacerbated by mismatches in the spatial registration of GF-1 optical data and SAR data [40]. Despite these accuracy limitations, several studies are currently addressing this issue, indicating that this new technology demonstrates considerable potential compared to traditional methods in the monitoring and management of SDAs. Moreover, the monitoring framework established in this study holds significant implications for promoting a circular economy. By accurately monitoring the dynamic changes within SDAs, the framework enables optimized resource management, reduces environmental impacts of waste, and promotes effective resource reutilization. For instance, precise estimation of earth and rock volume changes facilitates the rational allocation of construction resources, preventing resource wastage, and by optimizing deposition and transportation processes, it reduces carbon emissions. Additionally, the effective implementation of soil and water conservation measures helps prevent soil erosion, protect the ecological environment, and achieve the dual objectives of economic benefits and environmental protection. This comprehensive monitoring approach not only enhances the sustainability of HSR projects but also provides practical evidence for the application of a circular economy in infrastructure development. Despite the demonstrated effectiveness of the proposed framework, there remain several limitations that warrant attention in subsequent research: (1) Monitoring accuracy depends heavily on data resolution. UAV imagery provides high spatial resolution but limited coverage, whereas traditional RS imagery offers broader coverage with reduced detail. Ongoing efforts in multi-source data fusion could balance high resolution and large-area coverage, thereby enhancing both detail and scale.