In modern resource governance, the integration of Remote Sensing (RS) and Geographic Information Systems (GIS) has become a foundational technical solution, enabling the transition from traditional manual management to digital spatial data governance. Academic studies affirm the superior capability of this technological combination in analyzing land use/land cover (LULC) changes with high resolution and continuous real-time updates. From a technical perspective, deep learning algorithms in satellite image analysis have now achieved accuracy levels exceeding 90%, allowing detailed detection of surface structure changes, from land use classification to monitoring forest degradation (Chen et al., 2015).
GIS is not only limited to data visualization but also serves as a powerful spatial analysis tool, reducing up to 40% of the time required for processing and analyzing geospatial data compared to traditional field survey methods, which are often labor-intensive and costly. Furthermore, through spatial data modeling, researchers can accurately quantify the environmental impacts of urbanization. Specifically, the conversion of natural land into built-up areas reduces infiltration capacity and groundwater recharge by 30% to 50%. These findings provide an important data foundation for policymakers to design ecological buffer zones and make sustainable land allocation decisions. Standardizing spatial data systems ensures transparency, consistency, and interoperability in national resource management, thereby establishing a basis for developing modern, integrated land data infrastructure and playing a crucial role in supporting strategic decisions for economic development associated with natural resource conservation in the context of global climate change (Song, 2023).
Authors: Hao Phu Dong, Binh Thanh Nguyen*
References:
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X.,…Mills, J. (2015). Global land cover mapping at 30m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7-27. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2014.09.002
Song, X.-P. (2023). The future of global land change monitoring. International Journal of Digital Earth, 16(1), 2279-2300. https://doi.org/10.1080/17538947.2023.2224586
ⓒ 2026 BICE | Biochar-based Integrated Circular Economy for Paddy Rice.
