Research Progress in the Intelligent Identification of Ecologically Vulnerable Areas and its Prospects in the Mongolian Plateau

Identifying ecologically vulnerable areas is critical for constructing ecological barriers and precisely controlling ecological risks. With the rapid development of big data and Artificial Intelligence (AI) technologies, many intelligent methods have been developed to support the identification of vulnerable ecological areas. This paper reviews the methodological advancements in identifying ecologically vulnerable areas, including geographic zoning, expert integration, mathematical statistics, geographic information visualization, artificial neural networks, and unsupervised deep learning clustering methods. Additionally, we assessed several classic software tools used in ecology and natural resource management. Based on the review, several urgent research challenges for ecological function zoning research are proposed, such as the application of ecological vulnerability assessment intelligent algorithms, big data collaborative analysis, and the development of automated identification software. Considering the requirements in the Mongolian Plateau, this study proposes future development prospects of methods for identifying ecologically vulnerable area zoning, combined with the new AI research paradigm. They include enhancing the comprehensive analysis of multimodal data, increasing ecological barrier big data collaborative processing, advancing the interpretability of ecological function partitioning algorithms, developing automatic zoning software tools, and pushing the collaborative analysis of geographic big data and citizen science data.