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

生态脆弱区智能化识别研究现状及其在蒙古高原的发展展望

作　　者：WANG Meng WANG Juanle Ochir ALTANSUKH 王梦;王卷乐;Ochir ALTANSUKH(中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101;中国科学院大学资源与环境学院,北京100049;蒙古国立大学工程技术学院环境与森林工程系环境工程实验室,蒙古乌兰巴托14201;江苏省地理信息资源开发与应用协同创新中心,南京210023)

机构地区：[1]State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China [2]College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China [3]Environmental Engineering Laboratory,Department of Environment and Forest Engineering,School of Engineering and Technology,National University of Mongolia,Ulaanbaatar 14201,Mongolia [4]Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China

出　　处：《Journal of Resources and Ecology》2025年第1期1-10,共10页资源与生态学报(英文版)

基　　金：The National Key Research and Development Program(2022YFE0119200);The Key Research and Development and Achievement Transformation Plan Project of Inner Mongolia Autonomous Region(2023KJHZ0027);The Key Project of Innovation LREIS(KPI006);The Construction Project of China Knowledge Center for Engineering Sciences and Technology(CKCEST-2023-1-5)。

摘　　要：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.识别生态脆弱区对于构建生态屏障和精准控制生态风险至关重要。大数据和人工智能技术的快速发展,为传统生态脆弱区的识别提供了更多智能化方法支持。本文首先论述了生态脆弱区识别方法的进展,包括地理分区方法、专家集成与数理统计方法、地理信息可视化方法、人工神经网络方法和无监督深度学习聚类方法,并对所涉及的经典智能处理软件进行调研。在此基础上,提出了生态功能区划研究的若干紧迫研究挑战,诸如生态脆弱性评估智能算法应用、大数据协同分析及自动分区识别软件开发方面存在的难题。针对蒙古高原生态屏障建设的需求,结合新的大数据和人工智能科研范式转变,提出了生态脆弱区智能化识别方法研究展望,包括加强多模态数据综合分析、促进生态屏障大数据协同处理、提高生态功能分区算法解释性、研制生态脆弱区自动化分区软件工具、融合众源地理大数据和公民科学数据等五方面内容。

关 键 词：ecological barrier vulnerable zone identification ecological function zoning ecological geographical zoning intelligent technology resource ecology 

分 类 号：X171.1[环境科学与工程—环境科学]