2022年克鲁伦河流域草原面源污染管控分区数据集  

Dataset on Grassland Non-point Source Pollution Management and Control Zones for the Kherlen River Basin in 2022

作　　者：李淑华 李晓岚 刘玉[1] 高秉博 SUKHBAATAR Chinzorig 冯爱萍 李存军 任艳敏 LI ShuHua;LI XiaoLan;LIU Yu;GAO BingBo;Sukhbaatar Chinzorig;FENG AiPing;LI CunJun;REN YanMin(Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China;College of Land Science and Technology,China Agricultural University,Beijing 100083,China;Institute of Geography and Geoecology,Mongolian Academy of Sciences,Ulaanbaatar 15170,Mongolia;Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment,Beijing 100094,China)

机构地区：[1]北京市农林科学院信息技术研究中心,北京100097 [2]中国农业大学土地科学与技术学院,北京100083 [3]蒙古科学院地理与生态地质研究所,乌兰巴托15170,蒙古 [4]生态环境部卫星环境应用中心,北京100094

出　　处：《农业大数据学报》2025年第1期43-50,共8页Journal of Agricultural Big Data

基　　金：国家重点研发计划项目克鲁伦河流域面源污染遥感监测与评估技术研发(2021YFE0102300)。

摘　　要：克鲁伦河流域生态环境安全直接关乎中蒙两国流域可持续发展水平,科学划分面源污染管控单元对于该流域实现水环境精准施策和高效管理具有重要意义。然而,当前该区域在污染管控方面缺乏有效的分区数据来指导具体施策。传统的污染管控单元划分方法,难以精确反映草原面源污染的差异性,从而在一定程度上影响了管理效果。草原面源污染受多重因素影响,其地理属性在属性上呈现出重复性,同时在空间分布上又表现出连续性。为了更准确地捕捉这些特征,需要一种能够平衡处理属性重复与空间连续的聚类方法。本研究针对克鲁伦河流域,面向草原面源污染影响因素,综合考虑了年平均降水、气温、数字高程、草地载畜强度以及土壤全氮磷含量等关键连续数据,利用能有效处理属性依赖关系和空间一致性策略的空间Toeplitz逆协方差聚类(STICC)方法进行聚类分析,构建了2022年克鲁伦河流域面源污染管控分区数据集。为了验证该数据集的精确性,采用DUNN聚类精度评价指标对该分区效果与其他传统分区结果进行了对比,结果显示:STICC方法在聚类精度上优于K-Means、Spectral K-Means、GMM及Repeated Bisection等方法,能够更有效地识别污染的异质性区域,进而显著提升管理的精细度。此外,本研究还保留了数据的原始连续性,使得对污染特征的刻画更为准确。相较于传统方法,本研究提供的分区数据在细节展现上提升了50%以上。该数据集不仅为深入研究克鲁伦河流域的面源污染特征提供了有力支持,还为相关管控决策提供了坚实的数据基础。The ecological and environmental safety of the Kherlen River Basin is directly related to the sustainable development of both China and Mongolia.Scientific delineation of non-point source pollution control units is crucial for precise implementation of water environment policies and efficient management in the basin.However,currently,there is a lack of effective zoning data to guide specific measures in pollution control in this region.Traditional methods of dividing pollution control units struggle to accurately reflect the differences in grassland non-point source pollution,thereby affecting management effectiveness to some extent.Grassland non-point source pollution is influenced by multiple factors,exhibiting both attribute repetition and spatial continuity.To capture these characteristics more accurately,a clustering method that balances attribute repetition and spatial continuity is required.In this study,focusing on the Kherlen River Basin and targeting the influencing factors of grassland non-point source pollution,we comprehensively considered key continuous data such as annual average precipitation,temperature,digital elevation,grassland carrying capacity,and soil total nitrogen and phosphorus content.Utilizing the Spatial Toeplitz Inverse Covariance Clustering(STICC)method,which effectively handles attribute dependencies and spatial consistency strategies,we conducted clustering analysis and constructed a 2022 dataset for non-point source pollution control zoning in the Kherlen River Basin.To validate the accuracy of this dataset,we compared the zoning effects using the DUNN clustering accuracy evaluation index with other traditional zoning results.The results showed that the STICC method outperforms methods like K-Means,Spectral K-Means,GMM,and Repeated Bisection in clustering accuracy.It can more effectively identify heterogeneous pollution areas,significantly enhancing the precision of management.Additionally,this study preserved the original continuity of the data,resulting in a more accurate depic

关 键 词：克鲁伦河流域 面源污染 管控分区 空间聚类 STICC聚类 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]