A WebGIS-Based System for Supporting Saline-Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China

文献类型： 外文期刊

第一作者： Song, Yingqiang

作者： Song, Yingqiang;Pan, Yinxue;Xiang, Meiyan;Yang, Weihao;Zhan, Dexi;Wang, Xingrui;Lu, Miao;Lu, Miao

作者机构：

关键词： WebGIS; ecological monitoring; machine learning; Yellow River Delta

期刊名称：REMOTE SENSING （ 影响因子：4.2； 五年影响因子：4.9 ）

ISSN：

年卷期： 2024 年 16 卷 11 期

页码：

收录情况： SCI

摘要： Monitoring and evaluation of soil ecological environments are very important to ensure saline-alkali soil health and the safety of agricultural products. It is of foremost importance to, within a regional ecological risk-reduction strategy, develop a useful online system for soil ecological assessment and prediction to prevent people from suffering the threat of sudden disasters. However, the traditional manual or empirical parameter adjustment causes the mismatch of the hyperparameters of the model, which cannot meet the urgent need for high-performance prediction of soil properties using multi-dimensional data in the WebGIS system. To this end, this study aims to develop a saline-alkali soil ecological monitoring system for real-time monitoring of soil ecology in the Yellow River Delta, China. The system applied advanced web-based GIS, including front-end and back-end technology stack, cross-platform deployment of machine learning models, and a database embedded in multi-source environmental variables. The system adopts a five-layer architecture and integrates functions such as data statistical analysis, soil health assessment, soil salt prediction, and data management. The system visually displays the statistical results of air quality, vegetation index, and soil properties in the study area. It provides users with ecological risk assessment functions to analyze heavy metal pollution in the soil. Specially, the system introduces a tree-structured Parzan estimator (TPE)-optimized machine learning model to achieve accurate prediction of soil salinity. The TPE-RF model had the highest prediction accuracy (R2 = 94.48%) in the testing set in comparison with the TPE-GBDT model, which exhibited a strong nonlinear relationship between environmental variables and soil salinity. The system developed in this study can provide accurate saline-alkali soil information and health assessment results for government agencies and farmers, which is of great significance for agricultural production and saline-alkali soil ecological protection.

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