基于特征筛选算法的数字土壤制图研究  

Research on Digital Soil Mapping Based on Feature Selection Algorithm

作　　者：张晓婷 黄魏[1] 傅佩红[1] 孟可 王苏放 ZHANG Xiaoting;HUANG Wei;FU Peihong;MENG Ke;WANG Sufang(College of Resource and Environment,Huazhong Agricultural University,Wuhan 430070,China)

机构地区：[1]华中农业大学资源与环境学院,武汉430070

出　　处：《土壤学报》2024年第3期635-647,共13页Acta Pedologica Sinica

基　　金：国家自然科学基金项目(42171056,41877001)资助。

摘　　要：平缓地带数字土壤制图中,环境协变量的选择是提高制图精度的关键。已有研究证明遥感影像可作为推理制图的辅助因子,而如何确定环境因子推理制图时各自的权重已成为现阶段研究的重点。选取湖北省麻城市乘马岗镇为研究区,采用3种特征筛选方法进行有效环境变量筛选,探索参与平原-丘陵混合区域制图的因子并确定其重要性,依据选择的相对稳定的指标,进一步探索提高土壤类型制图准确性的途径。根据141个野外独立样点的检验结果表明:在推理制图中,遥感因子在平原区域的重要性程度高于丘陵区域,且遥感因子中归一化植被指数(NDVI)和均值(Mean)较为稳定;基于递归特征算法的按地形推理制图精度最高为75.89%,分别高于ReliefF算法和基于Tree的特征筛选算法13.48%和4.97%;此外3种特征筛选算法制图结果中,按地形因子分区制图的精度均高于整体区域制图。因此,遥感因子作为辅助手段参与推理过程可有效提高制图精度。本研究采用的特征挖掘与机器学习算法对提升土壤制图精度具有一定的理论意义。【Objective】Traditional digital soil mapping methods are unable to produce detailed soil maps within a reasonable cost and time.Digital soil mapping is a powerful technique,which is popular and widely used by scholars coupled with environmental covariates to map soil types or properties.The selection of environmental covariates is the key to ensuring the accuracy of mapping.Previous studies have proven that remote-sensing images can be used as auxiliary factors for reasoning mapping.Remote sensing data can provide rich soil landscape information,which is consistent with the core idea of using grids to express spatial changes of soil features in digital soil mapping.Moreover,remote sensing technology can obtain real-time information quickly.However,there are few relevant studies on how principal components and texture information of remote sensing factors contribute to the reasoning process.Thus,determining the weight of remote sensing factors in the reasoning process is the key content of this study,which is tested by the reliability of testing mapping results.【Method】Chengmagang Town,Macheng City,Hubei Province was selected as the study area.Using Chinese soil classification and soil type map with a spacing of 10 meters,which were extracted from the contour data and remote sensing image using a variety of feature selection algorithms to effective screening of variables,this study conducted the soil digital mapping by reasoning machine learning algorithms.Specifically,the recursive feature elimination screening algorithm,ReliefF algorithm and tree-based feature screening algorithm were used to rank all environmental factors in the whole area,plain and hilly areas of the study area,respectively.Then,it screened the effective environmental variables of environmental factors and analyzed the weight of remote sensing factors in the reasoning process.The factors involved in plant-hill region mapping were explored and their importance was determined.According to the selected relatively stable indicators,the grad

关 键 词：土壤-环境知识获取 特征筛选 数字土壤制图 贝叶斯优化 梯度提升树 

分 类 号：S159[农业科学—土壤学]