基于FSO算法的洱海流域土地利用分类适用性研究  

Applicability of land use classification in Erhai Basin based on FSO algorithm

作　　者：何紫玲 陈运春 郭晓飞 张莹 黄玉南 柳兴鹏 曾维军 HE Ziling;CHEN Yunchun;GUO Xiaofei;ZHANG Ying;HUANG Yunan;LIU Xingpeng;ZENG Weijun(College of Water Resources,Yunnan Agricultural University,Kunming Yunnan 650201,China;Yunnan Province University Green Smart Farmland and Carbon Emission Reduction Engineering Research Center,Yunnan International Joint R&D Center for Smart Agriculture and Water Security,Kunming Yunnan 650201,China)

机构地区：[1]云南农业大学水利学院,云南昆明650201 [2]云南省高校绿色智慧农田与碳减排工程研究中心,云南省智慧农业与水安全国际联合研发中心,云南昆明650201

出　　处：《农业工程》2024年第9期126-133,共8页AGRICULTURAL ENGINEERING

基　　金：云南省科技厅科技计划项目“农业联合专项面上项目”(202101BD070001-101)。

摘　　要：精确提取土地利用类型是治理流域生态环境的重要途径,而分类算法繁多,针对分类的对象效果不一。洱海流域作为高原断陷湖泊具有其独有的特征,开展分类方法适用性研究具有重要意义。以洱海流域作为研究区,基于Sentinel-2影像数据,创新地融合了地物的光谱特征、几何结构和纹理特征,筛选出34个最优分类特征值,运用特征空间优化算法,对比分析支持向量机、随机森林和决策树3种分类方法在洱海流域的适用性。结果表明,在特征重要性排名中,红边指数(NDREI)相对于归一化植被指数(NDVI)和比值植被指数(RVI)表现出更为显著的优势,在所有特征中贡献率排名居首位;在优化特征空间后,支持向量机(SVM)的分类总体精度和Kappa系数效果最好,提高3.75和5.06个百分点,决策树(DT)效果最差,仅提高了1和1.17个百分点;相对于其他两种分类方法,优化特征空间的随机森林(RF)分类总体精度和Kappa系数最高,达到90.63%和88.87%,并且在细节上更符合真实地物分布,如在地物分布的细碎区域、区分湿地、水体和建设用地的效果明显。利用特征空间优化算法提高了分类方法的精度,随机森林对洱海流域的土地利用分类具有最强的适用性。Accurate extraction of land use types is an important way to manage ecological environment of watershed,but there are many classification algorithms with different effects on classified objects,and the Erhai Basin,as a plateau fracture lake,has its unique characteristics,and it is of great significance to carry out research on applicability of classification methods.Based on the Sentinel-2 image data,34 optimal classification features were selected by innovatively integrating spectral features,geometric structure and texture features,and feature space optimization algorithm was applied to compare and analyze applicability of three classification methods of Support Vector Machine(SVM),random forest(RF),and decision tree(DT)in the Erhai Basin as study area.Results showed that,in ranking of feature importance,red edge index(NDREI)showed a more significant advantage over tnormalized vegetation index(NDVI)and ratio vegetation index(RVI),and ranked first in contribution rate among all features.After optimizing feature space,SVM has the best effect on overall accuracy and Kappa coefficient of classification,with an improvement of 3.75 and 5.06 percentage points,and DT has the worst effect,with an improvement of only 1 and 1.17 percentage points.Compared with the other two classification methods,RFF with optimized feature space has the highest overall classification accuracy and Kappa coefficient,reaching 90.63%and 88.87%,and was more in line with distribution of real features in details,such as distinguishing wetlands,water bodies and construction land in fine-grained area of feature distribution.The use of feature space optimization algorithm improved accuracy of classification method,and the RFFs has the strongest applicability to land use classification in Erhai Basin.

关 键 词：洱海流域 红边指数 土地利用分类 FSO算法 适用性研究 

分 类 号：S28[农业科学—农业水土工程]