Comparative Study on Remote Sensing Classification of Land Use in Chaohu Lake Area
ZHU Shujuan
CHEN Xiaofeng
XIONG Chun
PAN Jianjun
Abstract：With the acceleration of urbanization,the water ecological pressure in the Chaohu Lake Basin is increasing,and scientific and efficient monitoring and governance are urgently needed.Remote sensing technology has become an important technical means for land use research due to its wide coverage,high update frequency,and relatively low cost.Taking Chaohu Lake as the research area,based on the 16 m multispectral images of HJ-2A/B satellite for environmental disaster reduction,three land use classification methods were systematically compared:band analysis(single band,INDW,IRSW,INDV),unsupervised classification(cluster analysis),and supervised classification(random forest method).By constructing a classification system for water bodies,vegetation,buildings,cultivated land,and unused land,the accuracy of the classification results is evaluated using confusion matrix and K coefficient.The results show that the band threshold method is easy to operate,but it is greatly affected by subjective threshold settings and has limited ability to extract land cover information.Unsupervised classification method can effectively extract land cover types with obvious spectral features,but its classification performance for land cover with insignificant feature differences is poor.Supervised classification method can fully utilize prior sample information and multidimensional spectral features,accurately identify various types of land use,and effectively reduce the misjudgment rate.At a resolution scale of 16m,supervised classification is the optimal solution for extracting land use information in the Chaohu Lake area.The research results can provide current and reliable data support for the integrated management of"mountains,waters,forests,fields,lakes,and grasslands"in Chaohu Lake,as well as the establishment of national spatial control and water ecological compensation mechanisms.
Keywords：remote sensing image dataremote sensing classificationland use analysis
Publication Date：2026-03-20
Online Publishing Date：2026-09-16(First online date of this platform, not the publication date of the document)
Pages：11( 77-87 )