Land use classification in mine-agriculture compound area based on multi-feature random forest: a case study of Peixian

Abstract

IntroductionLand use classification plays a critical role in analyzing land use/cover change (LUCC). Remote sensing land use classification based on machine learning algorithm is one of the hot spots in current remote sensing technology research. The diversity of surface objects and the complexity of their distribution in mixed mining and agricultural areas have brought challenges to the classification of traditional remote sensing images, and the rich information contained in remote sensing images has not been fully utilized.MethodsA quantitative difference index was proposed quantify and select the texture features of easily confused land types, and a random forest (RF) classification method with multi-feature combination classification schemes for remote sensing images was developed, and land use information of the mine-agriculture compound area of Peixian in Xuzhou, China was extracted.ResultsThe quantitative difference index proved effective in reducing the dimensionality of feature parameters and resulted in a reduction of the optimal feature scheme dimension from 57 to 22. Among the four classification methods based on the optimal feature classification scheme, the RF algorithm emerged as the most efficient with a classification accuracy of 92.38% and a Kappa coefficient of 0.90, which outperformed the support vector machine (SVM), classification and regression tree (CART), and neural network (NN) algorithm.ConclusionThe findings indicate that the quantitative differential index is a novel and effective approach for discerning distinct texture features among various land types. It plays a crucial role in the selection and optimization of texture features in multispectral remote sensing imagery. Random forest (RF) classification method, leveraging a multi-feature combination, provides a fresh method support for the precise classification of intricate ground objects within the mine-agriculture compound area.

Land-Use Change Dynamics in Areas Subjected to Direct Urbanization Pressure: A Case Study of the City of Olsztyn

“…For example, within specific data repositories, like the CORINE Land Cover inventory, automated classification is employed. Yet, the varied color schemes, structures, and textures present can introduce inaccuracies when discerning particular land-use types, particularly in analyses involving numerous similar and overlapping land-cover categories [104,105].…”

Detailed Land Use Classification in a Rare Earth Mining Area Using Hyperspectral Remote Sensing Data for Sustainable Agricultural Development

“…There have only been a few studies on the detailed land use classification of complex surfaces in mining areas and most of them focused on using existing land use datasets or multispectral imagery to classify mining areas into simple land classes. For instance, Zhang Zemin et al [6] used 100 m spatial resolution land use datasets to study the change in land use classes in a typical mining area and Jiaxing Xu et al [7] combined 30 m spatial resolution OLI satellite images with 30 m spatial resolution to classify a mining area into simple land classes such as cropland, forest land, industrial and mining land, and water and proposed a random forest classification method based on a multi-feature combination classification scheme for remotely sensed images. However, these studies failed to achieve a detailed classification of complex land classes within mining areas.…”