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

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

Exploiting Overlapping Landsat Scene Classifications and Focal Context to Identify Boreal Disturbance Mapping Uncertainty

The BorealDB dataset is derived from a mosaic of Landsat scenes that were independently classified to identify historic fire and timber harvesting disturbances within Ontario. This thesis identifies and flags areas of classification uncertainty within BorealDB and scrutinizes them to assess classification confidence. The focal context of all orthogonal neighbour states was quantified to feed classification tree (CT) and random forest (RF) classifiers to predict focal disturbance classes. Uncertainty is deemed to exist where BorealDB and predicted CT or RF classes disagree. When RF and CT predictions were compared with the BorealDB classes, RF predicted more uncertainty (58%) than CT predictions (15%). Sampled locations compared with original satellite imagery and visual assessments suggested uncertainty depended on classifier, disturbance type, and spatial neighbours. Timber harvest disturbance classifications had the most uncertainty and CT predictions was the most consistent with neighbouring classifications and visual assessments indicating it is more effective than RF

Exploiting Overlapping Landsat Scene Classifications and Focal Context to Identify Boreal Disturbance Mapping Uncertainty

The BorealDB dataset is derived from a mosaic of Landsat scenes that were independently classified to identify historic fire and timber harvesting disturbances within Ontario. This thesis identifies and flags areas of classification uncertainty within BorealDB and scrutinizes them to assess classification confidence. The focal context of all orthogonal neighbour states was quantified to feed classification tree (CT) and random forest (RF) classifiers to predict focal disturbance classes. Uncertainty is deemed to exist where BorealDB and predicted CT or RF classes disagree. When RF and CT predictions were compared with the BorealDB classes, RF predicted more uncertainty (58%) than CT predictions (15%). Sampled locations compared with original satellite imagery and visual assessments suggested uncertainty depended on classifier, disturbance type, and spatial neighbours. Timber harvest disturbance classifications had the most uncertainty and CT predictions was the most consistent with neighbouring classifications and visual assessments indicating it is more effective than RF