Owing to the limitation of spatial resolution and spectral resolution, deep learning methods are rarely used for the classification of multispectral remote sensing images based on the real spectral dataset from multispectral remote sensing images. This study explores the application of a deep learning model to the spectral classification of multispectral remote sensing images. To address the problem of the large workload with respect to selecting training samples during classification by deep learning, first, linear spectral mixture analysis and the spectral index method were applied to extract the pixels of impervious surfaces, soil, vegetation, and water. Second, through the Euclidean distance threshold method, a spectral dataset of multispectral image pixels was established. Third, a deep learning classification model, ResNet-18, was constructed to classify Landsat 8 OLI images based on pixels’ real spectral information. According to the accuracy assessment, the results show that the overall accuracy of the classification results can reach 0.9436, and the kappa coefficient can reach 0.8808. This study proposes a method that allows for the more optimized establishment of the actual spectral dataset of ground objects, addresses the limitations of difficult sample selection in deep learning classification and of spectral similarity in traditional classification methods, and applies the deep learning method to the classification of multispectral remote sensing images based on a real spectral dataset.

We adopted a weight of evidence approach to establish a causal analysis of an impaired land ecosystem on a regional scale; namely, Daye, a traditional mining city in China. Working processes, including problem statements, a list of candidate causes, and a conceptual model were developed to represent a causal hypothesis for describing land degradation. Causal criteria were applied to integrate multiple lines of evidence. Then, various pieces of evidence were scored to either strengthen or weaken our causal assumptions. Results showed that habitat alteration, heavy metal accumulation, organic pollutants, water eutrophication, and nutrient runoff were the probable causes of land ecosystem impairment in Daye. Meanwhile, noxious gas, toxicants, altered underground runoff, atmospheric deposition, and acid rain were identified as possible causes. The most unlikely causes were altered hydrology, altered earth surface runoff, and soil erosion. Soil salinization, soluble inorganic salts, biological species invasion, and pathogens were deferred as delayed causes due to lack of adequate information. The causal analysis approach was applied to identify the primary causes of land degradation and implement accurate protective measures in an impaired land ecosystem.

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