Abstract
Land cover mapping (LCM) in complex surface-mined and agricultural landscapes could contribute greatly to regulating mine exploitation and protecting mine geo-environments. However, there are some special and spectrally similar land covers in these landscapes which increase the difficulty in LCM when employing high spatial resolution images. There is currently no research on these mixed complex landscapes. The present study focused on LCM in such a mixed complex landscape located in Wuhan City, China. A procedure combining ZiYuan-3 (ZY-3) stereo satellite imagery, the feature selection (FS) method, and machine learning algorithms (MLAs) (random forest, RF; support vector machine, SVM; artificial neural network, ANN) was proposed and first examined for both LCM of surface-mined and agricultural landscapes (MSMAL) and classification of surface-mined land (CSML), respectively. The mean and standard deviation filters of spectral bands and topographic features derived from ZY-3 stereo images were newly introduced. Comparisons of three MLAs, including their sensitivities to FS and whether FS resulted in significant influences, were conducted for the first time in the present study. The following conclusions are drawn. Textures were of little use, and the novel features contributed to improve classification accuracy. Regarding the influence of FS: FS substantially reduced feature set (by 68% for MSMAL and 87% for CSML), and often improved classification accuracies (with an average value of 4.48% for MSMAL using three MLAs, and 11.39% for CSML using RF and SVM); FS showed statistically significant improvements except for ANN-based MSMAL; SVM was most sensitive to FS, followed by ANN and RF. Regarding comparisons of MLAs: for MSMAL based on feature subset, RF achieved the greatest overall accuracy of 77.57%, followed by SVM and ANN; for CSML, SVM had the highest accuracies (87.34%), followed by RF and ANN; based on the feature subsets, significant differences were observed for MSMAL and CSML using any pair of MLAs. In general, the proposed approach can contribute to LCM in complex surface-mined and agricultural landscapes.
Highlights
Land cover information about the Earth’s surface features in terms of their quantity, diversity, and spatial distribution has been identified as one of the crucial data components for many aspects of global change studies and environmental applications [1,2]
This study focused on the following tasks: mapping of the surface-mined and agricultural landscapes (MSMAL), i.e., the entire study area, and classification of the surface-mined land (CSML)
Accuracy assessment was performed using the F1-measure, overall accuracy, percentage deviation, deviation, and statistical test for MSMAL and classification of surface-mined land (CSML) based on the feature subsets and all features and statistical test for MSMAL and CSML based on the feature subsets and all features using the random forest (RF), using the RF, support vector machine (SVM), and ANN algorithms
Summary
Land cover information about the Earth’s surface features in terms of their quantity, diversity, and spatial distribution has been identified as one of the crucial data components for many aspects of global change studies and environmental applications [1,2]. 2016, 8, 514 availability of high spatial resolution (HR) satellite remote sensing images, land cover mapping (LCM) at fine scales has increasingly attracted more attention [3,4,5]. It is noted that surface mining and subsequent reclamation are the dominant drivers of land cover change in many mine areas, resulting in deforestation, damage to ecosystems and natural landscapes, and threats to human health [12,21,22,23].
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