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Open AccessArticle

Tracking the Land Use/Land Cover Change in an Area with Underground Mining and Reforestation via Continuous Landsat Classification

1
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2
School of Management, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1719; https://doi.org/10.3390/rs11141719
Received: 6 June 2019 / Revised: 13 July 2019 / Accepted: 19 July 2019 / Published: 20 July 2019
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)
Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional research related to the methods in this field is still needed. In this study, we tracked the LULC change in the Nanjiao mining area, Shanxi Province, China between 1987 and 2017 via random forest classifier and continuous Landsat imagery, where years of underground mining and reforestation projects have occurred. We applied a Savitzky–Golay filter and a normalized difference vegetation index (NDVI)-based approach to detect the temporal and spatial change, respectively. The accuracy assessment shows that the random forest classifier has a good performance in this heterogeneous area, with an accuracy ranging from 81.92% to 86.6%, which is also higher than that via support vector machine (SVM), neural network (NN), and maximum likelihood (ML) algorithm. LULC classification results reveal that cultivated forest in the mining area increased significantly after 2004, while the spatial extent of natural forest, buildings, and farmland decreased significantly after 2007. The areas where vegetation was significantly reduced were mainly because of the transformation from natural forest and shrubs into grasslands and bare lands, respectively, whereas the areas with an obvious increase in NDVI were mainly because of the conversion from grasslands and buildings into cultivated forest, especially when villages were abandoned after mining subsidence. A partial correlation analysis demonstrated that the extent of LULC change was significantly related to coal production and reforestation, which indicated the effects of underground mining and reforestation projects on LULC changes. This study suggests that continuous Landsat classification via random forest classifier could be effective in monitoring the long-term dynamics of LULC changes, and provide crucial information and data for the understanding of the driving forces of LULC change, environmental impact assessment, and ecological protection planning in large-scale mining areas. View Full-Text
Keywords: LULC change; underground mining and reforestation; random forest classifier; heterogeneous area; continuous Landsat classification LULC change; underground mining and reforestation; random forest classifier; heterogeneous area; continuous Landsat classification
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MDPI and ACS Style

Mi, J.; Yang, Y.; Zhang, S.; An, S.; Hou, H.; Hua, Y.; Chen, F. Tracking the Land Use/Land Cover Change in an Area with Underground Mining and Reforestation via Continuous Landsat Classification. Remote Sens. 2019, 11, 1719. https://doi.org/10.3390/rs11141719

AMA Style

Mi J, Yang Y, Zhang S, An S, Hou H, Hua Y, Chen F. Tracking the Land Use/Land Cover Change in an Area with Underground Mining and Reforestation via Continuous Landsat Classification. Remote Sensing. 2019; 11(14):1719. https://doi.org/10.3390/rs11141719

Chicago/Turabian Style

Mi, Jiaxin; Yang, Yongjun; Zhang, Shaoliang; An, Shi; Hou, Huping; Hua, Yifei; Chen, Fuyao. 2019. "Tracking the Land Use/Land Cover Change in an Area with Underground Mining and Reforestation via Continuous Landsat Classification" Remote Sens. 11, no. 14: 1719. https://doi.org/10.3390/rs11141719

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