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

Abstract

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.

Recognition of Landscape Key Areas in a Coal Mine Area of a Semi-Arid Steppe in China: A Case Study of Yimin Open-Pit Coal Mine

“…Moreover, we conducted a field survey. Some studies have found that the Random Forest Method (RF) provided the highest accuracy (86%) [35]. Therefore, we applied the random forest algorithm for landscape classification.…”

The long‐term effects of underground mining on the growth of tree, shrub, and herb communities in arid and semiarid areas in China

“…Thus, we divided the vegetation into trees (natural forest, cultivated forest, mixed forest), shrubs, and herbs (high‐coverage grassland and low‐coverage grassland) based on existing LULC in the Nanjiao mining area; other types of LULC (farm land, bare land, buildings, and photovoltaic panels) were all categorized into one class: others. The results of each LULC come from our previous study (Mi et al, 2019), and a detailed process of LULC classification is also provided in the Supporting Information. Then, the tree, shrub, and herb communities that maintained the same type between 1987 and 2017 were extracted by combining the LULC classification results in 1987, 1997, 2007, and 2017.…”

Multitemporal Land Use and Land Cover Classification from Time-Series Landsat Datasets Using Harmonic Analysis with a Minimum Spectral Distance Algorithm

“…Lu et al [15] applied Landsat images from 1990 to 2015 at five-year intervals to classify impervious surface and non-impervious surface areas using a linear spectral mixture analysis of the six selected metropoles in the coastal and inland metropoles in 2015, achieving an overall accuracy varying from 94% to 95%. Mi et al [14] applied an RF classifier with time-series Landsat datasets (1987-2017) to detect the LULC changes in a mining area, achieving an average OA of about 84%. Buitre et al [13] applied a support vector machine with time-series Landsat datasets (1987-2016) to classify mangroves, non-mangroves, seawater, and clouds in the Philippines and attained an average OA of about 84%.…”

“…Accelerated urban growth and LULC changes exert pressure on the natural environment and human welfare and have become a global concern [8]. Several studies on LULC changes and their impacts have been conducted worldwide from multiple dimensions using satellite remote sensing and GIS technology [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. All of these studies require time-series datasets that are mostly derived from Earth observation satellites to classify multitemporal LULC maps.…”