Mapping Mining Areas in the Brazilian Amazon Using MSI/Sentinel-2 Imagery (2017)

Lobo, Felipe de Lucia; Souza-Filho, Pedro Walfir M.; Novo, Evlyn Márcia Leão de Moraes; Carlos, Felipe Menino; Barbosa, Cláudio Clemente Faria

doi:10.3390/rs10081178

Cited by 76 publications

(53 citation statements)

References 22 publications

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“…Previous studies have applied remote sensing to monitor the LULC in numerous mining areas. The changes in LULC of the Tortiya mining area within 46 years were detected by supervised classification via Corona and Landsat images [20]; the surface mines within the United States were classified to revealed the changes between 2001 and 2006 [21]; the distribution of land use/land covers in mining areas was also mapped by classification via multiple remote sensing imagery [22,23]. In these studies, supervised classification is the most important approach to obtaining the LULC data, such as using random forest, a support vector machine, as well as the maximum likelihood and neural network methods [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Zhang

et al. 2019

Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

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

Yang

Zhang

et al. 2019

Remote Sensing

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“…Some researchers have revealed the important effect of open pit mining on local land degradation [1][2][3][4][5]. Accordingly, land covers in complex open pit mining landscapes are being increasingly used as key datasets for global and local land degradation and development studies [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, high resolution satellite imagery and machine learning algorithms (MLAs) have been applied to land cover classification in open pit mining areas [6,9,[12][13][14]. MLAs can generally accept various features sets [10], which have proven to be valuable in open pit mining areas classification. Several algorithms with excellent performance have been widely used, for example, support vector machine (SVM) [15,16], artificial neural network (ANN) [17], and random forest (RF) [15].…”

Section: Introductionmentioning

confidence: 99%

Fine Land Cover Classification in an Open Pit Mining Area Using Optimized Support Vector Machine and WorldView-3 Imagery

Chen

Wang

2019

Remote Sensing

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Fine land cover classification in an open pit mining area (LCCOM) is essential in analyzing the terrestrial environment. However, researchers have been focusing on obtaining coarse LCCOM while using high spatial resolution remote sensing data and machine learning algorithms. Although support vector machines (SVM) have been successfully used in the remote sensing community, achieving a high classification accuracy of fine LCCOM using SVM remains difficult because of two factors. One is the lack of significant features for efficiently describing unique terrestrial characteristics of open pit mining areas and another is the lack of an optimized strategy to obtain suitable SVM parameters. This study attempted to address these two issues. Firstly, a novel carbonate index that was based on WorldView-3 was proposed and introduced into the used feature set. Additionally, three optimization methods—genetic algorithm (GA), k-fold cross validation (CV), and particle swarm optimization (PSO)—were used for obtaining the optimization parameters of SVM. The results show that the carbonate index was effective for distinguishing the dumping ground from other open pit mining lands. Furthermore, the three optimization methods could significantly increase the overall classification accuracy (OA) of the fine LCCOM by 8.40%. CV significantly outperformed GA and PSO, and GA performed slightly better than PSO. CV was more suitable for most of the fine land cover types of crop land, and PSO for road and open pit mining lands. The results of an independent test set revealed that the optimized SVM models achieved significant improvements, with an average of 8.29%. Overall, the proposed strategy was effective for fine LCCOM.

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“…This approach can remove "salt-and-pepper" effects, and a large set of features (e.g., objects generated from the spectral, spatial and textural properties of a group of pixels) can be produced as additional information Mining activity expanded mainly in the context of the Carajás mining projects, which started in the early 1980s. The role of mining activity in the process of clearing forests has been widely considered [18]; however, industrial mining is directly responsible for the conversion of restricted areas covered by forest or mountain savannas in the mining areas [19]. On the other hand, the main driver of LCLU changes in the IRW was associated with the opening of a rudimentary road network associated with settlements and cattle ranching that facilitated inland timber exploitation in the southeastern Amazon region [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, information is lacking regarding the conversion of forests and montane savanna regions to mining infrastructure, with the exception of a few studies on gold mining using high-resolution images [16]. Recent publications have demonstrated the influence of mining projects on the LCLU changes in the Brazilian Amazon from "pixel-to-pixel" approach [17][18][19]. Furthermore, these LCLU conversions have collaborated to climate and water discharge changes in the context of river watersheds in the southeastern Amazon [20][21][22].…”

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confidence: 99%

A GEOBIA Approach for Multitemporal Land-Cover and Land-Use Change Analysis in a Tropical Watershed in the Southeastern Amazon

et al. 2018

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The southeastern Amazon region has been intensively occupied by human settlements over the past three decades. To evaluate the effects of human settlements on land-cover and land-use (LCLU) changes over time in the study site, we evaluated multitemporal Landsat images from the years 1984, 1994, 2004, 2013 and Sentinel to the year 2017. Then, we defined the LCLU classes, and a detailed "from-to" change detection approach based on a geographic object-based image analysis (GEOBIA) was employed to determine the trajectories of the LCLU changes. Three land-cover (forest, montane savanna and water bodies) and three land-use types (pasturelands, mining and urban areas) were mapped. The overall accuracies and kappa values of the classification were higher than 0.91 for each of the classified images. Throughout the change detection period,~47% (19,320 km 2 ) of the forest was preserved mainly within protected areas, while almost 42% (17,398 km 2 ) of the area was converted from forests to pasturelands. An intrinsic connection between the increase in mining activity and the expansion of urban areas also exists. The direct impacts of mining activities were more significant throughout the montane savanna areas. We concluded that the GEOBIA approach adopted in this study combines the advantages of quality human interpretation and the capacities of quantitative computing.to improve image classification accuracy [5]. Primarily, GEOBIA has been able to be applied due to the advent of very high (spatial)-resolution images that show mainly land-cover and land-use (LCLU) changes within urban areas [6], forests [7], and agriculture areas [8], where image objects are digitally constructed from dozens to hundreds of pixels [9,10]. However, the use of GEOBIA has been increasingly expanded to include moderate-resolution images if a higher hierarchical image-object level is applied [11][12][13][14].In a global and regional context, the investigation of variations in the LCLU constitute a broad field in terms of the diversity of remote sensing methods that are available to map and monitor the different types of human-driven changes in the environments. Although many change detection techniques have been developed at the per-pixel level, new insights have been obtained from GEOBIA and hybrid methods centered on "from-to" change trajectories to better qualify and quantify LCLU change patterns [15].In the Amazon region, the dynamics of forest conversion to pastureland are well documented using a per-pixel approach [2], and the findings of these studies are mainly provided in annual reports on the Satellite Monitoring System of the Brazilian Amazon Forest (PRODES) (www.dpi.inpe. br/prodesdigital/prodes). Nevertheless, information is lacking regarding the conversion of forests and montane savanna regions to mining infrastructure, with the exception of a few studies on gold mining using high-resolution images [16]. Recent publications have demonstrated the influence of mining projects on the LCLU changes in the Brazilian Amazon from "pixel-to-...

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Mapping Mining Areas in the Brazilian Amazon Using MSI/Sentinel-2 Imagery (2017)

Cited by 76 publications

References 22 publications

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

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

Fine Land Cover Classification in an Open Pit Mining Area Using Optimized Support Vector Machine and WorldView-3 Imagery

A GEOBIA Approach for Multitemporal Land-Cover and Land-Use Change Analysis in a Tropical Watershed in the Southeastern Amazon

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