Land Cover Changes in Open-Cast Mining Complexes Based on High-Resolution Remote Sensing Data

Nascimento, Filipe Silveira; Gastauer, Markus; Souza-Filho, Pedro Walfir M.; Nascimento, Wilson R.; Santos, Diogo C.; Costa, Marlene Furtado da

doi:10.3390/rs12040611

Cited by 28 publications

(12 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remote sensing (RS)-aided derived in monitoring examples in terrain and surfaces, aeolian geomorphology, fluvial geomorphology and coastal geomorphology landslides and their traits. Mountain types, relief types, relief classes IKONOS OSA 3/M , DHM25 3/R , GTOPO30-DEM 3/R , LiDAR 2/L [330][331][332] Volcano types (volcanic full forms),volcanoes, lava flow fields, hydrothermal alteration, geothermal explorations, heat fluxes, volcanoes hazard monitoring Doves-PlanetScop, Terra/Aqua MODIS 3/M , EO-1 ALI 3/M , Landsat-8 OLI 3/M/TIR , Terra ASTER 3/M/TIR , MSG SEVIRI 3/M/TIR , LiDAR 2/L [333][334][335][336][337] Mountain hazards, mass movement (rock fall probability, boulders, denudation, mass erosion, rock decelerations, rotation changes, slope stability, rock shapes, particle shapes, patterns, structures, faults and fractures, holes and depressions) InSAR 3/R , SAR 3/R , LiDAR 2/L , Digital Orthophoto 1/RGB [338][339][340][341][342][343][344][345][346][347] Landslide chances, landslide evolution Digital Orthophoto 1/RGB [348] Above ground-chances, disturbances Opencast mining, sand mining and extraction, tipping, dumps TanDEM-X 3/R , SRTM DEM 3/R , ALOS PALSAR 3/R , ERS-1 3/R , GeoEye GIS 3/M , WorldView-3 Imager 3/M , IKONOS OSA 3/M , Landsat-5 TM/-7 ETM+/-8 OLI 3/M/TIR , IRS-P6 LISS-III 3/M , High resolution satellite data of Google 3/M , LiDAR 2/L [349][350][351][352][353][354][355] Vegetation traits as proxy of the geochemical parameters HyMAP 2/H [356] Below ground-chances, disturbances Salt mines, fracking ERS-1/-2 3/R , ASAR 3/R , ALOS PALSAR 3/R , Landsat-5 TM/-7 ETM+/-8 OLI 3/M/TIR [113,357] Table 5. Cont.…”

Section: Cosmo Skymedmentioning

confidence: 99%

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Lausch

Schaepman

Skidmore³

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

show abstract

Section: Cosmo Skymedmentioning

confidence: 99%

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Lausch

Schaepman

Skidmore³

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In [11], [12], [15], the authors employed a threshold segmentation for remote sensing image classification. In [11], the authors evaluated a region of the state of Mato Grosso with the objective of classifying deforestation and environmental degradation in the region.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The accuracy obtained by the authors was 95.7% for the LANDSAT-8/OLI images and 96.7% for the Sentinel-2 images. In [15], the authors carried out the classification of images in the region of the Carajás mines , located in the south of the state of Pará. The classes defined by the authors were cerrado, forest, water and mining area.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Methodology of Data Fusion Using Deep Learning for Semantic Segmentation of Land Types in the Amazon

Oliveira¹,

Costa

Filho

2020

IEEE Access

View full text Add to dashboard Cite

This study proposes a methodology using deep learning and a multi-resolution segmentation algorithm to perform the semantic segmentation of remote sensing images. Initially the image is segmented using a CNN, and then an image with homogeneous regions is generated using a multi-resolution segmentation algorithm. Finally, a data fusion process is performed with these two images, generating the final classified image. The field of study was the Brazilian Amazon region. The proposed methodology classifies images in the following classes: forest, pasture and agriculture. The input data used were LANDSAT-8/OLI images. The reference data were extracted from the results of the TerraClass project in 2014. Two datasets were evaluated: the first with six bands and the second with three bands. Three CNN architectures were evaluated together with three optimization methods: SGDM, ADAM, and RMSProp and the dropout and L2 regularization methods as methods for generalization improvement. The best model, CNN + optimization method + technique for generalization improvement, evaluated in the validation set, was submitted to a 5-fold cross validation methodology, and the results were compared with pre-trained networks using the learning transfer methodology; in this case the networks used for comparison were ResNet50, InceptionResnetv2, MobileNetv2 and Xception. The proposed methodology was evaluated through image segmentation of some regions of the Amazon. Finally, the proposed methodology was evaluated in regions used by other authors. The accuracy values obtained for the images evaluated were over 99%. INDEX TERMS deep learning; convolutional neural networks; remote sensing; image segmentation.

show abstract

“…High-resolution optical imagery has also been used to monitor land use and land cover (LULC) around open-pit mines and assess the impact of mining activities on the environment. (Nascimento et al [8]; Asner et al [9]; Wu et al [10]). Nevertheless, the limitations of UAV's flight range make multiple flight sessions necessary to carry out monitoring of a large-area [5], which is undoubtedly a challenge for areas that cannot be continuously powered [11].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Mining Activities Using Sentinel-1A InSAR Coherence in Open-Pit Coal Mines

et al. 2021

View full text Add to dashboard Cite

Long-term continuous monitoring of the mining activities in open-pit coal mines is conducive to planning and management of the mining operations. Additionally, this faciliatates assessment on their environmental impact and supervises illegal mining behaviors. Interferometric Synthetic Aperture Radar (InSAR) technology can be effectively applied in the monitoring of open-pit mines where vegetation is sparse and land cover is dominated by bare rock. The main objective of this study is to monitor the mining activities of four open-pit coal mines in the Wucaiwan mining area in China from 2018 to 2020, namely No. 1, No. 2 (containing two mining areas), and No. 3. We use the normalized differential activity index (NDAI) based on the coherence coefficient as an indicator of the mine activity due to its robustness to temporal and spatial decorrelation. After analyzing and removing the decorrelation caused by rain and snow weather, 70 NDAI images in 12-day intervals are obtained from Sentinel-1A InSAR coherence images. Then, the annually-averaged NDAI images are applied to an RGB composite technique (red for 2018, green for 2019, blue for 2020) to express the interannual variation of the mining activities. Points of interest are then selected for NDAI time series analysis. The RGB composite results indicated that No. 1 and 3 open-pit coal mines were continuously mined during the three years; whereas, the two mining areas of No. 2 were mainly active in 2018. The 12-day NDAI time-series graphs of No. 2 open-pit coal mine also indicate that the coal piles located in the coal transferring area of the first mining area were not completely removed until April 2019. It is also seen that the second mining area was decommissioned in November 2018 and became rehabilitated in July 2019. Results were validated using the Sentinel-2A images and related background information confirming the efficiency of the proposed approach for monitoring the mining activity in open-pit mines.

show abstract

Land Cover Changes in Open-Cast Mining Complexes Based on High-Resolution Remote Sensing Data

Cited by 28 publications

References 49 publications

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

Methodology of Data Fusion Using Deep Learning for Semantic Segmentation of Land Types in the Amazon

Monitoring Mining Activities Using Sentinel-1A InSAR Coherence in Open-Pit Coal Mines

Contact Info

Product

Resources

About