Surface coal mine area monitoring using multi-temporal high-resolution satellite imagery

Demirel, Nuray; Emil, M. K.; Düzgün, Şebnem

doi:10.1016/j.coal.2010.11.010

Cited by 102 publications

(50 citation statements)

References 19 publications

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“…For better understanding of the NDVI variable, Figure 5 presents the variability within the CBI plots of different levels of burn severity. The two GeoEye images were atmospherically corrected, using the ATCOR 2 software (Wessling, Germany), and ortho-rectified with the help of the 5 m Digital Elevation Model [40]. The total RMS errors associated with the ground control points (GCPs) did not exceed 0.5 pixels in either of the two images.…”

Section: Satellite Datamentioning

confidence: 99%

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Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

et al. 2016

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Abstract:Monitoring post-fire vegetation response using remotely-sensed images is a top priority for post-fire management. This study investigated the potential of very-high-resolution (VHR) GeoEye images on detecting the field-measured burn severity of a forest fire that occurred in Evros (Greece) during summer 2011. To do so, we analysed the role of topographic conditions and burn severity, as measured in the field immediately after the fire (2011) and one year after (2012) using the Composite Burn Index (CBI) for explaining the post-fire vegetation response, which is measured using VHR satellite imagery. To determine this relationship, we applied redundancy analysis (RDA), which allowed us to identify which satellite variables among VHR spectral bands and Normalized Difference Vegetation Index (NDVI) can better express the post-fire vegetation response. Results demonstrated that in the first year after the fire event, variations in the post-fire vegetation dynamics can be properly detected using the GeoEye VHR data. Furthermore, results showed that remotely-sensed NDVI-based variables are able to encapsulate burn severity variability over time. Our analysis showed that, in this specific case, burn severity variations are mildly affected by the topography, while the NDVI index, as inferred from VHR data, can be successfully used to monitor the short-term post-fire dynamics of the vegetation recovery.

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Section: Satellite Datamentioning

confidence: 99%

“…We used a five-meter (5 m) DEM [40] to derive three topographic parameters: (a) aspect; (b) slope; and (c) elevation. The produced grids of aspect and slope were calculated in radians, while the elevation in meters.…”

Section: Topographic Datamentioning

confidence: 99%

Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

et al. 2016

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“…Richter et al [34] classified acid mine drainage pollution zones in tailing areas, into tailings zone (very high), dry vegetation zone (high), transition zone (low to medium), and vegetated island (very low to low). Demirel et al [35,36] examined three mining related land cover types: mine, dump, and coal stockpile.…”

Section: Classification Thematic Resolution For Lccmamentioning

confidence: 99%

“…For LCCMA, the following remote sensing data have been utilized: (1) Landsat images [14,18,28,30,32]; (2) high-resolution images such as ZiYuan-3 [27,41], RapidEye [24][25][26], SPOT-5 [31], IKONOS [35,36], and QuickBird [35,36]; (3) airborne aerial images [24,29,33,34]. For details, see Table 2.…”

Section: Remote Sensing Data Sources and Featuresmentioning

confidence: 99%

“…ZiYuan-3 stereo satellite imagery [41] RapidEye [24] RapidEye, LiDAR [25,26] Landsat 5 TM, Landsat 8 OLI [28,30] Aerial imagery from Bing Maps, Google Maps imagery, NAIP [27,29] SPOT-5 imagery, DEM from aerial photographic stereo pairs [31] Landsat TM, Landsat ETM+, Landsat ETM [32] Landsat MSS, Landsat TM, Landsat ETM+ [18] Landsat MSS, Landsat TM, IRS LISS-II [14] Airborne high spatial and spectral resolution Compact Airborne Spectrographic Imager, hyperspectral data acquired with the Probe-1 airborne imager [33] Hyperspectral data acquired with the TRWIS III [34] IKONOS, QuickBird [35,36] HR images often have four bands; thus, identifying land cover at a fine scale using only spectral curves becomes difficult. In previous research employing HR images, the widely used spectral band and several spectral indexes, such as vegetation index [30,35], were considered as the input features of the classification process.…”

Section: Remote Sensing Data Sources Referencesmentioning

confidence: 99%

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A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

Chen

et al. 2017

Remote Sensing

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Abstract:Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines.

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Restoration of damaged ecosystems in desert steppe open‐pit coal mines: Effects on soil nematode communities and functions

Han

Gao

et al. 2021

Land Degrad Dev

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Repair of damaged areas in open-pit (opencast) coal mines has emerged as an important environmental concern. Our research investigated the mechanism by which different ecological restoration methods affected soil nematode communities in damaged areas of desert steppe coal mines. Using high-throughput sequencing technology, nematode community composition, diversity, and function were analyzed to determine the response of nematodes to different ecological restoration methods in damaged areas of coal mines. For slope rehabilitation, vegetation blanket restoration exhibited more favorable effects than those exhibited by vegetation bag restoration and natural restoration. For rehabilitation of the platform area under the slope, the diversity of soil nematodes and the soil fauna analysis under alien soil restoration conditions were performed and exhibited similar characteristics to those of the native vegetation. Findings based on Linear discriminant analysis Effect Size (LEfSe) multi-level discriminant analysis and determination of shared genera suggest that Paraphelenchus, Cervidellus, Panagrolaimus, Microdorylaimus, Cephalobus, and Ecumenicus may be the key genera of soil nematodes in the damaged ecosystem of open-pit coal mines in the desert steppe. We found that reasonable water and fertilizer management in slope restoration and under-slope platform area restoration may play a key role in the restoration of damaged ecosystems in open-pit coal mines. We comprehensively analyzed the response of soil nematode communities and their functions to different ecological restoration methods, and provided a reference for evaluating the quality of underground ecosystem restoration of damaged areas in abandoned desert steppe open-pit coal mines.

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Surface coal mine area monitoring using multi-temporal high-resolution satellite imagery

Cited by 102 publications

References 19 publications

Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

Restoration of damaged ecosystems in desert steppe open‐pit coal mines: Effects on soil nematode communities and functions

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