Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation

Maxwell, Aaron E.; Strager, Michael P.; Warner, Timothy A.; Zégre, Nicolas; Yuill, Charles

doi:10.1080/15481603.2014.912874

Cited by 52 publications

(53 citation statements)

References 56 publications

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“…For mining and mine reclamation classification, Maxwell et al [24][25][26] defined three land cover types related to mining areas. For details, see Table 1.…”

Section: Classification Thematic Resolution For Lccmamentioning

confidence: 99%

“…References opencast stope, mineral processing land, dumping site 3 [35] reclaimed herbaceous vegetation, reclaimed woody vegetation, barren (including haul roads, active quarries, land disturbed by mining) 3 [22][23][24] mine-reclaimed grassland (including reclaimed land within mine sites and valley fills dominated by herbaceous vegetation) 1 [25] open stope, stripping area, waste-dump area, mine industrial area 4 [26] active, disturbed vegetation and pasture, rehabilitation, remnant, spoil/waste, and water management 6 [27] coal deposit, over burden dump, mine dump 3 [28] tree cover, dense grass, sparse grass, bare ground 4 [29] coal mining excavation cities, coal dump areas 2 [30] active mine, reclaimed mine (including grass, woodland, forest) 2 [16] opencast mining (coal), overburden dump 2 [12] bare tailings, exposed lime, water/wet tailings 3 [31] tailings zone, dry vegetation zone, transition zone, vegetated islands 4 [32] mine, dump, coal stockpile 3 [33,34] …”

Section: Sub-classes Of Mining Areasmentioning

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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“…For mining and mine reclamation classification, Maxwell et al [24][25][26] defined three land cover types related to mining areas. For details, see Table 1.…”

Section: Classification Thematic Resolution For Lccmamentioning

confidence: 99%

Section: Sub-classes Of Mining Areasmentioning

confidence: 99%

Section: Remote Sensing Data Sources and Featuresmentioning

confidence: 99%

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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“…In addition, random forest provides the relative importance of a variable using out-of-bag data when the variable is permuted. Because of these strengths, random forest has proven robust in various remote sensing applications [61][62][63][64][65][66][67][68].…”

Section: Typical Waveform Over Leads Ice Floes and Oceanmentioning

confidence: 99%

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Lee

Kim

et al. 2016

Remote Sensing

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Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011-2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86-0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011-2013 and rebounded in 2014.

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Impacts of mountaintop mining on terrestrial ecosystem integrity: identifying landscape thresholds for avian species in the central Appalachians, United States

et al. 2014

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Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation

Cited by 52 publications

References 56 publications

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

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

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Impacts of mountaintop mining on terrestrial ecosystem integrity: identifying landscape thresholds for avian species in the central Appalachians, United States

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