Changes in the extent of surface mining and reclamation in the Central Appalachians detected using a 1976–2006 Landsat time series

Townsend, Philip A.; Helmers, David P.; Kingdon, Clayton C.; McNeil, Brenden E.; Beurs, Kirsten M. de; Eshleman, Keith N.

doi:10.1016/j.rse.2008.08.012

Cited by 228 publications

(154 citation statements)

References 35 publications

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“…The America National Land Cover Database (NLCD) based on Landsat TM was completed in the 1990s by the Multi-Resolution Land Characteristics (MRLC) Consortium, and revealed that the classification accuracy of LCCMA is lower than 60% with 48% omission [17]. In addition, maps of mine reclamation are rare, in large part because reclaimed mines are spectrally indistinguishable from grasslands or pastures [18]. For instance, bare exposed mine dumps and grasses and trees above it were distinguished from farmland, water, and forest using a single Landsat TM in Pennsylvania, but the total mapping accuracy was only 62.3% [19].…”

Section: Development Of Land Cover Classification In Open-pit Mining mentioning

confidence: 99%

“…However, coalfield represented both coal mining excavation cities and coal dump areas. Townsend et al [18] examined active and reclaimed mines. Prakash and Gupta [14] recognized 10 classes with two mining land types.…”

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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Section: Development Of Land Cover Classification In Open-pit Mining mentioning

confidence: 99%

Section: Classification Thematic Resolution For Lccmamentioning

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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“…Erener [1] analyzed the vegetation cover and health status of the reclaimed area of Seyitömer mine using NDVI, SRI, and reduced simple ratio. Townsend et al [17] examined the land use changes of central Appalachian Mountain region in the eastern United States using decision trees and Landsat time series data. Zhang et al [18] based their research on HyMap and Hyperion image data used in Vegetation Inferiority Index and adopted Water Absorption Disrelated Index and NDVI; they also investigated and evaluated the effect of mining activities on the environment.…”

Section: State Of the Artmentioning

confidence: 99%

Monitoring the ecological environment of open-pit coalfields in cold zone of Northeast China using Landsat time series images of 2000-2015

Sun¹,

Zhang²,

Zhang³

et al. 2017

Teh. vjesn.

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Wenbin Sun i dr.Praćenje ekološkog okruženja rudnika s otvorenim jamskim oknom u hladnim područjima sjeveroistočne Kine primjenom Landsat serije slika ... MONITORING THE ECOLOGICAL ENVIRONMENT OF OPEN-PIT COALFIELDS IN COLD ZONES OF NORTHEAST CHINA USING LANDSAT TIME SERIES IMAGES OF 2000-2015Wenbin Sun, Hongdan Zhang, Yungang Cao, Xiaolei Zhang, Xuefeng Ji, Fashuai LiOriginal scientific paper Evaluating the deterioration of ecological environment and vegetation of coalfields caused by China's large-scale coal mining activities is important because of the fragile ecological environment and low temperature in cold and arid areas. This study takes the open coal pits of Haizhou, Gulianhe, and Huolinhe as examples and proposes a method for evaluating their ecological environment using Landsat time series images based on the Normalized Difference Vegetation Index (NDVI) variations of open-pit coalfields in cold and arid zones. The average NDVI value of the mining area each month was calculated using Landsat image data from 2000 to 2015. The vegetation cover area in the coalfields was extracted according to the NDVI threshold, and the scatter plots of the annual maximum NDVI and vegetation cover area were drawn. We fitted the variation trend line of maximum NDVI value and vegetation cover area to reduce the effect of meteorological factors on NDVI values. Results show that after the closure of open pit and reclamation of dump area, the NDVI of open-pit coalfields and vegetation cover area have been increasing rapidly over the last decade, and the ecological environment of these coalfields has obviously improved. The coal mining activities have led to the rapid decline of annual maximum NDVI and vegetation cover area of the coalfields in permafrost zones, and the ecological environment of coalfields continues to deteriorate. Although the quarterly average NDVI remains unchanged in non-permafrost mining coalfields under coal exploitation, the vegetation cover area in the coalfields decreases linearly, indicating that the ecological environment of the coalfields tends to deteriorate. From an ecological environment protection perspective, the results of this study provide a basis for decision making in constructing large-scale open pits in cold and arid zones. Keywords: coalfield; ecological environment; Landsat data set; Normalized Difference Vegetation Index (NDVI); time series Praćenje ekološkog okruženja rudnika s otvorenim jamskim oknom u hladnim područjima sjeveroistočne Kine primjenom Landsat serije slika snimljenih u razdoblju 2000-2015Izvorni znanstveni članak Procjena pogoršanja ekološkog okruženja i vegetacije rudnika u Kini zbog prekomjernog vađenja ugljena važna je zbog osjetljivog ekološkog okruženja i niske temperature u hladnim i sušnim područjima. U ovom se istraživanju kao primjeri uzimaju rudnici Haizhou, Gulianhe i Huolinhe s otvorenim jamskim oknom te se predlaže metoda za procjenu njihovog ekološkog okruženja primjenom Landsat vremenske serije slika na temelju varijacija Normalized Difference Vegeta...

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Trade‐offs in moving citizen‐based anuran call surveys online during the SARS‐CoV‐2 pandemic: Lessons from rural Appalachia, USA

Smith

Hamed

2020

Ecology and Evolution

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Changes in the extent of surface mining and reclamation in the Central Appalachians detected using a 1976–2006 Landsat time series

Cited by 228 publications

References 35 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

Monitoring the ecological environment of open-pit coalfields in cold zone of Northeast China using Landsat time series images of 2000-2015

Trade‐offs in moving citizen‐based anuran call surveys online during the SARS‐CoV‐2 pandemic: Lessons from rural Appalachia, USA

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