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Article: Monitoring surface mining belts using multiple remote sensing datasets: A global perspective

TitleMonitoring surface mining belts using multiple remote sensing datasets: A global perspective
Authors
KeywordsSurface mining
Change detection
Global
Time series
Issue Date2018
Citation
Ore Geology Reviews, 2018, v. 101, p. 675-687 How to Cite?
Abstract© 2018 Elsevier B.V. Quantifying the spatiotemporal change of land cover and understanding their ecological, environmental, and socioeconomic impacts are important for sustainable development. Surface mining by the minerals industry is one driver of the changes in land cover, leading to loss of natural vegetation and top soils, and interruption of ecosystem service flows. This study investigates the effectiveness of remote sensing datasets to identify and map land cover changes, with the specific goal of understanding the impact of surface mining activities on land cover globally from 1980s to 2013. Diverse remote sensing datasets with long term observations are analyzed, including high-resolution images in Google Earth, Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI), the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) product and Defense Meteorological Satellites Program (DMSP)/Operational Linescan System (OLS) stable night-time light. The results indicated that after entering 21st century, North America (e.g., the United States and Canada) was the only continent to have more surface mining spots categorized as Shrink type (rehabilitated) rather than Expand type. South America (e.g., Chile and Brazil) and Asia (e.g., India and China) had the highest proportions of Expand Type of surface mining spots. Detailed demonstrations on how those remote sensing datasets could help in mining spot monitoring are presented.
Persistent Identifierhttp://hdl.handle.net/10722/296942
ISSN
2020 Impact Factor: 3.809
2020 SCImago Journal Rankings: 1.413
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYu, Le-
dc.contributor.authorXu, Yidi-
dc.contributor.authorXue, Yueming-
dc.contributor.authorLi, Xuecao-
dc.contributor.authorCheng, Yuqi-
dc.contributor.authorLiu, Xiaoxuan-
dc.contributor.authorPorwal, Alok-
dc.contributor.authorHolden, Eun Jung-
dc.contributor.authorYang, Jian-
dc.contributor.authorGong, Peng-
dc.date.accessioned2021-02-25T15:17:01Z-
dc.date.available2021-02-25T15:17:01Z-
dc.date.issued2018-
dc.identifier.citationOre Geology Reviews, 2018, v. 101, p. 675-687-
dc.identifier.issn0169-1368-
dc.identifier.urihttp://hdl.handle.net/10722/296942-
dc.description.abstract© 2018 Elsevier B.V. Quantifying the spatiotemporal change of land cover and understanding their ecological, environmental, and socioeconomic impacts are important for sustainable development. Surface mining by the minerals industry is one driver of the changes in land cover, leading to loss of natural vegetation and top soils, and interruption of ecosystem service flows. This study investigates the effectiveness of remote sensing datasets to identify and map land cover changes, with the specific goal of understanding the impact of surface mining activities on land cover globally from 1980s to 2013. Diverse remote sensing datasets with long term observations are analyzed, including high-resolution images in Google Earth, Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI), the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) product and Defense Meteorological Satellites Program (DMSP)/Operational Linescan System (OLS) stable night-time light. The results indicated that after entering 21st century, North America (e.g., the United States and Canada) was the only continent to have more surface mining spots categorized as Shrink type (rehabilitated) rather than Expand type. South America (e.g., Chile and Brazil) and Asia (e.g., India and China) had the highest proportions of Expand Type of surface mining spots. Detailed demonstrations on how those remote sensing datasets could help in mining spot monitoring are presented.-
dc.languageeng-
dc.relation.ispartofOre Geology Reviews-
dc.subjectSurface mining-
dc.subjectChange detection-
dc.subjectGlobal-
dc.subjectTime series-
dc.titleMonitoring surface mining belts using multiple remote sensing datasets: A global perspective-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.oregeorev.2018.08.019-
dc.identifier.scopuseid_2-s2.0-85051648055-
dc.identifier.volume101-
dc.identifier.spage675-
dc.identifier.epage687-
dc.identifier.isiWOS:000448092400036-
dc.identifier.issnl0169-1368-

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