Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine

Socioeconomic development is often dependent on the production of mining resources, but both opencast and underground mining harm vegetation and the eco-environment. Under the requirements of the construction for ecological civilization in China, more attention has been paid to the reclamation of mi...

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Autores principales:	Maoxin Zhang, Tingting He, Guangyu Li, Wu Xiao, Haipeng Song, Debin Lu, Cifang Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	continuous change detection google earth engine Landsat disturbance vegetation NDVI Science Q
Acceso en línea:	https://doaj.org/article/fb60ce5e8de2407e8c822c42ab44ff48
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spelling	oai:doaj.org-article:fb60ce5e8de2407e8c822c42ab44ff482021-11-11T18:52:26ZContinuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine10.3390/rs132142732072-4292https://doaj.org/article/fb60ce5e8de2407e8c822c42ab44ff482021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4273https://doaj.org/toc/2072-4292Socioeconomic development is often dependent on the production of mining resources, but both opencast and underground mining harm vegetation and the eco-environment. Under the requirements of the construction for ecological civilization in China, more attention has been paid to the reclamation of mines and mining management. Thus, it is the basement of formulating policies related to mining management and implementing reclamation that detection of mining disturbance rapidly and accurately. This research carries on an empirical study in the Dexing copper mine, Jiangxi, China, aiming at exploring the process of distance and reclamation. Based on the dense time-series stack derived from the Landsat archive on Google Earth Engine (GEE), the disturbance of surface mining in the 1986–2020 period has been detected using the continuous change detection and classification (CCDC) algorithm. The results are that: (1) the overall accuracy of damage and recovery is 92% and 88%, respectively, and the Kappa coefficient is 85% and 84% respectively. This means that we obtained an ideal detection effect; (2) the surface-mining area was increasing from 1986–2020 in the Dexing copper mine, and the accumulation of mining damage is approximately 2865.96 ha with an annual area of 81.88 ha. We also found that the area was fluctuating with the increase. The detected natural restoration was appraised at a total of 544.95 ha in the 1988–2020 period with an average restoration of 16.03 ha. This means that it just restores less in general; (3) it has always been the case that the Dexing mine is damaged by mining and reclamation in the whole year (it is most frequently damaged month is July). All imageries in the mine are detected by the CCDC algorithm, and they are classified as four types by disturbing number in pixel scale (i.e., 0, 1, 2, more than 2 times). Based on that, we found that the only once disturbed pixels account for 64.75% of the whole disturbed pixels, which is the majority in the four classes; (4) this method provides an innovative perspective for obtaining the mining disturbed dynamic information timely and accurately and ensures that the time and number of surface mining disturbed areas are identified accurately. This method is also valuable in other applications including the detection of other similar regions.Maoxin ZhangTingting HeGuangyu LiWu XiaoHaipeng SongDebin LuCifang WuMDPI AGarticlecontinuous change detectiongoogle earth engineLandsatdisturbancevegetationNDVIScienceQENRemote Sensing, Vol 13, Iss 4273, p 4273 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	continuous change detection google earth engine Landsat disturbance vegetation NDVI Science Q
spellingShingle	continuous change detection google earth engine Landsat disturbance vegetation NDVI Science Q Maoxin Zhang Tingting He Guangyu Li Wu Xiao Haipeng Song Debin Lu Cifang Wu Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine
description	Socioeconomic development is often dependent on the production of mining resources, but both opencast and underground mining harm vegetation and the eco-environment. Under the requirements of the construction for ecological civilization in China, more attention has been paid to the reclamation of mines and mining management. Thus, it is the basement of formulating policies related to mining management and implementing reclamation that detection of mining disturbance rapidly and accurately. This research carries on an empirical study in the Dexing copper mine, Jiangxi, China, aiming at exploring the process of distance and reclamation. Based on the dense time-series stack derived from the Landsat archive on Google Earth Engine (GEE), the disturbance of surface mining in the 1986–2020 period has been detected using the continuous change detection and classification (CCDC) algorithm. The results are that: (1) the overall accuracy of damage and recovery is 92% and 88%, respectively, and the Kappa coefficient is 85% and 84% respectively. This means that we obtained an ideal detection effect; (2) the surface-mining area was increasing from 1986–2020 in the Dexing copper mine, and the accumulation of mining damage is approximately 2865.96 ha with an annual area of 81.88 ha. We also found that the area was fluctuating with the increase. The detected natural restoration was appraised at a total of 544.95 ha in the 1988–2020 period with an average restoration of 16.03 ha. This means that it just restores less in general; (3) it has always been the case that the Dexing mine is damaged by mining and reclamation in the whole year (it is most frequently damaged month is July). All imageries in the mine are detected by the CCDC algorithm, and they are classified as four types by disturbing number in pixel scale (i.e., 0, 1, 2, more than 2 times). Based on that, we found that the only once disturbed pixels account for 64.75% of the whole disturbed pixels, which is the majority in the four classes; (4) this method provides an innovative perspective for obtaining the mining disturbed dynamic information timely and accurately and ensures that the time and number of surface mining disturbed areas are identified accurately. This method is also valuable in other applications including the detection of other similar regions.
format	article
author	Maoxin Zhang Tingting He Guangyu Li Wu Xiao Haipeng Song Debin Lu Cifang Wu
author_facet	Maoxin Zhang Tingting He Guangyu Li Wu Xiao Haipeng Song Debin Lu Cifang Wu
author_sort	Maoxin Zhang
title	Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine
title_short	Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine
title_full	Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine
title_fullStr	Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine
title_full_unstemmed	Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine
title_sort	continuous detection of surface-mining footprint in copper mine using google earth engine
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fb60ce5e8de2407e8c822c42ab44ff48
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Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine

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