Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data

Coal is the most prevalent energy source in China and plays an important role in ensuring energy security. The continuous monitoring of coal mining activities is helpful to clarify the incremental space of coal production and establish a rational framework for future coal production capacity. In thi...

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Main Authors: Fangzhou Hong, Guojin He, Guizhou Wang, Zhaoming Zhang, Yan Peng
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/15/13/3439
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author Fangzhou Hong
Guojin He
Guizhou Wang
Zhaoming Zhang
Yan Peng
author_facet Fangzhou Hong
Guojin He
Guizhou Wang
Zhaoming Zhang
Yan Peng
author_sort Fangzhou Hong
collection DOAJ
description Coal is the most prevalent energy source in China and plays an important role in ensuring energy security. The continuous monitoring of coal mining activities is helpful to clarify the incremental space of coal production and establish a rational framework for future coal production capacity. In this study, a multi-source remote sensing approach utilizing SPOT 4, GF, and Landsat data is employed to monitor land cover and vegetation changes in the Juhugeng mining area of the Muli coalfield over a span of nearly 20 years. The analysis incorporates an object-oriented classification method and a vegetation parameter to derive insights. The findings reveal that the mining operations can be divided into two periods, since their initiation in 2003 until their cessation in 2021, with a dividing point around 2013/2014. The initial phase witnessed rapid and even accelerated expansion of the mine, while the subsequent phase was characterized by more stable development and the implementation of some restorative measures for the mine environment. Although the vegetation parameter, Fractional Vegetation Cover (FVC), indicates some reclamation efforts within the mining area, the extent of the reclaimed land remains limited. This study demonstrates the effective application of object-oriented classification in conjunction with the vegetation parameter FVC for monitoring coal mining areas.
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spelling doaj.art-cef2195093114b66bce5860e14516e552023-11-18T17:26:18ZengMDPI AGRemote Sensing2072-42922023-07-011513343910.3390/rs15133439Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing DataFangzhou Hong0Guojin He1Guizhou Wang2Zhaoming Zhang3Yan Peng4Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaCoal is the most prevalent energy source in China and plays an important role in ensuring energy security. The continuous monitoring of coal mining activities is helpful to clarify the incremental space of coal production and establish a rational framework for future coal production capacity. In this study, a multi-source remote sensing approach utilizing SPOT 4, GF, and Landsat data is employed to monitor land cover and vegetation changes in the Juhugeng mining area of the Muli coalfield over a span of nearly 20 years. The analysis incorporates an object-oriented classification method and a vegetation parameter to derive insights. The findings reveal that the mining operations can be divided into two periods, since their initiation in 2003 until their cessation in 2021, with a dividing point around 2013/2014. The initial phase witnessed rapid and even accelerated expansion of the mine, while the subsequent phase was characterized by more stable development and the implementation of some restorative measures for the mine environment. Although the vegetation parameter, Fractional Vegetation Cover (FVC), indicates some reclamation efforts within the mining area, the extent of the reclaimed land remains limited. This study demonstrates the effective application of object-oriented classification in conjunction with the vegetation parameter FVC for monitoring coal mining areas.https://www.mdpi.com/2072-4292/15/13/3439Juhugeng mining areamulti-source remote sensing datamultiresolution segmentationland cover changeFVC
spellingShingle Fangzhou Hong
Guojin He
Guizhou Wang
Zhaoming Zhang
Yan Peng
Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data
Remote Sensing
Juhugeng mining area
multi-source remote sensing data
multiresolution segmentation
land cover change
FVC
title Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data
title_full Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data
title_fullStr Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data
title_full_unstemmed Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data
title_short Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data
title_sort monitoring of land cover and vegetation changes in juhugeng coal mining area based on multi source remote sensing data
topic Juhugeng mining area
multi-source remote sensing data
multiresolution segmentation
land cover change
FVC
url https://www.mdpi.com/2072-4292/15/13/3439
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AT guizhouwang monitoringoflandcoverandvegetationchangesinjuhugengcoalminingareabasedonmultisourceremotesensingdata
AT zhaomingzhang monitoringoflandcoverandvegetationchangesinjuhugengcoalminingareabasedonmultisourceremotesensingdata
AT yanpeng monitoringoflandcoverandvegetationchangesinjuhugengcoalminingareabasedonmultisourceremotesensingdata