Mapping annual land disturbance and reclamation in rare-earth mining disturbance region using temporal trajectory segmentation.

Land disturbance process Mining activity Multisource remote sensing monitoring Temporal trajectory segmentation Time-series analysis Vegetation restoration

Journal

Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769

Informations de publication

Date de publication:
Dec 2021
Historique:
received: 09 05 2021
accepted: 13 07 2021
pubmed: 23 7 2021
medline: 4 1 2022
entrez: 22 7 2021
Statut: ppublish

Résumé

Rare-earth mining has caused extensive damage to soil, vegetation, and water, significantly threatening ecosystems. Monitoring environmental disturbance caused by rare-earth mining is necessary to protect the ecological environment. A spatiotemporal remote sensing monitoring method for mining to reclamation processes in a rare-earth mining area using multisource time-series satellite images is described. In this study, the normalized difference vegetation index (NDVI) is used to evaluate the mining impact. Regression analysis is conducted to relate the HJ-1B CCD and Landsat 5/8 data to reduce the NDVI error related to sensor differences between different datasets. The analysis method of NDVI trajectory data of ground objects is proposed, and areas of environmental disturbance caused by rare-earth mining are identified. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and a temporal trajectory segmentation method is established based on the vegetation changes in different disturbance stages. The temporal trajectory of the rare-earth disturbance points is segmented to extract features in each stage to obtain the disturbance year, recovery year, and recovery cycle and evaluate the vegetation recovery after rare-earth mining disturbance. We applied the method to a stack of 20 multitemporal images from 2000 to 2019 to analyze vegetation disturbance due to rare-earth mining and vegetation recovery in the upper reaches of the Guangdong-Hong Kong-Macao Greater Bay Area, China. The results show the following. (1) Mining industry in the study area experienced rapid expansion before 2008, but growth slowed since the policies implemented by the government since 2009 to restrict rare-earth mining. (2) The continuous influence to the land caused by rare-earth mining can last for decades; however, the reclamation activities shorten the recovery cycle of mining land from 5 to 3 years.

Identifiants

pubmed: 34291411
doi: 10.1007/s11356-021-15480-3
pii: 10.1007/s11356-021-15480-3
doi:

Substances chimiques

Metals, Rare Earth 0
Soil 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

69112-69128

Subventions

Organisme : Education Department of Jiangxi Province
ID : No.JC20119
Organisme : Natural Science Foundation of Jiangxi Province
ID : 20181BAB206018

Informations de copyright

© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Références

Brom J, Nedbal V, Procházka J, Pecharová E (2012) Changes in vegetation cover, moisture properties and surface temperature of a brown coal dump from 1984 to 2009 using satellite data analysis. Ecol Eng 43:45–52. https://doi.org/10.1016/j.ecoleng.2011.03.001
doi: 10.1016/j.ecoleng.2011.03.001
Forkuor G, Ullmann T, Griesbeck M (2020) Mapping and monitoring small-scale mining activities in Ghana using sentinel-1 time series (2015–2019). Remote Sens 12:911. https://doi.org/10.3390/rs12060911
doi: 10.3390/rs12060911
Giuliani G, Dao H, De Bono A, Chatenoux B, Allenbach K, De Laborie P, Rodila D, Alexandris N, Peduzzi P (2017) Live monitoring of earth surface (LiMES): a framework for monitoring environmental changes from earth observations. Remote Sens Environ 202:222–233. https://doi.org/10.1016/j.rse.2017.05.040
doi: 10.1016/j.rse.2017.05.040
Gwenzi W, Mangori L, Danha C, Chaukura N, Dunjana N, Sanganyado E (2018) Sources, behaviour, and environmental and human health risks of high-technology rare earth elements as emerging contaminants. Sci Total Environ 636:299–313. https://doi.org/10.1016/j.scitotenv.2018.04.235
doi: 10.1016/j.scitotenv.2018.04.235
Jin S-L, Huang Y-z, Hu Y, Qiao M, Wang XL, Wang F, Li J, Xiang M, Xu F (2014) Rare earth elements content and health risk assessment of soil and crops in typical rare earth mine area in Jiangxi Province. Acta Sci Circumst 34:3084–3093
Jing L, Yan X, Cao Z, Yang Z, Liang J, Ma T, Liu Q (2020) Identification of successional trajectory over 30 Years and evaluation of reclamation effect in coal waste dumps of surface coal mine. J Clean Prod 269:122161. https://doi.org/10.1016/j.jclepro.2020.122161
doi: 10.1016/j.jclepro.2020.122161
Lassalle G, Fabre S, Credoz A, Dubucq D, Elger A (2020) Monitoring oil contamination in vegetated areas with optical remote sensing: a comprehensive review. J Hazard Mater 393:122427. https://doi.org/10.1016/j.jhazmat.2020.122427
doi: 10.1016/j.jhazmat.2020.122427
LeClerc E, Wiersma YF (2017) Assessing post-industrial land cover change at the Pine Point Mine, NWT, Canada using multi-temporal Landsat analysis and landscape metrics. Environ Monit Assess 189:185
doi: 10.1007/s10661-017-5893-7
Li J, Zipper CE, Donovan PF, Wynne RH, Oliphant AJ (2015a) Reconstructing disturbance history for an intensively mined region by time-series analysis of Landsat imagery. Environ Monit Assess 187:557. https://doi.org/10.1007/s10661-015-4766-1
doi: 10.1007/s10661-015-4766-1
Li N, Yan CZ, Xie JL (2015b) Remote sensing monitoring recent rapid increase of coal mining activity of an important energy base in northern China, a case study of Mu Us Sandy Land. Resour Conserv Recycl 94:129–135. https://doi.org/10.1016/j.resconrec.2014.11.010
doi: 10.1016/j.resconrec.2014.11.010
Li H, Wu L, Liu X  (2014) Change detection of ground-surface environment in rare earth mining area based on multi-temporal remote sensing: a case in Lingbei rare  earth minging area. J China Inst Min Technol 43(6):1087–1094. https://doi.org/10.13247/j.cnki.jcumt.000260
Li H, Lei J, Wu J (2018) Analysis of land damage and recovery process in rare earth mining area based on multi-source sequential NDVI. Trans Chin Soc Agric Eng 34(1):232–240
Li H, Li Q, Wang L, Lei J (2019) The Multi-source Image NDVI Interactive Corrective Method for Long-term Remote Sensing Monitoring of Rare Earth Mining Area. J Appl Eng Sci 22:549–556. https://doi.org/10.6180/jase.201909_22(3).0016
doi: 10.6180/jase.201909_22(3).0016
Li H, Feng X, Qin L (2020) Remote sensing monitoring of land damage and restoration in rare earth mining areas in 6 counties in southern Jiangxi based on multisource sequential images. J Environ Manag 267:110653. https://doi.org/10.1016/j.jenvman.2020.110653
doi: 10.1016/j.jenvman.2020.110653
Löw M, Koukal T (2020) Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 Time Series in Austria. Remote Sens 12:4191. https://doi.org/10.3390/rs12244191
doi: 10.3390/rs12244191
Mahmoud AW, Soliman AM, Ellah AHA, El-Sagheer RM (2013) Markov chain Monte Carlo to study the estimation of the coefficient of variation. IJCA 77:31–37. https://doi.org/10.5120/13384-1000
doi: 10.5120/13384-1000
MoLR (Ministry of Land and Resources of the People’s Republic of China) (2009) Regulations on the protection of the geological environment of mines. 3. Available online: http://www.gov.cn/flfg/2009-03/05/content_1251130.htm (accessed on 3 March 2020)
Pijl A, Quarella E, Vogel TA, D’Agostino V, Tarolli P (2021) Remote sensing vs. field-based monitoring of agricultural terrace degradation. Int Soil Water Conserv Res 9:1–10. https://doi.org/10.1016/j.iswcr.2020.09.001
doi: 10.1016/j.iswcr.2020.09.001
Sen S, Zipper CE, Wynne RH, Donovan PF (2012) Identifying revegetated mines as disturbance/recovery trajectories using an interannual Landsat chronosequence. Photogramm Eng Remote Sens 78:223–235. https://doi.org/10.14358/PERS.78.3.223
doi: 10.14358/PERS.78.3.223
Swab RM, Lorenz N, Byrd S, Dick R (2017) Native vegetation in reclamation: improving habitat and ecosystem function through using prairie species in mine land reclamation. Ecol Eng 108:525–536. https://doi.org/10.1016/j.ecoleng.2017.05.012
doi: 10.1016/j.ecoleng.2017.05.012
Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010) Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ 114:106–115. https://doi.org/10.1016/j.rse.2009.08.014
doi: 10.1016/j.rse.2009.08.014
Wang C, Jia D, Lei S, Mu S (2017) Analysis of dynamic characteristics of vegetation in semi-arid mining area based on time trajectory segmentation algorithm. J China Coal Soc 42:477–483
Wang J, Guo M, Liu M, Wei X (2020a) Long-term outlook for global rare earth production. Res Policy 65:101569. https://doi.org/10.1016/j.resourpol.2019.101569
doi: 10.1016/j.resourpol.2019.101569
Wang Z, Lechner AM, Yang Y, Baumgartl T, Wu J (2020b) Mapping the cumulative impacts of long-term mining disturbance and progressive rehabilitation on ecosystem services. Sci Total Environ 717:137214. https://doi.org/10.1016/j.scitotenv.2020.137214
doi: 10.1016/j.scitotenv.2020.137214
Wu B., Zhao Y, Fang C (2019) Detection of Spatiotemporal Changes of Surface Mining Area in Changting County Southeast China. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium 2019:1606–1609. https://doi.org/10.1109/IGARSS.2019.8900513
Wübbeke J (2013) Rare earth elements in China: policies and narratives of reinventing an industry. Res Policy 38:384–394. https://doi.org/10.1016/j.resourpol.2013.05.005
doi: 10.1016/j.resourpol.2013.05.005
Xiao W, Deng X, He T, Chen W (2020) Mapping annual land disturbance and reclamation in a surface coal mining region using Google Earth engine and the LandTrendr algorithm: a case study of the Shengli coalfield in inner Mongolia, China. Remote Sens 12:1612. https://doi.org/10.3390/rs12101612
doi: 10.3390/rs12101612
Xie L, Wu W, Huang X, Ou P, Lin Z, Zhiling W, Song Y, Lang T, Huangfu W, Zhang Y, Zhou X, Fu X, Li J, Jiang J, Zhang M, Zhang Z, Qin Y, Peng S, Shao C, Bai Y (2020) Mining and restoration monitoring of rare earth element (REE) exploitation by new remote sensing indicators in Southern Jiangxi, China. Remote Sens 12:3558. https://doi.org/10.3390/rs12213558
doi: 10.3390/rs12213558
Yang XJ, Lin A, Li X-L, Wu Y, Zhou W, Chen Z (2013) China’s ion-adsorption rare earth resources, mining consequences and preservation. Environ Dev 8:131–136. https://doi.org/10.1016/j.envdev.2013.03.006
doi: 10.1016/j.envdev.2013.03.006
Yang Z, Li J, Zipper CE, Shen Y, Miao H, Donovan PF (2018) Identification of the disturbance and trajectory types in mining areas using multitemporal remote sensing images. Sci Total Environ 644:916–927. https://doi.org/10.1016/j.scitotenv.2018.06.341
doi: 10.1016/j.scitotenv.2018.06.341
Yin J, Zuo X (2008) Analysis of global rare earth market in 2007 and prospect in 2008. World Nonferrous Metals 4(03):47–52
Yuan Y, Zhao Z, Niu S, Li X, Wang Y, Bai Z (2018) Reclamation promotes the succession of the soil and vegetation in opencast coal mine: a case study from Robinia pseudoacacia reclaimed forests, Pingshuo mine, China. Catena 165:72–79. https://doi.org/10.1016/j.catena.2018.01.025
doi: 10.1016/j.catena.2018.01.025
Zhang M, Wang J, Li S (2019) Tempo-spatial changes and main anthropogenic influence factors of vegetation fractional coverage in a large-scale opencast coal mine area from 1992 to 2015. J Clean Prod 232:940–952. https://doi.org/10.1016/j.jclepro.2019.05.334
doi: 10.1016/j.jclepro.2019.05.334

Auteurs

Zhenbang Wu (Z)

School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China.

Hengkai Li (H)

School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China. giskai@126.com.

Yuqing Wang (Y)

School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China.

Articles similaires

Populus Soil Microbiology Soil Microbiota Fungi
Humans Neoplasms Male Female Middle Aged
Humans Male Female Aged Middle Aged

Classifications MeSH