1 |
Gallwey J, Robiati C, Coggan J, et al. A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana: Applying deep learning to shallow mining[J]. Remote Sensing of Environment, 2020, 248: 111970-111984. DOI: 10.1016/j.rse.2020.111970 .
doi: 10.1016/j.rse.2020.111970
|
2 |
Johansen K, Erskine P, Mccabe M. Using unmanned aerial vehicles to assess the rehabilitation performance of open cut coal mines[J]. Journal of Cleaner Production, 2019, 209: 819-833. DOI: 10.1016/j.jclepro.2018.10.287 .
doi: 10.1016/j.jclepro.2018.10.287
|
3 |
Yang Jinzhong, Nie Hongfeng, Jing Qingqing. Preliminary analysis of mine geo-environment status and existing problems in China[J]. Remote Sensing for Land and Resources, 2017, 29(2): 1-7.
|
3 |
杨金中, 聂洪峰, 荆青青. 初论全国矿山地质环境现状与存在问题[J]. 国土资源遥感, 2017, 29(2): 1-7.
|
4 |
Chen W, Li X, He H, et al. A review of fine-scale land use and land cover classification in open-pit mining areas by remote sensing techniques[J]. Remote Sensing, 2017, 10(2): 10010015-10010033. DOI: 10.3390/rs10010015 .
doi: 10.3390/rs10010015
|
5 |
Li L, Zhang R, Sun J, et al. Monitoring and prediction of dust concentration in an open-pit mine using a deep-learning algorithm[J]. Journal of Environmental Health Science and Engineering, 2021, 19(1): 401-414. DOI: 10.1007/s40201-021-00613-0 .
doi: 10.1007/s40201-021-00613-0
|
6 |
Li X, Chen W, Cheng X, et al. A comparison of machine learning algorithms for mapping of complex surface-mined and agricultural landscapes using ZiYuan-3 stereo satellite imagery[J]. Remote Sensing, 2016, 8(6): 8060514-8060540. DOI: 10.3390/rs8060514 .
doi: 10.3390/rs8060514
|
7 |
Xie X, Xu C, Wen Y, et al. Monitoring groundwater storage changes in the Loess Plateau using GRACE satellite gravity data, hydrological models and coal mining data[J]. Remote Sensing, 2018, 10(4): 605-623. DOI: 10.3390/rs10040605 .
doi: 10.3390/rs10040605
|
8 |
Hu X, Oommen T, Lu Z, et al. Consolidation settlement of salt lake county tailings impoundment revealed by time-series InSAR observations from multiple radar satellites[J]. Remote Sensing of Environment, 2017, 202: 199-209. DOI: 10.1016/j.rse.2017.05.023 .
doi: 10.1016/j.rse.2017.05.023
|
9 |
Rauhala A, Tuomela A, Davids C, et al. UAV remote sensing surveillance of a mine tailings impoundment in sub-arctic conditions[J]. Remote Sensing, 2017, 9(12): 1318-1331. DOI: 10.3390/rs9121318 .
doi: 10.3390/rs9121318
|
10 |
Gong B, Shu C, Han S, et al. Mine vegetation identification via ecological monitoring and deep belief network[J]. Plants, 2021, 10(6): 10061099-10061120. DOI: 10.3390/plants10061099 .
doi: 10.3390/plants10061099
|
11 |
Wang W, Liu R, Gan F, et al. Monitoring and evaluating restoration vegetation status in mine region using remote sensing data: Case study in Inner Mongolia, China[J]. Remote Sensing,2021,13(7):13071350-13071372. DOI: 10.3390/rs 13071350 .
doi: 10.3390/rs 13071350
|
12 |
Yang C, Wu G, Ding K, et al. Improving land use/land cover classification by integrating pixel unmixing and decision tree methods[J]. Remote Sensing, 2017, 9(12): 9121222-9121237. DOI: 10.3390/rs9121222 .
doi: 10.3390/rs9121222
|
13 |
Chen W, Li X, Wang L. Fine land cover classification in an open pit mining area using optimized support vector machine and WorldView-3 imagery[J].Remote Sensing,2019,12(1): 12010082-12010097. DOI: 10.3390/rs12010082 .
doi: 10.3390/rs12010082
|
14 |
Hu Naixun, Chen Tao, Zhen Na, et al. Object-oriented open pit extraction based on convolutional neural network[J]. Remote Sensing Technology and Application, 2021, 36(2): 265-274.
|
14 |
胡乃勋, 陈涛, 甄娜, 等. 基于卷积神经网络的面向对象露天采场提取[J]. 遥感技术与应用, 2021, 36(2): 265-274.
|
15 |
Pour A, Zoheir B, Pradhan B, et al. Editorial for the special issue: multispectral and hyperspectral remote sensing data for mineral exploration and environmental monitoring of mined areas[J]. Remote Sensing, 2021, 13(3): 519-524. DOI: 10.3390/rs13030519 .
doi: 10.3390/rs13030519
|
16 |
Wempen J. Application of DInSAR for short period monitoring of initial subsidence due to longwall mining in the mountain west United States[J]. International Journal of Mining Science and Technology, 2020, 30(1): 33-37. DOI: 10.1016/j.ijmst.2019.12.011 .
doi: 10.1016/j.ijmst.2019.12.011
|
17 |
Wempen J, Mccarter M. Comparison of L-band and X-band differential interferometric synthetic aperture radar for mine subsidence monitoring in central Utah[J]. International Journal of Mining Science and Technology, 2017, 27(1): 159-163. DOI: 10.1016/j.ijmst.2016.11.012 .
doi: 10.1016/j.ijmst.2016.11.012
|
18 |
Gee D, Bateson L, Grebby S, et al. Modelling groundwater rebound in recently abandoned coalfields using DInSAR[J]. Remote Sensing of Environment, 2020, 249: 112021-112039. DOI: 10.1016/j.rse.2020.112021 .
doi: 10.1016/j.rse.2020.112021
|
19 |
Najafpour N, Afshin H, Firoozabadi B. The 20-22 february 2016 mineral dust event in Tehran, Iran: Numerical modeling, remote sensing, and in situ measurements[J]. Journal of Geophysical Research: Atmospheres, 2018, 123(10): 5038-5058. DOI: 10.1029/2017jd027593 .
doi: 10.1029/2017jd027593
|
20 |
Xiao D, Yin L, Fu Y. Open-Pit mine road extraction from high-resolution remote sensing images using RATT-UNet[J]. IEEE Geoscience and Remote Sensing Letters, 2021: 1-5. DOI: 10.1109/LGRS.2021.3065148 .
doi: 10.1109/LGRS.2021.3065148
|
21 |
He L, Wu L, Liu S, et al. Mapping two-dimensional deformation field time-series of large slope by coupling DInSAR-SBAS with MAI-SBAS[J]. Remote Sensing, 2015, 7(9): 12440-12458. DOI: 10.3390/rs70912440 .
doi: 10.3390/rs70912440
|
22 |
Maulik U, Chakraborty D. Remote sensing image classification: a survey of support-vector-machine-based advanced techniques[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(1): 33-52. DOI: 10.1109/mgrs.2016.2641240 .
doi: 10.1109/mgrs.2016.2641240
|
23 |
Rodriguez V Sanchez M, Chica M, et al. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines[J]. Ore Geology Reviews, 2015, 71: 804-818. DOI: 10.1016/j.oregeorev.2015.01.001 .
doi: 10.1016/j.oregeorev.2015.01.001
|
24 |
Ou D, Tan K, Du Q, et al. Decision fusion of D-InSAR and pixel offset tracking for coal mining deformation monitoring[J]. Remote Sensing, 2018, 10(7): 10071055-10071073. DOI: 10.3390/rs10071055 .
doi: 10.3390/rs10071055
|
25 |
Zhu Jianjun, Yang Zefa, Li Zhiwei. Recent progress in retrieving and predicting mining-induced 3D displacements using InSAR[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(2): 135-144.
|
25 |
朱建军, 杨泽发, 李志伟. InSAR矿区地表三维形变监测与预计研究进展[J]. 测绘学报, 2019, 48(2): 135-144.
|
26 |
Yang Z, Li Z, Zhu J, et al. Use of SAR/InSAR in mining deformation monitoring, parameter inversion, and forward predictions: A review[J]. IEEE Geoscience and Remote Sensing Magazine,2020,8(1):71-90. DOI:10.1109/mgrs. 2019. 2954824 .
doi: 10.1109/mgrs. 2019. 2954824
|
27 |
Ren H, Zhao Y, Xiao W, et al. A review of UAV monitoring in mining areas: Current status and future perspectives[J]. International Journal of Coal Science & Technology, 2019, 6(3): 320-333. DOI: 10.1007/s40789-019-00264-5 .
doi: 10.1007/s40789-019-00264-5
|
28 |
Bao N, Li W, Gu X, et al. Biomass Estimation for semiarid vegetation and mine rehabilitation using Worldview-3 and Sentinel-1 SAR Imagery[J]. Remote Sensing, 2019, 11(23): 2855-2874. DOI: 10.3390/rs11232855 .
doi: 10.3390/rs11232855
|
29 |
Zhang C, Ren H, Dai X, et al. Spectral characteristics of copper-stressed vegetation leaves and further understanding of the copper stress vegetation index[J]. International Journal of Remote Sensing, 2019, 40(12): 4473-4488. DOI: 10.1080/01431161.2018.1563842 .
doi: 10.1080/01431161.2018.1563842
|
30 |
Zeng B, Zhang Z, Yang M. Risk assessment of groundwater with multi-source pollution by a long-term monitoring programme for a large mining area[J]. International Biodeterioration & Biodegradation, 2018, 128: 100-108. DOI: 10.1016/j.ibiod.2017.01.002 .
doi: 10.1016/j.ibiod.2017.01.002
|
31 |
Yuan G, Wang Y, Zhao F, et al. Accuracy assessment and scale effect investigation of UAV thermography for underground coal fire surface temperature monitoring[J]. International Journal of Applied Earth Observation and Geoinformation,2021,102:102426-102442. DOI: 10.1016/j.jag.2021. 102426 .
doi: 10.1016/j.jag.2021. 102426
|
32 |
Yuan Q, Shen H, Li T, et al. Deep learning in environmental remote sensing: Achievements and challenges[J]. Remote Sensing of Environment, 2020, 241: 111716-111740. DOI: 10.1016/j.rse.2020.111716 .
doi: 10.1016/j.rse.2020.111716
|
33 |
Liu J, Liu R, Zhang Z, et al. A Bayesian Network-based risk dynamic simulation model for accidental water pollution discharge of mine tailings ponds at watershed-scale[J]. Journal of Environmental Management, 2019, 246: 821-831. DOI: 10.1016/j.jenvman.2019.06.060 .
doi: 10.1016/j.jenvman.2019.06.060
|
34 |
Wu Q, Song C, Liu K, et al. Integration of TanDEM-X and SRTM DEMs and spectral imagery to improve the large-scale detection of opencast mining areas[J]. Remote Sensing, 2020, 12(9): 1451-1470. DOI: 10.3390/rs12091451 .
doi: 10.3390/rs12091451
|
35 |
Wang J, Li L, Yu H. Application of domestic High-Resolution satellite data in remote sensing geological survey of the metallogenic belt in Zhejiang Province[J]. Sustainability, 2022, 14(8): 4397-4416. DOI: 10.3390/su14084397 .
doi: 10.3390/su14084397
|
36 |
Balaniuk R, Isupova O, Reece S. Mining and tailings dam detection in satellite imagery using deep learning[J]. Sensors, 2020, 20(23): 6936-6968. DOI: 10.3390/s20236936 .
doi: 10.3390/s20236936
|
37 |
Cai Xiang, Li Qi, Luo Yan, et al. Surface features extraction of mining area image based on object-oriented and deep-learning method[J].Remote Sensing for Land and Resources, 2021, 33(1): 63-71.
|
37 |
蔡祥, 李琦, 罗言, 等. 面向对象结合深度学习方法的矿区地物提取[J]. 国土资源遥感, 2021, 33(1): 63-71.
|
38 |
Lyu J, Hu Y, Ren S, et al. Extracting the tailings ponds from high spatial resolution remote sensing images by integrating a deep learning-based model[J]. Remote Sensing, 2021, 13(4): 743-759. DOI: 10.3390/rs13040743 .
doi: 10.3390/rs13040743
|
39 |
Xie H, Pan Y, Luan J, et al. Open-pit mining area segmentation of remote sensing images based on DUSegNet[J]. Journal of the Indian Society of Remote Sensing, 2021(12): 1-14. DOI: 10.1007/s12524-021-01312-x .
doi: 10.1007/s12524-021-01312-x
|
40 |
Sun T, Li H, Wu K, et al. Data-Driven predictive modelling of mineral prospectivity using machine learning and deep learning methods: A case study from Southern Jiangxi Province, China[J]. Minerals, 2020, 10(2): 102-129. DOI: 10.3390/min10020102 .
doi: 10.3390/min10020102
|
41 |
Camalan S, Cui K, Pauca V P, et al. Change detection of amazonian alluvial gold mining using deep learning and Sentinel-2 imagery[J]. Remote Sensing, 2022, 14(7): 1746-1768. DOI: 10.3390/rs14071746 .
doi: 10.3390/rs14071746
|
42 |
Carlà T, Farina P, Intrieri E, et al. Integration of ground-based radar and satellite InSAR data for the analysis of an unexpected slope failure in an open-pit mine[J]. Engineering Geology,2018,235:39-52. DOI:10.1016/j.enggeo.2018.01.021 .
doi: 10.1016/j.enggeo.2018.01.021
|
43 |
Zhao Y, Sun B, Liu S, et al. Identification of mining induced ground fissures using UAV and infrared thermal imager: Temperature variation and fissure evolution[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 180: 45-64. DOI: 10.1016/j.isprsjprs.2021.08.005 .
doi: 10.1016/j.isprsjprs.2021.08.005
|
44 |
Battulwar R, Zare-Naghadehi M, Emami E, et al. A state-of-the-art review of automated extraction of rock mass discontinuity characteristics using three-dimensional surface models[J].Journal of Rock Mechanics and Geotechnical Engineering,2021,13(4):920-936. DOI:10.1016/j.jrmge.2021.01.008 .
doi: 10.1016/j.jrmge.2021.01.008
|
45 |
Zhang F, Hu Z, Fu Y, et al. A new identification method for surface cracks from UAV images based on machine learning in coal mining areas[J]. Remote Sensing, 2020, 12(10): 1571-1590. DOI: 10.3390/rs12101571 .
doi: 10.3390/rs12101571
|
46 |
Yang K, Hu Z, Liang Y, et al. Automated extraction of ground fissures due to coal mining subsidence based on UAV photogrammetry[J]. Remote Sensing, 2022, 14(5): 1071-1090. DOI: 10.3390/rs14051071 .
doi: 10.3390/rs14051071
|
47 |
Liu Shanjun, Wu Lixin, Mao Yachun, et al. Spaceborne-airborne-ground collaborated intelligent monitoring on openpit slope and its typical applications[J]. Journal of China Coal Society, 2020, 45(6): 2265-2276.
|
47 |
刘善军, 吴立新, 毛亚纯, 等. 天—空—地协同的露天矿边坡智能监测技术及典型应用[J]. 煤炭学报, 2020, 45(6): 2265-2276.
|
48 |
Necsoiu M, Walter R. Detection of uranium mill tailings settlement using satellite-based radar interferometry[J]. Engineering Geology, 2015, 197: 267-277. DOI: 10.1016/j.enggeo.2015.09.002 .
doi: 10.1016/j.enggeo.2015.09.002
|
49 |
Wang G, Wu Q, Li P, et al. Mining subsidence prediction parameter inversion by combining GNSS and DInSAR deformation measurements[J]. IEEE Access, 2021, 9: 89043-89054. DOI: 10.1109/access.2021.3089820 .
doi: 10.1109/access.2021.3089820
|
50 |
Wu Z, Wang T, Wang Y, et al. Deep learning for the detection and phase unwrapping of mining-induced deformation in large-scale interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-18. DOI: 10.1109/tgrs.2021.3121907 .
doi: 10.1109/tgrs.2021.3121907
|
51 |
Yan Y, Li M, Dai L, et al. Construction of “Space-Sky-Ground” integrated collaborative monitoring framework for surface deformation in mining area[J]. Remote Sensing, 2022, 14(4): 840-866. DOI: 10.3390/rs14040840 .
doi: 10.3390/rs14040840
|
52 |
Liu Bin, Ge Daqing, Li Man, et al. Using ground-based InSAR to evaluate the stability of an open-pit slope under blasting operation[J].Journal of Remote Sensing,2018,22(Sup.1): 139-145.
|
52 |
刘斌, 葛大庆, 李曼, 等. 地基InSAR评估爆破作业对露天采矿边坡的稳定性影响[J]. 遥感学报, 2018, 22(): 139-145.
|
53 |
Carlà T, Farina P, Intrieri E, et al. On the monitoring and early-warning of brittle slope failures in hard rock masses: Examples from an open-pit mine[J]. Engineering Geology, 2017, 228: 71-81. DOI: 10.1016/j.enggeo.2017.08.007 .
doi: 10.1016/j.enggeo.2017.08.007
|
54 |
Carnec C, Massonnet D, King C. Two examples of the use of SAR interferometry on displacement fields of small spatial extent[J]. Geophysical Research Letters, 1996, 23(24): 3579-3582. DOI: 10.1029/96gl03042 .
doi: 10.1029/96gl03042
|
55 |
Bui X, Nguyen H, Choi Y, et al. Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm[J]. Scientific Reports, 2020, 10(1): 9939-9959. DOI: 10.1038/s41598-020-66904-y .
doi: 10.1038/s41598-020-66904-y
|
56 |
Du Z, Ge L, Ng A, et al. Risk assessment for tailings dams in Brumadinho of Brazil using InSAR time series approach[J]. Science of the Total Environment, 2020, 717: 137125-137137. DOI: 10.1016/j.scitotenv.2020.137125 .
doi: 10.1016/j.scitotenv.2020.137125
|
57 |
Entezari I, Rivard B, Vajihinejad V, et al. Monitoring tailings flocculation performance using hyperspectral imagery[J]. The Canadian Journal of Chemical Engineering, 2019, 97(9): 2465-2471. DOI: 10.1002/cjce.23493 .
doi: 10.1002/cjce.23493
|
58 |
Mishra R K, Pandey J K, Pandey J, et al. Detection and analysis of coal Fire in Jharia Coalfield (JCF) using Landsat remote sensing data[J]. Journal of the Indian Society of Remote Sensing, 2019, 48(2): 181-195. DOI: 10.1007/s12524-019-01067-6 .
doi: 10.1007/s12524-019-01067-6
|
59 |
Wei Lihui, Wan Dajuan, Bi Junping, et al. Dynamic monitoring and assessment of ecologic environment in Xikuangshan antimony mining area based on remote sensing[J]. Environmental Science & Technology, 2020, 43(6):230-236.
|
59 |
魏力辉, 万大娟, 毕军平, 等. 锡矿山锑矿区生态环境质量遥感动态监测与评价[J]. 环境科学与技术, 2020, 43(6):230-236.
|
60 |
Zhu D, Chen T, Zhen N, et al. Monitoring the effects of open-pit mining on the eco-environment using a moving window-based remote sensing ecological index[J]. Environmental Science and Pollution Research, 2020, 27(13): 15716-15728. DOI: 10.1007/s11356-020-08054-2 .
doi: 10.1007/s11356-020-08054-2
|
61 |
Xu W, Wang J, Zhang M, et al. Construction of landscape ecological network based on landscape ecological risk assessment in a large-scale opencast coal mine area[J]. Journal of Cleaner Production, 2021, 286: 125523-125534. DOI: 10.1016/j.jclepro.2020.125523 .
doi: 10.1016/j.jclepro.2020.125523
|
62 |
Hou X, Liu S, Zhao S, et al. The alpine meadow around the mining areas on the Qinghai-Tibetan Plateau will degenerate as a result of the change of dominant species under the disturbance of open-pit mining[J]. Environmental Pollution, 2019, 254(Pt2):113111-113125. DOI:10.1016/j.envpol. 2019. 113111 .
doi: 10.1016/j.envpol. 2019. 113111
|
63 |
Moudrý V, Gdulová K, Fogl M, et al. Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure: Prospects for monitoring hazards and restoration success[J]. Applied Geography, 2019, 104: 32-41. DOI: 10.1016/j.apgeog.2019.02.002 .
doi: 10.1016/j.apgeog.2019.02.002
|
64 |
Murray X, Apan A, Deo R, et al. Rapid assessment of mine rehabilitation areas with airborne LiDAR and deep learning: Bauxite strip mining in Queensland, Australia[J]. Geocarto International,2022:1-30. DOI:10.1080/10106049.2022. 2048902 .
doi: 10.1080/10106049.2022. 2048902
|
65 |
Ma D, Zhao S. Quantitative analysis of land subsidence and its effect on vegetation in Xishan coalfield of Shanxi Province[J]. ISPRS International Journal of Geo-Information, 2022, 11(3): 154-167. DOI: 10.3390/ijgi11030154 .
doi: 10.3390/ijgi11030154
|
66 |
Isgró M, Basallote M, Barbero L. Unmanned aerial system-based multispectral water quality monitoring in the Iberian Pyrite Belt (SW Spain)[J]. Mine Water and the Environment, 2022,41(1):30-41. DOI:10.1007/s10230-021-00837-4 .
doi: 10.1007/s10230-021-00837-4
|
67 |
Pyankov S, Maximovich N, Khayrulina E, et al. Monitoring acid mine Drainage’s effects on surface water in the Kizel coal basin with Sentinel-2 satellite images[J]. Mine Water and the Environment, 2021, 40(3): 606-621. DOI: 10.1007/s10230-021-00761-7 .
doi: 10.1007/s10230-021-00761-7
|
68 |
Zhu X, Ning Z, Cheng H, et al. A novel calculation method of subsidence waterlogging spatial information based on remote sensing techniques and surface subsidence prediction[J]. Journal of Cleaner Production, 2022, 335: 130366-130381. DOI: 10.1016/j.jclepro.2022.130366 .
doi: 10.1016/j.jclepro.2022.130366
|
69 |
Wang Y, Ma H, Wang J, et al. Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 257: 119739-119748. DOI: 10.1016/j.saa.2021.119739 .
doi: 10.1016/j.saa.2021.119739
|
70 |
Che H, Qi B, Zhao H, et al. Aerosol optical properties and direct radiative forcing based on measurements from the China Aerosol Remote Sensing Network (CARSNET) in Eastern China[J]. Atmospheric Chemistry and Physics, 2018, 18(1): 405-425. DOI: 10.5194/acp-18-405-2018 .
doi: 10.5194/acp-18-405-2018
|
71 |
Brinkman J, Johnson C. Effects of downwash from a 6-Rotor Unmanned Aerial Vehicle (UAV) on gas monitor concentrations[J]. Mining, Metallurgy & Exploration, 2021, 38(4): 1789-1800. DOI: 10.1007/s42461-021-00436-5 .
doi: 10.1007/s42461-021-00436-5
|