气候变化领域集成服务门户(CCPortal): Rapid assessment of mine rehabilitation areas with airborne LiDAR and deep learning: bauxite strip mining in Queensland, Australia

CCPortal

DOI	10.1080/10106049.2022.2048902
	Rapid assessment of mine rehabilitation areas with airborne LiDAR and deep learning: bauxite strip mining in Queensland, Australia
	Murray, Xavier; Apan, Armando; Deo, Ravinesh; Maraseni, Tek
通讯作者	Murray, X (通讯作者)，Univ Southern Queensland, Sch Civil Engn & Surveying, Toowoomba, Qld, Australia.
发表日期	2020
ISSN	1010-6049
EISSN	1752-0762
英文摘要	Rehabilitation of disturbed sites is a complex process requiring constant monitoring and assessment, with current methods being somewhat obsolete, manual, and rather fraught with risk in terms of beneficial outcomes of the evaluation. This study challenges the status quo of manual assessments and build a new methodology to recognise a clear need for a quasi-automated and reliable remote sensing method to achieve rapid mine rehabilitation monitoring. The aim of this study was to innovate and develop an ensemble analytic methodology for the use of airborne LiDAR, in conjunction with deep learning, to rapidly evaluate the rehabilitation status of post-mined areas. Airborne LiDAR datasets, obtained via an Optech ORION m/c 200 sensor, were collected over bauxite mining operations on the western coast of Cape York Peninsula, Queensland, Australia. These datasets were processed into a 3 D point cloud using the Esri ArcGIS Pro platform. An ensemble analytical approach was developed, leading with a Convolutional Neural Network (CNN) algorithm, which was trained to recognise, then classify images of the reference site (natural ecosystems) and active rehabilitation areas. The result of this classification was then leveraged, training in turn a support vector machine (SVM) algorithm which assessed the entire LiDAR collection, resulting in a predictive surface of rehabilitation status. This surface, as the final analytical product, was assessed for validity using ground-truth plot data and an ordinary least squares regression model, as well as comparison to subject matter expert evaluation through a Delphi expert panel assessment. The results indicate that the predictive surface, as the final analytical product, had statistically significant correlation coefficient with multiple attributes (or predictor) that correlated with the rehabilitation status interpretation. These predictor variables were primarily structural in nature and included the canopy height, sub-canopy height, key species as a proportion of canopy, herbaceous ground cover percentage, litter ground cover percentage, and distance from haul road (p < 0.05). A Delphi expert collaboration comprised of ten environmental science subject matter experts and 600 observations, indicated a high precision (r = 0.89 at p < 0.05) of the prediction surface when compared with a subject expert panels assessment, as well as a high Total Deviation Index (TDI) of 16% (p < 0.05). This indicated positive agreement between the panel's assessment and the predictive surface. This study suggests that airborne LiDAR and deep learning ensemble analytics can be used as a rapid means to estimate the rehabilitation status of disturbed sites.
关键词	CONCORDANCE CORRELATION-COEFFICIENT CONVOLUTIONAL NEURAL-NETWORKS TOTAL DEVIATION INDEX VEGETATION STRUCTURE DELPHI METHOD RESTORATION CLASSIFICATION AGREEMENT CNN PERFORMANCE
英文关键词	Mining; bauxite; rehabilitation; LiDAR; CNN; deep learning; remote sensing
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000777910100001
来源期刊	GEOCARTO INTERNATIONAL
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254894
作者单位	[Murray, Xavier; Apan, Armando] Univ Southern Queensland, Sch Civil Engn & Surveying, Toowoomba, Qld, Australia; [Apan, Armando] Univ Philippines Diliman, Inst Environm Sci & Meteorol, Quezon City, Philippines; [Deo, Ravinesh] Univ Southern Queensland, Sch Sci, Toowoomba, Qld, Australia; [Maraseni, Tek] Univ Southern Queensland, Inst Agr & Environm, Toowoomba, Qld, Australia; [Maraseni, Tek] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou, Peoples R China
推荐引用方式 GB/T 7714	Murray, Xavier,Apan, Armando,Deo, Ravinesh,et al. Rapid assessment of mine rehabilitation areas with airborne LiDAR and deep learning: bauxite strip mining in Queensland, Australia[J]. 中国科学院西北生态环境资源研究院,2020.
APA	Murray, Xavier,Apan, Armando,Deo, Ravinesh,&Maraseni, Tek.(2020).Rapid assessment of mine rehabilitation areas with airborne LiDAR and deep learning: bauxite strip mining in Queensland, Australia.GEOCARTO INTERNATIONAL.
MLA	Murray, Xavier,et al."Rapid assessment of mine rehabilitation areas with airborne LiDAR and deep learning: bauxite strip mining in Queensland, Australia".GEOCARTO INTERNATIONAL (2020).