Mapping Sensitive Vegetation Communities in Mining Eco-space using UAV-LiDAR
Article
Article Title | Mapping Sensitive Vegetation Communities in Mining Eco-space using UAV-LiDAR |
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ERA Journal ID | 210637 |
Article Category | Article |
Authors | Banerjee, Bikram Pratap and Raval, Simit |
Journal Title | International Journal of Coal Science and Technology |
Journal Citation | 9 (1), pp. 1-16 |
Article Number | 40 |
Number of Pages | 16 |
Year | 2022 |
Place of Publication | China |
ISSN | 2095-8293 |
2198-7823 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s40789-022-00509-w |
Web Address (URL) | https://link.springer.com/article/10.1007/s40789-022-00509-w |
Abstract | Near earth sensing from uncrewed aerial vehicles or UAVs has emerged as a potential approach for fine-scale environmental monitoring. These systems provide a cost-effective and repeatable means to acquire remotely sensed images in unprecedented spatial detail and a high signal-to-noise ratio. It is increasingly possible to obtain both physiochemical and structural insights into the environment using state-of-art light detection and ranging (LiDAR) sensors integrated onto UAVs. Monitoring sensitive environments, such as swamp vegetation in longwall mining areas, is essential yet challenging due to their inherent complexities. Current practices for monitoring these remote and challenging environments are primarily ground-based. This is partly due to an absent framework and challenges of using UAV-based sensor systems in monitoring such sensitive environments. This research addresses the related challenges in developing a LiDAR system, including a workflow for mapping and potentially monitoring highly heterogeneous and complex environments. This involves amalgamating several design components, including hardware integration, calibration of sensors, mission planning, and developing a processing chain to generate usable datasets. It also includes the creation of new methodologies and processing routines to establish a pipeline for efficient data retrieval and generation of usable products. The designed systems and methods were applied to a peat swamp environment to obtain an accurate geo-spatialised LiDAR point cloud. Performance of the LiDAR data was tested against ground-based measurements on various aspects, including visual assessment for generation LiDAR metrices maps, canopy height model, and fine-scale mapping. |
Keywords | Upland swamps; Mine surveying; Monitoring; Environment sustainability; Drones; laser scanning |
ANZSRC Field of Research 2020 | 401304. Photogrammetry and remote sensing |
401399. Geomatic engineering not elsewhere classified | |
410402. Environmental assessment and monitoring | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Agriculture Victoria |
University of New South Wales |
https://research.usq.edu.au/item/z3084/mapping-sensitive-vegetation-communities-in-mining-eco-space-using-uav-lidar
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