Mapping Sensitive Vegetation Communities in Mining Eco-space using UAV-LiDAR

Article


Banerjee, Bikram Pratap and Raval, Simit. 2022. "Mapping Sensitive Vegetation Communities in Mining Eco-space using UAV-LiDAR." International Journal of Coal Science and Technology. 9 (1), pp. 1-16. https://doi.org/10.1007/s40789-022-00509-w
Article Title

Mapping Sensitive Vegetation Communities in Mining Eco-space using UAV-LiDAR

ERA Journal ID210637
Article CategoryArticle
AuthorsBanerjee, Bikram Pratap and Raval, Simit
Journal TitleInternational Journal of Coal Science and Technology
Journal Citation9 (1), pp. 1-16
Article Number40
Number of Pages16
Year2022
Place of PublicationChina
ISSN2095-8293
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.

KeywordsUpland swamps; Mine surveying; Monitoring; Environment sustainability; Drones; laser scanning
ANZSRC Field of Research 2020401304. Photogrammetry and remote sensing
401399. Geomatic engineering not elsewhere classified
410402. Environmental assessment and monitoring
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Byline AffiliationsAgriculture Victoria
University of New South Wales
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