Mapping sensitive vegetation communities in mining eco-space using UAV-LiDAR
Bikram Pratap Banerjee1
Simit Raval2
1.Agriculture Victoria,Grains Innovation Park,110 Natimuk Road,3400 Horsham,VIC,Australia;School of Minerals and Energy Resources Engineering,University of New South Wales,2052 Sydney,NSW,Australia2.School of Minerals and Energy Resources Engineering,University of New South Wales,2052 Sydney,NSW,Australia
摘要：Near earth sensing from uncrewed aerial vehicles or UAVs has emerged as a potential approach for fine-scale environ-mental 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 challeng-ing 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 f or m apping a nd p otentially m onitoring h ighly h eterogeneous a nd c omplex e nvironments.This involves amalgamating several design components,including hardware integration,calibration of sensors,mission plan-ning,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.
机标关键词：vegetationmappingcommunitieseco-spaceminingsensitiveuav-lidarusing
论文发表日期：2022-06-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：16( 185-200 )
英文信息