Development of UAV-based hyperspectral and LiDAR systems for environmental monitoring

Near earth sensing from unmanned 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 high signal-to-noise ratio. It is becoming increasingly possible to obtain both physiochemical and structural insights of the environment using state-of-art lightweight hyperspectral and light detection and ranging (LiDAR) sensors integrated onto UAVs. Although these systems are becoming increasingly popular in agriculture and forestry research, their application in other areas such as monitoring of complex ecosystem is still unproven. Monitoring of sensitive environments, such as swamp vegetation in longwall mining areas is important, yet challenging due to their inherent complexities. Current practices for monitoring these remote and difficult 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 the development of a set of UAV-based hyperspectral and LiDAR systems including a workflow for mapping and potentially monitoring highly heterogeneous and complex environments. This involves the amalgamation of several design components, which include hardware integration, calibration of sensors, mission planning, and designing of a processing chain to generate usable datasets. It also includes the creation of new algorithms and processing routines to establish a pipeline for efficient data retrieval and generation of usable products. Several types of data process analyses were performed at distinct intermediate processing stages to ensure the quality of data propagation throughout the workflow. The designed systems and methods were applied on a peat swamp environment to obtain accurate geo-spatialised hyperspectral raster and LiDAR point cloud. Performance of the UAV-based hyperspectral and LiDAR data was tested against ground-based measurements on various aspects including visual assessment for fine-scale mapping, generation of parameters such as vegetation indices and LiDAR metrices maps, leaf area index, canopy height model, and classification. Overall, this study provides a quantifiable basis to assess the condition of vegetation in sensitive environments through the development of integrated UAV-hyperspectral and LiDAR data acquisition systems and their associated processing protocols