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 light detection and ranging (LiDAR) sensors integrated onto UAVs. 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 LiDAR system 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 methodologies and processing routines to establish a pipeline for efficient data retrieval and generation of usable products. The designed systems and methods were applied on a peat swamp environment to obtain 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.