<p>LIDAR (light detection and ranging) data has proven to be a very useful tool for analyzing vegetation structure and above-ground biomass in forest ecosystems. This can support important current applications such as forest management, biodiversity research, carbon cycle assessment and other.<br />A large variety of LIDAR data that differ in terms of sensor and platform are currently available.<br />Traditionally, airborne LIDAR (see, e.g., <a class="theme markdown__link" href="https://openlidar.io/en/" target="_blank" rel="noreferrer">https://openlidar.io/en/</a>) is acquired with small aircraft to generate three-dimensional discrete-return point clouds (4-10 pts/m²). Drones have lately been used to carry similar sensors, resulting in much denser point clouds (>1000 pts/m²).<br />Space-borne sensors (e.g. <a class="theme markdown__link" href="https://gedi.umd.edu/" target="_blank" rel="noreferrer">GEDI</a> integrate reflected energy over a footprint and return a waveform, that gives insight into the density of an object. While not achieving the level of detail and accuracy of airborne LIDAR, their advantage is a near-global coverage.<br />In this study project, we will investigate (and potentially combine) LIDAR data from different sensors and platforms to assess their applicability for applications in forest analysis (e.g., detecting/monitoring forest disturbance, forest succession, assessment of forest biomass, ...).</p>
- Lehrende/r: Johannes Heisig
- Lehrende/r: Christian Knoth
- Lehrende/r: Edzer Pebesma