Leonard Hambrecht is a highly motivated researcher with a strong commitment to biodiversity conservation and a member of the Integrated Remote Sensing Studio, GitHub. He is researching the use of copter and fixed-wing drones for surveys of wildlife populations, a long-standing career goal. Specifically, his research aims to leverage the benefits of drones in combination with thermal imaging technology and AI-based image analysis to enhance and support existing aerial survey methods. As part of the TerraLuma research group, Leonard completed his PhD at the University of Tasmania, AU, in 2024, where he gained experience in drone-based remote sensing techniques, particularly lidar. Originally from Germany, he holds an M.Sc. in Wildlife Conservation & Drone Technology from Liverpool John Moores University, UK, and a B.Sc. in Wildlife Management from Van Hall University of Applied Sciences, NL.
From 2019-2024, Leonard was a member of the TerraLuma, where he had the privilege of contributing to lidar data collection for the documentary The Giants, highlighting the captivating story of Bob and the natural forests in Tasmania.
In addition, Leonard did volunteer as a data scientist at the respected NGO Sensing Clues. He actively supported their efforts to employ remote sensing products for environmental monitoring and the detection of human disturbance in Africa and Europe. Through his contributions, Leonard aims to contribute to the collective endeavour of preserving and protecting our planet's invaluable ecosystems.
Examining the current practicality and potential of copter and fixed-wing drones for wildlife surveys, with comparisons of currently employed survey methods. The project aims to demonstrate the practicality and potential of drones through structured stages. These include assessing the statistical validity of drone imagery for detecting moose in Alberta using historical survey data, exploring AI and machine learning for automated wildlife identification, and validating thermal and RGB camera capabilities in diverse environmental conditions.
Furthermore, the project plans to conduct large-scale trials with fixed-wing drone operators and commercial aviation to compare drone-based wildlife population estimates with traditional aerial surveys. A key outcome will be the development of a best practice guide summarising recommendations for drone specifications, camera settings, flight planning, and data processing. This guide aims to support wildlife specialists and land managers in Alberta, contributing to improved wildlife management practices through innovative drone technologies.
The ARC project aims to assess biodiversity using remote sensing data, focusing on the characterization of biodiversity through trait-based functional diversity. This approach utilizes ecologically meaningful traits derived from high-resolution remote sensing RS data. Part of the project leverages the benefits of high point density lidar data obtained from both UAV-based (ULS) and Terrestrial Laser Scanner (TLS) technologies.
By employing these advanced lidar technologies, the project enables the segmentation of individual trees, facilitating a detailed analysis of their structure. This segmentation process not only allows for a comprehensive assessment of functional diversity but also enables the evaluation of functional diversity based on the specific traits exhibited by each individual tree.
Multiple study sites serve as practical case studies for comparing and assessing functional diversity. By analysing and comparing the functional diversity across these study sites, the project aims to contribute valuable insights into the understanding and management of biodiversity.
- Addressing human wildlife conflict and biodiversity loss with remote sensing tools
- FOSS
- lidR