The lack of high-resolution measurements of 3D ecosystem structure across broad spatial extents impedes major advancements in animal ecology and biodiversity science. We aim to fill this gap by using Light Detection and Ranging (LiDAR) technology to characterize the vertical and horizontal complexity of vegetation and landscapes at high resolution across regional to continental scales.
The newly LiDAR- derived 3D ecosystem structures will be applied in species distribution models for breeding birds in forests and marshlands, for insect pollinators in agricultural landscapes, and songbirds at stopover sites during migration. This will allow novel insights into the hierarchical structure of animal-habitat associations, into why animal populations decline, and how they respond to habitat fragmentation and ongoing land use change.
The processing of these massive amounts of LiDAR point cloud data will be achieved by developing a generic interactive eScience environment with multi-scale object-based image analysis (OBIA) and interpretation of LiDAR point clouds, including data storage, scalable computing, tools for machine learning and visualization (feature selection, annotation/segmentation, object classification, and evaluation), and a PostGIS spatial database. The classified objects will include trees, forests, vegetation strata, edges, bushes, hedges, reedbeds etc. with their related metrics, attributes and summary statistics (e.g. vegetation openness, height, density, vertical biomass distribution etc.).
The newly developed eScience tools and data will be available to other disciplines and applications in ecology and the Earth sciences, thereby achieving high impact. The project will foster new multi-disciplinary collaborations between ecologists and eScientists and contribute to training a new generation of geo-ecologists.
Image: Sagar (CC License)