Spaceborne Lidar is for the Birds: Applications of GEDI lidar to improve species distribution and hotspot mapping in a National Park in Africa

In 2019, an alarming study of bird populations revealed that 3 billion birds have been lost since the 1970s. Bird populations are predicted to decrease further as a result of climate change and land conversion. Additionally, the usefulness of bird diversity as an indicator of overall biodiversity and ecosystem health has been well researched, as has the connection between forest structure and avian species. However, on-the-ground studies of forest structural diversity are often limited by time and financial resources. Remote sensing of forest structure, such as discrete return and aerial lidar, demonstrably improve species model performance in regional studies to predict bird species occurrence. In addition, the ability of spaceborne lidar to improve biodiversity predictions is still being explored and offers a publicly available method for measuring forest structure across large regions. Here, we will use the Global Ecosystem Dynamics Investigation (GEDI) lidar instrument to improve existing species habitat suitability models and predict biodiversity hotspots in a National Park in Africa. GEDI is a space-borne lidar instrument aboard the International Space Station that is capable of measuring the height and complexity of vegetation. In addition to sociocultural and ecological data, we will compile GEDI derived metrics of forest structure, including canopy height (RH98), foliage height diversity (FHD), plant area index (PAI), waveform structural complexity index (WSCI) to apply structural variables to an ensemble model of species distribution. Using species occupancy gathered from in-situ point count data from 2022-2023, along with the SSDM package in R, we will create stacked species distribution models and species richness predictions. Models with and without GEDI data will be compared to understand the impact of GEDI metrics on model accuracy. The results of this work will inform park management and bolster efforts to conserve species biodiversity in the park using remote sensing tools. Results of this work will also inform biodiversity hotspot mappings across larger regions of Africa where in situ data is sparse.