With on-going climate change, there has never been a greater need to prioritize conservation through frequent and spatially detailed assessments of the planet s biodiversity, ideally achieved at…

This federal contract award with NASA's Shared Services Center (value: $405,359.30) was granted to Tall Timbers Research, Inc., a non-profit organization specializing in fire ecology and natural resource management. The contract aims to integrate ground-based and remotely sensed data to develop algorithms for identifying plant communities and their biodiversity in relation to recent fire regimes in the Southeastern U.S. coastal plain. It leverages the high-resolution spectral and thermal infrared imagery capabilities of the Surface Biology and Geology (SBG) designated observable to improve biodiversity assessments. The contract does not have a set-aside designation and is a grant for research, with a targeted completion date of August 30, 2025. This award demonstrates Tall Timbers' expertise in applying advanced remote sensing and data integration techniques to support federal agencies' environmental conservation and wildfire management objectives.

With on-going climate change, there has never been a greater need to prioritize conservation through frequent and spatially detailed assessments of the planet s biodiversity, ideally achieved at global scales through remote sensing. While wide-ranging remote sensing products typically cannot identify individual plant species in complex communities, key environmental parameters with remotely detectable properties often drive the distribution and abundance of species across landscapes. The surface biology and geology (SBG) designated observable, a novel combination of high spatial resolution spectral and thermal infrared imagery, is uniquely designed to advance linkages between observable parameters and biodiversity. Wildland fire, increasingly well monitored through remote sensing techniques, is chief among the parameters predicting plant community resilience and biodiversity. In fire-effected ecosystems, the accuracy of estimates of vegetation community characteristics, including biodiversity, using hyperspectral and thermal data provided by SBG could be further increased by pre-classification of pixels by time since fire derived from burn severity algorithms of comparable resolution (30 m). This proposal will integrate ground-based and remotely sensed data to develop algorithms for identifying natural and anthropogenic plant communities and their biodiversity as they relate to recent fire regimes in the southeastern U.S. Coastal plain.