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Airborne Remote Sensing of Structural & Functional DiversityAirborne remote sensing (hyperspectral and LiDAR) offers an unprecedented opportunity to map plant traits, structure, and biochemistry across landscapes. We are using these data to address critical questions in both community ecology and ecosystem modeling.
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This work is currently supported by Kyla's NSF CAREER award - CAREER: Plant traits link disturbance history to carbon uptake across spatiotemporal scales.
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Land Surface ModelingComparing ESMs to remotely sensed data ("benchmarking") is an important part of how we evaluate models, though many factors need to be considered. Projects like ILAMB are making this a systematic part of model evaluation. We are interested in using land surface models to improve our understanding of the carbon cycle across scales.
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This work is currently funded by an NSF Macrosystems Biology Collaborative award led by Dr. Danica Lombardozzi at Colorado State University.
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Spatiotemporal Patterns in Semi-arid and Savanna-type EcosystemsUnderstanding how vegetation varies in space and time phenology works dry environments is a critical component of the Earth system. We are approaching this challenge through a combination of remote sensing, statistical modeling, and meta-analysis.
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Currently this work was most recently led by former ERSAM Lab grad student Dr. Donald Akanga, who is now an assistant professor at Montana State University, Billings.
 Photo credit: Aaron Kamoske
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Biodiversity across ScalesBiodiversity, or the variety of life on Earth, can take many forms, including taxonomic, functional, and phylogenetic. We are interested in connecting point level data, like species counts, to remotely sensed products across a wide variety of scales and concepts.
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Recently Kyla was involved in a collaborative publication on this topic in Nature Ecology & Evolution.