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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 is being led by ERSAM Lab grad students Ryan Nagelkirk and Gloria Desanker, who graduated in 2018 (co-advised with Andy Finley)
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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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Currently this work is funded by a NSF Macrosystems Early Career award to Kyla - MSB-ECA: Ecosystems in four dimensions: Measuring changes to forest structure and function in the Anthropocene - with Scott Stark (MSU) and Shawn Serbin (BNL) and ERSAM Lab grad student Aaron Kamoske.
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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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Currently this work is funded by a NASA Carbon Cycle grant to Jiquan Chen (MSU), Kyla, and others. More information on that project is here.
 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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This work was funded by a NASA Biodiversity grant to Phoebe Zarnetske (MSU), Kyla, and Sydne Record (Bryn Mawr).