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Research Interests




  • forest structure and dynamics, fire ecology, biodiversity, biogeography, macroecology

Remote sensing:

  • LiDAR, image spectroscopy, broad-band optical time series 


  • geospatial analysis, ecoinformatics, data visualization 


  • multivariate, spatial, hierarchical, nonparametric, prediction and inference


  • conservation, human-environment, wildfires, land cover change, urbanization

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Wildfire severity prediction and treatment effectiveness 

  • I am working with with academic, state, and federal partners to employ hindcasted and interpolated space-borne lidar, with field and airborne data to assess the relationship between pre-fire fuels, weather and climate conditions, fire severity, and fuels treatment effectiveness across America’s West, and especially in the wildland-urban interface (WUI). 












Global forest community dynamics  

  • My work on global-scale forest community dynamics relies on a diverse array of data spanning spatial and temporal scales, including high spatial resolution optical imagery, image spectroscopy (hyperspectral), LiDAR, and ground plots to model interrelations among emergent properties of forest ecosystems like biodiversity, composition, structure, and function. Space-borne sensors like GEDI, DESIS, ECOSTRESS, and upcoming SBG coupled with global networks of airborne LiDAR-hyperspectral networks like NEON and G-LiHT make scaling properties from landscapes to global scales increasing feasible.

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Climate, environment, structure, and diversity 

  • I am employing satellite and aerial data to examine environmental and climatological constraints on the relationship between vegetation structure, diversity, and ecosystem function across the biomes of North America. Results will assist collaborative efforts to model multi-scale community processes at a global extent using space-borne LiDAR and hyperspectral sensors like GEDI and SBG.

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Ecological scaling 

  • Ecological phenomena are fundamentally scale dependent in that they vary as a function of biological, spatial, and temporal grain and extent. Inferential conclusions from any one scale of observation are potentially misleading when applied to another. For this and other reasons, multiple-scale analyses are better suited to uncovering the combined effect of multiple drivers of diversity acting at multiple scales. One fundamental pillar of my research approach is to systemically assess the role of scale: including the characteristic scales at which organisms disperse, assemble, and compete, considerations of resolution and extent in multiple sources of remotely-sensed data, to tradeoffs in parsimony and complexity in models of ecosystem properties.

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Other research

  • My work on land cover change time series employs repeat imagery from constellations of space-borne sensors to create large-extent, temporally-dense multi-decadal land cover time series. My interest in the topic is both methods-driven and applied. I have developed a series of algorithms for automating data fusion and predictive modeling of multi-temporal imagery data cubes. In addition, I led studies to characterize higher-order spatio-temporal land cover change morphologies in the Greater Houston area (summarized here).

  • NASA Earth and Space Science Fellowship. In this three-part study, I use field plots, high spatial resolution optical imagery, image spectroscopy, and laser altimetry from NASA Goddard’s LiDAR, Thermal, and Hyperspectral (G-LiHT) airborne imager to map forest communities over a continuous environmental gradient and predict vascular plant species richness at seven spatial scales using predictive Bayesian spatial modeling.

  • Spatially Explicit Plot-Level Forest Dynamics: An Interactive Tool. Fast and flexible three-dimensional forest dynamics spatio-temporal data visualization. Provides ecological statistics based on selected criteria, spatially-explicit forest dynamics map, and change vectors. Requires one .txt file with three data points per tree: species type, diameter at breast height (dbh), xy location.

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