In my postdoctoral research, we are forecasting how species and communities are reorganizing under climate change using generalized joint attribute models. We are incorporating remote sensing data including satellite imagery and lidar for improved habitat and vegetation structure characterization. I also developed a python package (geedataextract) using Google Earth Engine’s python API, which allows for efficient pre-processing and spatial/temporal averaging of remotely sensed data (e.g., MODIS, SMOS, TRMM, Landsat) as well as other climate, elevation, and soil datasets.
For my doctoral research, I explored how forests will respond to increases in droughts and heatwaves projected under climate change, by studying the impacts of the 2011 drought on the forests and woodlands of Texas. We developed remote sensing approaches that improve monitoring of forest disturbances from droughts and heatwaves at regional scales. We have also explored two modeling approaches, to improve forecasts of forest vulnerability to future droughts and heatwaves. Firstly, at the regional scale, across Texas we identified climatic anomalies associated with spatial patterns of canopy loss and forecasted how often these thresholds were likely to be surpassed in the future. Secondly, at the watershed scale, we built a quantitative model of soil moisture dynamics across a landscape, while accounting for spatial differences in aspect, topography, and soils. We then tested for the importance of incorporating landscape diversity when projecting future tree water stress.
My research interests include forest disturbance, global change ecology, remote sensing, geospatial analysis, and landscape ecology. I'm drawn to ecological questions relevant to large spatial scales, that require remote sensing & geospatial analysis and the integration of spatial datasets across agencies and platforms. In my research I predominantly use the following programing languages: R, Python, IDL/ENVI API, and MATLAB.