Monitoring plant stress
Monitoring ecosystem function with high resolution over large spatial scale is extremely difficult due to the inherent cost and complexity managing ground-based operations and sensors. To overcome these challenges, I use computational spatial methods to characterize the vegetation function and the effect of physiology on the optical and thermal properties of plants. These characterizations enable the ability to track vegetation responses to various threats.
Temperature condition index (TCI) for nine image dates with ecoregion boundaries shown in black.
In 2012 – 2015, California experienced a severe drought. Using fused hyperspectral and thermal airborne imagery, I performed a large scale analysis of plant species’ annual and seasonal temperature variability throughout a prolonged drought in Southern California. This work integrates not only remote sensing products but in situ and field measurements. Results revealed the heterogeneity of water stress across the landscape as the drought progressed, highlighting areas more prone to wildfires, as well as insight into structural and geographical determinants of plant stress.
Miller, D.L., Alonzo, M., Meerdink, S.K., Allen, M.A., Tague, C.L., Roberts, D.A., & McFadden, J.P. (2022). Seasonal and interannual drought responses of vegetation in a California urbanized area measured using complementary remote sensing indices. ISPRS Journal of Photogrammetry and Remote Sensing, 183, 178–195. https://doi.org/10.1016/j.isprsjprs.2021.11.002
Kibler, C. L., Parkinson, A. L., Peterson, S. H., Roberts, D. A., Antonio, C. M. D., Meerdink, S.K., & Sweeney, S. H. (2019). Monitoring post-fire recovery of chaparral and conifer species using field surveys and Landsat time series. Remote Sensing, 11(2963), 1–25. https://doi.org/10.3390/rs11242963