Seasonal and diurnal drone and ground-based thermal, multispectral and hyperspectral imaging to quantify responses of California oak woodland productivity and evapotranspiration to extreme climate conditions

As climate change progresses, many questions are emerging about how terrestrial ecosystem carbon, water and nutrient cycles will change, interact, and affect landscape to global-scale ecosystem function. The front lines in time and space where climate change is affecting ecosystems and people are the increasingly frequent periods of extreme temperature and hydrologic conditions occurring globally. Within landscapes, locations and organisms can experience these extremes very differently by species, terrain or community structure. Studying responses of vegetation productivity and evapotranspiration (ET) during these periods of climate extremes within landscapes, using satellite and airborne remote sensing, remains challenging. Many spacecraft-based observations occur at limited times of day, face barriers from seasonal environmental conditions, and lack the spatial resolution needed to resolve ecosystem processes at more granular scales across diverse terrain and vegetation communities.

Here, we use seasonal and diurnal near-surface remote sensing to assess how productivity, water stress and ET in California oak woodlands are responding to extreme heat, low atmospheric and soil moisture conditions across species and hillslope positions. Using drone-borne multispectral, thermal, and ground-based hyperspectral data collected across late wet and dry seasons (May-October), we examine how canopy and hillslope-scale productivity, ET, water and nutrient-stress indicators vary across evergreen live oaks, deciduous blue oaks and valley oaks, and how hillslope position affects species responses to extreme drought and temperature. We also experiment with diurnal, drone-based thermal imaging at individual and stand scales to calculate canopy-scale conductance, ET, and test how daily fluctuations of air temperature and atmospheric moisture affect organism and tree stand-scale water losses. We anticipate our near-surface remotely sensed data, accompanied by field collection of leaf-level spectroscopic, thermal and porometry data, can refine use of data from Landsat, Sentinel and new ECOSTRESS instruments to track more granular ecosystem responses to climate. We will also touch on applications of our near-surface remote sensing work for conservation land and water management.