HyspIRI prepatory activity
Alexander Antonarakis and Stacy Bogan
In this study, we examine how simulated HyspIRI and MASTER imaging spectrometry can be used to provide accurate and comprehensive measurements of current ecosystem state -- specifically plant functional composition, and canopy and soil temperatures -- that can be used to constrain terrestrial biosphere model predictions of the current and future carbon, water and energy fluxes of the land surface. First, remotely-sensed imaging spectrometry-derived estimates of ecosystem composition and canopy temperatures for the diverse range of terrestrial ecosystems found in California are linked to the definition of above ground ecosystem in ED2, a state-of-the-art process-based terrestrial biosphere model. The remote-sensing derived information on the current state of the ecosystem is then used to constrain and improve predictions for the current carbon, water and energy balance of terrestrial ecosystems across the range of ecosystem types.
Enhancing vegetation structure for terrestrial biosphere modeling using Lidar and Radar techniques: Harvard Forest
There are uncertainties in regional and global terrestrial carbon budgets due to the current state heterogeneity of forest ecosystems, the dynamics of carbon storage, and the changes in forest ecosystems resulting from disturbance and recovery processes. Therefore, consistent measurements of canopy structure and forest attributes such as canopy height, vegetation age, trunk diameter and height, canopy gap, aboveground biomass, and species identification amongst others, may help in providing information regarding the current state forest structure that is vital for assessments of current and future forest sustainability, biodiversity, and terrestrial carbon budgets.
Lidar and radar remote sensing techniques are capable of these measurements, with full waveform lidar measurements at near-infrared pulse emissions being sensitive to the vertical vegetation profile, and radar measurements a P, L, and C-bands sensitive to forest volume and density.
Lidar and radar remote sensing techniques on their own right are powerful tools in determining forest structure. In this project they will be investigated separately and also fused at the signal and parameter level to extract 3D forest structure and biomass at a variety of distinct ecosystems. The uncertainties in determining these parameters at different scales and spatial heterogeneity will be quantified by simulating regional carbon fluxes and performing sensitivity analyses using the Ecosystem Demography (ED) model. An end goal of this research is to assimilate lidar and radar remote sensing measurements of vegetation structure into the ED biosphere model in order to improve predictions of long-term vegetation responses to climate change. With this information the two active remote sensing techniques will be considered for global coverage on a spaceborne mission concept.