- Effects of deforestation and climate change on carbon & water cycling in Amazonia
- Improving the predictive ability of terrestrial biosphere models
- Mechanistic models of animal movement
- Enhancing vegetation structure for terrestrial biosphere modeling using Lidar and Radar techniques
- Development of the AIRMOSS Level 4 Net Ecosystem Exchange (NEE) products using the ED2 terrestrial biosphere model
- HyspIRI: Linking Terrestrial Biosphere Models with Imaging Spectrometry Measurements of Ecosystem Composition, Structure, and Function
- Integrated assessment of land-use and hydrology for sustainable development of the Amazon under changing climate (SSP)
- Previous research projects
Enhancing vegetation structure for terrestrial biosphere modeling using Lidar and Radar techniques
Lidar and Radar estimation techniques for the ED model
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.