Chris Hakkenberg, a doctoral candidate in the Curriculum for the Environment and Ecology, has been awarded a three-year NASA Earth and Space Science Fellowship (NESSF) for his dissertation project entitled: “Understanding the Impact of Land Cover/Land Use Change on Plant Diversity: Scaling from Plots to Landscapes Using Multi-Platform Remote Sensing”.
The NESSF is a competitive fellowship program under NASA’s Earth Science Research Program that supports students for up to three years of graduate research. The Earth System Science component of the NESSF encourages research that places particular emphasis on the utilization of NASA’s fleet of Earth observing satellites to “study the Earth from space to advance scientific understanding and meet societal needs”.
Chris’s research focuses on the use of remote sensing – including high spatial resolution broadband optical imagery, image spectroscopy (hyperspectral), and laser altimetry (such as LiDAR) – to test hypotheses in the fields of forest community ecology and landscape ecology. His dissertation project aims to quantify the effect of land cover change on plant diversity in forest ecosystems of the North Carolina Piedmont. Hypotheses are guided by the more fundamental question: How do interacting land cover change trends impact plant diversity over large spatio-temporal extents? The North Carolina Piedmont is marked by species-rich forests as well as rapid (and accelerating) land cover change trends – two factors making it an ideal location to investigate this question.
Chris will undertake a three-part dissertation project. First, forest composition and structure will be mapped for the Piedmont study area by fusing remotely sensed imagery from active and passive sensors to exploit subtle variations in each product to better characterize forest conditions over large scales. Second, he will establish a predictive model of plant species richness based on remotely sensed predictors as well as ground data from the Carolina Vegetation Survey (CVS), Duke Forest long-term vegetation plots, and the Forest Inventory Analysis (FIA). In the third and final step, he will compile a thirty-year time series of historical imagery of the study area to identify changes in the spatial distribution of predicted plant diversity. When constructing large spatially-explicit ecological models of this sort, particular attention will be paid to algorithm efficiency in data processing using UNC’s Linux computing cluster, as well as model validation and the quantification of error and uncertainty. As such, Chris and a small team will sample vegetation in a diversity of forest types in several NC counties over the next two summers to supplement input datasets and validate model predictions.
This study commences from the desire to move towards a more automated, active monitoring of vegetation change – including structural, compositional, and phenological change – to better capture a realistic picture of forest dynamics from which conservation planning can commence. While remote sensing technologies are optimally suited to monitor land cover change over large spatio-temporal extents, direct approaches to the remote measurement of biodiversity still remain a challenge. This research is proposed at a critical time in the field of spatial ecology, when cutting edge technologies are increasingly enabling the fluid integration of fine-scale plot data with large-scale wall-to-wall satellite imagery.