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Terrestrial ecosystems provide a substantial carbon sink that helps mitigate climate change, sequestering roughly 30% of anthropogenic carbon emissions annually. However, the long-term future of this sink is not well understood. In this dissertation, I use satellite remote sensing, in-situ measurements, and models to improve understanding of the nature and magnitude of spatial and temporal variation in the primary productivity of Eastern Deciduous Forests of the United States. In my first research chapter, I use remote sensing to model to the phenology of two key variables that control forest productivity: leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (fAPAR). Results show that the relationship between remotely sensed vegetation indices and both LAI and fAPAR is strongly influenced by systematic variation in near infrared reflectance arising from seasonal changes in canopy shadow fraction that are independent of physical changes in forest canopy properties. In my second research chapter, I use estimates of gross primary production (GPP) derived from eddy covariance measurements at four temperate deciduous sites to model the phenology and controls on light use efficiency (LUE) within and across sites. Results show that multiple modes of variation in incoming radiation dominate daily and seasonal variation in LUE, and provide a refined basis for understanding how variability in environmental controls affect LUE and how the strength of these drivers change throughout the growing season. In my third research chapter, I use the long-term record of Landsat imagery, in-situ phenological observations, and estimates of GPP derived from eddy covariance measurements at two temperate deciduous forest sites to investigate how phenology controls interannual variability in GPP at these sites. Results demonstrate that phenology metrics derived from remote sensing are consistent with in-situ measurements, and that interaction between the timing of growing season anomalies and incoming radiation explains a significant proportion of interannual variation in GPP. Taken together, results from this dissertation demonstrate how variation in phenology and LUE control variation in deciduous forest productivity, which is essential for reducing uncertainty in how future climate changes will impact the carbon budget of deciduous forest ecosystems.

Where once the Arctic basin held predominantly old, thick perennial sea ice, it is now increasingly occupied by young, thin seasonal ice. The sea ice surface topography, which affects and is affected by many of the physical processes operating at the interface between ocean, sea ice and atmosphere, is closely related to the age and type of sea ice cover. In this thesis, new methods are presented for measuring and understanding sea ice topography using Light Detection and Ranging (LiDAR) technology. A new technique is presented for parameterizing the micro-scale roughness of sea ice using terrestrial LiDAR. Field, laboratory and numerical experiments have been carried out to test the precision and accuracy of the technique, and calibrations have been developed for correcting field observations of surface roughness for known biases. Results obtained using this technique have been applied in several microwave remote sensing and electromagnetic-wave scattering model studies of snow-covered and melting sea ice. Terrestrial and satellite LiDAR observations are acquired and combined in a further study to examine how sea ice surface topography regulates the melting of ice during the Arctic summer. Observations from a field program in the Canadian Arctic show that minor variations in the roughness of pre-melt sea ice topography can affect significant variations in the melt pond coverage at the ice surface in summer. Numerical simulations are used to develop a quantitative understanding of these findings and, when applied to satellite observations, explain most of the spatial variation in Arctic summer ice melting rates. Results suggest that a recent reduction in sea ice roughness, caused by progressive changes in the type of sea ice resident in the Arctic Ocean, has accelerated the summer melting and decline of the Arctic sea ice cover. / May 2016

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The overall topic of the research described in this dissertation was the partitioning of available energy at the Earth's surface into sensible and latent heat flux, with an emphasis on the development of techniques which utilize remotely sensed data. One of the major objectives was to investigate the modification of existing techniques, developed over agricultural surfaces, to "natural" ecosystems (i.e., non -agricultural vegetation types with variable and incomplete canopy cover). Ground -based measurements of surface fluxes, vegetation cover, and surface and root -zone soil moisture from the First ISLSCP (International Land Surface Climatology Program) Field Experiment (FIFE) were used to examine the factors controlling the partitioning of energy at ground stations with contrasting surface characteristics. Utilizing helicopter -based and satellite -based data acquired directly over ground -based flux stations at the FINE experimental area, relatively simple algorithms were developed for estimating the soil heat flux and sensible heat flux from remotely sensed data. The root mean square error (RMSE) between the sensible heat flux computed with the remotely sensed data and the sensible heat flux measured at the ground stations was 33 Wm 2. These algorithms were then applied on a pixel -by -pixel basis to data from a Landsat -TM (Thematic Mapper) scene acquired over the FIFE site on August 15, 1987 to produce spatially distributed surface energy- balance components for the FIFE site. A methodology for quantifying the effect of spatial scaling on parameters derived from remotely sensed data was presented. As an example of the utility of this approach, NDVI values for the 1,IFE experimental area were computed with input data of variable spatial resolution. The differences in the values of NDVI computed at different spatial resolutions were accurately predicted by an equation which quantified those differences in terms of variability in input observations.

Understanding how deltas build and maintain themselves is critical to predicting how they will respond to perturbations such as sea level rise. This is especially an issue of interest in coastal Louisiana, where land loss is exacerbated due to subsidence and decreased sediment supply. Feedbacks between ecology and geomorphology have been well documented in tidal environments, but the role of vegetation in delta morphodynamics is not well understood. This study investigates spatial and temporal correlations between vegetation succession and sediment accumulation at the Wax Lake Delta in Louisiana. I established a 2500 m long transect along the western levee of Pintail Island, capturing the full range of island elevations and the transition from bare sediment to herbaceous plants and trees. Shallow (50-100 cm deep) sediment cores taken along this transect were analyzed for particle size, organic matter content, and bulk density, and dated using ²¹⁰Pb. The resulting sedimentation rates and composition trends over time were compared to remote sensing-based analyses of temporal changes in island topography and flooding frequency derived from historical Landsat images. We found that the topography of Pintail Island has developed from a non-systematic arrangement of elevations to a discrete set of levees and intra-island platforms with distinct vegetation types, designated as high marsh, low marsh, and mudflat habitat. This elevation zonation is consistent with alternative stable state theory as so far applied to tidal salt marsh systems. At all but the youngest sampling site, sediment cores showed a significant decrease in organic matter content and a significant increase in grain size with depth. The total organic matter contribution to vertical growth was not sufficient to account for all the elevation change required to achieve the differentiation from low marsh to high marsh deduced from the time-lapse Landsat imagery analysis. Mineral sediment accumulation rates suggested that elevation growth was accelerating or holding steady over time, in contrast to theory suggesting rates should slow as elevation increases. These results provide an empirical foundation for future mechanistic models linking mineral sedimentation, organic sedimentation, vegetation succession, elevation change, and flood frequency in the delta. / text

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