Landscape dynamics of northeastern forests

This project involves collaborative research with Stephen W. Pacala and Simon A. Levin of Princeton University to calibrate, test, and analyze models of heterogeneous forested landscapes containing a diverse array of habitats. The project is an extension of previous, NASA-supported research to develop a spatially-explicit model of forest dynamics at the scale of an individual forest stand (hectares to square kilometer spatial scales). That model (SORTIE) has been thoroughly parameterized from field studies in the modal upland environment of western Connecticut. Under our current funding, we are scaling-up the model and parameterizing it for the broad range of upland environments in the region. Our most basic goal is to understand the linkages between stand-level dynamics (as revealed in our previous research) and landscape-level dynamics of forest composition and structure

TWELVE DATA FUSION ALGORITHMS FOR USE IN RAPID DAMAGE MAPPING WORKFLOWS: AN EVALUATION

ABSTRACT Fused images form the basis for manual, semi-, and fully-automated classification steps in the disaster information retrieval chain. Many fusion algorithms have been developed and tested for different remote sensing applications; however, they are weakly assessed in the context of rapid mapping workflows. We examined how well different fusion algorithms would perform when applied to very high spatial resolution (VHSR) satellite images that encompass post-disaster scenes. The evaluation entailed twelve fusion algorithms: Brovey transform, colour normalization spectral sharpening (CN) algorithm, Ehlers fusion algorithm, Gram-Schmidt fusion algorithm, high-pass filter (HPF) fusion algorithm, local mean matching algorithm, local mean variance matching (LMVM) algorithm, modified intensity-hue-saturation (HIS) fusion algorithm, principal component analysis (PCA) fusion algorithm, subtractive resolution merge (SRM) fusion algorithm, University of New Brunswick (UNB) fusion algorithm, and the wavelet-PCA fusion algorithm. These algorithms were applied to GeoEye-1 satellite images taken over two geographical settings: the 2010 earthquake-damaged sites in Haiti and the 2010 flood-impacted sites in Pakistan. Fused images were assessed for spectral and spatial fidelity using sixteen quality indicators and visual inspection methods. Under each metric, fusion algorithms were ranked and best competitors were identified. Ehlers, WV-PCA, and HPF had the best scores for the majority of spectral quality indices. UNB and Gram-Schmidt algorithms had the best scores for spatial metrics. HPF emerged as the overall best performing fusion algorithm