Paul M. Montesano

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Best merge region growing normally produces segmentations with closed connected region objects. Recognizing that spectrally similar objects often appear in spatially separate locations, we present an approach for tightly integrating best merge region growing with nonadjacent region object aggregation, which we call hierarchical segmentation or HSeg.(More)
The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard’s(More)
Satellite-based estimates of vegetation structure capture broad-scale vegetation characteristics as well as differences in vegetation structure at plot-scales. Active remote sensing from laser altimetry and radar systems is regularly used to measure vegetation height and infer vegetation structural attributes, however, the current uncertainty of their(More)
a r t i c l e i n f o Accurate estimate of biomass and its changes at local to regional scales are important for a better understanding of ecosystem function, biodiversity and sustainability. In this study we explored the forest biomass prediction and dynamic monitoring from Light detection and ranging (LiDAR) waveform metrics at different key map scales.(More)
Title of Document: THE UNCERTAINTY OF SPACEBORNE OBSERVATION OF VEGETATION STRUCTURE IN THE TAIGA-TUNDRA ECOTONE: A CASE STUDY IN NORTHERN SIBERIA. Paul Mannix Montesano, Doctor of Philosophy, 2015 Directed By: Dr. Ralph Dubayah, Department of Geographical Sciences The ability to characterize vegetation structure in the taiga-tundra ecotone (TTE) at fine(More)
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