MEASURING AND MAPPING FOREST WILDLIFE HABITAT CHARACTERISTICS USING LIDAR REMOTE SENSING AND MULTI-SENSOR FUSION

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dc.contributor.advisorDubayah, Ralph O.en_US
dc.contributor.authorHyde, Peteren_US
dc.contributor.departmentGeographyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2006-02-04T07:36:10Z
dc.date.available2006-02-04T07:36:10Z
dc.date.issued2005-12-05en_US
dc.description.abstractManaging forests for multiple, often competing uses is challenging; managing Sierra National Forest's fire regime and California spotted owl habitat is difficult and compounded by lack of information about habitat quality. Consistent and accurate measurements of forest structure will reduce uncertainties regarding the amount of habitat reduction or alteration that spotted owls can tolerate. Current methods of measuring spotted owl habitat are mostly field-based and emphasize the important of canopy cover. However, this is more because of convenience than because canopy cover is a definitive predictor of owl presence or fecundity. Canopy cover is consistently and accurately measured in the field using a moosehorn densitometer; comparable measurements can be made using airphoto interpretation or from examining satellite imagery, but the results are not consistent. LiDAR remote sensing can produce consistent and accurate measurements of canopy cover, as well as other aspects of forest structure (such as canopy height and biomass) that are known or thought to be at least as predictive as canopy cover. Moreover, LiDAR can be used to produce maps of forest structure rather than the point samples available from field measurements. However, LiDAR data sets are expensive and not available everywhere. Combining LiDAR with other, remote sensing data sets with less expensive, wall-to-wall coverage will result in broader scale maps of forest structure than have heretofore been possible; these maps can then be used to analyze spotted owl habitat. My work consists of three parts: comparison of LiDAR estimates of forest structure with field measurements, statistical fusion of LiDAR and other remote sensing data sets to produce broad scale maps of forest structure, and analysis of California spotted owl presence and fecundity as a function of LiDAR-derived canopy structure. I found that LiDAR was able to replicate field measurements accurately. Additionally, I was able to statistically combine LiDAR with passive optical and RaDAR (SAR backscatter and InSAR range) data to produce broad scale maps of forest structure that are consistent and accurate relative to field data and LiDAR data alone. Finally, I was able to demonstrate that these forest structural attributes predict spotted owl presence and absence as well as productivity.en_US
dc.format.extent4518367 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/3205
dc.language.isoen_US
dc.subject.pqcontrolledGeographyen_US
dc.subject.pqcontrolledBiology, Ecologyen_US
dc.subject.pqcontrolledAgriculture, Forestry and Wildlifeen_US
dc.subject.pquncontrolledlidaren_US
dc.subject.pquncontrolledspotted owlen_US
dc.subject.pquncontrolledforest structureen_US
dc.subject.pquncontrolledbiomassen_US
dc.subject.pquncontrolledcanopy coveren_US
dc.subject.pquncontrolledcanopy heighten_US
dc.titleMEASURING AND MAPPING FOREST WILDLIFE HABITAT CHARACTERISTICS USING LIDAR REMOTE SENSING AND MULTI-SENSOR FUSIONen_US
dc.typeDissertationen_US

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