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MAPPING FOREST STRUCTURE AND HABITAT CHARACTERISTICS USING LIDAR AND MULTI-SENSOR FUSION

dc.contributor.advisorDubayah, Ralphen_US
dc.contributor.authorSwatantran, Anuradhaen_US
dc.date.accessioned2011-07-06T05:38:27Z
dc.date.available2011-07-06T05:38:27Z
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/1903/11462
dc.description.abstractThis dissertation explored the combined use of lidar and other remote sensing data for improved forest structure and habitat mapping. The objectives were to quantify aboveground biomass and canopy dynamics and map habitat characteristics with lidar and /or fusion approaches. Structural metrics from lidar and spectral characteristics from hyperspectral data were combined for improving biomass estimates in the Sierra Nevada, California. Addition of hyperspectral metrics only marginally improved biomass estimates from lidar, however, predictions from lidar after species stratification of field data improved by 12%. Spatial predictions from lidar after species stratification of hyperspectral data also had lower errors suggesting this could be viable method for mapping biomass at landscape level. A combined analysis of the two datasets further showed that fusion could have considerably more value in understanding ecosystem and habitat characteristics. The second objective was to quantify canopy height and biomass changes in in the Sierra Nevada using lidar data acquired in 1999 and 2008. Direct change detection showed overall statistically significant positive height change at footprint level (ΔRH100 = 0.69 m, +/- 7.94 m). Across the landscape, ~20 % of height and biomass changes were significant with more than 60% being positive, suggesting regeneration from past disturbances and a small net carbon sink. This study added further evidence to the capabilities of waveform lidar in mapping canopy dynamics while highlighting the need for error analysis and rigorous field validation Lastly, fusion applications for habitat mapping were tested with radar, lidar and multispectral data in the Hubbard Brook Experimental Forest, New Hampshire. A suite of metrics from each dataset was used to predict multi-year presence for eight migratory songbirds with data mining methods. Results showed that fusion improved predictions for all datasets, with more than 25% improvement from radar alone. Spatial predictions from fusion were also consistent with known habitat preferences for the birds demonstrating the potential of multi- sensor fusion in mapping habitat characteristics. The main contribution of this research was an improved understanding of lidar and multi-sensor fusion approaches for applications in carbon science and habitat studies.en_US
dc.titleMAPPING FOREST STRUCTURE AND HABITAT CHARACTERISTICS USING LIDAR AND MULTI-SENSOR FUSIONen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentGeographyen_US
dc.subject.pqcontrolledGeographyen_US
dc.subject.pquncontrolledBiomassen_US
dc.subject.pquncontrolledDynamicsen_US
dc.subject.pquncontrolledHabitaten_US
dc.subject.pquncontrolledHyperspectralen_US
dc.subject.pquncontrolledLidaren_US
dc.subject.pquncontrolledRadaren_US


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