ASSESSING FOREST BIOMASS AND MONITORING CHANGES FROM DISTURBANCE AND RECOVERY WITH LIDAR AND SAR

Simple item page
dc.contributor.advisorDubayah, Ralphen_US
dc.contributor.advisorSun, Guoqingen_US
dc.contributor.authorHuang, Wenlien_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.accessioned2015-10-02T05:30:37Z
dc.date.available2015-10-02T05:30:37Z
dc.date.issued2015en_US
dc.description.abstractThis dissertation research investigated LiDAR and SAR remote sensing for assessing aboveground biomass and monitoring changes from anthropogenic forest disturbance and post-disturbance recovery. First, waveform LiDAR data were applied to map forest biomass and its changes at different key map scales for the two study sites: Howland Forest and Penobscot Experimental Forest. Results indicated that the prediction model at the scale of individual LVIS footprints is reliable when the geolocation errors are minimized. The evaluation showed that the predictions were improved markedly (20% R2 and 10% RMSE) with the increase of plot sizes from 0.25 ha to 1.0 ha. The effect of disturbance on the prediction model was strong at the footprint level but weak at the 1.0 ha plot-level. Errors reached minimum when footprint coverage approached about 50% of the area of 1.0 ha plots (16 footprints) with no improvement beyond that. Then, a sensitivity analysis was conducted for multi-source L-band SAR signatures, to change in forest biomass and related factors such as incidence angle, soil moisture, and disturbance type. The effect of incidence angle on SAR backscatter was reduced by an empirical model. A cross-image normalization was used to reduce the radiometric distortions due to changes in acquisition conditions such as soil moisture. Results demonstrated that the normalization ensured that the derived biomass of regrowth forests was cross-calibrated, and thus made the detection of biomass change possible. Further, the forest biomass was mapped for 1989, 1994 and 2009 using multi-source SAR data, and changes in biomass were derived for a 15- and a 20-year period. Results improved our understanding of issues concerning the mapping of biomass dynamic using L-ban SAR data. With the increase of plot sizes, the speckle noise and geolocations errors were reduced. Multivariable models were found to outperform the single-term models developed for biomass estimation. The main contribution of this research was an improved knowledge concerning waveform LiDAR and L-band SAR’s ability in monitoring the changes in biomass in a temperate forest. Results from this study provide calibration and validation methods as a foundation for improving the performance of current and future spaceborne systems.en_US
dc.identifierhttps://doi.org/10.13016/M2GS9B
dc.identifier.urihttp://hdl.handle.net/1903/17148
dc.language.isoenen_US
dc.subject.pqcontrolledRemote sensingen_US
dc.subject.pqcontrolledGeographyen_US
dc.subject.pqcontrolledForestryen_US
dc.subject.pquncontrolledBIOMASSen_US
dc.subject.pquncontrolledFOREST DISTURBANCEen_US
dc.subject.pquncontrolledLIDARen_US
dc.subject.pquncontrolledMONITORINGen_US
dc.subject.pquncontrolledSARen_US
dc.subject.pquncontrolledTEMPERATE FORESTen_US
dc.titleASSESSING FOREST BIOMASS AND MONITORING CHANGES FROM DISTURBANCE AND RECOVERY WITH LIDAR AND SARen_US
dc.typeDissertationen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Huang_umd_0117E_16532.pdf
Size:
7.78 MB
Format:
Adobe Portable Document Format
Download

Collections

UMD Theses and Dissertations
Geography Theses and Dissertations