Sangam: A Confluence of Knowledge Streams

FUSING GEDI LIDAR AND TANDEM-X INSAR OBSERVATIONS FOR IMPROVED FOREST STRUCTURE AND BIOMASS MAPPING

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dc.contributor Dubayah, Ralph
dc.contributor Digital Repository at the University of Maryland
dc.contributor University of Maryland (College Park, Md.)
dc.contributor Geography
dc.creator Qi, Wenlu
dc.date 2018-09-07T05:36:27Z
dc.date 2018-09-07T05:36:27Z
dc.date 2018
dc.date.accessioned 2022-05-20T08:39:15Z
dc.date.available 2022-05-20T08:39:15Z
dc.identifier doi:10.13016/M2W37KZ9B
dc.identifier http://hdl.handle.net/1903/21138
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/117697
dc.description The upcoming NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission presents an unprecedented opportunity to advance current global biomass estimates. However, gaps are expected between GEDI’s ground tracks, requiring the development of fusion-based methodologies to contiguously map forest biomass at satisfactory resolutions and accuracies. This dissertation is built on the complementary advantages of observations from GEDI and DLR’s TerraSAR-X/TanDEM-X (TDX)) Interferometric Synthetic Aperture Radar (InSAR) mission. To meet the goal of mapping forest structure and biomass contiguously and accurately, three types of fusion strategies have been investigated. First, a simulated GEDI-derived digital terrain model (DTM) was utilized to improve height estimation from TDX. Forest heights were initially derived from TDX coherence alone as a baseline using the widely used Random Volume over Ground (RVoG) scattering model. Here, assumptions about RVoG parameters – extinction coefficient (σ) and ground-to-volume amplitude ratio (µ) – were made. Using an external DTM derived from simulated GEDI lidar data, RVoG model was used to calculate spatially varied σ values and derived forest heights with better accuracy. TDX forest height estimation was further improved with the aid of simulated GEDI-derived DTM and canopy heights. The additional use of simulated GEDI canopy heights as RVoG input not just refined σ but also enabled the estimation of µ. Based on these parameters, forest heights were improved across three different forest types; biases were reduced from 1.7–3.8 m using only simulated GEDI DTMs to -0.9–1.1 m by using both simulated GEDI DTMs and canopy heights. Finally, wall-to-wall TDX heights were used to improve biomass estimates from simulated GEDI data over three contrasting forest types. When using simulated GEDI sampled observations alone, uncertainties were estimated statistically to be 9.0–19.9% at 1 km. These were improved to 5.2–11.7% at the same resolution by upscaling simulated GEDI footprint biomass with TDX heights. The GEDI/TDX data fusion also enabled the generation of biomass maps at a fine spatial resolution of 100 m, with uncertainties estimated to be 6.0–14.0%. Through the exploration of these fusion strategies, it has been demonstrated that a fusion-based mapping method could realize the generation of forest biomass products from GEDI with unprecedented resolutions and accuracies, while taking advantage of global seamless observations from TDX.
dc.format application/pdf
dc.language en
dc.subject Geography
dc.subject Biomass
dc.subject Forest structure
dc.subject GEDI
dc.subject Lidar
dc.subject Synthetic Aperture Radar
dc.subject TanDEM-X
dc.title FUSING GEDI LIDAR AND TANDEM-X INSAR OBSERVATIONS FOR IMPROVED FOREST STRUCTURE AND BIOMASS MAPPING
dc.type Dissertation


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