Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
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Item Advanced Modeling Using Land-use History and Remote Sensing to Improve Projections of Terrestrial Carbon Dynamics(2021) Ma, Lei; Hurtt, George; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Quantifying, attributing, and projecting terrestrial carbon dynamics can provide valuable information in support of climate mitigation policy to limit global warming to 1.5 °C. Current modeling efforts still involve considerable uncertainties, due in part to knowledge gaps regarding efficient and accurate scaling of individual-scale ecological processes to large-scale dynamics and contemporary ecosystem conditions (e.g., successional states and carbon storage), which present strong spatial heterogeneity. To address these gaps, this research aims to leverage decadal advances in land-use modeling, remote sensing, and ecosystem modeling to improve the projection of terrestrial carbon dynamics at various temporal and spatial scales. Specifically, this research examines the role of land-use modeling and lidar observations in determining contemporary ecosystem conditions, especially in forest, using the latest land-use change dataset, developed as the standard forcing for CMIP6, and observations from both airborne lidar and two state-of-the-art NASA spaceborne lidarmissions, GEDI and ICESat-2. Both land-use change dataset and lidar observations are used to initialize a newly developed global version of the ecosystem demography (ED) model, an individual-based forest model with unique capabilities to characterize fine-scale processes and efficiently scale them to larger dynamics. Evaluations against multiple benchmarking datasets suggest that the incorporation of land-use modeling into the ED model can reproduce the observed spatial pattern of vegetation distribution, carbon dynamics, and forest structure as well as the temporal dynamics in carbon fluxes in response to climate change, increased CO2, and land-use change. Further, the incorporation of lidar observations into ED, largely enhances the model’s ability to characterize carbon dynamics at fine spatial resolutions (e.g., 90 m and 1 km). Combining global ED model, land-use modeling and lidar observation together can has great potential to improve projections of future terrestrial carbon dynamics in response to climate change and land-use change.Item FUSING GEDI LIDAR AND TANDEM-X INSAR OBSERVATIONS FOR IMPROVED FOREST STRUCTURE AND BIOMASS MAPPING(2018) Qi, Wenlu; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)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.Item Lidar Remote Sensing of Vertical Foliage Profile and Leaf Area Index(2015) Tang, Hao; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Leaf Area Index (LAI) and Vertical Foliage Profile (VFP) are among the most important forest structural parameters, and characterization of those parameters in high biomass forests remains a major challenge in passive remote sensing due to signal saturation problem. Recently an active remote sensing technology, light detection and ranging (lidar), has shown a great promise in this task recognizing its accuracy in measuring aboveground biomass and canopy height. This dissertation further expands current application of lidar on ecosystem monitoring, and explores the capacity of deriving LAI and VFP from lidar data in particular. The overall goal of this study is to derive large scale forest LAI and VFP using data from the Geoscience Laser Altimeter System (GLAS) on board of ICESat, and provide a framework of validating such LAI products from plot level to global scale. To achieve this goal, a physically based Geometry Optical and Radiative Transfer (GORT) model was first developed using high quality airborne waveform lidar data over a tropical rainforest in La Selva, Costa Rica. The excellent agreement between lidar data and field destructively sampled data demonstrated the effectiveness of the Lidar-LAI model and suggested large footprint waveform lidar can provide accurate vertical LAI profile estimates that do not saturate even at the highest possible LAI levels. Next, an intercomparative study of ground-based, airborne and spaceborne retrievals of total LAI was conducted over the conifer-dominated forests of Sierra Nevada in California. Good relationships were discovered in their comparisons, following a scaling-up validation strategy where ground-based LAI observations were related to aircraft observations of LAI, which in turn were used to validate GLAS LAI derived from coincident data. Successful implementation of this strategy can pave the way for the future recovery of vertical LAI profiles globally. LAI and VFP products were then derived over both the entire state of California and Contiguous United States as an efficacy demonstration of the method. These products were the first ever attempts to obtain large scale estimates of LAI and VFP from lidar observations. Such forest structural measurement can be used not only to quantify carbon stock and flux of terrestrial ecosystem, but also to provide spatial information of specie abundance in biodiversity. Results from this study can also greatly help broaden scientific applications of future spaceborne lidar missions (e.g. ICESat-2 and GEDI).Item LINKING ALLOMETRIC SCALING THEORY WITH LIDAR REMOTE SENSING FOR IMPROVED BIOMASS ESTIMATION AND ECOSYSTEM CHARACTERIZATION(2015) Duncanson, Laura; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Accurate quantification of forest carbon stocks and fluxes is critical for the successful modeling and mitigation of climate change. This research focuses on forest carbon stock quantification, both in terms of testing emerging remote sensing approaches to forest carbon modeling, and examining allometric equations used to estimate biomass stocks in field plots. First, we test controversial theoretical predictions of forest allometry through the mapping of the allometric variability using field plots across the U.S. we find that there is considerable variability in forest allometry across space, largely driven by local environment and life history. However, in tall forests, allometries tend to converge toward theoretical predictions, suggesting that theory may be a useful constraint on allometry in certain forests. Second, we shift to an analysis of empirical allometries by developing an algorithm to extract individual crown information from forest systems and using it for biomass mapping and allometric equation testing. Third, we test whether individual tree structure bolsters biomass modeling capabilities in comparison to tradition, plot-aggregated LiDAR metrics. As part of this analysis we also test an allometric scaling-based approach to biomass mapping. We find that individual tree-level structure only improves biomass models when there is considerable spatial heterogeneity in the forest. Also, allometric scaling-based only worked in one study site, and failed in the other two sites because there was little or no relationship between basal area and maximum canopy height. Finally, we applied LiDAR datasets to an analysis of the effects of sample size on empirical allometry development. We found that small samples sizes tend to result in an under sampling of large stems, which yields a more linear fit than the true allometry. An assessment of the potential carbon implications of this problem yielded site-level biomass predictions with biases of 10-178%. We suggest that empirical allometric equations developed on small sample sizes, as applied in the U.S., yield potentially large errors in biomass and therefore require careful reassessment. In combination with our findings regarding the spatial variability of forest allometry, we believe that the limiting factor to forest carbon estimation is the use of allometric equations.Item ASSESSING THE RELATIONSHIPS BETWEEN VERTICAL STRUCTURE, BIODIVERSITY, AND SUCCESSION IN A FOREST ECOSYSTEM USING LIDAR REMOTE SENSING(2014) Whitehurst, Amanda Sharon; Dubayah, Ralph O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis used lidar remote sensing to explore the role of vertical structure in forest ecosystem dynamics. In particular, relationship between the vertical distribution, biodiversity, and succession was examined in Hubbard Brook Experimental Forest, NH (HBEF). The first objective was to develop metrics characterizing vertical foliage distribution or canopy layering. Two novel metrics (canopy layer structure categories and number of foliage profile layers) were created, allowing canopy layering to be mapped HBEF. The canopy layer structure metric categorizes areas by comparing the amount of vegetation in under, mid, and overstories. The number of foliage profile layers is related to peaks in the foliage area profile, representing area of dense of "clumped" foliage. Both these metrics varied with canopy height and elevation, areas with taller trees and lower elevations tended to have more foliage profile layers and were classified as categories with a dominant overstory. The second objective was to examine the relationship between vertical canopy structure and avian species diversity. Multiple vertical structure metrics were derived for 370 bird plots in HBEF. Foliage height diversity (FHD) varied greatly in relation to bird species diversity. Of the foliage distribution metrics, vegetation ratio and number of foliage profile layers explained the most variability in bird species diversity. The lidar metric of height at median return (HOME) had the strongest correlation with bird species diversity (r = - 0.56). This study showed a moderate correlation between bird species diversity and foliage distribution metrics. It further supports previous studies which question the applicability of FHD. Finally, change in vertical structure in HBEF was examined using lidar data from 1999 and 2009. Due to significant change in canopy height, canopy cover, vegetation ratio and understory cover during the time period, it was determined that HBEF had not reached steady-state. Recently disturbed areas had significantly higher canopy height growth than undisturbed areas, despite being at higher elevations. This research presents standardized metrics for the characterization and mapping of canopy foliage distribution. It also provides ecological links between lidar metrics and ecological concepts to enabling these measurements of forest structure to be applied in other areas.Item MAPPING FOREST STRUCTURE AND HABITAT CHARACTERISTICS USING LIDAR AND MULTI-SENSOR FUSION(2011) Swatantran, Anuradha; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This 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.