College of Behavioral & Social Sciences

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations..

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    Monitoring Aboveground Biomass in Forest Conservation and Restoration Areas Using GEDI and Optical Data Fusion
    (2024) Liang, Mengyu; Duncanson, Laura I; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Forests play a critical role in the global carbon cycle by sequestering carbon in the form of aboveground biomass. Area-based conservation measures, such as protected areas (PAs), are a cornerstone conservation strategy for preserving some of the world's most at-risk forest ecosystems. Beyond PAs, tree planting and forest restoration have been lauded as solutions to combat climate change and criticized as ways for polluters to offset carbon emissions. Consistent monitoring and quantification of forest restoration can impact decisions on future restoration activities. In this dissertation, I utilized a fusion of remote sensing assets and a combination of remote sensing with impact assessment techniques, to obtain objective baseline information for reconstructing past forest biomass conditions, and for monitoring and quantifying the patterns and success of forest regrowth in areas that underwent different forest management interventions. This overarching research goal is approached in three studies corresponding to chapters 2-4. In chapter 2, PAs’ effectiveness in storing biomass carbon and preserving forest structure is assessed on a regional scale using Global Ecosystem Dynamics Investigation (GEDI) lidar data in combination with a counterfactual analysis using statistical matching. This chapter provides an assessment of the reference condition of the biomass carbon storage capacity by one of the most stringent forest management means. The study finds that analyzed PAs in Tanzania possess 24.4% higher biomass densities than their unprotected counterparts and highlights that community-governed PAs are the most effective category of PAs at preserving forest structure and aboveground biomass density (AGBD). In chapter 3, empirical models are developed to link current (2019-2020) AGBD estimates from the GEDI with Landsat (2007-2019) at a regional scale. This will allow both current wall-to-wall biomass mapping and estimation of biomass dynamics across time. We demonstrate the utility of the method by applying it to quantify the AGBD dynamics associated with forest degradation for charcoal production. In chapter 4, the same modeling framework laid out in chapter 3 will be used to derive AGBD trajectories for 27 forest restoration sites across three biomes in East Africa. To assess the effectiveness of and compare Assisted Natural Regeneration (ANR) and Active Restoration (AR) in enhancing forest AGBD growth compared to natural regeneration (NR), we used staggered difference-in-difference (staggered DiD) to analyze the average annual AGBD change. We controlled for pre-intervention AGBD change rate between AR/ANR and NR and estimated the effectiveness with explicit consideration of intervention duration. This study finds that AR and ANR outperform NR during long-term restoration. Using the most suitable restoration interventions in each biome and timeframe, 4% suitable areas could enhance 2.40 ± 0.78 Gt (billion metric tons) forest carbon uptake over 30 years, equivalent to 3.6 years of African-wide emissions. Overall, this dissertation develops remote sensing methodological frameworks for using GEDI data and its fusion with Landsat time series to quantify and monitor forest AGBD. Moreover, by combining remote sensing-derived AGBD dynamics with impact assessment techniques, such as statistical matching and staggered DiD, the dissertation further assesses and compares different conservation and restoration means’ effectiveness in increasing AGBD and carbon uptake in forests. The dissertation therefore advances the applications of state-of-the-art remote sensing data and techniques for sustainably managing forests towards climate mitigation targets.
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    Advances in Mapping Forest Biomass and Old-Growth Conditions Using Waveform Lidar
    (2023) Bruening, Jamis; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne waveform lidar sys- tem that has transformed scientific understanding of the world’s forests through billions of pre- cise measurements of ecosystem structure. Relative to forest processes that operate on decadal to millennial timescales, the four year period during which GEDI collected these measurements is short, and GEDI’s ability to analyze how forest structure changes over time is mostly unproven. However, fusion efforts that integrate GEDI data with forest inventory measurements and ecosys- tem models hold immense potential for discovery. In this dissertation, I explore the limitations and capabilities of GEDI data for inference into structural and successional dynamics within east- ern US forests. First, I used a forest gap model to quantify uncertainty in biomass predictions for individual GEDI waveforms, and discovered a relationship between biomass uncertainty and successional stage. Next, I investigated uncertainties and errors in large-scale GEDI biomass estimates relative to unbiased estimates from the US forest inventory. I developed a novel mod- eling framework based on fusion between GEDI and the US forest inventory data that corrected these errors, and I produced unbiased and precise maps of forest biomass for the continental US. Lastly, I assessed GEDI’s ability to identify and map different types of old-growth forests, and discovered that GEDI can detect some old forests more effectively than others. This research identified key limitations associated with using GEDI to study forest dynamics, and I leveraged these discoveries to develop new ways of using GEDI data for ecological and successional in- ference. These discoveries will inform new uses of GEDI data and its integration with inventory data and ecosystem modeling to better characterize changes within forest ecosystems.
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    Characterization of Canopy Layering in Forested Ecosystems Using Full Waveform Lidar
    (MDPI, 2013-04-22) Whitehurst, Amanda S.; Swatantran, Anu; Blair, J. Bryan; Hofton, Michelle A.; Dubayah, Ralph
    Canopy structure, the vertical distribution of canopy material, is an important element of forest ecosystem dynamics and habitat preference. Although vertical stratification, or “canopy layering,” is a basic characterization of canopy structure for research and forest management, it is difficult to quantify at landscape scales. In this paper we describe canopy structure and develop methodologies to map forest vertical stratification in a mixed temperate forest using full-waveform lidar. Two definitions—one categorical and one continuous—are used to map canopy layering over Hubbard Brook Experimental Forest, New Hampshire with lidar data collected in 2009 by NASA’s Laser Vegetation Imaging Sensor (LVIS). The two resulting canopy layering datasets describe variation of canopy layering throughout the forest and show that layering varies with terrain elevation and canopy height. This information should provide increased understanding of vertical structure variability and aid habitat characterization and other forest management activities.
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    Drone Laser Scanning for Modeling Riverscape Topography and Vegetation: Comparison with Traditional Aerial Lidar
    (MDPI, 2019-04-12) Resop, Jonathan P.; Lehmann, Laura; Hession, W. Cully
    Lidar remote sensing has been used to survey stream channel and floodplain topography for decades. However, traditional platforms, such as aerial laser scanning (ALS) from an airplane, have limitations including flight altitude and scan angle that prevent the scanner from collecting a complete survey of the riverscape. Drone laser scanning (DLS) or unmanned aerial vehicle (UAV)-based lidar offer ways to scan riverscapes with many potential advantages over ALS. We compared point clouds and lidar data products generated with both DLS and ALS for a small gravel-bed stream, Stroubles Creek, located in Blacksburg, VA. Lidar data points were classified as ground and vegetation, and then rasterized to produce digital terrain models (DTMs) representing the topography and canopy height models (CHMs) representing the vegetation. The results highlighted that the lower-altitude, higher-resolution DLS data were more capable than ALS of providing details of the channel profile as well as detecting small vegetation on the floodplain. The greater detail gained with DLS will provide fluvial researchers with better estimates of the physical properties of riverscape topography and vegetation.
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    Quantifying the Spatial Variability of Annual and Seasonal Changes in Riverscape Vegetation Using Drone Laser Scanning
    (MDPI, 2021-09-07) Resop, Jonathan P.; Lehmann, Laura; Hession, W. Cully
    Riverscapes are complex ecosystems consisting of dynamic processes influenced by spatially heterogeneous physical features. A critical component of riverscapes is vegetation in the stream channel and floodplain, which influences flooding and provides habitat. Riverscape vegetation can be highly variable in size and structure, including wetland plants, grasses, shrubs, and trees. This vegetation variability is difficult to precisely measure over large extents with traditional surveying tools. Drone laser scanning (DLS), or UAV-based lidar, has shown potential for measuring topography and vegetation over large extents at a high resolution but has yet to be used to quantify both the temporal and spatial variability of riverscape vegetation. Scans were performed on a reach of Stroubles Creek in Blacksburg, VA, USA six times between 2017 and 2019. Change was calculated both annually and seasonally over the two-year period. Metrics were derived from the lidar scans to represent different aspects of riverscape vegetation: height, roughness, and density. Vegetation was classified as scrub or tree based on the height above ground and 604 trees were manually identified in the riverscape, which grew on average by 0.74 m annually. Trees had greater annual growth and scrub had greater seasonal variability. Height and roughness were better measures of annual growth and density was a better measure of seasonal variability. The results demonstrate the advantage of repeat surveys with high-resolution DLS for detecting seasonal variability in the riverscape environment, including the growth and decay of floodplain vegetation, which is critical information for various hydraulic and ecological applications.
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    Characterizing tree species diversity in the tropics using full-waveform lidar data
    (2019) Marselis, Suzanne; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Tree species diversity is of paramount value to maintain forest health and to ensure that forests are able to provide all vital functions, such as creating oxygen, that are needed for mankind to survive. Most of the world’s tree species grow in the tropical region, but many of them are threatened with extinction due to increasing natural and human-induced pressures on the environment. Mapping tree species diversity in the tropics is of high importance to enable effective conservation management of these highly diverse forests. This dissertation explores a new approach to mapping tree species diversity by using information on the vertical canopy structure derived from full-waveform lidar data. This approach is of particular interest in light of the recently launched Global Ecosystem Dynamics Investigation (GEDI), a full-waveform spaceborne lidar. First, successful derivation of vertical canopy structure metrics is ensured by comparing canopy profiles from airborne lidar data to those from terrestrial lidar. Then, the airborne canopy profiles were used to map five successional vegetation types in Lopé National Park in Gabon, Africa. Second, the relationship between vertical canopy structure and tree species richness was evaluated across four study sites in Gabon, which enabled mapping of tree species richness using canopy structure information from full-waveform lidar. Third, the relationship between canopy structure and tree species richness across the tropics was established using field and lidar data collected in 16 study sites across the tropics. Finally, it was evaluated how the methods and applications developed here could be adapted and used for mapping pan-tropical tree species diversity using future GEDI lidar data products.
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    MEASURING AND MAPPING FOREST WILDLIFE HABITAT CHARACTERISTICS USING LIDAR REMOTE SENSING AND MULTI-SENSOR FUSION
    (2005-12-05) Hyde, Peter; Dubayah, Ralph O.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Managing 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.
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    Canopy Fuels Inventory and Mapping Using Large-Footprint Lidar
    (2005-12-05) Peterson, Birgit; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This dissertation explores the efficacy of large-footprint, waveform-digitizing lidar for the inventory and mapping of canopy fuels for utilization in fire behavior simulation models. Because of its ability to measure the vertical structure of forest canopies lidar is uniquely suited among remote sensing instruments to observe the canopy structure characteristics relevant to fuels characterization and may help address the lack of high-quality fuels data for many regions, especially in more remote areas. Lidar data were collected by the Laser Vegetation Imaging Sensor (LVIS) over the Sierra National Forest in California. Various waveform metrics were calculated from the waveforms. Field data were collected at 135 plots co-located with a subset of the lidar footprints. The field data were used to calculate ground-based observations of canopy bulk density (CBD) and canopy base height (CBH). These observed values of CBD and CBH were used as dependent variables in a series of regression analyses using the derived lidar metrics as independent variables. Comparisons of observed and predicted resulted in an r2 of 0.71 for CBD and an r2 of 0.59 for CBH. These regression models were then used to generate grids of CBD and CBH from all of the lidar waveform data in the study area. These grids, along with lidar-derived grids of canopy height, were then used as inputs to the FARSITE (Fire Area Simulator-Model) fire behavior model in a series of simulations. Comparisons between conventionally derived and lidar-based model inputs showed differences between the two sets of data. Specifically, the lidar-derived inputs contained much more spatial heterogeneity. Outputs from FARSITE using the lidar-derived inputs were also compared to outputs using input maps of CBD and CBH generated from field observations. There were significant differences between the two sets of outputs, especially in the frequency and spatial distribution of crown fire. Experiments in manipulating the effective resolution of the lidar-based inputs confirmed that FARSITE outputs are affected by the spatial variability of the input data. Furthermore, a sensitivity analysis demonstrated that FARSITE is sensitive to potential errors in the canopy structure input grids. The results of this dissertation show that lidar can be used effectively to predict CBD and CBH for the purpose of fire behavior modeling and that investment in these lidar-based canopy structure data is worthwhile, especially for forests characterized by significant heterogeneity. This work affirms that lidar is a useful tool for future canopy fuels inventory and mapping.