Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
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Item 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.Item MULTISCALE, MULTITEMPORAL ASSESSMENT OF CHIMPANZEE (Pan troglodytes) HABITAT USING REMOTELY SENSED DATASETS(2023) Jantz, Samuel M; Hansen, Matthew C; Geography/Library & Information Systems; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)All four sub-species of our closest living relative, the chimpanzee, are listed as endangered by the International Union for the Conservation of Nature (IUCN), and their populations continue to decline due to human activities. Effective conservation efforts require information on their population status and distribution. Traditional field surveys are expensive and impractical for covering large areas at regular time intervals, making it difficult to track population trends. Given that chimpanzees occupy a large range (2.3 x 106 km2), new cost-effective methods and data are needed to provide relevant information on population status and trends across large geographic and time scales. The objective of this dissertation is to help fill this gap by leveraging freely available and regularly updated remotely sensed datasets to map and monitor chimpanzee habitat across their range. This research begins by first producing annual forest cover and change maps for the Greater Gombe (GGE) and Greater Mahale ecosystems (GME) in western Tanzania using Landsat phenological metrics and machine learning methods. Canopy cover was predicted at 30-meter resolution and the Cumulative Sums (CuSum) algorithm was applied to the canopy cover time series to detect forest loss and gain events between 2000-2020. An accuracy assessment showed the CuSum algorithm was able to detect forest loss well but had more difficulty detecting gradual forest gain events. A total of 276,000 ha (+/- 27,000 ha) of gross forest loss was detected between 2000 and 2020 in the GGE and GME; however, loss was not spread equally among the two ecosystems. The results show widespread forest loss in the GME, contrasted with net forest cover gain in the GGE. Next, the annual forest cover maps, and additional derived variables, were used to train an ensemble model to predict the relative encounter rate of chimpanzee nest sightings in the GGE and GME. Model output exhibited a strong linear relationship to chimpanzee abundances and population density estimated from a recent ground survey, enabling model output to be linearly transformed into population estimates. The model predicted the two ecosystems harbor just over 3,000 individuals, which agrees with the upper limit of population estimates from ground surveys. Most importantly, the model can be applied to annually updated variables enabling the detection of potential population shifts caused by changes in landscape condition. Model output indicates a possible population reduction in portions of the GME, while the GGE is predicted to have increased its ability to sustain a larger population. Finally, Random Forests regression was used to relate predictor variables, primarily derived from Landsat data to a coarse resolution, range-wide habitat suitability map enabling the prediction of habitat suitability at 30 meter resolution. The model showed good agreement with the calibration data; however, there was considerable variation in predictive capability among the four chimpanzee sub-species. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. The model can detect changes in suitability to support monitoring and conservation planning across the chimpanzee range. Results from this dissertation highlight the promise of integrating continuously updated satellite data into habitat suitability models to detect changes through time and inform conservation efforts for chimpanzees at multiple scales.Item QUANTIFYING VULNERABILITY OF PENINSULAR MALAYSIA’S TIGER LANDSCAPE TO FUTURE FOREST LOSS(2018) Shevade, Varada; Loboda, Tatiana V.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Agricultural expansion has been the dominant driver of tropical deforestation and increased consumption of commodities and resulting global trade have become distal drivers of land cover change. Habitat loss and fragmentation threaten biodiversity globally. Peninsular Malaysia, particularly, has a long history of land cover land use change and expansion of plantations like those of oil palm (Elaeis guineensis). Deforestation and plantation expansion threaten the Malayan tiger (Panthera tigris jacksonii), a critically endangered subspecies of the tiger endemic to the Malay Peninsula. Conservation of tigers and their long-term viability requires not only the protection of habitat patches but also maintenance of corridors connecting habitat patches. The goal of this dissertation was to understand patterns of recent forest loss and conversions, determine the drivers of these changes, and model future forest loss and changes to landscape connectivity for tigers. Satellite remote sensing data were used to map and estimate the extent of forest loss and forest conversions to plantations within Peninsular Malaysia. Mapped forest conversions to industrial oil palm plantations were used to model the factors influencing such conversions and the constraints to recent and future conversions. Finally, the mapped forest loss was used to model the deforestation probability for the region and develop scenarios of future forest loss. This study indicates that despite the history of land cover change and an extensive area under plantations, natural forest loss has continued within Peninsular Malaysia with about half of the cleared forests being converted to plantations. Proximity to pre-existing oil palm plantations is the most important determinant of forest conversions to oil palm. Such conversions are increasingly in more marginal lands indicating that biophysical suitability alone cannot determine where future conversions might take place. Forest conversions to oil palm plantations within the region are more constrained by accessibility to infrastructure rather than biophysical suitability for oil palm. The projected patterns of loss indicate lowland forests along the southeastern coast and in the center of the Peninsula are most vulnerable to future loss. This projected loss will likely reduce the connectivity between forest patches further isolating tiger populations in the southern part of the Peninsula. This study demonstrates the continued pressure on Peninsular Malaysia’s forests, the potential impact of persistent deforestation on forest connectivity, and draws attention to the need for conservation and restoration of forest linkages to ensure viability of the remaining Malayan tiger population.