Geography Research Works
Permanent URI for this collectionhttp://hdl.handle.net/1903/1641
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Item Optimization of herbaceous feedstock delivery to a network of supply depots for a biorefinery in the Piedmont, USA(Wiley, 2023-09-13) Resop, Jonathan P.; Cundiff, John S.The southeastern USA has the potential to be a significant producer of bio-based products; however, research is still needed to demonstrate the most cost-effective feedstock delivery system for this region. A logistic system that has shown promise is one utilizing a network of supply depots. This study calculated the cost to produce a stream of size-reduced herbaceous biomass (i.e., switchgrass) for five theoretical depots in the Piedmont province. Three depots were located in south central Virginia and two in north central North Carolina. A logistics system with a 20-bale handling unit was used for load-out operations at 199 theoretical satellite storage locations (SSLs) within a 48 km radius of each depot location. The distribution of potential production fields and the transportation distance from SSLs to the depots were determined with spatial and network analyses. Based on an analysis of potential land cover available for feedstock production, the annual capacity per depot ranged from 80 839 to 170 830 Mg, resulting in a total annual capacity of 555 195 Mg for all five depots. Cost to deliver feedstock for 24/7 operation, 48 weeks per year ranged from 46.03 to 62.86 USD Mg−1 annual capacity. At the low end, these costs were: SSL operation (22%), truck (29%), receiving facility (26%), and debaling-size-reduction (23%). The principle economy-of-scale factors were the receiving facility and debaling-size-reduction costs. To minimize per-Mg cost, depot capacity should be chosen such that equipment can be operated as close to 80% of design capacity as possible.Item Including Campus Forest Carbon Estimates Into Climate Mitigation Planning -- Year 3(2023-05-01) Howerton, Michael; James, Jarrett; Kopp, Katelyn; Panday, Frances Marie; Hurtt, George C.; Lamb, RachelSummary of project led by student researchers in the UMD Department of Geographical Sciences to integrate high-resolution forest carbon estimates into the University of Maryland's Climate Action Plan and GHG Inventory. Covers year 3 progress of a three-year project funded by the UMD Sustainability Fund.Item Characterizing Low-Lying Coastal Upland Forests to Predict Future Landward Marsh Expansion(Ecological Society of America, 2024) Powell, Elisabeth; Dubayah, Ralph; Stovall, Atticus, E.L.Sea level rise (SLR) is causing vegetation regime shifts on both the seaward and landward sides of many coastal ecosystems, with the Eastern coast of North America experiencing accelerated impacts due to land subsidence and the weakening of the Gulf Stream. Tidal wetland ecosystems, known for their significant carbon storage capacity, are crucial but vulnerable blue carbon habitats. Recent observations suggest that SLR rates may exceed the threshold for elevation gain primarily through vertical accretion in many coastal regions. Therefore, research has focused on mapping the upslope migration of marshes into suitable adjacent lands, as this landward gain may be the most salient process for estimating future wetland resiliency to accelerated rates of SLR. However, our understanding of coastal vegetation characteristics and dynamics in response to SLR is limited due to a lack of in-situ data and effective mapping strategies for delineating the boundaries, or ecotones, of these complex coastal ecosystems. In order to effectively study these transitioning ecosystems, it is necessary to employ reliable and scalable landscape metrics that can differentiate between marsh and coastal forests. As such, integrating vegetation structure metrics from Light detection and ranging (Lidar) could enhance traditional mapping strategies compared to using optical data alone. Here, we used terrestrial laser scanning (TLS) to measure changes in forest structure along elevation gradients that may be indicative of degradation associated with increased inundation in the Delaware Bay estuary. We analyzed a set of TLS-derived forest structure metrics to investigate their relationships with elevation, specifically seeking those that showed consistent change from the forest edge to the interior. Our findings revealed a consistent pattern between elevation and the Plant Area Index (PAI), a metric that holds potential for enhancing the delineation of complex coastal ecosystem boundaries, particularly in relation to landward marsh migration. This work provides support for utilizing lidar-derived forest structural metrics to enable a more accurate assessment of future marsh landscapes and the overall coastal carbon sink under accelerated sea-level rise conditions.Item A New Set of MODIS Land Products (MCD18): Downward Shortwave Radiation and Photosynthetically Active Radiation(MDPI, 2020-01-03) Wang, Dongdong; Liang, Shunlin; Zhang, Yi; Gao, Xueyuan; Brown, Meredith G. L.; Jia, AolinSurface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR), its visible component, are key parameters needed for many land process models and terrestrial applications. Most existing DSR and PAR products were developed for climate studies and therefore have coarse spatial resolutions, which cannot satisfy the requirements of many applications. This paper introduces a new global high-resolution product of DSR (MCD18A1) and PAR (MCD18A2) over land surfaces using the MODIS data. The current version is Collection 6.0 at the spatial resolution of 5 km and two temporal resolutions (instantaneous and three-hour). A look-up table (LUT) based retrieval approach was chosen as the main operational algorithm so as to generate the products from the MODIS top-of-atmosphere (TOA) reflectance and other ancillary data sets. The new MCD18 products are archived and distributed via NASA’s Land Processes Distributed Active Archive Center (LP DAAC). The products have been validated based on one year of ground radiation measurements at 33 Baseline Surface Radiation Network (BSRN) and 25 AmeriFlux stations. The instantaneous DSR has a bias of −15.4 W/m2 and root mean square error (RMSE) of 101.0 W/m2, while the instantaneous PAR has a bias of −0.6 W/m2 and RMSE of 45.7 W/m2. RMSE of daily DSR is 32.3 W/m2, and that of the daily PAR is 13.1 W/m2. The accuracy of the new MODIS daily DSR data is higher than the GLASS product and lower than the CERES product, while the latter incorporates additional geostationary data with better capturing DSR diurnal variability. MCD18 products are currently under reprocessing and the new version (Collection 6.1) will provide improved spatial resolution (1 km) and accuracy.Item Contextualizing Landscape-Scale Forest Cover Loss in the Democratic Republic of Congo (DRC) between 2000 and 2015(MDPI, 2020-01-16) Molinario, Giuseppe; Hansen, Matthew; Potapov, Peter; Tyukavina, Alexandra; Stehman, StephenShifting cultivation has been shown to be the primary cause of land use change in the Democratic Republic of Congo (DRC). Traditionally, forested and fallow land are rotated in a slash and burn cycle that has created an agricultural mosaic, including secondary forest, known as the rural complex. This study investigates the land use context of new forest clearing (during 2000–2015) in primary forest areas outside of the established rural complex. These new forest clearings occur as either rural complex expansion (RCE) or isolated forest perforations (IFP), with consequent implications on the forest ecosystem and biodiversity habitat. During 2000–2015, subsistence agriculture was the dominant driver of forest clearing for both extension of settled areas and pioneer clearings removed from settled areas. Less than 1% of clearing was directly attributable to land uses such as mining, plantations, and logging, showing that the impact of commercial operations in the DRC is currently dwarfed by a reliance on small-holder shifting cultivation. However, analyzing the landscape context showed that large-scale agroindustry and resource extraction activities lead to increased forest loss and degradation beyond their previously-understood footprints. The worker populations drawn to these areas create communities that rely on shifting cultivation and non-timber forest products (NTFP) for food, energy, and building materials. An estimated 12% of forest loss within the RCE and 9% of the area of IFP was found to be within 5 km of mines, logging, or plantations. Given increasing demographic and commercial pressures on DRC’s forests, it will be crucial to factor in this landscape-level land use change dynamic in land use planning and sustainability-focused governance.Item Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping(MDPI, 2020-01-29) Potapov, Peter; Hansen, Matthew C.; Kommareddy, Indrani; Kommareddy, Anil; Turubanova, Svetlana; Pickens, Amy; Adusei, Bernard; Tyukavina, Alexandra; Ying, QingThe multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.Item Assessing Terrestrial Ecosystem Resilience using Satellite Leaf Area Index(MDPI, 2020-02-11) Wu, Jinhui; Liang, ShunlinQuantitative approaches to measuring and assessing terrestrial ecosystem resilience, which expresses the ability of an ecosystem to recover from disturbances without shifting to an alternative state or losing function and services, is critical and essential to forecasting how terrestrial ecosystems will respond to global change. However, global and continuous terrestrial resilience measurement is fraught with difficulty, and the corresponding attribution of resilience dynamics is lacking in the literature. In this study, we assessed global terrestrial ecosystem resilience based on the long time-series GLASS LAI product and GIMMS AVHRR LAI 3g product, and validated the results using drought and fire events as the main disturbance indicators. We also analyzed the spatial and temporal variations of global terrestrial ecosystem resilience and attributed their dynamics to climate change and environmental factors. The results showed that arid and semiarid areas exhibited low resilience. We found that evergreen broadleaf forest exhibited the highest resilience (mean resilience value (from GLASS LAI): 0.6). On a global scale, the increase of mean annual precipitation had a positive impact on terrestrial resilience enhancement, while we found no consistent relationships between mean annual temperature and terrestrial resilience. For terrestrial resilience dynamics, we observed three dramatic raises of disturbance frequency in 1989, 1995, and 2001, respectively, along with three significant drops in resilience correspondingly. Our study mapped continuous spatiotemporal variation and captured interannual variations in terrestrial ecosystem resilience. This study demonstrates that remote sensing data are effective for monitoring terrestrial resilience for global ecosystem assessment.Item Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices—Prospects and Case Study(MDPI, 2020-02-07) Liang, Mengyu; Pause, Marion; Prechtel, Nikolas; Schramm, MatthiasSurface soil moisture (SSM) plays a critical role in many hydrological, biological and biogeochemical processes. It is relevant to farmers, scientists, and policymakers for making effective land management decisions. However, coarse spatial resolution and complex interactions of microwave radiation with surface roughness and vegetation structure present limitations within active remote sensing products to directly monitor soil moisture variations with sufficient detail. This paper discusses a strategy to use vegetation indices (VI) such as greenness, water stress, coverage, vigor, and growth dynamics, derived from Earth Observation (EO) data for an indirect characterization of SSM conditions. In this regional-scale study of a wetland environment, correlations between the coarse Advanced SCATterometer-Soil Water Index (ASCAT-SWI or SWI) product and statistical measurements of four vegetation indices from higher resolution Sentinel-2 data were analyzed. The results indicate that the mean value of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) correlates most strongly to the SWI and that the wet season vegetation traits show stronger linear relation to the SWI than during the dry season. The correlation between VIs and SWI was found to be independent of the underlying dominant vegetation classes which are not derived in real-time. Therefore, fine-scale vegetation information from optical satellite data convey the spatial heterogeneity missed by coarse synthetic aperture radar (SAR)-derived SSM products and is linked to the SSM condition underneath for regionalization purposes.Item Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation(MDPI, 2020-01-23) Brown, Meredith G. L.; Skakun, Sergii; He, Tao; Liang, ShunlinSatellite-derived estimates of downward surface shortwave radiation (SSR) and photosynthetically active radiation (PAR) are a part of the surface radiation budget, an essential climate variable (ECV) required by climate and vegetation models. Ground measurements are insufficient for generating long-term, global measurements of surface radiation, primarily due to spatial limitations; however, remotely sensed Earth observations offer freely available, multi-day, global coverage of radiance that can be used to derive SSR and PAR estimates. Satellite-derived SSR and PAR estimates are generated by computing the radiative transfer inversion of top-of-atmosphere (TOA) measurements, and require ancillary data on the atmospheric condition. To reduce computational costs, often the radiative transfer calculations are done offline and large look-up tables (LUTs) are generated to derive estimates more quickly. Recently studies have begun exploring the use of machine-learning techniques, such as neural networks, to try to improve computational efficiency. Here, nine machine-learning methods were tested to model SSR and PAR using minimal input data from the Moderate Resolution Imaging Spectrometer (MODIS) observations at 1 km spatial resolution. The aim was to reduce the input data requirements to create the most robust model possible. The bootstrap aggregated decision tree (Bagged Tree), Gaussian Process Regression, and Neural Network yielded the best results with minimal training data requirements: an 𝑅2 of 0.77, 0.78, and 0.78 respectively, a bias of 0 ± 6, 0 ± 6, and 0 ± 5 W/m2, and an RMSE of 140 ± 7, 135 ± 8, and 138 ± 7 W/m2, respectively, for all-sky condition total surface shortwave radiation and viewing angles less than 55°. Viewing angles above 55° were excluded because the residual analysis showed exponential error growth above 55°. A simple, robust model for estimating SSR and PAR using machine-learning methods is useful for a variety of climate system studies. Future studies may focus on developing high temporal resolution direct and diffuse estimates of SSR and PAR as most current models estimate only total SSR or PAR.Item Understanding the Knowledge and Data Landscape of Climate Change Impacts and Adaptation in the Chesapeake Bay Region: A Systematic Review(MDPI, 2020-04-17) Teodoro, Jose Daniel; Nairn, BruceClimate change is increasingly threatening coastal communities around the world. This article reviews the literature on climate change impacts and adaptation in the Chesapeake Bay region (USA). We reviewed both climate impacts and adaptation literature (n = 283) published in the period 2007–2018 to answer the questions: (i) how are indicators of climate impacts measured and reported by different types of authors (e.g., scientists, government, and NGOs), document types (e.g., academic articles or reports), and geographic focus (e.g., State, region, county, or municipal level)? (ii) what are the current approaches for measuring the most pressing climate impacts in the Chesapeake Bay? We found that scientists produce the most amount of data but are increasingly shifting towards engaging with practitioners through reports and online resources. Most indicators focus on the Chesapeake Bay scale, but data is most needed at the local level where adaptive policies are implemented. Our analysis shows emerging approaches to monitoring climate hazards and areas where synergies between types of authors are likely to increase resilience in the 21st century. This review expands the understanding of the information network in the Chesapeake Bay and explores the institutional landscape of stakeholders involved in the production and consumption of environmental and social change data. The analysis and insights of this review may be extended to similar regions around the planet experiencing or anticipating similar climate hazards to the Chesapeake Bay.