Geography Theses and Dissertations

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End date: 2009Subject: search.filters.subject.MODIS

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    SEASONAL AND INTERANNUAL VARIABILITY OF EMISSIONS FROM CROP RESIDUE BURNING IN THE CONTIGUOUS UNITED STATES
    (2009) McCarty, Jessica; Justice, Chrisopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Crop residue burning is a global agricultural practice used to remove excess residues before or after harvest. Crop residue burning in the contiguous United States (CONUS) has been documented at the regional and state-level by governmental organizations and in the scientific literature. Emissions from crop residue burning in the CONUS have been found to impair local and regional air quality, leading to serious health impacts and legal disputes. Currently, there is no baseline estimate for the area and emissions of crop residue burning in the CONUS. A bottom-up model for emissions calculations is employed to calculate CO2, CO, CH4, NO2, SO2, PM2.5, PM10, and Pb emissions from crop residue burning in the CONUS for the years 2003 through 2007. These atmospheric species have negative impacts on air quality and human health and are important to the carbon cycle. Spatially and temporally explicit cropland burned area and crop type products for the CONUS, necessary for emissions calculations, are developed using remote sensing approaches. The majority of crop residue burning and emissions in the CONUS are shown to occur during the spring (April - June) and fall harvests (October - December). On average, 1,239,000 ha of croplands burn annually in the CONUS with an average interannual variability of ± 91,200 ha. In general, CONUS crop residue burning emissions vary less than ±10% interannually. The states of Arkansas, California, Florida, Idaho, Texas, and Washington emit 50% of PM10, 51% of CO2, 52% of CO, and 63% of PM2.5 from all crop residue burning in the CONUS. Florida alone emits 17% of all annual CO2, CO, and PM2.5 emissions and 12% of annual PM10 emissions from crop residue burning. Crop residue burning emissions in the CONUS account for as little as 1% of global agricultural emissions and as much as 15% of all agricultural burning emissions estimates in North America, including Mexico and Canada. The results have implications for international, federal, and state-level reporting and monitoring of air quality and greenhouse gas and carbon emissions aimed at protecting human health, mitigating climate change, and understanding the carbon cycle.
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    Changes in Amazon Forest Structure from Land-Use Fires: Integrating Satellite Remote Sensing and Ecosystem Modeling
    (2008-11-17) Morton, Douglas; DeFries, Ruth S; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Fire is the dominant method of deforestation and agricultural maintenance in Amazonia, and these land-use fires frequently escape their intended boundaries and burn into adjacent forests. Initial understory fires may increase forest flammability, thereby creating a positive fire feedback and the potential for long-term changes in Amazon forest structure. The four studies in this dissertation describe the development and integration of satellite remote sensing and ecosystem modeling approaches to characterize land-use fires and their consequences in southern Amazon forests. The dissertation contributes three new methods: use of the local frequency of satellite-based active fire detections to distinguish between deforestation and maintenance fires, use of satellite data time series to identify canopy damage from understory fires, and development of a height-structured fire sub-model in Ecosystem Demography, an advanced ecosystem model, to evaluate the impacts of a positive fire feedback on forest structure and composition. Conclusions from the dissertation demonstrate that the expansion of mechanized agricultural production in southern Amazonia increased the frequency and duration of fire use compared to less intensive methods of deforestation for pasture. Based on this increase in the frequency of land-use fires, fire emissions from current deforestation may be higher than estimated for previous decades. Canopy damage from understory fires was widespread in both dry and wet years, suggesting that drought conditions may not be necessary to burn extensive areas of southern Amazon forests. Understory fires were five times more common in previously-burned than unburned forest, providing satellite-based evidence for a positive fire feedback in southern Amazonia. The impact of this positive fire feedback on forest structure and composition was assessed using the Ecosystem Demography model. Scenarios of continued understory fires under current climate conditions show the potential to trap forests in a fire-prone structure dominated by early-successional trees, similar to secondary forests, reducing net carbon storage by 20-46% within 100 years. In summary, satellite and model-based results from the dissertation demonstrate that fire-damaged forests are an extensive and long-term component of the frontier landscape in southern Amazonia and suggest that a positive fire feedback could maintain long-term changes in forest structure and composition in the region.
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    Estimating High Spatial Resolution Clear-Sky Land Surface Longwave Radiation Budget from MODIS and GOES Data
    (2008-05-06) Wang, Wenhui; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The surface radiation budget (SRB) is important in addressing a variety of scientific and application issues related to climate trends, hydrological and biogeophysical modeling, and agriculture. The three longwave components of SRB are surface downwelling, upwelling, and net longwave radiation (LWDN, LWUP, and LWNT). Existing surface longwave radiation budget (SLRB) datasets have coarse spatial resolution and their accuracy needs to be greatly improved. This study develops new hybrid methods for estimating instantaneous clear-sky high spatial resolution land LWDN and LWUP from the Moderate Resolution Imaging Spectroradiometer (MODIS, 1km) and the Geostationary Operational Environmental Satellites (GOES, 2-10 km) data. The hybrid methods combine extensive radiation transfer (physical) and statistical analysis (statistical) and share the same general framework. LWNT is derived from LWDN and LWUP. This study is the first effort to estimate SLRB using MODIS 1 km data. The new hybrid methods are unique in at least two other aspects. First, the radiation transfer simulation accounted for land surface emissivity effect. Second, the surface pressure effect in LWDN was considered explicitly by incorporating surface elevation in the statistical models. Nonlinear models were developed using the simulated databases to estimate LWDN from MODIS TOA radiance and surface elevation. Artificial Neural Network (ANN) models were developed to estimate LWUP from MODIS TOA radiance. The LWDN and LWUP models can explain more than 93.6% and 99.6% of variations in the simulated databases, respectively. Preliminary study indicates that similar hybrid methods can be developed to estimate LWDN and LWUP from the current GOES-12 Sounder data and the future GOES-R data. The new hybrid methods and alternative methods were evaluated using two years of ground measurements at six validation sites from the Surface Radiation Budget Network (SURFRAD). Validation results indicate the hybrid methods outperform alternative methods. The mean RMSEs of MODIS-derived LWDN, LWUP, and LWNT using the hybrid methods are 16.88, 15.23, and 17.30 W/m2. The RMSEs of GOES-12 Sounder-derived LWDN and LWUP are smaller than 23.70 W/m2. The high spatial resolution MODIS and GOES SLRB derived in this study is more accurate than existing datasets and can be used to support high resolution numerical models.
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    Fire Dynamics and Woody Cover Changes in the Serengeti-Mara Ecosystem 2000 to 2005 - A Remote Sensing Approach
    (2007-01-21) Dempewolf, Jan; DeFries, Ruth; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The Serengeti-Mara savanna environment in East Africa is characterized by changing levels of woody cover and a dynamic fire regime. The relative proportion of woodland to grassland savanna affects animal habitat, biodiversity, and carbon storage, and is regulated by factors such as the fire regime (frequency, intensity, seasonality), and precipitation. The main objectives of this dissertation are to determine recent changes in woody cover at a regional scale and identify fire regimes and climate associated with these changes. Understanding these relationships is important for the assessment of future trajectories of woody cover under changing climate. Required spatially coherent data layers can only be obtained at the regional scale through the analysis of remote sensing data. Woody cover changes between 2000 and 2005 were derived from field data and a time series of MODIS satellite imagery at 500 m spatial resolution. Data layers on the controlling variables (fire frequency, seasonality, intensity and rainfall) were developed using a combination of remote sensing and model-based approaches. Burned areas were mapped using daily MODIS imagery at 250 m resolution. Outputs were used to make the requisite layers depicting fire frequency and seasonality. Fire intensity was derived using a model based on empirical relationships, mainly estimating fire fuel load as a function of rainfall and grazing. The combined data layers were analyzed using regression and decision tree techniques. Results suggest woody cover in central and northern Serengeti National Park continued to increase after 2000. Woody cover decreases were strongest in the wider Maswa Game Reserve area (MSW) under low precipitation conditions and late season burning. Woody cover losses in burned areas were also higher in the low fire frequency region of the Maasai Mara National Reserve (MNR). Fire seasonality was the most important fire regime parameter controlling woody cover in burned woodland savanna areas while fire intensity was most relevant for grassland savanna areas. Continued late season burning in drought years might cause further decrease of woody cover in MSW. MNR is expected to continue to be dominated by grassland savanna at similar fire frequency and browsing levels.
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    Improving Predictive Capabilities of Environmental Change with GLOBE Data
    (2006-07-25) Robin, Jessica; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This dissertation addresses two applications of Normalized Difference Vegetation Index (NDVI) essential for predicting environmental changes. The first study focuses on whether NDVI can improve model simulations of evapotranspiration for temperate Northern (> 35) regions. The second study focuses on whether NDVI can detect phenological changes in start of season (SOS) for high Northern (> 60) environments. The overall objectives of this research were to (1) develop a methodology for utilizing GLOBE data in NDVI research; and (2) provide a critical analysis of NDVI as a long-term monitoring tool for environmental change. GLOBE is an international partnership network of K-12 students, teachers, and scientists working together to study and understand the global environment. The first study utilized data collected by one GLOBE school in Greenville, Pennsylvania and the second utilized phenology observations made by GLOBE students in Alaska. Results from the first study showed NDVI could predict transpiration periods for environments like Greenville, Pennsylvania. In phenological terms, these environments have three distinct periods (QI, QII, and QIII). QI reflects onset of the growing season (mid March - mid May) when vegetation is greening up (NDVI < 0.60) and transpiration is less than 2mm/day. QII reflects end of the growing season (mid September - October) when vegetation is greening down and transpiration is decreasing. QIII reflects height of the growing season (mid May - mid September) when transpiration rates average between 2 and 5 mm per day and NDVI is at its maximum (>0.60). Results from the second study showed that a climate threshold of 153 ± 22 growing degree days was a better predictor of SOS for Fairbanks than a NDVI threshold applied to temporal AVHRR and MODIS datasets. Accumulated growing degree days captured the inter-annual variability of SOS better than the NDVI threshold and most closely resembled actual SOS observations made by GLOBE students. Overall, biweekly composites and effects of clouds, snow, and conifers limit the ability of NDVI to monitor phenological changes in Alaska. Both studies did show that GLOBE data provides an important source of input and validation information for NDVI research.
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    Detection, Evaluation, and Analysis of Global Fire Activity Using MODIS Data
    (2006-04-26) Giglio, Louis; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Global biomass burning plays a significant role in regional and global climate change, and spaceborne sensors offer the only practical way to monitor fire activity at these scales. This dissertation primarily concerns the development, evaluation, and use of the NASA Terra and Aqua MODIS instruments for fire monitoring. MODIS is the first satellite sensor designed specifically for global monitoring of fires. An improved operational fire detection algorithm was developed for the MODIS instrument. This algorithm offers a sensitivity to small, cool fires and minimizes false alarm rates. To support the accuracy assessment of the MODIS global fire product, an operational fire detection algorithm was developed and evaluated for the ASTER instrument, which provides higher resolution observations coincident with the Terra MODIS. The unique data set of multi-year MODIS day and night fire observations was used to analyze the global distribution of biomass burning using five different temporal metrics which included, for the first time, mean fire radiative power, a measure of fire intensity. The metrics show the planetary extent, seasonality, and interannual variability of fire. Recognizing differences in fire activity between morning and afternoon overpasses, the impact of the diurnal cycle of fire activity was addressed using seven years of fire data from the VIRS sensor on-board the TRMM satellite. A strong diurnal cycle was found in all regions, with the time of peak burning varying between approximately 13:00 and 18:30 local time. Given interest in area burned among atmospheric chemical transport and carbon cycle modelers, a data set was developed utilizing the MODIS global fire and vegetation cover products to estimate monthly burned area at 1-degree spatial resolution. The methods, products and results presented in this thesis provide the global change research and fire management communities with products for global fire monitoring and are currently being used in the development of the next generation of operational satellite fire monitoring systems.
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    MODELING OF SEASONAL TRACE GAS AND PARTICULATE EMISSIONS FROM VEGETATION FIRES IN SOUTHERN AFRICA
    (2004-04-29) Korontzi, Stefania; Justice, Christopher O; Geography
    Fire is widespread in southern African savannas with important implications for tropical and global atmospheric chemistry. However, previous regional emission studies have not fully accounted for the variability of the emissions throughout the burning season and the associated impacts on emissions quantification. The main aim of this study is to address this gap. The complexity of the emissions process is described using a spatially and temporally explicit modeling approach that integrates recently published satellite-driven fuel load amounts, satellite burned area products, and empirically derived parameterizations of combustion completeness and emission factors. To represent fire behavior characteristics, land cover is classified into grasslands and woodlands, using a satellite-derived percent tree cover product. The combustion completeness is modeled as a function of grass fuel moisture and the emission factors as a function of grass fuel moisture in grasslands and fuel mixture in woodlands. Fuel moisture is derived from a fuel load model and by using satellite vegetation index time series. A sensitivity analysis with respect to three satellite burned area products reveals large differences in emissions due to differences in their amounts and spatial distribution. The analysis at the regional scale shows, that early burning in grasslands may lead to higher amounts of products of incomplete combustion despite the lower amounts of fuel consumed, compared with late dry season burning. In contrast, early burning in woodlands results in lower emissions because less fuel gets consumed. These seasonal emissions trends become more pronounced when the fuels are wetter. Burning in woodlands dominates the regional emissions budgets. Emissions estimates for various atmospheric species, many of which are modeled for the first time, are reported and compared with other regional sources of pyrogenic emissions and global biomass burning and fossil fuel emissions. The modeled estimates for 2000 are (in Tg): 537 CO<sub>2</sub>, 23.2 CO, 0.726 CH<sub>4</sub>, 0.661 NMHC, 2.4 particulates (< 2.5 micron), 1.0 NO<sub>x</sub> and account for significant fractions of regional emissions from all pyrogenic sources. Especially high is the previously undetermined contribution of Oxygenated Volatile Organic Compounds (1.8 Tg). The methodology and results have direct implications for national reporting of savanna fire emissions.