Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
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Item THE EFFECTS OF CHANGES IN LAND COVER AND LAND USE ON NUTRIENT LOADINGS TO THE CHESAPEAKE BAY USING FORECASTS OF URBANIZATION(2009) Roberts, Allen Derrick; Prince, Stephen D.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examined the effects of land cover and land use (LC/LU) change on nutrient loadings (mass for a specified time) to the Chesapeake Bay, after future projections of urbanization were applied. This was accomplished by quantifying the comprehensive impacts of landscape on nutrients throughout the watershed. In order to quantify forecasted impacts of future development and LC/LU change, the current (2000) effects of landscape composition and configuration on total nitrogen (TN) and total phosphorus (TP) were examined. The effects of cover types were examined not only at catchment scales, but within riparian stream buffer to quantify the effects of spatial arrangement. Using the SPAtially Referenced Regressions On Watershed Attributes (SPARROW) model, several compositional and configurational metrics at both scales were significantly correlated to nutrient genesis and transport and helped estimate loadings to the Chesapeake Bay with slightly better accuracy and precision. Remotely sensed forecasts of future (2030) urbanization were integrated into SPARROW using these metrics to project TN and TP loadings into the future. After estimation of these metrics and other LC/LU-based sources, it was found that overall nutrient transport to the Chesapeake Bay will decrease due to agricultural land losses and fertilizer reductions. Although point and non-point source urban loadings increased in the watershed, these gains were not enough to negate decreased agricultural impacts. In catchments forecasted to undergo urban sprawl conditions by 2030, the response of TN locally generated within catchments varied. The forecasted placement of smaller patches of development within agricultural lands of higher nutrient production was correlated to projected losses. However, shifting forecasted growth onto or adjacent to existing development, not agricultural lands, resulted in projected gains. This indicated the importance of forecasted spatial arrangement to projected TN runoff from the watershed. In conclusion, comprehensive landscape analysis resulted in differences in simulations of current and future nutrient loadings to the Chesapeake Bay, as a result of urbanization and LC/LU change. With eutrophication from excess nutrients being the primary challenge to the estuary, information gained from the estimation of these effects could improve the future management and regulation of the Chesapeake Bay.Item AN APPROACH TO ESTIMATE GLOBAL BIOMASS BURNING EMISSIONS OF ORGANIC AND BLACK CARBON FROM MODIS FIRE RADIATIVE POWER(2009) Ellicott, Evan Andrew; Justice, Christopher O; Vermote, Eric; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Biomass burning is an important global phenomenon affecting atmospheric composition with significant implications for climatic forcing. Wildland fire is the main global source of fine primary carbonaceous aerosols in the form of organic carbon (OC) and black carbon (BC), but uncertainty in aerosol emission estimates from biomass burning is still rather large. Application of satellite based measures of fire radiative power (FRP) has been demonstrated to offer an alternative approach to estimate biomass consumed with the potential to estimate the associated emissions from fires. To date, though, no study has derived integrated FRP (referred to as fire radiative energy or FRE) at a global scale, in part due to limitations in temporal or spatial resolution of satellite sensors. The main objective of this research was to quantify global biomass burning emissions of organic and black carbon aerosols and the corresponding effect on planetary radiative forcing. The approach is based on the geophysical relationship between the flux of FRE emitted, biomass consumed, and aerosol emissions. Aqua and Terra MODIS observations were used to estimate FRE using a simple model to parameterize the fire diurnal cycle based on the long term ratio between Terra and Aqua MODIS FRP and cases of diurnal satellite measurements of FRP made by the geostationary sensor SEVIRI, precessing sensor VIRS, and high latitude (and thus high overpass frequency) observations by MODIS. Investigation of the atmospheric attenuation of MODIS channels using a parametric model based on the MODTRAN radiative transfer model indicates a small bias in FRE estimates which was accounted for. Accuracy assessment shows that the FRE estimates are precise (R2 = 0.85), but may be underestimated. Global estimates of FRE show that Africa and South America dominate biomass burning, accounting for nearly 70% of the annual FRE generated. The relationship between FRE and OCBC estimates made with a new MODIS-derived inversion product of daily integrated biomass burning aerosol emissions was explored. The slope of the relationship within each of several biomes yielded a FRE-based emission factor. The biome specific emission factors and FRE monthly data were used to estimate OCBC emissions from fires on a global basis for 2001 to 2007. The annual average was 17.23 Tg which was comparable to previously published values, but slightly lower. The result in terms of global radiative forcing suggests a cooling effect at both the top-of-atmosphere (TOA) and surface approaching almost -0.5 K which implies that biomass burning aerosols could dampen the warming effect of green house gas emissions. An error budget was developed to explore the sources and total uncertainty in the OCBC estimation. The results yielded an uncertainty value of 58% with specific components of the process warranting future consideration and improvement. The uncertainty estimate does not demonstrate a significant improvement over current methods to estimate biomass burning aerosols, but given the simplicity of the approach should allow for refinements to be made with relative ease.Item 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.Item Estimation of land surface radiation budget from MODIS data(2008-08-04) Kim, Hye-Yun; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Land Surface Radiation Budget (SRB) is responsible for the available energy between the Earth and atmosphere system. Net radiation is the driving force for the transportation and exchange of all matter at the interface between the Earth's surface and the atmosphere, and therefore, significantly affects the climatic forming and change. Accurate estimation of shortwave net radiation (Sn), cloudy-sky allwave net radiation (Rn), and daily integrated Sn at high spatial resolution is essential in regional and global land surface models. The current SRB products have fine temporal and coarse spatial resolutions not suitable for land applications. New hybrid algorithm for Sn estimation has been developed in this study. Sn is estimated from MODIS data under both clear- and cloudy-sky conditions without requiring coarser resolution ancillary data. Therefore, estimated Sn retains the spatial resolution of the raw input data. Surface all-wave (both shortwave and longwave) net radiation (Rn) controls the input of latent and sensible heat flux into the atmosphere over the Earth's surface. Meteorological datasets are spatially limited and satellite data have the advantage of global spatial coverage; however, difficulty in accurately estimating cloudy-sky longwave net radiation (Ln) undermines efforts to estimate cloudy-sky all-wave net radiation. This study presents methods for estimating cloudy-sky Rn using Sn and other surface variables at 1 km spatial resolution. Daily integrated Sn is closely related to carbon, water and energy flux simulations. A daily integrated Sn product with a 1-km spatial resolution supports recent high resolution numerical climate and ecosystem simulations. This study describes a method for estimating daily integrated Sn in 1 km resolution based on instantaneous Sn data. All these algorithms have been validated using seven sites of a SURFace RADiation budget observing network (SURFRAD) in United States, instantaneous Sn is also compared with GEWEX/SRB and ISCCP data. The new hybrid algorithm developed in the study can be easily implemented to generate operational global products. These finer spatial resolution datasets capture the specific sequence of the redistribution of the available energy at the Earth's surface; therefore, they support recent high resolution land surface models.Item 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.Item Idenfitying and Understanding North American Carbon Cycle Perturbations from Natural and Anthropogenic Disturbances(2008-05-05) Neigh, Christopher Sean; Townshend, John R.G.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Carbon dioxide accumulating in our atmosphere is one of the most important environmental threats of our time. Humans and changing climate, separately or in concert, have affected global vegetation, biogeochemical cycles, biophysical processes, and primary production. Recent studies have found temporary carbon stores in North American vegetation due to land-cover land-use change, but have yet to characterize regional mechanisms across the continent. This research implemented multi-resolution remote sensing data, coupled with ecosystem simulations, to determine the importance of fine-scale disturbance in our understanding of dynamics that drove and/or perturbed carbon sequestration in North America from 1982 through 2005. The research involved three components: 1) identified large regions with natural and anthropogenic vegetation disturbances; 2) determined causes of disturbances with high-spatial resolution data and mapped associative fine-scale land cover dynamics; and 3) used prior empirical observations in simulations to quantify mechanisms that altered carbon pathways. Investigation of normalized difference vegetation index data from the NOAA series of Advanced Very High Resolution Radiometers found regions in North America that experienced marked increases in photosynthetic capacity at various times from 1982 to 2005. Inspection of anomalies with multi-resolution data from Landsat, IKONOS, aerial photography, and ancillary data revealed a wide range of causes: climatic influences; severe drought and subsequent recovery; irrigated agriculture expansion; insect outbreaks followed by logging and subsequent regeneration; and forest fires with subsequent regeneration. Fine-scale land cover change dynamics were included in Carnegie-Ames-Stanford approach simulations to enhance replication of carbon cycle processes found in empirical observations. Integration of multi-resolution remote sensing data, with carbon ecosystem process modeling, improved regional understanding and accounting of dynamic fine-scale spatial-temporal North American ecosystem carbon balance by a total of ~10 − 250 teragrams of carbon. Coarse resolution simulations could fail to identify important local scale drivers which impact regional carbon balance.Item Towards an integrated system for vegetation fire monitoring in the Amazon basin(2008-04-25) Schroeder, Wilfrid; Justice, Christopher; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Biomass burning is a major environmental problem in Amazonia. Satellite fire detections represent the primary source of information for fire alert systems, decision makers, emissions modeling groups and the scientific community in general. Those various users create a growing demand for good quality fire data of higher spatial and temporal resolution that can only be achieved via integration of multiple satellite fire detection products. The main objective of this dissertation was to develop an integrated fire product capable of improved monitoring and characterization of fire activity in Brazilian Amazonia. Two major active fire detection algorithms based on MODIS and GOES data were used to meet the users demand for fire information. Large differences involving the performance of the MODIS and GOES fire products required the quantification of omission and commission errors in order to allow for appropriate treatment of individual detections produced by each data set. Relatively small omission errors due to cloud obscuration were estimated for Brazilian Amazonia. Regional climate conditions result in reduced cloud coverage in areas of high fire activity during the peak of the dry season, therefore minimizing the effects of cloud obscuration on fire detection omission errors. Clear sky omission and commission errors were largely dependent on the vegetation and background conditions. Relatively large commission errors occurring in high percentage tree cover areas suggested that fire detection algorithms must either be regionalized or incorporate additional tests to provide more consistent fire information across a broader range of surface conditions. Integration of MODIS and GOES fire products using a physical parameter describing fire energy (i.e., fire radiative power) was proven difficult due to limitations involving the interplay between sensor characteristics and the types of fires that occur in Amazonia. As part of this research, a new integrated product was generated based on binary fire detection information derived from MODIS and GOES data, incorporating adjustments to reduce commission and omission errors and optimizing the complementarities among individual detections. These findings made a significant contribution to fire monitoring science in Amazonia and could play an important role in the development of future fire detection algorithms for tropical regions.Item Evaluating the potential of the Normalized Burn Ratio and other spectral indices for assessment of fire severity in Alaskan Black Spruce forests.(2007-05-04) Hoy, Elizabeth Embury; Kasischke, Eric S.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis presents an assessment of the potential of the differenced Normalized Burn Ratio (dNBR) and other spectral indices for mapping fire severity in Alaskan black. Using simple linear regression, the dNBR derived from Landsat TM and ETM+ data was correlated with ground measures of fire severity including the Composite Burn Index (CBI), depth of the organic soil remaining after the fire, reduction in the depth of the organic layer, and Canopy Fire Severity Index; these being measures of fire severity used to assess the ecological effects of fire. Regression analyses yielded weak correlations: the highest R2 for a comparison between the dNBR and CBI was 0.52, p<0.0001. However, the mid-infrared ratio showed higher potential than other spectral indices in many comparisons. Overall, these results indicate 1) validation of the dNBR is needed and 2) burn severity mapping schemes which are more comprehensive than the dNBR should be developed.Item 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.Item 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.