Geography Theses and Dissertations
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Item QUANTIFYING FIRE-INDUCED SURFACE FORCING IN SIBERIAN LARCH FORESTS(2017) Chen, Dong; Loboda, Tatiana V.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Wildfires are a common disturbance agent in the global boreal forests. In the North American boreal forests, they have been shown to exert a strong cooling effect through post-fire changes in surface albedo that has a larger overall impact on the climate system than associated carbon emissions. However, these findings are not directly applicable to the Siberian larch forests, a major component of the boreal biome where species composition are dominated by a deciduous needleleaf species and fire regimes are characterized by the common occurrence of both stand-replacing and less-severe surface fires. This dissertation quantifies the post-fire surface forcing imposed by both fire types in these forests over 14 years since fire, and determines that both surface and stand replacing fires impose cooling effects through increased albedo during snow season. The magnitude of the cooling effect from stand replacing fires is much larger than that of surface fires, and this is likely a consequence of higher levels of canopy damage after stand-replacing fires. At its peak (~ year 11 after fire occurrence), the cooling magnitude is similar to that of the North American boreal fires. Strong cooling effect and the wide-spread occurrence of stand-replacing fires lead to a net negative surface forcing over the entire region between 2002 and 2013. Based on the extended albedo trajectory which was made possible by developing a 24-year stand age map, it was shown that the cooling effect of stand-replacing fires lasts for more than 26 years. The overall cooling effect of surface fires is of lower magnitude and is likely indicative of damages not only to the canopies but also the shrubs in the understory. Based on the identified difference in their influences on post-fire energy budget, this dissertation also identified a remote sensing method to separate surface fires from stand-replacing fires in Siberian larch forests with an 88% accuracy. The insights gained from this dissertation will allow for accurate representation of wildfires in the regional or global climate models in the future.Item A Spatial-Temporal Analysis of Wetland Loss and Section 404 Permitting on the Delmarva Peninsula from 1980 to to 2010(2017) Stubbs, Quentin A.; Yeo, In-Young; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Geospatial approaches for wetland change analyses have emphasized the evaluation of landscape change on a local level, but have often neglected to examine and integrate regional trends and patterns of land use and land cover change as well as the impacts of wetland management policies. This study attempts to bridge the gaps by integrating a geospatial assessment of land cover change and a geostatistical analysis of the physical and anthropogenic drivers of wetland change. The aim is to demonstrate how urban development, conservation, and climate change policy decisions influenced wetland change trends and patterns on the Delmarva Peninsula from 1980 to 2010. Historical data on the nine counties on the Delmarva Peninsula illustrated the dynamism of population growth, sprawl, and different wetland management strategies. Data sets from the National Oceanic and Atmospheric Administration, the Chesapeake Bay Program, the U.S. Army Corps of Engineers, the U.S. Fish and Wildlife Service, and the U.S. Census Bureau, and other sources were gathered and assessed. A land cover database was developed and analyzed using geospatial techniques, such as cross tabulation matrices and hot spot density analyses, in order to quantify and locate land cover change between 1984 and 2010. The results highlighted that anthropogenic drivers such as urbanization and agriculture were associated with the loss of wetlands in coastal areas as well as in upland, forested, suburban areas that were at low risk to flooding, but required deforestation in order to expand residential and commercial development. The greatest quantity and percentage of loss occurred between 1992 and 2001, and it was likely the result of increases in tourism and suburban sprawl (e.g., the Housing Boom and roadway expansion). The majority of wetland loss tapered off in 2000, except on coastal areas suffering from sea level rise and shoreline erosion. The results also reinforced the need to address the negative impacts from certain activities related to agriculture and silviculture, which are exempt from Section 404 of the Clean Water Act, have on wetlands. Physical drivers and processes like inundation from sea level rise and soil erosion from surface runoff force communities to simultaneously adapt to multiple drivers of wetland loss and alteration. This study supports the hypothesis that an increase in development and wetland permitting indicates an increased a risk of wetland loss. In the end, the study demonstrates that geostatistical modelling techniques can be used to predict wetland loss, and that model performance and accuracy can be improved by reducing the multicollinearity of independent variables. Planners and policymakers can use these models to better understand the wetland locations that are at greatest risk to loss, as well as the drivers and landscape conditions that have the greatest influence on the probability of wetland loss.Item Factors Influencing Remote Sensing Measurements of Winter Cover Crops(2016) Prabhakara, Kusuma; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. In addition to remote sensing imagery, advances have been made in the use of proximal sensors integrated with GPS for on-field measurements, and the comparability of such measurements between platforms, as well as based on processing level is important. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012-2013 winter growing season. There was a strong relationship between the Normalized Difference Vegetation Index (NDVI) and percent groundcover (r2 =0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The Triangular Vegetation Index (TVI) was most accurate in estimating high ranges of biomass (r2=0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). Accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops. Surface reflectance measurements were more correlated with proximal sensors compared to top of atmosphere, with intercepts closer to zero, regression slopes nearer to the 1 to 1 line, and less variance between measured values. NDVI was highly correlated with percent vegetative groundcover, though surface reflectance products did not necessarily improve the relationships. When the Scattering for Arbitrarily Inclined Leaves (SAIL) model was used with measured field variables reflective of realistic winter cover crop scenarios, there were not large differences between NDVI despite differences in residue cover and moisture. At low LAI, NDVI is not capable of differentiating between residue and vegetative cover.Item Tree Cover Variability in the District of Columbia(2013) Johnston, Andrew; Goward, Samuel N; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Urban forests are increasingly a focus of interest as urbanized populations grow and urban areas expand. Urban forests change as trees are planted, grow, die, and are removed. These processes alter a city's tree cover over time, but this inherent dynamism is poorly understood. Better understanding of how tree cover is a variable land cover component will enhance knowledge of the urban environment and provide new perspectives for management of urban resources. In this study, tree cover variability within a major urban center was observed over a 20 year period. Changes in tree cover proportion were measured in the District of Columbia between 1984-2004 utilizing highly calibrated satellite remote sensing data. Testing of alternate methodologies demonstrated that an approach utilizing support vector regression provided most consistent accuracy across land use types. Tree cover maps were validated using aerial photography imagery and data from field surveys. Between 1984-2004, the city-wide tree cover remained between 22.1(+/-2.9)% and 28.8(+/-2.9)% of total land surface area. The District of Columbia did not experience an overall increase or decrease in total tree canopy area. Spatial patterns of tree cover variability were investigated to identify local scale changes in tree cover and connections with urban land use. Within the city, greatest variability was observed in low density residential zones. Tree cover proportion in these zones declined 7.4(+/-5.4)% in the years between 1990-1996 and recovered after 1996. Changes in tree cover were observed with high resolution aerial photography to determine relative contribution from fluctuation in the number of standing trees and changes in crown sizes. Land cover conversion removed dense tree cover from 50.2 hectares of the city's land surface between 1984-2004. The results demonstrate that tree cover variability in the District of Columbia occurred primarily within low population density residential areas. Neighborhoods within these zones were analyzed to identify factors correlated with tree cover. Implications of the results include enhanced understanding of the possible impact of urban forest management, and how a focus on low density residential zones is appropriate in setting goals for expansion of urban tree cover.Item A GENERALIZED APPROACH TO WHEAT YIELD FORECASTING USING EARTH OBSERVATIONS: DATA CONSIDERATIONS, APPLICATION, AND RELEVANCE.(2012) Becker-Reshef, Inbal; Justice, Christopher C; Vermote, Eric; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. The issue of food security has rapidly risen to the top of government agendas around the world as the recent lack of food access led to unprecedented food prices, hunger, poverty, and civil conflict. Timely information on global crop production is indispensable for combating the growing stress on the world's crop production, for stabilizing food prices, developing effective agricultural policies, and for coordinating responses to regional food shortages. Earth Observations (EO) data offer a practical means for generating such information as they provide global, timely, cost-effective, and synoptic information on crop condition and distribution. Their utility for crop production forecasting has long been recognized and demonstrated across a wide range of scales and geographic regions. Nevertheless it is widely acknowledged that EO data could be better utilized within the operational monitoring systems and thus there is a critical need for research focused on developing practical robust methods for agricultural monitoring. Within this context this dissertation focused on advancing EO-based methods for crop yield forecasting and on demonstrating the potential relevance for adopting EO-based crop forecasts for providing timely reliable agricultural intelligence. This thesis made contributions to this field by developing and testing a robust EO-based method for wheat production forecasting at state to national scales using available and easily accessible data. The model was developed in Kansas (KS) using coarse resolution normalized difference vegetation index (NDVI) time series data in conjunction with out-of-season wheat masks and was directly applied in Ukraine to assess its transferability. The model estimated yields within 7% in KS and 10% in Ukraine of final estimates 6 weeks prior to harvest. The relevance of adopting such methods to provide timely reliable information to crop commodity markets is demonstrated through a 2010 case study.Item Impacts of Conflict on Land Use and Land Cover in the Imatong Mountain Region of South Sudan and Northern Uganda(2012) Gorsevski, Virginia; Kasischke, Eric S; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Imatong Mountain region of South Sudan makes up the northern most part of the Afromontane conservation `biodiversity hotspot' due to the numerous species of plants and animals found here, some of which are endemic. At the same time, this area (including the nearby Dongotana Hills and the Agoro-Agu region of northern Uganda) has witnessed decades of armed conflict resulting from the Sudan Civil War and the presence of the Ugandan Lord's Resistance Army (LRA). The objective of my research was to investigate the impact of war on land use and land cover using a combination of satellite remote sensing data and semi-structured interviews with local informants. Specifically, I sought to 1) assess and compare changes in forest cover and location during both war and peace; 2) compare trends in fire activity with human population patterns; and 3) investigate the underlying causes influencing land use patterns related to war. I did this by using a Disturbance Index (DI), which isolates un-vegetated spectral signatures associated with deforestation, on Landsat TM and ETM+ data in order to compare changes in forest cover during conflict and post-conflict years, mapping the location and frequency of fires in subsets of the greater study area using MODIS active fire data, and by analyzing and summarizing information derived from interviews with key informants. I found that the rate of forest recovery was significantly higher than the rate of disturbance both during and after wartime in and around the Imatong Central Forest Reserve (ICFR) and that change in net forest cover remained largely unchanged for the two time periods. In contrast, the nearby Dongotana Hills experienced relatively high rates of disturbance during both periods; however, post war period losses were largely offset by gains in forest cover, potentially indicating opposing patterns in human population movements and land use activities within these two areas. For the Agoro-Agu Forest Reserve (AFR) region northern Uganda, the rate of forest recovery was much higher during the second period, coinciding with the time people began leaving overcrowded Internally Displaced Persons (IDP) camps. I also found that fire activity largely corresponded to coarse-scale human population trends on the South Sudan and northern Uganda side of the border in that post-war fire activity decreased for all areas in South Sudan and northern Uganda except for areas near the larger towns and villages of South Sudan, where people have begun to resettle. Fires occurred most frequently in woodlands on the South Sudan side, while the greatest increase in post-war, northern Ugandan fires occurred in croplands and the forested area around the Agoro-Agu reserve, Interviews with key informants revealed that while some people fled the area during the war, many others remained in the forest to hide; however, their impact on the forests during and after the conflict has been minimal; in contrast, those interviewed believed that wildlife has been largely depleted due to the widespread access to firearms and lack of regulations and enforcement. This study demonstrates the utility of using a multi-disciplinary approach to examine aspects of forest dynamics and fire activity related to human activities and conflict and as such contributes to the nascent but growing body of research on armed conflict and the environment.Item Semantic integration of geospatial concepts - a study on land use land cover classification systems(2011) Wei, Hua; Townshend, John; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In GI Science, one of the most important interoperability is needed in land use and land cover (LULC) data, because it is key to the evaluation of LULC's many environmental impacts throughout the globe (Foley et al. 2005). Accordingly, this research aims to address the interoperability of LULC information derived by different authorities using different classificatory approaches. LULC data are described by LULC classification systems. The interoperability of LULC data hinges on the semantic integration of LULC classification systems. Existing works on semantically integrating LULC classification systems has a major drawback in finding comparable semantic representations from textual descriptions. To tackle this problem, we borrowed the method of comparing documents in information retrieval, and applied it to comparing LULC category names and descriptions. The results showed significant improvement comparing to previous works. However, lexical semantic methods are not able to solve the semantic heterogeneities in LULC classification systems: the confounding conflict - LULC categories under similar labels and descriptions have different LULC status in reality, and the naming conflict - LULC categories under different labels represent similar LULC type. Without confirmation of their actual land cover status from remote sensing, lexical semantic method cannot achieve reliable matching. To discover confounding conflicts and reconcile naming conflicts, we developed an innovative method of applying remote sensing to the integration of LULC classification systems. Remote sensing is a means of observation on actual LULC status of individual parcels. We calculated parcel level statistics from spectral and textural data, and used these statistics to calculate category similarity. The matching results showed this approach fulfilled its goal - to overcome semantic heterogeneities and achieve more reliable and accurate matching between LULC classifications in the majority of cases. To overcome the limitations of either method, we combined the two by aggregating their output similarities, and achieve better integration. LULC categories that post noticeable differences between lexical semantics and remote sensing once again remind us of semantic heterogeneities in LULC classification systems that must to be overcome before LULC data from different sources become interoperable and serve as the key to understanding our highly interrelated Earth system.Item mapping photosynthetically active radiation (PAR) using multiple remote sensing data(2007-07-11) zheng, tao; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Incident Photosynthetically Active Radiation (PAR) is an important parameter for terrestrial ecosystem models. Presently, deriving PAR using remotely sensed data is the only practical approach to meet the needs for large scale ecosystem modeling. The usefulness of the currently available PAR products is constricted by their limited spatial and temporal resolution. In addition, the applicability of the existing algorithms for deriving PAR using remotely sensed data are limited by their requirements for external atmospheric information. This study develops new algorithms to estimate incident PAR using remotely sensed data from MODIS (Moderate Resolution Imaging Spectroradiometer), GOES (Geostationary Operational Environmental Satellite), and AVHRR (Advanced Very High Resolution Radiometer). The new PAR algorithms differ from existing algorithms in that the new algorithms derive surface properties and atmospheric optical properties using time-series of at-sensor radiance without external atmospheric information. First, a new PAR algorithm is developed for MODIS visible band data. The validity of the algorithm's underpinning theoretical basis is examined and associated errors are analyzed in light of their impact on PAR estimation accuracy. Second, the MODIS PAR algorithm is adapted to AVHRR in order to take advantage of the long data acquisition record of AVHRR. In addition, the scaling of remote sensing derived instantaneous PAR to daily PAR is addressed. Last, the new algorithm is extended to GOES visible band data. Two major improvements of GOES PAR algorithm over that of MODIS and AVHRR are the inclusion of the bi-directional reflectance distribution function for deriving surface reflectance, and the procedure for excluding cloud-shadowed pixels in searching for observations made under clear skies. Furthermore, the topographic impact on PAR is accessed and corrected. To assess the effectiveness of the newly developed PAR algorithms, validation efforts have been made using ground measurements made at FLUXNET sites. The validations indicate that the new PAR algorithms for MODIS, GOES, and AVHRR are capable of reaching reasonably high accuracy with no need for external atmospheric information. This work is the first attempt to develop a unified PAR estimation algorithm for both polar-orbiting and geostationary satellite data. The new algorithms developed in this study have been used to produce incident PAR over North America routinely to support the North America Carbon Program.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 ENDANGERED DRY DECIDUOUS FORESTS OF UPPER MYANMAR (BURMA): A MULTI-SCALE APPROACH FOR RESEARCH AND CONSERVATION(2006-09-11) Songer, Melissa A.; DeFries, Ruth S.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Tropical dry forests are critically endangered and largely unprotected ecosystem. I used a multi-scale research approach to study Upper Myanmar's dry deciduous forests. At the broad scale I assessed how well existing land cover data can be used to map and monitor dry forests, and estimated the extent, distribution, and level of protection of these forests. At the landscape level I assessed spatial and temporal dynamics of deforestation in and around a dry forest protected area, Chatthin Wildlife Sanctuary (CWS), investigated land use pressures driving these changes, and evaluated effectiveness of protection efforts within the sanctuary. At the local scale I studied the degree to which people rely on dry forests for subsistence and the socioeconomic variables correlated with dependence on forest products. Using MODerate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data to delineate remaining dry deciduous forests, I found that only 24,000 km2 of this forest type remain in Upper Myanmar--only 4% inside protected areas. At 81% accuracy, this map scored higher than existing global and regional land cover classifications for predicting dry forest. Employing satellite images covering the landscape in and around CWS (Landsat MSS, TM, ETM+ and ASTER) between the years 1973-2005 , I found that 62% of forest was lost (1.93% annual rate) primarily from agricultural conversion and hydroelectric development. Sanctuary protection has been effective in slowing decline: loss rates inside CWS were 0.49% annually (16% total). However, forest inside the sanctuary is still declining at a rate above the global average and shows evidence of impact from forest product extraction around the boundaries. Based on interviews with 784 people living in 28 subsistence-based agricultural communities located in and around CWS, I found virtually all survey respondents depended on CWS for food, medicine, housing materials, and, above all, fuelwood. Poverty and socioeconomic limitations drive extractive activities. While CWS has been effective in slowing deforestation rates, alternative use strategies that benefit people will improve prospects for long-term conservation in the area. My results demonstrate that a multi-scaled research approach is essential for understanding the drivers impacting the rapidly-declining dry forests of Upper Myanmar.