Doctor of Philosophy / Department of Geography / Marcellus M. Caldas / Biofuel ethanol has been proposed as the most viable solution to mitigate greenhouse gas emissions (GHG) from the transportation sector; however, the impact of such production on the environment is not completely known. Environmental impacts are of more concern when ethanol production occurs in areas of high biodiversity value such as the Cerrado (Brazilian savanna). The Cerrado is a global biodiversity hotspot and an important breadbasket—at the same time, it is on a path to becoming the major sugarcane ethanol-producing region in Brazil. The main goal of this dissertation is to examine the impacts of sugarcane expansion on farmers’ land use decision processes in the Cerrado and to consider its consequences on biodiversity and the impacts of climate change. In the following chapters, land change dynamics are investigated using a combination of theory and methods from geography, GIScience, economics, and ecology. Chapter 2 presents an examination of the drivers for the sugarcane expansion. The findings suggest that the Cerrado attracted mills because of the good agricultural conditions, affordable land prices, and favorable state-level fiscal incentive policies, while factors that have prevented traditional sugarcane-producing regions from meeting the increasing demand for ethanol. Chapter 3 develops a procedure to identify intensification and extensification responses at the field level. The main finding is that extensification is the main response. Additionally, this response has a higher probability of occurrence the farther an area is from a mill. Chapter 4 applies the partial adjustment framework to understand farmers’ land use decisions regarding sugarcane production. Estimates found that price of cattle have the largest cross-price elasticity with sugarcane acreage. In addition, the results suggest that acreage of sugarcane and soybean double-crop are positively correlated. Chapter 5 focuses on the impacts of climate change on land suitability for sugarcane and amphibian species. The findings show that land suitability for sugarcane is vulnerable to climate change and that the Brazilian zoning policy for sugarcane is not addressing this issue. Additionally, amphibians are affected by climate change and conflict with areas suitable for sugarcane in climate change scenarios.

Doctor of Philosophy / Department of Geography / Lisa M. Harrington / Land-change science has become a foundational element of global environmental change. Understanding how complex coupled human and natural systems (CHANS) affect land change is part of understanding our planet and also helps us determine how to mitigate current and future problems. Upland birds such as the Ring-Necked Pheasant (Phasianus colchicus) have been widely studied. While myriad studies have been done that show relationships between land change driving forces and the pheasant, what are not found are long-term, comprehensive approaches that show the historical importance of how past land change drivers can be used to gain knowledge about what is happening today or what may happen in the future. This research set out to better understand how human and natural driving forces have affected land change, pheasants, and pheasant hunting in South Dakota from the early 1900s to the present. A qualitative historical geography approach was used to assemble information from historic literature and South Dakota Game, Fish, and Parks Department annual reports to show the linkages between human and natural systems and how they affect pheasant populations. A quantitative approach was used to gather information from hunters who participated in the 2006 pheasant hunting season. Two-thousand surveys were mailed that gathered socioeconomic data, information on types of land hunted, thoughts on land accessibility issues, as well as spatial information on where hunters hunted in South Dakota. Results from the hunter surveys provided some significant information. Non-resident and resident hunters tended to hunt in different parts of the state. Non-resident hunters were older, better educated, and had higher incomes than resident hunters. Resident hunters, when asked about issues such as crowded public hunting grounds and accessibility to private lands had more negative responses, whereas non-resident hunters, especially those who hunt on privately-held lands, were more satisfied with their hunting experiences. Linkages were also seen between changes in human and natural systems and pheasant populations. Some of the most important contributors to population changes were large-scale conservation policies (Conservation Reserve Program) and agricultural incentives, as well as broader economic issues such as global energy production and national demands for increases in biofuel production (ethanol and biodiesel). Many of the changes in pheasant populations caused by changes in human systems have been exacerbated by changes in natural systems, such as severe winter weather and less-than-optimal springtime breeding conditions.

abstract: The Dhofar Cloud Forest is one of the most diverse ecosystems on the Arabian Peninsula. As part of the South Arabian Cloud Forest that extends from southern Oman to Yemen, the cloud forest is an important center of endemism and provides valuable ecosystem services to those living in the region. There have been various claims made about the health of the cloud forest and its surrounding region, the most prominent of which are: 1) variability of the Indian Summer Monsoon threatens long-term vegetation health, and 2) human encroachment is causing deforestation and land degradation. This dissertation uses three independent studies to test these claims and bring new insight about the biodiversity of the cloud forest. Evidence is presented that shows that the vegetation dynamics of the cloud forest are resilient to most of the variability in the monsoon. Much of the biodiversity in the cloud forest is dominated by a few species with high abundance and a moderate number of species at low abundance. The characteristic tree species include Anogeissus dhofarica and Commiphora spp. These species tend to dominate the forested regions of the study area. Grasslands are dominated by species associated with overgrazing (Calotropis procera and Solanum incanum). Analysis from a land cover study conducted between 1988 and 2013 shows that deforestation has occurred to approximately 8% of the study area and decreased vegetation fractions are found throughout the region. Areas around the city of Salalah, located close to the cloud forest, show widespread degradation in the 21st century based on an NDVI time series analysis. It is concluded that humans are the primary driver of environmental change. Much of this change is tied to national policies and development priorities implemented after the Dhofar War in the 1970’s. / Dissertation/Thesis / Doctoral Dissertation Geography 2015

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given.

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given.