Anthropogenic disturbance in a landscape can take various forms, including residential development, which has substantial impact on the world’s ecosystems. Exurban development, characterized by low density residential development outside urban areas, was and continues to be one of the fastest growing forms of residential development in North America. It has disproportionately large ecological impacts relative to its footprint, yet is mostly overlooked in scientific studies. Specifically, a lack of spatially explicit (disaggregate) data on exurban development at regional level has contributed to a very limited understanding of this interspersed low density development. The main goal of this dissertation is to provide an increased understanding of exurban development in terms of its location, locating factors, and conservation and ecological implications at regional level, especially to enable incorporation of exurban information in the decision making processes. For this I asked four specific questions in this dissertation: (i) Where exactly is exurban development? (ii) What are the most likely factors that influence exurban development location? (iii) How does current and future development conflict with conservation goals? And (iv) What is the extent of the exurban development’s ecological impacts? Using a heterogeneous landscape, the County of Peterborough (Ontario, Canada), as the case study this dissertation undertook a number of separate yet related analyses that collectively provided the improved understanding of exurban development. The investigation of traditionally used surrogates for development, like roads and census data, and a more direct remote sensing method, using moderate resolution SPOT/HRVIR imagery, provided insights and contributed to development of spatially explicit data on exurban development. The evaluation of several commonly hypothesized locating factors in relation to exurban development revealed some of the major influences on the location of this development, especially in the context of Ontario. This research contributed to our understanding of the future risks of land conversion and identification of potential conflict areas between development and conservation plans in the study area. Lastly, examining the ecological impact of exurban development including associated roads, in terms of functions such as barrier effects and landscape connectivity, highlighted the importance of these seldom included anthropogenic disturbances in land and conservation planning. The contributions of this research to the existing body of knowledge are threefold. First, this dissertation reveals the limitations associated with existing methods used to map exurban development and presents a relatively easy, effective, automated and operational method to delineate exurban built areas at regional level using GIS and remote sensing. Second, the analyses conducted in this dissertation repeatedly highlights the importance of incorporating fine level details on exurban development in land and conservation planning as well as ecological impact assessments and presents methods and tools that can systematically and scientifically integrate this information in decision making framework. Third, this study conducted one of a kind, comprehensive and spatially explicit study on exurban development in Canada, where there is near absence of such research. With the rarely available exurban built footprint data delineated for the study area, this study not only identified the potential locating factors, future conversion risk, and conflict areas between development and conservation plans, but also quantified ecological impact in terms of landscape function, namely barrier effects and landscape connectivity, using a relatively novel circuit theoretic approach that can directly inform land and conservation decision planning process.

Anthropogenic disturbance in a landscape can take various forms, including residential development, which has substantial impact on the world’s ecosystems. Exurban development, characterized by low density residential development outside urban areas, was and continues to be one of the fastest growing forms of residential development in North America. It has disproportionately large ecological impacts relative to its footprint, yet is mostly overlooked in scientific studies. Specifically, a lack of spatially explicit (disaggregate) data on exurban development at regional level has contributed to a very limited understanding of this interspersed low density development. The main goal of this dissertation is to provide an increased understanding of exurban development in terms of its location, locating factors, and conservation and ecological implications at regional level, especially to enable incorporation of exurban information in the decision making processes. For this I asked four specific questions in this dissertation: (i) Where exactly is exurban development? (ii) What are the most likely factors that influence exurban development location? (iii) How does current and future development conflict with conservation goals? And (iv) What is the extent of the exurban development’s ecological impacts? Using a heterogeneous landscape, the County of Peterborough (Ontario, Canada), as the case study this dissertation undertook a number of separate yet related analyses that collectively provided the improved understanding of exurban development. The investigation of traditionally used surrogates for development, like roads and census data, and a more direct remote sensing method, using moderate resolution SPOT/HRVIR imagery, provided insights and contributed to development of spatially explicit data on exurban development. The evaluation of several commonly hypothesized locating factors in relation to exurban development revealed some of the major influences on the location of this development, especially in the context of Ontario. This research contributed to our understanding of the future risks of land conversion and identification of potential conflict areas between development and conservation plans in the study area. Lastly, examining the ecological impact of exurban development including associated roads, in terms of functions such as barrier effects and landscape connectivity, highlighted the importance of these seldom included anthropogenic disturbances in land and conservation planning. The contributions of this research to the existing body of knowledge are threefold. First, this dissertation reveals the limitations associated with existing methods used to map exurban development and presents a relatively easy, effective, automated and operational method to delineate exurban built areas at regional level using GIS and remote sensing. Second, the analyses conducted in this dissertation repeatedly highlights the importance of incorporating fine level details on exurban development in land and conservation planning as well as ecological impact assessments and presents methods and tools that can systematically and scientifically integrate this information in decision making framework. Third, this study conducted one of a kind, comprehensive and spatially explicit study on exurban development in Canada, where there is near absence of such research. With the rarely available exurban built footprint data delineated for the study area, this study not only identified the potential locating factors, future conversion risk, and conflict areas between development and conservation plans, but also quantified ecological impact in terms of landscape function, namely barrier effects and landscape connectivity, using a relatively novel circuit theoretic approach that can directly inform land and conservation decision planning process.

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<p>This research explores the impact of road infrastructure on deforestation, the role of forest governance and a carbon tax/credit mechanism in mitigating the effect on land use change and subsequent GHG emissions, with application to the states of Pará and Mato Grosso in Brazil. Few studies have addressed how policies to protect forested land affect the rate of deforestation associated with road and infrastructure improvement. This research makes three main contributions to the literature of roads and deforestation: 1) the concept of cost of access to the “closest” market in terms of time (expressed in person hours per ten ton load) is introduced to reflect variations in the road network infrastructure; 2) development of empirical evidence of the role of forest governance in diminishing the rate of deforestation linked to roads, using data from Brazil; and 3) and assessment of the efficacy of a carbon tax/credit scheme for mitigating the impact of infrastructure investment on land use and resultant changes in GHG emissions. Access cost ranged between 0.01 and 3084 person hours per load, however 80 percent of the pixels measured less than 784 person hours across the three years analyzed (2003, 2013, and 2018). This measure facilitated a contrast in spatial accessibility due to road infrastructure across pixels within the same year and across years on a same pixel. The use of a fractional logit model allowed the incorporation of proportions of different land uses within a same pixel at the same resolution of other <a></a>variables not available at the same fine scale. Strong forest governance reduced up to 25% the elasticities on forest lands with respect to access cost; in other words, the impact of roads on deforestation is reduced by one fourth when forest governance is strengthened. These larger impacts occur at the frontier where most of the efforts need to be addressed. Finally, provided a shock in road infrastructure, a carbon tax/credit level of $82/tCO2e permitted to abate an additional amount of GHG emissions estimated in 244 million tons of CO2e released due to changes in carbon stocks and flow emissions from agricultural activities induced from changes in road infrastructure. More importantly, this research provided insights of a proportion of GHG emissions that could be abated at different levels of a carbon tax/credit.</p>

Der Bezirk Xilingol wurde als geeignetes Beispiel ausgewählt, weil es zu einem großen Flächenanteil von Grassteppe bedeckt ist und fast alle Phasen der Umweltpolitik Chinas durchlaufen hat. Es wurden zwei deutlich voneinander abgrenzbare Phasen identifiziert, von 1975 bis 2000 und von 2000 bis 2015. Während der ersten Phase, bis 2000, war Landdegradation der dominante Landnutzungswandelprozess, der 11.4 % der Gesamtfläche betraf. In dieser Phase war die menschliche Einflussnahme der Hauptfaktor in acht Landkreisen, die sich ändernden Wasserverhältnisse war es in sechs Landkreisen. Während der zweiten Phase, ab 2000, setzte ein spürbare Erholung des Zustandes auf 12 % des Gesamtgebietes ein, während die Degradation jedoch weiter voranschritt und zusätzliche 9,5 % des Landes veränderte. Während dieser Phase wurde die Städtebildung zum dominanten Treiber für die Landdegradierung in sieben Landkreisen, während der Einfluss menschlicher Störungen und der Wasserverfügbarkeit wieder zurückging. Nach der Identifizierung der Haupttreiber für die Landdegradation, wurde die komplexe Beziehung zwischen verschiedenen Treibern und der Grassteppen-Degradation untersucht. Die Ergebnisse zeigten, dass die Beziehung zwischen dicht bedeckter, moderat bedeckter, und spärlich bedeckter Grassteppe und die Dichte des Schafbesatzes für die Degradationsdynamik in der Grassteppe verantwortlich waren. In dieser Arbeit wurden die Methoden der Clusteranalyse, der Partial-Order-Theorie, und der Hasse Diagramme eingesetzt, um die Haupttreiber der Landdegradation auf Landkreisebene zu identifizieren. Dann wurde ein Ansatz aus dem maschinellen Lernen, XGBoost (eXtreme Gradient Boosting) verwendet, um die Dynamik der Grassteppen-Degradation vorauszusagen. Darüber hinaus wurde SHAP (SHapley Additive exPlanations) eingesetzt, um das von XGBoost erstellte Black-Box-Modell zu in seine Bestanteile zu zerlegen und für jedes Degradations-Pixel in der Karte den Haupttreiber zu extrahieren. / The aims of this thesis are to gain an integrated and systematic understanding of the processes and determinants of land degradation on the Mongolian Plateau. Xilingol was chosen as a suitable example, mainly since it is covered by vast grassland, and has experienced almost all ecological policies that have been implemented in China. Two distinct phases were identified in this region: 1975-2000 and 2000-2015. During the first phase (up to 2000), land degradation was the dominant land use change process, accounting for 11.4% of the total area. During this phase, human disturbance was the major driver in eight counties, whereas the water condition was the dominant driver in six counties. During the second phase (post-2000), land restoration increased (12.0% of the total area), whereas degradation continued, resulting in a further 9.5% of degraded land. During this phase, urbanisation became the dominant driver of land degradation in seven counties, while effects resulting from human disturbance and water availability decreased after 2000. After identifying the major drivers of degradation, the complex relationships between drivers and grassland degradation were captured. The results indicated that the distance to dense, moderately dense grass and sparse grass and sheep density were responsible for the grassland degradation dynamics. In this thesis, a clustering method, partial order theory and Hasse diagram techniques were first used to identify the major drivers of land degradation at the county level. Subsequently, an approach from machine learning, XGBoost (eXtreme Gradient Boosting), was used to predict the dynamics of grassland degradation. Moreover, SHAP (SHapley Additive exPlanations) values were used to open up the black box model, and the primary driver was extracted for each pixel showing degradation.