Modelling land use and land cover changes in California’s landscapes
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Modelling land use and land cover changes in California’s landscapes


Land use land cover change (LULCC) patterns are constantly changing the Earth’s surface. Growing population numbers and an increased demand for housing, energy and food have not only expanded the human footprint into natural ecosystems but have also accelerated LULCC processes. Climate-driven land cover changes due to prolonged droughts and shifts in temperatures and precipitation patterns have also influenced the spatial configuration of land uses, and the distribution of ecological communities. Natural and anthropogenic forces along with their feedbacks and interdependencies are major drivers of global environmental change. Land use dynamics affect the distribution, composition, condition and vulnerability of ecosystems, influence soil properties and impact the livelihoods of people dependent on the productivity and health of the land and its natural resources. Given future climate uncertainties and increased resource demands, it is important to study land use change itself, understand the drivers behind land cover changes and assess the impacts of LULCC processes on socio-ecological systems. This dissertation investigates the causes and effects of land cover changes across various ecosystems in California, exploring the linkages between human activities and landscape changes. The first chapter provides a general overview of the framework that motivated my research, showcasing ways through which land system science as a field has improved our understanding of the world. The second chapter examines the role that land markets can play in conservation. Land conversion from natural vegetation to other uses such as development or agricultural uses is a dominant trend in California and results in habitat fragmentation, loss of natural ecosystems, loss of ecosystem services, and atmospheric carbon (C) emissions. To investigate the effects that conservation purchases have on C emissions and loss of vegetation, I analyze 73 conservation easements owned by the California State Coastal Conservancy (SCC). I develop counterfactual scenario simulations that show likely outcomes of land cover changes in the absence of conservation actions and calculate the benefits of protecting these lands in terms of avoided C emissions and avoided vegetation loss. I base my counterfactual scenario simulations on expert opinions gathered through property-specific appraisal reports provided by the SCC. I combine the information found in these reports with a comprehensive analysis of land cover changes in the vicinity of the areas studied to develop likely pathways of rural development and/or agricultural conversion. In this chapter I show that measuring the benefits of conservation purchases through the development of counterfactuals reveals that many of the properties purchased by the SCC did not experience a high risk of being converted to development and/or agricultural uses. A second important finding highlights that the location of the property and its vegetation significantly influence the likelihood of conversion and associated avoided carbon emissions. In particular high-carbon ecosystems, such as redwood forests, are less likely to become developed than lower-carbon ecosystems, such as grasslands. In my third chapter, I study one of the natural disturbances ingrained in the history of California: wildfire. With a landscape that is both fire prone as well as fire adapted, California often experiences active fire seasons. Recent years have been marked by large, catastrophic fire events that have burned hundreds of thousands of acres, destroying everything in their path, and affecting numerous communities and ecosystems. Fire modelling efforts help us better understand fire behavior and predict future fire occurrences. Conceptualizing fire-risk in fire prone landscapes and mapping how this risk will evolve through time are key components in effectively managing forest ecosystems, while minimizing and mitigating the impacts of large wildfire events. Yet, understanding the results of fire models can be challenging and difficult to represent using traditional geographic techniques. Since most of the variables that influence fire behavior are space and time variant, in this chapter, I propose a new approach of interpreting modeled wildfire predictions in 3D - across a continuum of space and time. Using modeled wildfire data created by Westerling (2018) and geographic information systems (GIS) space time mining capabilities (the Space Time Cube and Emerging Hot Spots Analysis functions), I identify different categories of wildfire hot spots and cold spots across California for different time periods, between 2000 and 2100. Furthermore, I show how wildfire patterns affect communities located at the wildland urban interface, and how wildfire hot spot patterns change across California’s ecoregions. To aid in the understanding of modeled wildfire activity, I create 3D visualizations to capture the evolution of fire activity. Adopting a space-time approach and identifying areas where fire threat is predicted to increase in the future can help prioritize high risk areas and direct fuel reduction and fire prevention efforts to vulnerable areas. In the fourth chapter, I investigate the effects of land cover changes in agricultural landscapes and study the main drivers behind these changes within one of the top agricultural producing counties in California – Kern County. I document the factors that influence landowners’ decisions to allocate their land in one of four main categories (nut trees, fruit trees, field & vegetable crops, and barren & rangeland). To achieve this goal, I build a series of multinomial logit models with panel data. At the parcel level, I analyze the effect of variables such as: parcel characteristics (size, slope, elevation), and parcel location (distance to: urban areas, roads, canals, wells, and protected lands) on land use transitions across a 10-year time interval (between 2008 and 2018). I further study the ways in which climatic variables might influence land use transitions among the four categories previously defined by documenting any noticeable trends in land cover changes associated with the extensive drought that California has experienced between 2012-2016. A better understanding of the drivers behind land use transitions is important in developing sustainable and resilient land management practices. For example, the change from annual crops to perennial crops (such as the expansion of almond orchards) has numerous repercussions for water use and for the environment. In addition to this, modelling land use transitions enables the development of predictive models that show what future land cover might look like. This is especially relevant in the context of climate variability (such as changes in the number of frost days), water shortages (depletion of groundwater resources), and increased temperatures. Throughout this dissertation, I explore ways in which geospatial techniques and land use modelling approaches can be used to provide a roadmap for environmental policy and land management. In the final chapter of this dissertation (Chapter 5), I discuss potential policy implications, highlight avenues for future research, and provide suggestions for adaptive management. Statistical and spatial land use modelling techniques can document the tradeoffs and feedbacks of land conversion and provide key insights on land cover dynamics. This information can be further used to reach conservation goals, improve management of working landscapes, and enhance ecosystem resilience to climate-related stressors.

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