Climate change impacts are evident throughout the world, particularly in the low lying coastal areas. The multidimensional nature and cross-scale impacts of climate change require a concerted effort from different organizations operating at multiple levels of governance. The efficiency and effectiveness of the adaptation actions of these organizations rely on the problem framings, network structure, and power dynamics of the organizations and the challenges they encounter. Nevertheless, knowledge on how organizations within multi-level governance arrangements frame vulnerability, how the adaptation governance structure shapes their roles, how power dynamics affect the governance process, and how barriers emerge in adaptation governance as a result of multi-level interactions is limited. In this dissertation research, a multilevel governance perspective has been adopted to address these knowledge gaps through a case study of flood risk management in coastal Bangladesh. Key-informant interviews, systematic literature review, spatial multi-criteria decision analysis, social network analysis (SNA), and content analysis techniques have been used to collect and analyze data. This research finds that the organizations involved in adaptation governance generally have aligned framings of vulnerability, irrespective of the level at which they are operated, thus facilitating adaptation decision-making. However, this alignment raises concerns of a neglect of socio-economic aspects of vulnerability, potentially undermining adaptation initiatives. This study further finds that the adaptation governance process is elite-pluralistic in nature, but has a coexistence of top-down and bottom-up processes in different phases of adaptation actions. The analysis of power dynamics discloses the dominance of a few national level organizations in the adaptation governance process in Bangladesh. Lastly, four mechanisms have been found that can explain how organizational culture, practices, and preferences dictate the emergence of barriers in the adaptation governance process. This dissertation research overall advances our understanding on the significance of multilevel governance approach in climate change adaptation governance.

Use of off-highway vehicles (OHV) in natural landscapes is a popular outdoor activity around the world. Rapid-growing OHV activity causes impacts on vegetation and land cover within these landscapes and can be an important factor in land degradation and ecosystem change. The Algodones Dunes in southeastern California is one of the largest inland sand dune complexes in the United States and hosts many endangered species. This study examines changes in land cover and OHV activity within two OHV active sites in comparison to an adjoined protected area. The study also investigates potential associations between land cover changes, climate trends, and OHV activity over recent decades. Time-series analysis was used to investigate the spatial-temporal changes and trends in the land cover in the Algodones Dunes from 2001 to 2016. In addition, high-resolution aerial photographs were analyzed to determine spatial patterns of OHV usage in comparison to visitor estimation collected by the Bureau of Land Management and observed changes in land cover composition between the control site and OHVs areas.

A decreasing trend in Normalized Difference Vegetation Index over time indicates a decline in the amount of vegetation cover, which corresponds with an increasing trend in albedo and land surface temperature. Results also show a substantial difference in land cover between the control site and OHVs areas, which typically have a lower amount of vegetation cover, higher exposed sand surface, and increased anthropogenic features. Both climatic variations and OHV activity are statistically associated with land cover change in the dune field, although distinct causal mechanisms for the observed declines in vegetation cover could not be separated. The persistence of drought could inhibit vegetation growth and germination that, in turn, would hinder vegetation recovery in OHV areas. Meanwhile, repeated OHV driving has direct physical impacts on vegetation and landscape morphology, such as canopy destruction, root exposure, and increased aeolian sand transport. Active ecosystem protection and restoration is recommended to mitigate the response of declining vegetation cover and habitat loss to the impacts of OHV activity and climatic variability and allow natural recovery of re-establishement of nebkha dune ecosystems in the Algodones Dunes.

The global increase in urbanization has raised questions about urban sustainability to which multiple research communities have entered. Those communities addressing interest in the urban heat island (UHI) effect and extreme temperatures include land system science, urban/landscape ecology, and urban climatology. General investigations of UHI have focused primarily on land surface and canopy layer air temperatures. The surface temperature is of prime importance to UHI studies because of its central rule in the surface energy balance, direct effects on air temperature, and outdoor thermal comfort. Focusing on the diurnal surface temperature variations in Phoenix, Arizona, especially on the cool (green space) island effect and the surface heat island effect, the dissertation develops three research papers that improve the integration among the abovementioned sub-fields. Specifically, these papers involve: (1) the quantification and modeling of the diurnal cooling benefits of green space; (2) the optimization of green space locations to reduce the surface heat island effect in daytime and nighttime; and, (3) an evaluation of the effects of vertical urban forms on land surface temperature using Google Street View. These works demonstrate that the pattern of new green spaces in central Phoenix could be optimized such that 96% of the maximum daytime and nighttime cooling benefits would be achieved, and that Google Street View data offers an alternative to other data, providing the vertical dimensions of land-cover for addressing surface temperature impacts, increasing the model accuracy over the use of horizontal land-cover data alone. Taken together, the dissertation points the way towards the integration of research directions to better understand the consequences of detailed land conditions on temperatures in urban areas, providing insights for urban designs to alleviate these extremes.

Rapid urban expansion and the associated landscape modifications have led to significant changes of surface processes in built environments. These changes further interact with the overlying atmospheric boundary layer and strongly modulate urban microclimate. To capture the impacts of urban land surface processes on urban boundary layer dynamics, a coupled urban land-atmospheric modeling framework has been developed. The urban land surface is parameterized by an advanced single-layer urban canopy model (SLUCM) with realistic representations of urban green infrastructures such as lawn, tree, and green roof, etc. The urban atmospheric boundary layer is simulated by a single column model (SCM) with both convective and stable schemes. This coupled SLUCM-SCM framework can simulate the time evolution and vertical profile of different meteorological variables such as virtual potential temperature, specific humidity and carbon dioxide concentration. The coupled framework has been calibrated and validated in the metropolitan Phoenix area, Arizona. To quantify the model sensitivity, an advanced stochastic approach based on Markov-Chain Monte Carlo procedure has been applied. It is found that the development of urban boundary layer is highly sensitive to surface characteristics of built terrains, including urban land use, geometry, roughness of momentum, and vegetation fraction. In particular, different types of urban vegetation (mesic/xeric) affect the boundary layer dynamics through different mechanisms. Furthermore, this framework can be implanted into large-scale models such as Weather Research and Forecasting model to assess the impact of urbanization on regional climate.

Urban growth, from regional sprawl to global urbanization, is the most rapid, drastic, and irreversible form of human modification to the natural environment. Extensive land cover modifications during urban growth have altered the local energy balance, causing the city warmer than its surrounding rural environment, a phenomenon known as an urban heat island (UHI). How are the seasonal and diurnal surface temperatures related to the land surface characteristics, and what land cover types and/or patterns are desirable for ameliorating climate in a fast growing desert city? This dissertation scrutinizes these questions and seeks to address them using a combination of satellite remote sensing, geographical information science, and spatial statistical modeling techniques.

This dissertation includes two main parts. The first part proposes to employ the continuous, pixel-based landscape gradient models in comparison to the discrete, patch-based mosaic models and evaluates model efficiency in two empirical contexts: urban landscape pattern mapping and land cover dynamics monitoring. The second part formalizes a novel statistical model called spatially filtered ridge regression (SFRR) that ensures accurate and stable statistical estimation despite the existence of multicollinearity and the inherent spatial effect.

Results highlight the strong potential of local indicators of spatial dependence in landscape pattern mapping across various geographical scales. This is based on evidence from a sequence of exploratory comparative analyses and a time series study of land cover dynamics over Phoenix, AZ. The newly proposed SFRR method is capable of producing reliable estimates when analyzing statistical relationships involving geographic data and highly correlated predictor variables. An empirical application of the SFRR over Phoenix suggests that urban cooling can be achieved not only by altering the land cover abundance, but also by optimizing the spatial arrangements of urban land cover features. Considering the limited water supply, rapid urban expansion, and the continuously warming climate, judicious design and planning of urban land cover features is of increasing importance for conserving resources and enhancing quality of life.

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.

This dissertation investigates spatial and temporal changes in land cover and plant species distributions on Cyprus in the past, present and future (1973-2070). Landsat image analysis supports inference of land cover changes following the political division of the island of Cyprus in 1974. Urban growth in Nicosia, Larnaka and Limasol, as well as increased development along the southern coastline, is clearly evident between 1973 and 2011. Forests of the Troodos and Kyrenia Ranges remain relatively stable, with transitions occurring most frequently between agricultural land covers and shrub/herbaceous land covers. Vegetation models were constructed for twenty-two plant species of Cyprus using Maxent to predict potentially suitable areas of occurrence. Modern vegetation models were constructed from presence-only data collected by field surveys conducted between 2008 and 2011. These models provide a baseline for the assessment of potential species distributions under two climate change scenarios (A1b and A2) for the years 2030, 2050, and 2070. Climate change in Cyprus is likely to influence habitat availability, particularly for high elevation species as the relatively low elevation mountain ranges and small latitudinal range prevent species from shifting to areas of suitable environmental conditions. The loss of suitable habitat for some species may allow the introduction of non-native plant species or the expansion of generalists currently excluded from these areas. Results from future projections indicate the loss of suitable areas for most species by the year 2030 under both climate regimes and all four endemic species (Cedrus brevifolia, Helianthemum obtusifolium, Pterocephalus multiflorus, and Quercus alnifolia) are predicted to lose all suitable environments as soon as 2030. As striking exceptions Prunus dulcis (almond), Ficus carica (fig), Punica granatum (pomegranate) and Olea europaea (olive), which occur as both wild varieties and orchard cultigens, will expand under both scenarios. Land cover and species distribution maps are evaluated in concert to create a more detailed interpretation of the Cypriot landscape and to discuss the potential implications of climate change for land cover and plant species distributions.

Accurate characterization of forest canopy cover from satellite imagery hinges on the development of a model that considers the level of detail achieved by field methods. With the improved precision of both optical sensors and various spatial techniques, models built to extract forest structure attributes have become increasingly robust, yet many still fail to address some of the most important characteristics of a forest stand's intricate make-up. The objective of this study, therefore, was to address canopy cover from the ground, up. To assess canopy cover in the field, a vertical densitometer was used to acquire a total of 2,160 percent-cover readings from 30 randomly located triangular plots within a 6.94 km2 study area in the central highlands of the Bradshaw Ranger District, Prescott National Forest, Arizona. Categorized by species with the largest overall percentage of cover observations (Pinus ponderosa, Populus tremuloides, and Quercus gambelii), three datasets were created to assess the predictability of coniferous, deciduous, and mixed (coniferous and deciduous) canopies. Landsat-TM 5 imagery was processed using six spectral enhancement algorithms (PCA, TCT, NDVI, EVI, RVI, SAVI) and three local windows (3x3, 5x5, 7x7) to extract and assess the various ways in which these data were expressed in the imagery, and from those expressions, develop a model that predicted percent-cover for the entire study area. Generally, modeled cover estimates exceeded actual cover, over predicting percent-cover by a margin of 9-13%. Models predicted percent-cover more accurately when treated with a 3x3 local window than those treated with 5x5 and 7x7 local windows. In addition, the performance of models defined by the principal components of three vegetation indices (NDVI, EVI, RVI) were superior to those defined by the principal components of all four (NDVI, EVI, RVI, SAVI), as well as the principal and tasseled cap components of all multispectral bands (bands 123457). Models designed to predict mixed and coniferous percent-cover were more accurate than deciduous models.

In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and behavior of mobile objects is an important issue for extracting useful information and knowledge about mobile phenomena. Potential applications across a wide range of fields include urban and transportation planning, Location-Based Services, and logistics. This research is designed to contribute to the existing state-of-the-art in tracking and modeling mobile objects, specifically targeting three challenges in investigating spatio-temporal patterns and processes; 1) a lack of space-time analysis tools; 2) a lack of studies about empirical data analysis and context awareness of mobile objects; and 3) a lack of studies about how to evaluate and test agent-based models of complex mobile phenomena. Three studies are proposed to investigate these challenges; the first study develops an integrated data analysis toolkit for exploration of spatio-temporal patterns and processes of mobile objects; the second study investigates two movement behaviors, 1) theoretical random walks and 2) human movements in urban space collected by GPS; and, the third study contributes to the research challenge of evaluating the form and fit of Agent-Based Models of human movement in urban space. The main contribution of this work is the conceptualization and implementation of a Geographic Knowledge Discovery approach for extracting high-level knowledge from low-level datasets about mobile objects. This allows better understanding of space-time patterns and processes of mobile objects by revealing their complex movement behaviors, interactions, and collective behaviors. In detail, this research proposes a novel analytical framework that integrates time geography, trajectory data mining, and 3D volume visualization. In addition, a toolkit that utilizes the framework is developed and used for investigating theoretical and empirical datasets about mobile objects. The results showed that the framework and the toolkit demonstrate a great capability to identify and visualize clusters of various movement behaviors in space and time.