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- Genre: Masters Thesis
Description
The sacred San Francisco Peaks in northern Arizona have been at the center of a series of land development controversies since the 1800s. Most recently, a controversy arose over a proposal by the ski area on the Peaks to use 100% reclaimed water to make artificial snow. The current state of the San Francisco Peaks controversy would benefit from a decision-making process that holds sustainability policy at its core. The first step towards a new sustainability-focused deliberative process regarding a complex issue like the San Francisco Peaks controversy requires understanding the issue's origins and the perspectives of the people involved in the issue. My thesis provides an historical analysis of the controversy and examines some of the laws and participatory mechanisms that have shaped the decision-making procedures and power structures from the 19th century to the early 21st century.
ContributorsMahoney, Maren (Author) / Hirt, Paul W. (Thesis advisor) / Tsosie, Rebecca (Committee member) / White, Dave (Committee member) / Arizona State University (Publisher)
Created2011
Description
Urbanization, a direct consequence of land use and land cover change, is responsible for significant modification of local to regional scale climates. It is projected that the greatest urban growth of this century will occur in urban areas in the developing world. In addition, there is a significant research gap in emerging nations concerning this topic. Thus, this research focuses on the assessment of climate impacts related to urbanization on the largest metropolitan area in Latin America: Mexico City.
Numerical simulations using a state-of-the-science regional climate model are utilized to address a trio of scientifically relevant questions with wide global applicability. The importance of an accurate representation of land use and land cover is first demonstrated through comparison of numerical simulations against observations. Second, the simulated effect of anthropogenic heating is quantified. Lastly, numerical simulations are performed using pre-historic scenarios of land use and land cover to examine and quantify the impact of Mexico City's urban expansion and changes in surface water features on its regional climate.
Numerical simulations using a state-of-the-science regional climate model are utilized to address a trio of scientifically relevant questions with wide global applicability. The importance of an accurate representation of land use and land cover is first demonstrated through comparison of numerical simulations against observations. Second, the simulated effect of anthropogenic heating is quantified. Lastly, numerical simulations are performed using pre-historic scenarios of land use and land cover to examine and quantify the impact of Mexico City's urban expansion and changes in surface water features on its regional climate.
ContributorsBenson-Lira, Valeria (Author) / Georgescu, Matei (Thesis advisor) / Brazel, Anthony (Committee member) / Vivoni, Enrique (Committee member) / Arizona State University (Publisher)
Created2015
Description
Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a potential to locate several types of features; for example, forests, parking lots, airports, residential areas, or freeways in the imagery. However, the appearances of these things vary based on many things including the time that the image is captured, the sensor settings, processing done to rectify the image, and the geographical and cultural context of the region captured by the image. This thesis explores the use of deep convolutional neural networks to classify land use from very high spatial resolution (VHR), orthorectified, visible band multispectral imagery. Recent technological and commercial applications have driven the collection a massive amount of VHR images in the visible red, green, blue (RGB) spectral bands, this work explores the potential for deep learning algorithms to exploit this imagery for automatic land use/ land cover (LULC) classification. The benefits of automatic visible band VHR LULC classifications may include applications such as automatic change detection or mapping. Recent work has shown the potential of Deep Learning approaches for land use classification; however, this thesis improves on the state-of-the-art by applying additional dataset augmenting approaches that are well suited for geospatial data. Furthermore, the generalizability of the classifiers is tested by extensively evaluating the classifiers on unseen datasets and we present the accuracy levels of the classifier in order to show that the results actually generalize beyond the small benchmarks used in training. Deep networks have many parameters, and therefore they are often built with very large sets of labeled data. Suitably large datasets for LULC are not easy to come by, but techniques such as refinement learning allow networks trained for one task to be retrained to perform another recognition task. Contributions of this thesis include demonstrating that deep networks trained for image recognition in one task (ImageNet) can be efficiently transferred to remote sensing applications and perform as well or better than manually crafted classifiers without requiring massive training data sets. This is demonstrated on the UC Merced dataset, where 96% mean accuracy is achieved using a CNN (Convolutional Neural Network) and 5-fold cross validation. These results are further tested on unrelated VHR images at the same resolution as the training set.
ContributorsUba, Nagesh Kumar (Author) / Femiani, John (Thesis advisor) / Razdan, Anshuman (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2016
Description
The United States has a long history of providing public parks and amenities, especially for children. Unfortunately, children today are spending less time in public parks, less time getting physical activity and more time being indoors and sedentary. While multiple factors may be responsible for this lack of activity, multiple researchers have found the availability of parks is a significant influence on the physical activity levels of children as well as on the occurrence of obesity related illness. Public parks are ideal locations for children to get physical activity, however they are not always equitably distributed within communities. Income and race/ethnicity especially are common variables found to impact availability of parks. Such socioeconomic variables typically have an impact on the availability of public parks within a community. Such variables may also impact the quality of the parks provided. A case study of Scottsdale, Arizona was conducted analyzing the availability of public parks within the City between the years of 1990 and 2000 and the current quality of the parks. Statistical analysis and observation were utilized to assess the amount of park space available (in acres) and the quality of the parks in comparison to selected socioeconomic variables including ethnicity, income and total percent housing type (single family or multi-family). All analysis was conducted using U.S. Census data from the years 1990 and 2000 and was at the tract level. The results of the analysis indicate that in contrast to the initial hypothesis and past research, within the City of Scottsdale, lower income neighborhoods actually have more public park space available to them than higher income neighborhoods. Between 1990 and 2000 the difference in park space between the lowest and highest income quartiles increased considerably, approximately 230% over the ten years. The quality analysis results indicate that the overall quality of parks is slightly higher in the highest income neighborhoods, which also have no parks that could be considered of poor quality. Given the atypical results of this analysis, further research is necessary to better understand the impacts of socioeconomic characteristics on park, especially regarding children.
ContributorsSamples, Samantha (Author) / Crewe, Katherine (Thesis advisor) / Booze, Randy (Committee member) / Pijawka, David (Committee member) / Arizona State University (Publisher)
Created2011