• Housing & Race By Location Affordability in Washington State

      Lukinbeal, Chris; Stultz, Sierra (The University of Arizona., 2022)
      This Master’s Report focuses on how price-to-income ratio and race by location affordability affects housing in Washington State. Three types of analyses were used throughout this project. First, a price-to-income ratio using an affordability index was created to show areas of affordable to non-affordable housing. Price-to-income ratio calculates median home value divided by median household income resulting in an affordability ratio. The State of Washington has an affordability ratio of 5.6 and the ten highest ratios were in the following counties: San Juan, Whatcom, Chelan, Jefferson, Whitman, Skamania, King, Skagit, Kittitas, and Douglas. From county to block group level, King County tends to have the highest and most frequent affordability ratio in Washington. Cities and suburban areas tend to have a higher price-to-income ratio compared to the small-town rural areas. Second, race was added to a second affordability index. Race was compared to median home value through dot density and bivariate symbology to visually show race by location affordability. Third, race was compared to median home value and median household income through ordinary least squares linear regression to determine if there is a relationship which was shown. In terms of race by location affordability, majority of Washington State’s White population can afford a house compared to Hispanic/Latino, Black, Asian, American Indian or Alaskan Native, Hawaiian or Other Pacific Islander, and other races able to afford housing. The goal of this project is to bring further insight on where to focus efforts in providing equitable housing opportunities for racial disparities.
    • Identifying Housing Patterns in Pima County, Arizona Using the DEYA Affordability Index and Geospatial Analysis

      Nevarez Martinez, Deyanira (The University of Arizona., 2015)
      When the Fair Housing Act of 1968 was passed 47 years ago, the United States was in the midst of the civil rights movement and fair housing was identified as a pillar of equality. While, progress has been made, there is much work that needs to be done in order to achieve integration. As a country, the United States is a highly segregated country. It is important to understand the factors that contribute to this and it is important to understand the relationships that exists between them in order to attempt to solve the problem. While the legal barriers to integration have been lifted choices continue to be limited to families of color that lack the resources to live in desirable neighborhoods. The ultimate goal of this study is to examine the relationship between the impact of individual indicators and housing patterns in the greater Tucson/Pima county region. An affordability index, the DEYA index, was created to determine where affordability is at its highest. The index includes different weights for foreclosure, Pima County spending on affordable housing, the existence of Pima County general obligations bond affordable housing projects, land value and inclusion in the community land trust. Once this was determined a regression analysis was used to determine the relationship between affordability and individual factors that may be affecting integration. The indicators used were broken down into 3 categories: the categories were education, housing and neighborhoods and employment and economic health.
    • Identifying Opportunities for Community Solar: A Study of Maricopa and Pinal Counties

      Francis, Karol (The University of Arizona., 2016)
      Photovoltaic (PV) solar electricity generation has the potential to reduce the demand for more traditional fossil and nuclear power generation. Community PV solar installations allow energy users to share the electricity generated by these plants. Optimal siting of community solar installations will allow for maximum electricity generation while avoiding environmental conflicts, as well as, minimizing construction costs. This study identifies opportunities for community solar plants in Maricopa and Pinal Counties, Arizona, of ¼-acre in size. Input parameters fall into economic, physical, and environmental categories. Each of the input parameters were classified from 1 (not suitable) to 9 (highly suitable). Next, the classified rasters in each category were weighed according to importance, and Esri’s Weighted Sum tool was used to generate a combined raster for the category. The three resulting environmental, economic, and physical characteristic rasters were weighed again, and the Weighted Sum tool was used to generate a raster of community solar suitability scores. Next, a mask of locations inappropriate for community-scale solar development was created, including lakes, rivers, streams, and residential rooftops, which are too small to accommodate ¼-acre community solar installations. The masked areas were removed from the suitability raster, and the suitability raster was reclassified using standard deviations to generate a preference map with values ranging from 1 (low preference) to 3 (high preference). The model output reveals 68 percent of the study area is of medium or high preference for community solar installations. Maricopa and Pinal counties provide many opportunities for community solar installations.
    • The Inequitable Distribution of the Urban Heat Island in Dallas County, TX

      Korgaonkar, Yoga; Segovia, Isaias (The University of Arizona., 2021-12-16)
      The Urban Heat Island (UHI) effect is a phenomenon where an urban area has a higher Land Surface Temperature (LST) than surrounding rural areas due to human activities. This phenomenon is caused by the increasing urbanization and the removal of green vegetation. The combination of urbanization and climate change has intensified the UHI within urban areas. Certain urban areas can see slightly higher UHI temperatures than other urban areas due to certain demographic, socioeconomic, and land-use factors. This study sought to see how the UHI effect is being distributed within Dallas County, TX. Dallas County has the 8th largest city in the United States which is the city of Dallas. The City of Dallas was one of the cities within the United States that was redlined by the US government. Redlining was the practice that was used to bar minorities from moving into predominantly white communities and obtaining financial resources. This study used data from the Landsat 8 satellite to determine the UHI within Dallas County and how it is being distributed within certain demographic, socioeconomic, land-use, and historical practices. The assessment was done at the census tract level to determine if tracts with higher UHI had differences in the covariates. Thru the assessment, predominantly White areas, with a high median household income, and have areas graded by the Home Owner Loan Corporation (HOLC) as “Best” or “Still Desirable” had a lower UHI temperature. While areas that are predominantly Black or Hispanic with a high poverty rate, a large percentage of areas covered by impervious surfaces, and have areas graded by HOLC as “Definitely Declining” or “Hazardous” have a higher UHI temperature.
    • An Introduction to Identifying Nonpoint Sources of Water Pollution Using a Modified Land Use Conflict Analysis Identification Strategy (LUCIS) Model, Non-point Source Identification Strategy: NPSIS

      Cziesch, Jarrett (The University of Arizona., 2015)
      This paper examines the Non-Point Source Identification Strategy (NPSIS); a modification of the Land Use Conflict Identification Strategy (LUCIS): NPSIS is a raster model useful for identifying non-point sources of water pollution from three known contributors (agriculture, domestic, and natural background). By using a standard operating procedure, developers are able to create standardized datasets useful for identifying non-point sources of water pollution throughout the contiguous United States. The NPSIS model process requires the use of three “non-point source water pollution” contributors. A contributor is termed as a Non-Point Category (NPC) that contains collective elements (i.e. nutrient applications for agricultural purposes and urban runoff from highly developed areas). Using a survey, water resource professionals familiar with chosen study areas rank each NPC element according to potential impact to water quality. Following the survey, raster datasets that represent each NPC and impact to water quality are created using a lowest to highest (“1-9”) ordinal rank system derived from survey results after which each dataset is normalized using a (“1-3”) ordinal rank. Finally, the normalized NPC datasets are combined into one final model useful for identifying each dominant NPC by rank and location within a specified USGS watershed. In conclusion, the modifications to the LUCIS method yields results beneficial for identifying non-point source loads of water pollution.
    • Investigating Vulnerable Populations Inhabiting Sea Level Rise Resilient Geography in Miami, FL

      Korgaonkar, Yoga; Pachito, Samuel (The University of Arizona., 2022)
      Sea level rise (SLR) in Miami demands attention from policymakers to consider environmental benefits such as higher elevation as potential disadvantages when possessed by vulnerable populations. Without examining higher elevation landscapes, certain demographic features within historically segregated neighborhoods risk unfair exposure to climate gentrification. To find communities most affected by SLR per select neighborhood and census tract, ArcGIS Pro was used to create bathtub models from USGS digital elevation models, and polygons containing American Community Survey census data, which were spatially joined to illustrate those affected by SLR per half meter interval. Finding that while three of the four contemporary neighborhoods retain predominate racial and ethnic character of each respective historical community, 25.6% of the total population were in poverty, and 2.8% were 85 and older. Little Havana (92.8% Hispanic & Latino) was most affected by SLR in area and by population count. The area lost per census tract across all SLR intervals ranged from 0% - 96%, with the most resilient census tract found in Little Haiti with < 1.5% area lost at 3.0 m of SLR. This study elucidates the demographic details of higher elevation locations possessing varying degrees of resilience but that are at risk to climate gentrification.
    • Land Cover Change across Barbados using Remote Sensing and GIS Technology

      Sanchez Trigueros, Fernando; Browne, Tia (The University of Arizona., 2022)
      This paper focuses on the use of GIS (Geographical Information Systems) technology to determine land cover change in Barbados between 2014 and 2021. The island has experienced drought and urban expansion over the years which has raised concern about the availability of arable land on the island. Data acquired from the U.S geological survey Earth Explorer portal for February 26th, 2014, and March 2nd, 2021, were used to compute the Normalized Difference Vegetation Index (NDVI) for both years. Supervised classification using Support Vector Machines was used to determine seven (7) identified classes and their changes over the eight (8) year period. Results from the NDVI showed a general decrease in healthy vegetation from 2014 to 2021. 43.22% of the island experienced vegetation loss with 56.52% having vegetation remaining unchanged. Interestingly, only 0.26% of vegetation experienced regrowth mainly in forested areas. The validation of the supervised classification method used yielded an overall medium level of agreement with between 64% and 67% accuracy. The greatest change in land cover was from bare soil/barren land to urban areas which accounted for 23.2% change. 10.4% of grassy areas in 2014 changed to urban areas in 2021 with less than 10% change from forest to urban and agriculture to urban.
    • Land Suitability Analysis of the Fredericksburg Viticulture Area in the Texas Hill Country

      Sánchez-Trigueros, Fernando; Teet, Stacy (The University of Arizona., 2022)
      In the last 50 years, commercial vineyards in Texas have increased to more than five hundred. Wine production has tripled since 2012, making Texas the fifth largest wine producer in the United States. Like California’s Napa Valley, the Texas Hill Country is ripe for agritourism and wine cultivation bringing millions of visitors and billions of dollars to the state annually. Vineyards continue to increase, but most new owners lack agricultural experience. Due to its unique climate and lack of historical data, Texas growers and winemakers are still determining the best use of terrain while navigating harsh weather and regional hazards. Proper site selection is crucial. Spatial analysis of climate, soil and terrain characteristics was used to determine variables with the most impact on land suitability in the Fredericksburg viticulture region of the Texas Hill Country. Geospatial software was used to create a weighted overlay model of potential variables. Surface analysis found aspect, slope, solar radiation, flood frequency, drainage class, current land usage and available water storage to be statistically significant to this study. Potential areas were ranked on a scale of one to five, with one being permanently unsuitable and five being highly suitable for viticulture. Results found 594 acres or 27% to be highly suitable, 1,158 acres or 53% to be moderately suitable, and 430 acres or 20% not suitable for viticulture. Results of this study could help growers select prime areas for viticulture, but site-specific climate, environmental, and varietal specific factors should also be taken into consideration.

      Mason, Jennifer; Bollinger, Kyle (The University of Arizona., 2022-05-02)
      Maricopa County of Arizona is the 4th most populous county in the US, growing over 20% in population between 2010 and 2020. The Urban Heat Island (UHI) phenomenon in the county has increased alongside. The continued growth of urban and suburban structures, roads, and vegetation removal have created a heating effect near the ground that can be measured by the Land Surface Temperature (LST). By comparing Landsat-8 Thermal Infrared Sensor (TIRS) data the LST and thus UHI can be analyzed to better understand the long-term costs associated with urbanization. This effect is commonly associated with the removal of vegetation and using low reflective building and paving materials which can disproportionally influence the surface temperatures and thus heat in the area. Due to the sparse desert vegetation of Maricopa County, one would suspect that the newly developed areas may not be much warmer but due to the nature of the built materials that can absorb and release more energy after the sun sets than typical Arizona dirt. However, newly planted, and harvested farmland had the largest mean LST shifts within the study period contributing to the UHI problem even though farming occurs in rural areas. The urban space needs additional considerations and model variables that county officials could consider. Using an exploratory regression with an average land use per American Community Survey census tract and a generalized linear regression, results show which areas might exacerbate UHI issues so that the associated costs can be considered as part of future planning.
    • Las Vegas Metropolitan Area Urban Sprawl Assessment Using Shannon's Entropy

      Mason, Jennifer; Stuht, Casey M (The University of Arizona., 2022-05-03)
      A population center’s growth, known as urbanization, can pressure delicate environments and place strain on a region’s natural resources. Remote sensing combined with Geographic Information Systems can analyze and map the phenomenon of urban sprawl. This study quantifies growth within the Las Vegas, Nevada urban boundary using the aforementioned tools and Shannon’s Entropy method for 2000 and 2020. Shannon’s Entropy measures urban morphology, calculating compactness and dispersion of binary categorization, in this case, ‘developed’ and ‘undeveloped’ land cover. Eighteen multi-ring buffers were placed around Las Vegas City Hall at 1-mile intervals and found entropy values of 1.10 and 1.15 respectively. In comparison for the same years mentioned, five multi-ring buffers were set around the study area’s three main highways at 1-mile intervals and found entropy values of .608 and .628 respectively. All entropy values using the multi-ring buffer method were > 50% of log(n) for each dataset, meaning that the ‘developed’ land cover spatial variable is evenly dispersed across the study area with compactness or clustering of the ‘developed’ class found within each buffer zone. Temporally, over the 20-year period, the dispersion of development continued, with an increase in entropy values. Further, a geographic quadrant assessment revealed that the greatest land cover change-over from ‘undeveloped’ to ‘developed’ occurred in the northwestern and southwestern portions of the study area. This exercise provides a framework for developing municipalities that seek a cost effective, accessible, and expeditious method to better recognize sprawl patterns with the aim of correcting inefficient land and resource management.
    • Lead and Copper Rule Revisions: A Case Study in Identifying and Tracking Lead Water Service Lines with ArcGIS Field Maps

      Lukinbeal, Chris; Martin, Robert (The University of Arizona., 2022)
      The United States Environmental Protection Agency enacted the Lead and Copper Rule in 1991 to protect community water system consumers from exposure to lead and copper. The rule ensures levels of lead and copper in drinking water systems are below action levels. If the action level is exceeded, additional steps are required from water utilities to control corrosion in water systems. Significant quantities of lead in naturally occurring water sources are rare. However, with the use of certain plumbing fixtures containing brass, bronze or lead pipe prior to the Lead and Copper Rule of 1991, these materials can dissolve, flake or be found as small particles posing serious health risks. Corrosion can be a serious problem and is controlled through chemical treatment of source water. Considering events of the Flint, Michigan water crisis, revisions to improve the existing rule have been promulgated. The Lead and Copper Rule Revisions published on January 15, 2021, require service line material inventories, public outreach, and equitable replacement of lead service lines. Compliance is October 16, 2024. This case study includes a GIS based approach to identify and document all service line materials within the Ute Water District in Grand Junction, Colorado. GIS data architecture, methods and procedures utilizing ArcGIS software particularly ArcGIS Field Maps are shown to improve workflows, reduce time and redundancy over traditional paper record keeping methods. Data collection will be ongoing due to the large service area; however, a subset area will be analyzed within this study.

      Sánchez-Trigueros, Fernando; Garritson, David (The University of Arizona., 2022)
      Working in law enforcement is a challenging task, having the right tools available could mean success over failure. A proven method to identify and deter crime is mapping incidents from existing data to analyze and identify patterns. Small to medium size law enforcement agencies do not have the resources to utilize crime analysis mapping. Whether it is a matter of knowledge, time, staffing, or other factors; the benefits of mapping crime are unfortunately missing. By offering an understanding of the benefits of GIS, it will lead law enforcement agencies to use mapping in their crime analysis. With a clear understanding of where crime is being committed, policing trouble areas to reduce crime. Through creation of simple maps that depict criminal activity for a given area, it is possible to deter that crime. Learning how to utilize GIS tools that are currently available to prepare a visualization of crime in the form of a map, could lead to improved policing. Safer communities are possible with the proper training, a clear understanding of problem areas, and using mapping as a solution.
    • Mapping the Retreat of the Debris-Covered Tasman Glacier in the Aoraki-Mount Cook National Park, New Zealand

      Sánchez-Trigueros, Fernando; Garcia, Rose (The University of Arizona., 2022)
      As anthropogenic global climate change continues to accelerate, glaciers around the world are rapidly retreating. The Tasman Glacier offers a unique opportunity to demonstrate the challenge of mapping a debris covered glacier with a contemporary and rapid loss of ice at the terminus. Landsat 4, ETM+, and 8 OLI satellite Level-2 Reflectance imagery for years 1990, 2000, 2010 and 2022, are utilized for mapping the debris-covered glacier using a semi-automatic Support Vector Machine (SVM) classification. Normalized difference snow and ice index (NDSI) and normalized difference vegetation index (NDVI) are threshold and used as supplemental data for interpretation and optimization of machine learning training samples. To support delineation of the debris-covered glacier at the terminus location, slope data are derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (DEM). In addition to morphometric parameters, the DEM is used to calculate the glacier flow direction required to delineate the Tasman watershed. The 2013 Global Land Ice Measurements from Space (GLIMS) digital glacier outline is modified to delineate the Tasman Glacier System to derive the total area. Variability of the terminus retreat is quantified by area changes of the debris- covered glacier and proglacial lake. Post classification confusion matrices are computed to assess the quality and performance of the classified images. The overall level of agreement between the ground truth data and the predicted classes using the SVM classifier is strong, with an average Kappa statistic of 85 percent and average overall accuracy of 90 percent.
    • Measuring Ground Deformation Using Interferometry

      Summerlin, Tyler (The University of Arizona., 2022)
      The 2018 earthquake near Big Island, Hawaii caused landslides and ground deformation along the east coast. Ground deformation from seismic activity is of interest to scientists as it gives indications of volcanic activity below the Earth’s surface. Measuring this deformation can be challenging and typically requires Global Positioning System (GPS) monitors in place prior to an event to measure change, however, radar satellites provide a clear picture of wide scale movements. Interferometric Synthetic Aperture Radar (InSAR) is a collection method that compares Synthetic Aperture Radar (SAR) collections to measure vertical and horizontal ground displacement. This paper will outline the processes and methods used to process raw SAR data into an interferogram, a deformation map that precisely measures the ground shift after seismic events, glacial movements, or biomass change. Processing an interferogram started with reading raw radar collections from a SAR satellite such as Sentinel-1 and subsequently applying a series of conversions and transformations to create measurable data in the form of a displacement map. The calculated displacement indicates ground a sinking or downslope movement of -0.405 meters over the most active seismic area in Hawaii. The result from the interferogram and displacement map quantifies the effects of seismic activity and how InSAR can be used to accurately measure deformation for use in planning safe urban and infrastructure growth in areas of seismic activity.
    • Migration Centers of Virginia

      Korgaonkar, Yoga; Reyes, Neil (The University of Arizona., 2022-04-28)
      As the foreign-born population continues to grow in the United States, analyzing migration factors is crucial for continued growth. Immigration can be integral to the overall economy of an area as it leads to an increase of workers, business owners, taxpayers, and consumers. Virginia, specifically the Northern Virginia metropolitan region, is prime example of this correlation between a high foreign-born population and a bolstering economy. To ensure the large foreign-born population is maintained in Virginia, this study focuses on the significance and causes of migration. Several socioeconomic demographics were examined through regression and suitability analyses to understand the relationship between immigrants and an economy and migration. Based on the knowledge of push and pull migration factors, various demographics were chosen to represent these factors. The regression analysis assessed the relationship between the high foreign-born population and economic demographics, while the location suitability analysis mapped potential sites for immigration based on established migration criteria. The regression analysis proved an overall positive relationship between a large-foreign born population and an area’s overall economy, highlighting the importance of migration. The location suitability analysis demonstrated the draw, in conjunction with current immigrant population demographics, to those large urban centers with higher levels of socioeconomic advancement. The final cartographic products will demonstrate the importance of immigration to stimulate an area’s economy and produce recommendations for migration centers.
    • Modeling Postfire Soil Erosion and Sediment Deposition on the Tonto National Monument with the Unit Stream Power Erosion and Deposition Model

      Sanchez, Fernando; Macias, Michael (The University of Arizona., 2022)
      A major consequence of wildfire events is the acceleration of soil erosion by surface runoff. During a rainfall event, soil may become detached, transported, and eventually deposited elsewhere on the landscape. One approach to predict whether and where this erosion process could occur requires determining six empirically established factors, namely, rainfall erosivity, soil erodibility, slope length, slope steepness, vegetation cover, and erosion management methods. This project analyzed these landscape factors on the Tonto National Monument, an archaeologically rich site containing 14th century cliff dwellings in central Arizona’s Tonto Basin. In the summer of 2019, over 80% of the monument burned, threatening its natural and cultural resources both from the fire itself and from the postfire erosion that followed. Chosen for its ability to predict both soil erosion and sediment deposition, the Unit-Stream-Power-Erosion-and-Deposition Model identified areas of the monument where the erosion process may have occurred and to what extent. This project used high resolution data to obtain each factor in raster format followed by further calculations based on changes in sediment transport capacity using a Geographic Information System (GIS) called ArcGIS Pro. The model predicted that 13.5% of the monument had high erosion, 27% moderate erosion, 15.5% low erosion, 8.7% stable, 3.2% low deposition, 6.2% moderate deposition, and 25.7% high deposition. Although this project’s methodology focused on the 2019 fire event, it offers resource managers on the monument an approach to monitor and mitigate potential future fire events, reducing costs and focusing efforts to areas of highest risk.
    • Modeling the Change in Distribution of an Endangered Lichen Species Under Projected Climate Conditions

      Sánchez-Trigueros, Fernando; Jones, Julia (The University of Arizona., 2022)
      Sulcaria spiralifera, or Dune Hair Lichen, is endemic to coastal dune forests along the Pacific coast in the continental United States. The species’ habitat is vulnerable to drought and temperature extremes. Modeling the possible impact of climate change can assist with conservation planning and bolster preservation of the entire ecosystem. This study investigates the impact of climate change on the distribution of a rare and endangered species by using the maximum entropy probability distribution principal to build a predictive species distribution model. The approach has demonstrated success in predicting the distribution of rare species that may include limited data and lack points of absence. The probable distribution of the species was modeled under current and historic climate conditions and used to train new models that would predict distribution under future climate conditions. Results of the project show a spatial change in habitat between 2021 and 2100 with suitable locations becoming more abundant. Positive changes in presence predict a shift inland while locations along the coast experience negative change. Despite an overall increase in suitable habitat, the predicted point of presence remains relatively stable with gradual increases around 2% every 20 years until a decrease of 4% between 2080 and 2100. Although the models show an increase in habitat suitability over time, it is unclear whether the Dune Hair Lichen could survive potential relocation as habitat shifts inland. The species distribution model under future climate conditions can help conservationists monitor and inventory the species to assess adaptation success.
    • Modeling the Hillside Development Overlay Zone

      Psillas, Jennifer; Avis, Jack; Jackson, Chloe (The University of Arizona., 2016-12)
      Sustainable urban growth can be achieved in part by increasing density through infill development. Done right, infill development encourages already developed areas to become more diverse and livable, while limiting urban sprawl and all the public health, environmental, and infrastructure problems that accompany it. In Pima County’s 2015 update to the Comprehensive Plan, infill development is identified as a goal for land use policy. This study utilizes a Python script to build a model of the Hillside Development Overlay Zone (HDZ) to aid in removing zoning barriers to this goal. This a) improves the permitting process; b) encourages purchase of parcels outside of hillside areas and; c) encourages innovative design on hillside areas. The visualization is available on Pima County’s MapGuide website, allowing developers to make informed decisions about purchasing, permitting, and designing on HDZ parcels. In addition, this study uses a Kernel Density analysis to suggest areas where HDZ can be removed, without losing protection for mountainous areas. These suggestions are submitted to Pima County Development Services.
    • Monitoring the Mega Drought and the effects it has on Reservoirs in Southwest Colorado using a Change Detection Analysis

      Sánchez-Trigueros, Fernando; Busby, Blake (The University of Arizona., 2022)
      All across the Western portion of the United States water is an increasing topic of concern. A majority of the mainstream discussion revolves around Lake Mead and Lake Powel the two largest reservoirs in the United States. This project aims to shed light on the “Mega Drought” impacting three reservoirs in Southwest Colorado, McPhee Reservoir, Lemon Reservoir, and Vallecito Reservoir. The way the impact of the “Mega Drought” will be monitored is by generating a Normalized Difference Water Index (NDWI) every year from 2013-2021. The NDWIs were generated by using Landsat 8 OLI data. The data was compiled into ArcGIS Pro software. That data was compiled into a multidimensional raster format so a time series analysis could be performed as well as the generation of a change detection raster. To quantify the results of the NDWI sample points were generated to extract the pixel values. The results of this study showed that over the nine-year study that reservoir levels rose to the highest value in 2016 and have continued to fall to the year 2021. In 2021 all their reservoirs are registered with an average NDWI value that is classified as a moderate drought, non-aqueous surfaces. The results of this research are showing that these 3 reservoirs in Southwest Colorado are decreasing in volume year after year. Mostly caused by decreasing snowpack, warmer spring and summer temperatures, and increasingly unproductive monsoon seasons.
    • Monitoring urban land-use trends using remote sensed imagery and GIS for the Tucson, Arizona metropolitan region

      Danloe, John; Labadie, Philippe-Luis; Psillas, Jen; Lukinbeal, Chris (The University of Arizona., 2016-12-18)
      This project demonstrates the usefulness of using remotely sensed imagery in conjunction with GIS for urban studies. Utilizing 1-meter high-resolution imagery and GIS, this project provides land-cover change statistics and spatial variables describing new urban development. Statistics of land-cover change were used to quantify the amount of new urban development in acreage. The project then employed a global logistic regression to determine the significant topographic variables influencing the new urban development. The project focused on urban growth from 1998 to 2010 for the Greater Tucson Metropolitan Region. These methods provide accurate and useful information for quantifying urban growth.