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1
Pritsolas, Joshua. "Principal Component Analysis and Spatial Regression Techniques to Model and Map Corn and Soybean Yield Variability with Radiometrically Calibrated Multitemporal and Multispectral Digital Aerial Imagery." Thesis, Southern Illinois University at Edwardsville, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10807753.
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<p> Remotely sensed data has been discussed as a possible alternative to the standard precision agriculture systems of combine-mounted yield monitors because of the burden, cost, end of season use, and inherent errors that are associated with these systems. Due to the potential quantitative use of remote sensing in precision agriculture, the primary focus of this study was to test the relationship between multitemporal/multispectral digital aerial imagery with corn (<i>Zea mays</i> L.) and soybean (<i>Glycine max </i> L.) yield. Digital aerial imagery was gathered on nine different dates throughout the 2015 growing season from two fields (one corn and one soybean) located on a farm in Story County, Iowa. To begin assessing this relationship, the digital aerial imagery was radiometrically calibrated. The radiometric calibration process used calibration tarps with known reflectance values (3, 6, 12, 22, 44, and 56 percent). The calibrated imagery was then used to calculate and output 12 different vegetation indices (VIs) and three calibrated wavebands (red, green, and near-infrared). </p><p> Next, the calibrated VIs and wavebands from the 2015 growing season were used to examine their relationship with the corn and soybean yield data collected from a combine yield monitor system. This relationship between multitemporal/multispectral digital aerial imagery with corn and soybean yield was investigated with principal component analysis and spatial modeling techniques. The results from spatial modeling of corn revealed that VIs utilizing the green waveband performed strongly. VIs such as, chlorophyll index-green, chlorophyll vegetation index, and green normalized difference vegetation index accounted for 81.6, 83.0, and 82.4 percent of the yield variability, respectively. Strong modeling relationships were also found in soybean using just the near-infrared waveband or VIs that utilized the near-infrared waveband. The near-infrared waveband captured 89.1 percent of the yield variation, while VIs such as, difference vegetation index, triangular vegetation index, soil adjusted vegetation index, and optimized soil adjusted vegetation index accounted for 87.3, 87.3, 83.9, and 83.8 percent of soybean yield variability, respectively. The temporal assessment of the remotely sensed data also identified certain VIs and wavebands that captured pivotal growth stages for detecting potential yield limiting factors. These specific growth stages varied for different VIs and wavebands for both corn and soybean. Overall, the results from this study identified that mid-to-late vegetative growth stages (prior to tasseling) and late-season reproductive stages were important parameters that provided unique information in the modeling of corn yield variability, while the later reproductive stages (just prior to senescence) were essential to capturing soybean yield variability. </p><p> Lastly, this research produced corn and soybean yield maps from the digital aerial imagery. The digital aerial imagery yield maps were then compared with maps that used kriging interpolation of the combine yield monitor data gathered from the same corn and soybean fields. The results indicated that both corn and soybean yield maps produced with multitemporal/multispectral digital aerial imagery were comparable with a standard method of kriging interpolation from yield monitor data.</p><p>
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Gwenzi, David. "Lidar remote sensing of savanna biophysical attributes." Thesis, Colorado State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3720536.
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<p> Although savanna ecosystems cover approximately 20 % of the terrestrial land surface and can have productivity equal to some closed forests, their role in the global carbon cycle is poorly understood. This study explored the applicability of a past spaceborne Lidar mission and the potential of future missions to estimate canopy height and carbon storage in these biomes. </p><p> The research used data from two Oak savannas in California, USA: the Tejon Ranch Conservancy in Kern County and the Tonzi Ranch in Santa Clara County. In the first paper we used non-parametric regression techniques to estimate canopy height from waveform parameters derived from the Ice Cloud and land Elevation Satellite’s Geoscience Laser Altimeter System (ICESat-GLAS) data. Merely adopting the methods derived for forests did not produce adequate results but the modeling was significantly improved by incorporating canopy cover information and interaction terms to address the high structural heterogeneity inherent to savannas. Paper 2 explored the relationship between canopy height and aboveground biomass. To accomplish this we developed generalized models using the classical least squares regression modeling approach to relate canopy height to above ground woody biomass and then employed Hierarchical Bayesian Analysis (HBA) to explore the implications of using generalized instead of species composition-specific models. Models that incorporated canopy cover proxies performed better than those that did not. Although the model parameters indicated interspecific variability, the distribution of the posterior densities of the differences between composition level and global level parameter values showed a high support for the use of global parameters, suggesting that these canopy height-biomass models are universally (large scale) applicable. </p><p> As the spatial coverage of spaceborne lidar will remain limited for the immediate future, our objective in paper 3 was to explore the best means of extrapolating plot level biomass into wall-to-wall maps that provide more ecological information. We evaluated the utility of three spatial modeling approaches to address this problem: deterministic methods, geostatistical methods and an image segmentation approach. Overall, the mean pixel biomass estimated by the 3 approaches did not differ significantly but the output maps showed marked differences in the estimation precision and ability of each model to mimic the primary variable’s trend across the landscape. The results emphasized the need for future satellite lidar missions to consider increasing the sampling intensity across track so that biomass observations are made and characterized at the scale at which they vary. </p><p> We used data from the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne photon counting lidar sensor developed by NASA Goddard to simulate ICESat-2 data. We segmented each transect into different block sizes and calculated canopy top and mean ground elevation based on the structure of the histogram of the block’s aggregated photons. Our algorithm was able to compute canopy height and generate visually meaningful vegetation profiles at MABEL’s signal and noise levels but a simulation of the expected performance of ICESat-2 by adjusting MABEL data's detected number of signal and noise photons to that predicted using ATLAS instrument model design cases indicated that signal photons will be substantially lower. The lower data resolution reduces canopy height estimation precision especially in areas of low density vegetation cover. </p><p> Given the clear difficulties in processing simulated ATLAS data, it appears unlikely that it will provide the kind of data required for mapping of the biophysical properties of savanna vegetation. Rather, resources are better concentrated on preparing for the Global Ecosystem Dynamics Investigation (GEDI) mission, a waveform lidar mission scheduled to launch by the end of this decade. In addition to the full waveform technique, GEDI will collect data from 25 m diameter contiguous footprints with a high across track density, a requirement that we identified as critically necessary in paper 3. (Abstract shortened by UMI.)</p>
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Brailo, Courtney M. "A Light Detecting and Ranging (LiDAR) and Global Positioning System (GPS) Study of the Truckee Meadows, NV. Quaternary Fault Mapping with ArcGIS, 3D Visualization and Computational Block Modeling of the Greater Reno area." Thesis, University of Nevada, Reno, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10126167.
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<p> The Truckee Meadows (Reno, NV) sits in a tectonically complex area of western Nevada, where Walker Lane-style transtension is dominant throughout the region. A new Light Detection and Ranging (LiDAR) study focuses on the Truckee Meadows region of western Nevada, including the Reno/Sparks metropolitan area in Washoe County. We use the airborne LiDAR imagery (1485 sq. km) to create high quality, bare-earth topographic maps that were previously unattainable in vegetated, populated or alpine terrain. This approach gives us an opportunity to improve fault maps that may be outdated or incomplete in the area. Here we provide LiDAR imagery of a large section of Washoe County and an updated fault map of the greater Truckee Meadows region. </p><p> We also use this new LiDAR survey of the Truckee Meadows and nearby basins to constrain geometry, length, distribution, and slip rates along faults imaged by this new dataset. Estimated slip rates are compared to those derived from a geodetic block model constrained by Global Positioning Station (GPS) data to test for consistency. GPS station data and geologic mapping show that both east-west oriented extension and northwest-oriented right-lateral strike slip accommodate transtension as a backdrop for tectonics studies of region, with some northeast-oriented left-lateral strike slip. This study aims to better understand how this transtension is partitioned along remapped faults and newly identified structures in this urban setting, as the framework for strain accommodation in this area remains poorly understood. </p><p> Faults with normal offset were measured along strike using bare-earth LiDAR returns to determine the amount of vertical separation across geomorphic surfaces, and then converted to extension assuming a fault dip of 60 (+/-10) degrees. Since the primary geomorphic surfaces in this region are the result of Sierra Nevadan glacial outwash episodes, we use previously published geologic maps to link each surface to an associated date. When integrated across several basin perpendicular transects within the Mt. Rose pediment, we calculate a total extension rate of 0.87 (+0.40/-0.48) mm/yr for the southern Truckee Meadows basin. Integrated slip rates from fault scarp offsets are within the bounds of 1.23 (+/-0.70) mm/yr suggested by geodetic modeling. Block modeling highlights that north-striking faults primarily accommodate east-west extension, and so northwest-striking faults and/or block rotations must accommodate the northwest-directed shear seen in GPS velocities. This trend is bolstered by the discovery of a new northwest-oriented fault on Peavine Mountain 6 km east of the Mogul (2008) seismicity trend. Our study provides further evidence that the Truckee Meadows sits at a critical transition from north-striking normal faults in the southern part of the basin to northwest-oriented strike-slip faults to the north, an observation that mimics regional tectonics and geomorphology of the adjacent Lake Tahoe/Truckee system to the west.</p>
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Grubbs, Melodie. "Beach Morphodynamic Change Detection using LiDAR during El Nino Periods in Southern California." Thesis, University of Southern California, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10257407.
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<p> Light Detection and Ranging (LiDAR) technology combined with high-resolution differential Global Positioning Systems (dGPS) provide the ability to measure coastal elevation with high precision. This study investigates the use of LiDAR data and GIS to conduct time-series analyses of coastal sediment volume shifts during the 2006-2007 El Niño winter, Summer of 2007 and following 2007-2008 La Niña winter in the Oceanside Littoral Cell (OLC). The OLC, located in Southern California, spans from Dana Point to La Jolla and includes over 84 km of coastline. The ability to quantify sediment volume changes contributes to the scientific understanding of the role El Niño storms play in the OLC sand budget. This study provides a method to analyze LiDAR data to evaluate coastal geomorphologic changes over time. Additionally, identifying specific areas of coastal beach erosion associated with historical El Niño events can aid beach managers, planners, and scientists in protecting the valuable coastline. LiDAR datasets were prepared and formatted which included ground classifying millions of elevation points. Formatted datasets were inputted into an Empirical Bayesian Kriging (EBK) model, creating high-resolution, 1-meter grid cell, Digital Elevation Models (DEMs). The EBK model also incorporated uncertainty into the workflow by producing prediction error surfaces. LiDAR-derived DEMs were used to calculate sediment volume changes through a technique called DEM differencing. Results were visualized through a series of maps and tables. Overall results show that there was a higher rate of beach sediment erosion during the 2006-2007 El Niño winter than the 2007-2008 La Niña winter. Sediment accretion was evident during the intermediary Summer of 2007. Future applications of this study include incorporating bathymetric datasets to understand near-shore sediment transport, evaluating sediment contribution through cliff erosion, and conducting decadal scale studies to evaluate long-term trends with sea level rise scenarios. </p>
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Rafiq, Talha. "A temporal and ecological analysis of the Huntington Beach Wetlands through an unmanned aerial system remote sensing perspective." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1597786.
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<p>Wetland monitoring and preservation efforts have the potential to be enhanced with advanced remote sensing acquisition and digital image analysis approaches. Progress in the development and utilization of Unmanned Aerial Systems (UAS) and Unmanned Aerial Vehicles (UAV) as remote sensing platforms has offered significant spatial and temporal advantages over traditional aerial and orbital remote sensing platforms. Photogrammetric approaches to generate high spatial resolution orthophotos of UAV acquired imagery along with the UAV?s low-cost and temporally flexible characteristics are explored. A comparative analysis of different spectral based land cover maps derived from imagery captured using UAV, satellite, and airplane platforms provide an assessment of the Huntington Beach Wetlands. This research presents a UAS remote sensing methodology encompassing data collection, image processing, and analysis in constructing spectral based land cover maps to augment the efforts of the Huntington Beach Wetlands Conservancy by assessing ecological and temporal changes at the Huntington Beach Wetlands.
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Thompson, James. "Identifying Subsurface Tile Drainage Systems Utilizing Remote Sensing Techniques." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1290141705.
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Tu, Denise Shao-Wai. "Assessment of Methods for Monitoring Responses to River Restoration: Riverbed and Channel Form Changes." Thesis, University of Oregon, 2011. http://hdl.handle.net/1794/11505.
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xi, 54 p. : ill. (some col.)<br>On the Middle Fork John Day River (MFJD), a low gradient, meandering river in eastern Oregon, restoration includes engineered log structures intended to increase in-stream complexity and habitat diversity. Effects of log structures on riverbed topography can be captured through repeat topographic surveys, digital elevation model (DEM) of differencing (DoD), and aerial imagery. This study evaluates the (1) potential for remote sensing analysis, (2) effect of survey point density on DEMs, and (3) application of DoDs, in monitoring riverbed changes in the MFJD. An average point spacing and density finer than 0.50m and 1.25pts/m<super>2</super> captures riverbed complexities. Although elevation changes were expected to be minimal, DoDs revealed -0.9 to 0.5m elevation changes associated with log structure designs. Incorporating numerical thresholds into future monitoring survey methods will improve the modeling of MFJD riverbed surfaces. Monitoring riverbed changes through DoDs can inform improvements to future restoration design and the effectiveness of log structures.<br>Committee in charge: Patricia McDowell, Chairperson;
Andrew Marcus, Member
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Anibas, Kyle Lawrence. "Land cover, land use and habitat change in Volyn, Ukraine : 1986-2011." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/17682.
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Master of Science<br>Department of Geography<br>Douglas G. Goodin<br>Volyn Oblast in Western Ukraine has experienced substantial land use/land cover change over the last 25 years as a result of a change in political systems. Remote sensing provides a framework to quantify this change without extensive field work or historical land cover records. In this study, land change is quantified utilizing a post-classification change detection technique comparing Landsat imagery from 1986-2011(Post-Soviet era began 1991). A variety of remote sensing classification methods are explored to take advantage of spectral and spatial variation within this complex study area, and a hybrid scheme is ultimately utilized. Land cover from the CORINE classification scheme is then converted to the EUNIS habitat classification scheme to analyze how land cover change has affected habitat fragmentation. I found large scale agricultural abandonment, increases in forested areas, shifts towards smaller scale farming practices, shifts towards mixed forest structures, and increases in fragmentation of both forest and agricultural habitat types. These changes could have several positive and negative on biodiversity, ecosystems, and human well-being.
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Haynes, Keelin. "Modeling Land-Cover/Land-Use Change: A Case Study of a Dynamic Agricultural Landscape in An Giang and Dong Thap, Vietnam." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1596032711477172.
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Sanchez, Luna Maria M. "MAPPING SMALL SCALE FARMING IN HETEROGENEOUS LANDSCAPES: A CASE STUDY OF SMALLHOLDER SHADE COFFEE AND PLASTIC AGRICULTURE FARMERS IN THE CHIAPAS HIGHLANDS." Miami University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1564228778095931.
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Burchfield, David Richard. "Mapping eastern redcedar (Juniperus Virginiana L.) and quantifying its biomass in Riley County, Kansas." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/18404.
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Master of Arts<br>Department of Geography<br>Kevin P. Price<br>Due primarily to changes in land management practices, eastern redcedar (Juniperus virginiana L.), a native Kansas conifer, is rapidly invading onto valuable rangelands. The suppression of fire and increase of intensive grazing, combined with the rapid growth rate, high reproductive output, and dispersal ability of the species have allowed it to dramatically expand beyond its original range. There is a growing interest in harvesting this species for use as a biofuel. For economic planning purposes, density and biomass quantities for the trees are needed. Three methods are explored for mapping eastern redcedar and quantifying its biomass in Riley County, Kansas. First, a land cover classification of redcedar cover is performed using a method that utilizes a support vector machine classifier applied to a multi-temporal stack of Landsat TM satellite images. Second, a Small Unmanned Aircraft System (sUAS) is used to measure individual redcedar trees in an area where they are encroaching into a pasture. Finally, a hybrid approach is used to estimate redcedar biomass using high resolution multispectral and light detection and ranging (LiDAR) imagery. These methods showed promise in the forestry, range management, and bioenergy industries for better understanding of an invasive species that shows great potential for use as a biofuel resource.
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Athreya, Brinda K. "Spatially Assessing the perceptions and motivations of farmers implementing Best Management Practices (BMPs) in the Western Lake Erie Basin." University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1588932667586433.
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Blackmore, Debra Sue. "Use of Water Indices Derived from Landsat OLI Imagery and GIS to Estimate the Hydrologic Connectivity of Wetlands in the Tualatin River National Wildlife Refuge." Thesis, Portland State University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10191067.
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<p> This study compared two remote sensing water indices: the Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI). Both indices were calculated using publically-available data from the Landsat 8 Operational Land Imager (OLI). The research goal was to determine whether the indices are effective in locating open water and measuring surface soil moisture. To demonstrate the application of water indices, analysis was conducted for freshwater wetlands in the Tualatin River Basin in northwestern Oregon to estimate hydrologic connectivity and hydrological permanence between these wetlands and nearby water bodies. Remote sensing techniques have been used to study wetlands in recent decades; however, scientific studies have rarely addressed hydrologic connectivity and hydrologic permanence, in spite of the documented importance of these properties. Research steps were designed to be straightforward for easy repeatability: 1) locate sample sites, 2) predict wetness with water indices, 3) estimate wetness with soil samples from the field, 4) validate the index predictions against the soil samples from the field, and 5) in the demonstration step, estimate hydrologic connectivity and hydrological permanence. Results indicate that both indices predicted the presence of large, open water features with clarity; that dry conditions were predicted by MNDWI with more subtle differentiation; and that NDWI results seem more sensitive to sites with vegetation. Use of this low-cost method to discover patterns of surface moisture in the landscape could directly improve the ability to manage wetland environments.</p>
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Hollinger, David L. "Crop Condition and Yield Prediction at the Field Scale with Geospatial and Artificial Neural Network Applications." Kent State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=kent1310493197.
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Panozzo, Kimberly A. "A Validation of Nass Crop Data Layer in the Maumee River Watershed." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470417001.
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Valdez-Zamudio, Diego 1953. "Land cover and land use change detection in northwestern Sonora, Mexico using geographic information system and remote sensing techniques." Thesis, The University of Arizona, 1994. http://hdl.handle.net/10150/278469.
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Remote sensing and geographic information system techniques have proved to be effective tools to detect, analyze, and evaluate land cover and land use changes over time. In this research project, changes in land cover and land use were detected in northwestern Sonora, Mexico between 1972 and 1992 using Landsat MSS imagery. About 40% of the entire land cover in the study area changed during that period of time. Of the six classes assigned to the imagery, cropland had the highest rate of change being modified into riparian areas by more than 60%, more than 20% into plains vegetation, and about 8% into bajadas with vegetation. From the two classification methods utilized in this study, the seeding pixels method yielded an over all accuracy over 96%, while the seeking polygons method generated overall accuracy values smaller than 82% probably to user's error.
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"GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.53688.
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abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.<br>Dissertation/Thesis<br>Doctoral Dissertation Geography 2019
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"Reconstruction of a Tornado Disaster Employing Remote Sensing Techniques: A Case Study of the 1999 Moore, Oklahoma Tornado." Master's thesis, 2011. http://hdl.handle.net/2286/R.I.8969.
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abstract: Remote sensing has demonstrated to be an instrumental tool in monitoring land changes as a result of anthropogenic change or natural disasters. Most disaster studies have focused on large-scale events with few analyzing small-scale disasters such as tornadoes. These studies have only provided a damage assessment perspective with the continued need to assess reconstruction. This study attempts to fill that void by examining recovery from the 1999 Moore, Oklahoma Tornado utilizing Landsat TM and ETM+ imagery. Recovery was assessed for 2000, 2001 and 2002 using spectral enhancements (vegetative and urban indices and a combination of the two), a recovery index and different statistical thresholds. Classification accuracy assessments were performed to determine the precision of recovery and select the best results. This analysis proved that medium resolution imagery could be used in conjunction with geospatial techniques to capture recovery. The new indices, Shortwave Infrared Index (SWIRI) and Coupled Vegetation and Urban Index (CVUI), developed for disaster management, were the most effective at discerning reconstruction using the 1.5 standard deviation threshold. Recovery rates for F-scale damages revealed that the most incredibly damaged areas associated with an F5 rating were the slowest to recover, while the lesser damaged areas associated with F1-F3 ratings were the quickest to rebuild. These findings were consistent for 2000, 2001 and 2002 also exposing that complete recovery was never attained in any of the F-scale damage zones by 2002. This study illustrates the significance the biophysical impact has on recovery as well as the effectiveness of using medium resolution imagery such as Landsat in future research.<br>Dissertation/Thesis<br>M.A. Geography 2011
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"Assessing Usable Ground and Surface Water Level Correlation Factors in the Western United States." Master's thesis, 2018. http://hdl.handle.net/2286/R.I.51704.
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abstract: The Western Continental United States has a rapidly changing and complex ecosystem that provides valuable resources to a large portion of the nation. Changes in social and environmental factors have been observed to be significantly correlated to usable ground and surface water levels. The assessment of water level changes and their influences on a semi-national level is needed to support planning and decision making for water resource management at local levels. Although many studies have been done in Ground and Surface Water (GSW) trend analysis, very few have attempted determine correlations with other factors. The number of studies done on correlation factors at a semi-national scale and near decadal temporal scale is even fewer. In this study, freshwater resources in GSW changes from 2004 to 2017 were quantified and used to determine if and how environmental and social variables are related to GSW changes using publicly available remotely sensed and census data. Results indicate that mean annual changes of GSW of the study period are significantly correlated with LULC changes related to deforestation, urbanization, environmental trends, as well as social variables. Further analysis indicates a strong correlation in the rate of change of GSW to LULC changes related to deforestation, environmental trends, as well as social variables. GSW slope trend analysis also reveals a negative trend in California, New Mexico, Arizona, and Nevada. Whereas a positive GSW trend is evident in the northeast part of the study area. GSW trends were found to be somewhat consistent in the states of Utah, Idaho, and Colorado, implying that there was no GSW changes over time in these states.<br>Dissertation/Thesis<br>Masters Thesis Geography 2018
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"Spatial Growth of Informal Settlements in Delhi and Factors Affecting Growth Rate; An Application of Remote Sensing." Master's thesis, 2011. http://hdl.handle.net/2286/R.I.9111.
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abstract: Slum development and growth is quite popular in developing countries. Many studies have been done on what social and economic factors are the drivers in establishment of informal settlements at a single cross-section of time, however limited work has been done in studying their spatial growth patterns over time. This study attempts to study a sample of 30 informal settlements that exist in the National Capital Territory of India over a period of 40 years and identify relationships between the spatial growth rates and relevant factors identified in previous socio-economic studies of slums using advanced statistical methods. One of the key contributions of this paper is indicating the usefulness of satellite imagery or remote sensing data in spatial-longitudinal studies. This research utilizes readily available LANDSAT images to recognize the decadal spatial growth from 1970 to 2000, and also in extension, calculate the BI (transformed NDVI) as a proxy for the intensity of development for the settlements. A series of regression models were run after processing the data, and the levels of significance were then studied and compared to see which relationships indicated the highest levels of significance. It was observed that the change in BI had a higher strength of relationships with the change in independent variables than the settlement area growth. Also, logarithmic and cubic models showed the highest R-Square values than any other tested models.<br>Dissertation/Thesis<br>M.U.E.P. Environmental Design and Planning 2011
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Brun, Julien. "Investigating the Eco-Hydrological Impact of Tropical Cyclones in the Southeastern United States." Diss., 2013. http://hdl.handle.net/10161/8046.
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<p>Tropical Cyclones (TCs) intensity and frequency are expected to be impacted by climate change. Despite their destructive potential, these phenomena, which can produce heavy precipitation, are also an important source of freshwater. Therefore any change in frequency, seasonal timing and intensity of TCs is expected to strongly impact the regional water cycle and consequently the freshwater availability and distribution. This is critical, due to the fact that freshwater resources in the US are under stress due to the population growth and economic development that increasingly create more demands from agricultural, municipal and industrial uses, resulting in frequent over-allocation of water resources. </p><p>In this study we concentrate on monitoring the impact of hurricanes and tropical storms on vegetation activity along their terrestrial tracks and investigate the underlying physical processes. To characterize and monitor the spatial organization and time of recovery of vegetation disturbance in the aftermath of major hurricanes over the entire southeastern US, a remote sensed framework based on MODIS enhanced vegetation index (EVI) was developed. At the SE scale, this framework was complemented by a water balance approach to estimate the variability in hurricane groundwater recharge capacity spatially and between events. Then we investigate the contribution of TCs (season totals and event by event) to the SE US annual precipitation totals from 2002 to 2011. A water budget approach applied at the drainage basins scale is used to investigate the partitioning of TCs' precipitation into surface runoff and groundwater system in the direct aftermath of major TCs. This framework allows exploring the contribution of TCs to annual precipitation totals and the consequent recharge of groundwater reservoirs across different physiographic regions (mountains, coastal and alluvial plains) versus the fraction that is quickly evacuated through the river network and surface runoff. </p><p>Then a Land surface Eco-Hydrological Model (LEHM), combining water and energy budgets with photosynthesis activity, is used to estimate Gross Primary Production (GPP) over the SE US The obtained data is compared to AmeriFlux and MODIS GPP data over the SE United States in order to establish the model's ability to capture vegetation dynamics for the different biomes of the SE US. Then, a suite of numerical experiments is conducted to evaluate the impact of Tropical Cyclones (TCs) precipitation over the SE US. The numerical experiments consist of with and without TC precipitation simulations by replacing the signature of TC forcing by NARR-derived climatology of atmospheric forcing ahead of landfall during the TC terrestrial path. The comparison of these GPP estimates with those obtained with the normal forcing result in areas of discrepancies where the GPP was significantly modulated by TC activity. These areas show up to 10% variability over the last decade.</p><br>Dissertation
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"A Spatial Statistical Framework for Evaluating Landscape Pattern and Its Impacts on the Urban Thermal Environment." Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.39433.
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abstract: 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.<br>Dissertation/Thesis<br>Doctoral Dissertation Geography 2016
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"Understanding Open Spaces in an Arid City." Doctoral diss., 2011. http://hdl.handle.net/2286/R.I.14309.
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abstract: This doctoral dissertation research aims to develop a comprehensive definition of urban open spaces and to determine the extent of environmental, social and economic impacts of open spaces on cities and the people living there. The approach I take to define urban open space is to apply fuzzy set theory to conceptualize the physical characteristics of open spaces. In addition, a 'W-green index' is developed to quantify the scope of greenness in urban open spaces. Finally, I characterize the environmental impact of open spaces' greenness on the surface temperature, explore the social benefits through observing recreation and relaxation, and identify the relationship between housing price and open space be creating a hedonic model on nearby housing to quantify the economic impact. Fuzzy open space mapping helps to investigate the landscape characteristics of existing-recognized open spaces as well as other areas that can serve as open spaces. Research findings indicated that two fuzzy open space values are effective to the variability in different land-use types and between arid and humid cities. W-Green index quantifies the greenness for various types of open spaces. Most parks in Tempe, Arizona are grass-dominant with higher W-Green index, while natural landscapes are shrub-dominant with lower index. W-Green index has the advantage to explain vegetation composition and structural characteristics in open spaces. The outputs of comprehensive analyses show that the different qualities and types of open spaces, including size, greenness, equipment (facility), and surrounding areas, have different patterns in the reduction of surface temperature and the number of physical activities. The variance in housing prices through the distance to park was, however, not clear in this research. This dissertation project provides better insight into how to describe, plan, and prioritize the functions and types of urban open spaces need for sustainable living. This project builds a comprehensive framework for analyzing urban open spaces in an arid city. This dissertation helps expand the view for urban environment and play a key role in establishing a strategy and finding decision-makings.<br>Dissertation/Thesis<br>Ph.D. Geography 2011
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"West Nile virus in Maricopa County, Arizona: Investigating human, vector, and environmental interactions." Master's thesis, 2013. http://hdl.handle.net/2286/R.I.18663.
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abstract: Despite the arid climate of Maricopa County, Arizona, vector-borne diseases have presented significant health challenges to the residents and public health professionals of Maricopa County in the past, and will continue to do so in the foreseeable future. Currently, West Nile virus is the only mosquitoes-transmitted disease actively, and natively, transmitted throughout the state of Arizona. In an effort to gain a more complete understanding of the transmission dynamics of West Nile virus this thesis examines human, vector, and environment interactions as they exist within Maricopa County. Through ethnographic and geographic information systems research methods this thesis identifies 1) the individual factors that influence residents' knowledge and behaviors regarding mosquitoes, 2) the individual and regional factors that influence residents' knowledge of mosquito ecology and the spatial distribution of local mosquito populations, and 3) the environmental, demographic, and socioeconomic factors that influence mosquito abundance within Maricopa County. By identifying the factors that influence human-vector and vector-environment interactions, the results of this thesis may influence current and future educational and mosquito control efforts throughout Maricopa County.<br>Dissertation/Thesis<br>M.S. Sustainability 2013
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"Remote Sensing and Modeling of Stressed Aquifer Systems and the Associated Hazards." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.50435.
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abstract: Aquifers host the largest accessible freshwater resource in the world. However, groundwater reserves are declining in many places. Often coincident with drought, high extraction rates and inadequate replenishment result in groundwater overdraft and permanent land subsidence. Land subsidence is the cause of aquifer storage capacity reduction, altered topographic gradients which can exacerbate floods, and differential displacement that can lead to earth fissures and infrastructure damage. Improving understanding of the sources and mechanisms driving aquifer deformation is important for resource management planning and hazard mitigation.
Poroelastic theory describes the coupling of differential stress, strain, and pore pressure, which are modulated by material properties. To model these relationships, displacement time series are estimated via satellite interferometry and hydraulic head levels from observation wells provide an in-situ dataset. In combination, the deconstruction and isolation of selected time-frequency components allow for estimating aquifer parameters, including the elastic and inelastic storage coefficients, compaction time constants, and vertical hydraulic conductivity. Together these parameters describe the storage response of an aquifer system to changes in hydraulic head and surface elevation. Understanding aquifer parameters is useful for the ongoing management of groundwater resources.
Case studies in Phoenix and Tucson, Arizona, focus on land subsidence from groundwater withdrawal as well as distinct responses to artificial recharge efforts. In Christchurch, New Zealand, possible changes to aquifer properties due to earthquakes are investigated. In Houston, Texas, flood severity during Hurricane Harvey is linked to subsidence, which modifies base flood elevations and topographic gradients.<br>Dissertation/Thesis<br>Doctoral Dissertation Geological Sciences 2018
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Zeleke, Walelegn Mengist. "Wildfire Hazard Mapping using GIS-MCDA and Frequency Ratio Models : A Case Study in Eight Counties of Norway." Thesis, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-31369.
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Abstract A wildfire is an uncontrollable fire in an area of combustible fuel that occurs in the wild or countryside area. Wildfires are becoming a deadly and frequent event in Europe due to extreme weather conditions. In 2018, wildfires profoundly affected Sweden, Finland, and Norway, which were not big news before. In Norway, although there is well–organized fire detection, warning, and mitigation systems, mapping wildfire risk areas before the fire occurrence with georeferenced spatial information, are not yet well-practiced. At this moment, there are freely available remotely sensed spatial data and there is a good possibility that analysing wildfire hazard areas with geographical information systems together with multicriteria decision analysis (GIS–MCDA) and frequency ratio models in advance so that subsequent wildfire warning, mitigation, organizational and post resilience activities and preparations can be better planned. This project covers eight counties of Norway: Oslo, Akershus, Østfold, Vestfold, Telemark, Buskerud, Oppland, and Hedmark. These are the counties with the highest wildfire frequency for the last ten years in Norway. In this study, GIS-MCDA integrated with analytic hierarchy process (AHP), and frequency ratio models (FR) were used with selected sixteen–factor criteria based on their relative importance to wildfire ignition, fuel load, and other related characteristics. The produced factor maps were grouped under four main clusters (K): land use (K1), climate (K2), socioeconomic (K3), and topography (K4) for further analysis. The final map was classified into no hazard, low, medium, and high hazard level rates. The comparison result showed that the frequency ratio model with MODIS satellite data had a prediction rate with 72% efficiency, followed by the same model with VIIRS data and 70% efficiency. The GIS-MCDA model result showed 67% efficiency with both MODIS and VIIRS data. Those results were interpreted in accordance with Yesilnacar’s classifications such as the frequency ratio model with MODIS data was considered a good predictor, whereas the GIS-MCDA model was an average predictor. When testing the model on the dependent data set, the frequency ratio model showed 72% with MODIS & VIIRS data, and the GIS-MCDA model showed 67% and 68% performance with MODIS and VIIRS data, respectively. In the hazard maps produced, the frequency ratio models for both MODIS and VIIRS showed that Hedmark and Akershus counties had the largest areas with the highest susceptibility to wildfires, while the GIS-MCDA method resulted to Østfold and Vestfold counties. Through this study, the best independent wildfire predictor criteria were selected from the highest to the lowest of importance; wildfire constraint and criteria maps were produced; wildfire hazard maps with high-resolution georeferenced data using three models were produced and compared; and the best, reliable, robust, and applicable model alternative was selected and recommended. Therefore, the aims and specific objectives of this study should be considered and fulfilled.