Browsing by Subject "Remote Sensing"
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Item Applications of geospatial analysis techniques for public health(2016-05-02) Stanforth, Austin Curran; Filippelli, Gabriel; Johnson, Daniel P.; Wang, Lixin; Wilson, Jeffrey; Moreno-Madriñán, Max J.; Jacinthe, Pierre-AndréGeospatial analysis is a generic term describing several technologies or methods of computational analysis using the Earth as a living laboratory. These methods can be implemented to assess risk and study preventative mitigation practices for Public Health. Through the incorporation Geographic Information Science and Remote Sensing tools, data collection can be conducted at a larger scale, more frequent, and less expensive that traditional in situ methods. These techniques can be extrapolated to be used to study a variety of topics. Application of these tools and techniques were demonstrated through Public Health research. Although it is understand resolution, or scale, of a research project can impact a study’s results; further research is needed to understand the extent of the result’s bias. Extreme heat vulnerability analysis was studied to validate previously identified socioeconomic and environmental variables influential for mitigation studies, and how the variability of resolution impacts the results of the methodology. Heat was also investigated for the implication of spatial and temporal resolution, or aggregation, influence on results. Methods studying the physical and socioeconomic environments of Dengue Fever outbreaks were also studied to identify patters of vector emergence.Item Comparison of Urban Tree Canopy Classification With High Resolution Satellite Imagery and Three Dimensional Data Derived From LIDAR and Stereoscopic Sensors(2008-08-22T13:59:51Z) Baller, Matthew Lee; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Tedesco, Lenore P.; Li, LinDespite growing recognition as a significant natural resource, methods for accurately estimating urban tree canopy cover extent and change over time are not well-established. This study evaluates new methods and data sources for mapping urban tree canopy cover, assessing the potential for increased accuracy by integrating high-resolution satellite imagery and 3D imagery derived from LIDAR and stereoscopic sensors. The results of urban tree canopy classifications derived from imagery, 3D data, and vegetation index data are compared across multiple urban land use types in the City of Indianapolis, Indiana. Results indicate that incorporation of 3D data and vegetation index data with high resolution satellite imagery does not significantly improve overall classification accuracy. Overall classification accuracies range from 88.34% to 89.66%, with resulting overall Kappa statistics ranging from 75.08% to 78.03%, respectively. Statistically significant differences in accuracy occurred only when high resolution satellite imagery was not included in the classification treatment and only the vegetation index data or 3D data were evaluated. Overall classification accuracy for these treatment methods were 78.33% for both treatments, with resulting overall Kappa statistics of 51.36% and 52.59%.Item Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed(MDPI, 2016-09-19) Stanforth, Austin; Moreno-Madriñán, Max J.; Ashby, Jeffrey; Department of Environmental Health Sciences, FSPHPrevious research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellites were combined with population variables to be statistically compared against reported cases of Dengue Fever in the Río Magdalena watershed, Colombia. Three-factor analysis models were investigated to analyze variable patterns, including a population, population density, and empirical Bayesian estimation model. Results identified varying levels of Dengue Fever transmission risk, and environmental characteristics which support, and advance, the research literature. Multiple temperature metrics, elevation, and vegetation composition were among the more contributory variables found to identify future potential outbreak locations.Item Exploring the Utility of High Resolution Imagery for Determining Wetland Signatures(2012-07-03) DeLury, Judith Ann; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Ottensmann, John R.; Tedesco, Lenore P.Wetland habitats are characterized by periodic inundation and saturation by water creating anaerobic conditions that generate hydric soils and support hydrophytic vegetation. Wetland habitats provide important ecological functions including breeding grounds for fish, other wildlife, water purification, reduction in flooding, species diversity, recreation, food production, aesthetic value, and transformation of nutrients (Tiner, 1999). The multiple benefits of wetlands make them an important resource to monitor. A literature review suggests a combination of geospatial variables and methods should be tested for appropriateness in wetland delineation within local settings. Advancements in geospatial data technology and ease of accessing new, higher resolution geospatial data make study at local levels easier and more feasible (Barrette et al, 2000). The purpose of the current study is to evaluate new sources of geospatial data as potential variables to improve wetland identification and delineation. High resolution multispectral digital imagery, topographic data, and soils information are used to derive and evaluate independent variables. Regression analysis was used to analyze the data.Item Identifying enhanced urban heat island convection areas for Indianapolis, Indiana using space-borne thermal remote sensing methods(2015-04-02) Boyd, Kelly D.; Johnson, Daniel P.; Wilson, Jeffery S.; Martin, Pamela A.Heat is one of the most important factors in our atmosphere for precipitation (thunderstorm) formation. Thermal energy from local urban land-cover is also a common source of heat in the lower atmosphere. This phenomenon is known as the urban heat island effect (UHI) and is identified as a substantial cause to a changing climate in surface weather modification. The proceeding study investigates this connection between the UHI and surface weather using remote sensing platforms A ten-year analysis of the Indianapolis UHI and thunderstorms were studied from the summer months of May, June, July, August and September (MJJAS) from 2002 until 2011. LANDSAT space borne satellite technology and land-surface based weather radar technology was used in this analysis for thunderstorm investigation. Precipitation areas identified from land-based NEXRAD WSR-88D technology were used to identify convection from non-synoptic forcing and non-normal surface diurnal heating scenarios. Only convection appearing from the UHI were studied and analyzed. Results showed twenty-one events over eighteen days with the year 2005 and 2011 having the largest frequency of events. The month of August had the largest concentration with seven events during the late afternoon hours. UHI results showed July had the largest heat island magnitude with April and September having the lowest magnitude in UHI temperatures. Three events of the twenty-one storm paths did not had a significant mean temperature difference in the heat island below the storm reflectivity. The nineteen storm paths that were significant had a warmer area underneath storm path development by an average 6.2°C than surrounding areas. UHI initiation points showed twelve of the twenty-one events having statistically significant differences between 2 km initiation areas and the rest of the study areas. Land-cover results showed low intensity developed areas had the most land-cover type (48%) in the 2km initiation buffer regions.Item Measurement and Modeling of Ground-Level Ozone Concentration in Catania, Italy using Biophysical Remote Sensing and GIS(Research India Publications, 2017) Famoso, Fabio; Wilson, Jeffrey S.; Monforte, Pietro; Lanzafame, Rosario; Brusca, Sebastian; Lulla, VijayThis experimental study examined spatial variation of ground level ozone (O3) in the city of Catania, Italy using thirty passive samplers deployed in a 500-m grid pattern. Significant spatial variation in ground level O3 concentrations (ranging from 12.8 to 41.7 g/m3) was detected across Catania’s urban core and periphery. Biophysical measures derived from satellite imagery and built environment characteristics from GIS were evaluated as correlates of O3 concentrations. A land use regression model based on four variables (land surface temperature, building area, residential street length, and distance to the coast) explained 74% of the variance (adjusted R2) in measured O3. The results of the study suggest that biophysical remote sensing variables are worth further investigation as predictors of ground level O3 (and potentially other air pollutants) because they provide objective measurements that can be tested across multiple locations and over time.Item The Primary Advantage in Utilizing Remote Sensing Assets for Extreme Heat Vulnerability Studies(Earthzine, 2014-07-10) Johnson, Daniel P.Remotely sensed imagery provides an alternate view of spatial characteristics that in situ measurements typically lack. This is an advantage to utilizing such datasets for the analysis of environmental health vulnerabilities.Item Retrieval of aerosol optical depth from MODIS data at 500 m resolution compared with ground measurement in the state of Indiana(2015-05-05) Alhaj Mohamad, Fahed; Johnson, Daniel P.; Lulla, Vijay O.; Bein, Frederick L.Objective: "The purpose of this research is: Study the use of Moderate Resolution Imaging Spectroradiometer (MODIS) data in retrieving the aerosol optical depth (AOD) over Indiana State at high resolution of 500 meters. Examine the potential of using the resulted AOD data as an indicator of particulate air pollution by comparing the satellite derived AOD data with the ground measurements (provided from the continuous air monitors available over the study area). If an association should be found, AOD data would be used to map particulate matter (PM) concentration. Assess current and future ambient concentrations of air pollutants in the State of Indiana using the AOD."Item Spatiotemporal analysis of extreme heat events in Indianapolis and Philadelphia for the years 2010 and 2011(2014-03-12) Beerval Ravichandra, Kavya Urs; Johnson, Daniel P. (Daniel Patrick), 1971-; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Bein, Frederick L. (Frederick Louis), 1943-Over the past two decades, northern parts of the United States have experienced extreme heat conditions. Some of the notable heat wave impacts have occurred in Chicago in 1995 with over 600 reported deaths and in Philadelphia in 1993 with over 180 reported deaths. The distribution of extreme heat events in Indianapolis has varied since the year 2000. The Urban Heat Island effect has caused the temperatures to rise unusually high during the summer months. Although the number of reported deaths in Indianapolis is smaller when compared to Chicago and Philadelphia, the heat wave in the year 2010 affected primarily the vulnerable population comprised of the elderly and the lower socio-economic groups. Studying the spatial distribution of high temperatures in the vulnerable areas helps determine not only the extent of the heat affected areas, but also to devise strategies and methods to plan, mitigate, and tackle extreme heat. In addition, examining spatial patterns of vulnerability can aid in development of a heat warning system to alert the populations at risk during extreme heat events. This study focuses on the qualitative and quantitative methods used to measure extreme heat events. Land surface temperatures obtained from the Landsat TM images provide useful means by which the spatial distribution of temperatures can be studied in relation to the temporal changes and socioeconomic vulnerability. The percentile method used, helps to determine the vulnerable areas and their extents. The maximum temperatures measured using LST conversion of the original digital number values of the Landsat TM images is reliable in terms of identifying the heat-affected regions.Item USING AIRBORNE HYPERSPECTRAL IMAGERY TO ESTIMATE CHLOROPHYLL A AND PHYCOCYANIN IN THREE CENTRAL INDIANA MESOTROPHIC TO EUTROPHIC RESERVOIRS(2007-08-08T15:35:17Z) Sengpiel, Rebecca Elizabeth; Lin, Li; Tedesco, Lenore P.; Wilson, Jeffrey S. (Jeffrey Scott), 1967-This thesis presents the results of an analysis of predicting phytoplankton pigment concentrations (chlorophyll a and phycocyanin) from remotely sensed imagery. Hyperspectral airborne and hand-held reflectance spectra were acquired on three reservoirs (Geist, Morse and Eagle Creek) in Central Indiana, USA. Concurrent with the reflectance acquisition, in situ samples were collected and analyzed in laboratories to quantify the pigment concentration and other water quality parameters. The resultant concentration was then linked to Airborne Imaging Spectrometer for Applications (AISA) reflectance spectra for the sampling stations to develop predictive models. AISA reflectance spectra were extracted from the imagery which had been processed for radiometric calibration and geometric correction. Several previously published algorithms were examined for the estimation of pigment concentration from the spectra. High coefficients of determination were achieved for predicting chlorophyll a in two of the three reservoirs (Geist R2 = 0.712, Morse R2 = 0.895 and Eagle Creek Reservoir R2 = 0.392). This situation was similar for PC prediction, where two of the three reservoirs had high coefficients of determination between pigment concentration and reflectance (Geist R2 = 0.805, Morse R2 = 0.878 and Eagle Creek Reservoir R2 = 0.316). The results of this study show that reflectance spectra collected with an airborne hyperspectral imager are statistically significant, p < 0.03, in predicting chlorophyll a and phycocyanin pigment concentration in all three reservoirs in this study without the consideration of other parameters. The algorithms were then applied to the AISA image to generate high spatial resolution (1 m2) maps of Chlorophyll a and Phycocyanin distribution for each reservoir.