20 research outputs found
Social vulnerability to heat in Greater Atlanta, USA: Spatial pattern of heat, NDVI, socioeconomics and household composition
No full text© 2017 SPIE. The purpose of the article is evaluating spatial patterns of social vulnerability to heat in Greater Atlanta in 2015. The social vulnerability to heat is an index of socioeconomic status, household composition, land surface temperature and normalized differential vegetation index (NDVI). Land surface temperature and NDVI were derived from the red, NIR and thermal infrared (TIR) of a Landsat OLI/TIRS images collected on September 14, 2015. The research focus is on the variation of heat vulnerability in Greater Atlanta. The study found that heat vulnerability is highly clustered spatially, resulting in hot spots and cool spots . The results show significant health disparities. The hotspots of social vulnerability to heat occurred in neighborhoods with lower socioeconomic status as measured by low education, low income and more poverty, greater proportion of elderly people and young children. The findings of this study are important for identifying clusters of heat vulnerability and the relationships with social factors. These significant results provide a basis for heat intervention services
Spatio-temporal dynamics in Seoul Metropolitan Region: Linking remote sensing into urban theory
No full textSocial vulnerability to heat in Greater Atlanta, USA: spatial pattern of heat, NDVI, socioeconomics and household composition
No full textCharacterizing spatial burn severity patterns of 2016 Chimney Tops 2 fire using multi-temporal Landsat and NEON LiDAR data
No full textThe Chimney Tops 2 wildfire (CT2) in 2016 at Great Smoky Mountains National Park (GSMNP) was recorded as the largest fire in GSMNP history. Understanding spatial patterns of burn severity and its underlying controlling factors is essential for managing the forests affected and reducing future fire risks; however, this has not been well understood. Here, we formulated two research questions: 1) What were the most important factors characterizing the patterns of burn severity in the CT2 fire? 2) Were burn severity measures from passive and active optical remote sensing sensors providing consistent views of fire damage? To address these questions, we used multitemporal Landsat- and lidar-based burn severity measures, i.e., relativized differenced Normalized Burn Ratio (RdNBR) and relativized differenced Mean Tree Height (RdMTH). A random forest approach was used to identify key drivers in characterizing spatial variability of burn severity, and the partial dependence of each explanatory variable was further evaluated. We found that pre-fire vegetation structure and topography both play significant roles in characterizing heterogeneous mixed burn severity patterns in the CT2 fire. Mean tree height, elevation, and topographic position emerged as key factors in explaining burn severity variation. We observed generally consistent spatial patterns from Landsat- and lidar-based burn severity measures. However, vegetation type and structure-dependent relations between RdNBR and RdMTH caused locally inconsistent burn severity patterns, particularly in high RdNBR regions. Our study highlights the important roles of pre-fire vegetation structure and topography in understanding burn severity patterns and urges to integrate both spectral and structural changes to fully map and understand fire impacts on forest ecosystems
Exploratory Spatial Data Analysis of the Distribution of Multiple Crimes: A Case Study of Three Coastal Cities
No full text© 2016, © 2016 Applied Geography Conferences. The study aims to explore global and local spatial autocorrelation and characterize the way crime activities are located in three coastal cities. Using three crime types (Burglary, Larceny, and Auto theft) in the cities of Mobile, Alabama and Pensacola, Florida, as well as Miami, Florida over the years 2005–2006, we compute a global spatial autocorrelation statistic, as well as local Moran autocorrelation statistics (Moran scatterplot) in order to detect clusters of high and low crime rates. Results showed high in global spatial autocorrelation statistic in three cities. Also, all three crimes show significant spatial autocorrelation at local level. There are little “atypical” regions i.e. deviating from the global pattern of positive autocorrelation
Object-Based Feature Extraction of Google Earth Imagery for Mapping Termite Mounds in Amazon's Savannas
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Context-Based Neighborhood Sustainability Assessment in Birmingham, Alabama
No full textSustainability assessment is widely used to monitor public policy toward sustainable development (SD). However, such tools have been less developed at the local level. This research examined sustainability indicators (SIs) applied at the neighborhood scale. The indicators were developed by examination of previously developed sustainability rating systems and issues specific to the City of Birmingham, Alabama, USA. The indicators of Neighborhood Sustainability Assessment (NSA) systems addressed the three major dimensions of sustainability: economic, environmental, and social. The rating system was applied to all neighborhoods, and geographical patterns were analyzed. The ability to analyze the sustainability of all neighborhoods within the city provides information on which areas are performing well and which areas need more attention to become more sustainable
Measuring and Modeling of Urban Growth and Its Impacts On Vegetation and Species Habitats in Greater Orlando, Florida
No full textUrban growth is widely regarded as an important driver of environmental and social problems. It causes the loss of informal open space and wildlife habitats. Timely and accurate assessments of future urban growth scenarios and associated environmental impacts are crucial for urban planning, policy decision, and natural resource management. In this study, five distinct scenarios ( no constraints , compact development , transit-oriented development , agriculture protection and environmental protection scenarios) were tested on Greater Orlando, Florida, along with conservation objectives and projections for future land use/cover from development demands. The study examined the consequences of alternative scenarios of urban growth on potential habitat loss for a suite of species and vegetation habitats. As a result, the maximum impact is projected in no constraints scenario while minimum impact occurred in Scenario 5 ( environmental protection ) across almost all vegetation and species habitats. The results indicated that the big challenge is how to manage compact growth to protect ecosystems. Florida has one of the biggest land acquisition programs in the US and a tradition in implementing sustainable development through growth management. The big challenge is how to allocate the fast-growing new population in the future along with these sustainable development objectives
The Application of Hyperspectral Sensing Data for Seabed Classification in the Coastal Area of Korea
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