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Local and regional urban surface heterogeneity produces diverse urban climates, significantly impacting water use, energy consumption, and human health. This study examines urban energy flux variability across landcover and climate gradients for 2123 km2 of urbanized Los Angeles County, USA, by using high resolution remote sensing combined with spatially distributed simulations with an urban energy balance model, covering the complete diurnal cycle of the remote sensing flights. Localized parameters, including landcover, plant type, albedo, building and tree height, and irrigation, were derived from hyperspectral and LiDAR imagery, and GIS. Modeled latent heat fluxes (LE) were linearly correlated with measured land surface temperature at the neighborhood scale (R2 = 0.51), and modeled Bowen ratio values closely agreed with previous values derived from eddy covariance observations. LE across the region displayed spatial and temporal responses to irrigation and regional climate. Peak LE occurred 3 h before solar noon, driven by early morning irrigation characteristic of the region. This suggests that schedule-based water conservation policies could reduce the cooling capacity of urban vegetation in late afternoon. Additionally, a negative effect of distance from the coast on LE was observed to strengthen through the day, reducing LE in interior areas relative to coastal areas.

Integrating Scale in Remote Sensing and GIS serves as the most comprehensive documentation of the scientific and methodological advances that have taken place in integrating scale and remote sensing data. This work addresses the invariants of scale, the ability to change scale, measures of the impact of scale, scale as a parameter in process models, and the implementation of multiscale approaches as methods and techniques for integrating multiple kinds of remote sensing data collected at varying spatial, temporal, and radiometric scales. Researchers, instructors, and students alike will benefit from a guide that has been pragmatically divided into four thematic groups: scale issues and multiple scaling; physical scale as applied to natural resources; urban scale; and human health/social scale. Teeming with insights that elucidate the significance of scale as a foundation for geographic analysis, this book is a vital resource to those seriously involved in the field of GIScience.

Estuarine ecosystems are largely influenced by watersheds directly connected to them. In the Mobile Bay, Alabama watersheds we examined the effect of land cover and land use (LCLU) changes on discharge rate, water properties, and submerged aquatic vegetation, including freshwater macrophytes and seagrasses, throughout the estuary. LCLU scenarios from 1948, 1992, 2001, and 2030 were used to influence watershed and hydrodynamic models and evaluate the impact of LCLU change on shallow aquatic ecosystems. Overall, our modeling results found that LCLU changes increased freshwater flows into Mobile Bay altering temperature, salinity, and total suspended sediments (TSS). Increased urban land uses coupled with decreased agricultural/pasture lands reduced TSS in the water column. However, increased urbanization or agricultural/pasture land coupled with decreased forest land resulted in higher TSS concentrations. Higher sediment loads were usually strongly correlated with higher TSS levels, except in areas where a large extent of wetlands retained sediment discharged during rainfall events. The modeling results indicated improved water clarity in the shallow aquatic regions of Mississippi Sound and degraded water clarity in the Wolf Bay estuary. This integrated modeling approach will provide new knowledge and tools for coastal resource managers to manage shallow aquatic habitats that provide critical ecosystem services.

The seasonal and spatial variability of surface heat fluxes is crucial to the understanding of urban heat island phenomenon and dynamics. To estimate energy fluxes, remote sensing derived biophysical variables need to be integrated with surface atmospheric parameters measured in meteorological stations or in situ field measurements. In this study, based on the two-source energy balance algorithm, we applied a method to estimate surface energy fluxes by combined use of multispectral ASTER images and routine meteorological data, and applied it to the City of Indianapolis, United States, aiming at in-depth understanding of the spatial patterns of energy fluxes. By computing the fluxes by land use and land cover (LULC) type, we further investigated the spatial variability of heat fluxes. Results show that the energy fluxes possessed a strong seasonality, with the highest net radiation in summer, followed by spring, fall and winter. Sensible heat flux tended to change largely with surface temperature, while latent heat was largely modulated by the change in vegetation abundance and vigor and the accompanying moisture condition. The fluctuation in all heat fluxes tended to be high in the summer months and low in the winter months. Sensible and latent heat fluxes showed a stronger spatial variability than net radiation and ground heat flux. The variations of net radiation among the land use/cover types were mainly attributable to surface albedo and temperature, while the within-class variability in the turbulent heat fluxes was more associated with the changes in vegetation, water bodies, and other surface factors.

Using a geographic transect in Central Mexico, with an elevation/climate gradient, but uniformity in socio-economic conditions among study sites, this study evaluates the applicability of three widely-used remote sensing (RS) products to link weather conditions with the local abundance of the dengue virus mosquito vector, Aedes aegypti (Ae. aegypti). Field-derived entomological measures included estimates for the percentage of premises with the presence of Ae. aegypti pupae and the abundance of Ae. aegypti pupae per premises. Data on mosquito abundance from field surveys were matched with RS data and analyzed for correlation. Daily daytime and nighttime land surface temperature (LST) values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua cloud-free images within the four weeks preceding the field survey. Tropical Rainfall Measuring Mission (TRMM)-estimated rainfall accumulation was calculated for the four weeks preceding the field survey. Elevation was estimated through a digital elevation model (DEM). Strong correlations were found between mosquito abundance and RS-derived night LST, elevation and rainfall along the elevation/climate gradient. These findings show that RS data can be used to predict Ae. aegypti abundance, but further studies are needed to define the climatic and socio-economic conditions under which the correlations observed herein can be assumed to apply.

Previous studies showed that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM(sub 2.5) concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM(sub 2.5) exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM(sub 2.5) characteristics that are crucial to population-based PM(sub 2.5) health effects research. In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM(sub 2.5) concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R(sup 2) of 0.83, mean prediction error (MPE) of 1.89 micrograms/cu m, and square root of the mean squared prediction errors (RMSPE) of 2.73 micrograms/cu m in model fitting, and R(sup 2) of 0.67, MPE of 2.54 micrograms/cu m, and RMSPE of 3.88 micrograms/cu m in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM(sub 2.5) concentrations.

In this paper, we assessed and compared land surface temperature (LST) in urban centers using data from Landsat, MODIS, and the Simple Biosphere model (SiB2). We also evaluated the sensitivity of the model’s LST to different land cover types, fractions (percentages), and emissivities compared to reference points derived from Landsat thermal data. This was demonstrated in three climatologically- and morphologically-different cities of Atlanta, GA, New York, NY, and Washington, DC. Our results showed that in these cities SiB2 was sensitive to both the emissivity and the land cover type and fraction, but much more sensitive to the latter. The practical implications of these results are rather significant since they imply that the SiB2 model can be used to run different scenarios for evaluating urban heat island (UHI) mitigation strategies. This study also showed that using detailed emissivities per land cover type and fractions from Landsat-derived data caused a convergence of the model results towards the Landsat-derived LST for most of the studied cases. This study also showed that SiB2 LSTs are closer in magnitude to Landsat-derived LSTs than MODIS-derived LSTs. It is important, however, to emphasize that both Landsat and MODIS LSTs are not direct observations and, as such, do not represent a ground truth. More studies will be needed to compare these results to in situ LST data and provide further validation.

Introduction: An ongoing Zika virus pandemic in Latin America and the Caribbean has raised concerns that travel-related introduction of Zika virus could initiate local transmission in the United States (U.S.) by its primary vector, the mosquito Aedes aegypti. Methods: We employed meteorologically driven models for 2006-2015 to simulate the potential seasonal abundance of adult Aedes aegypti for fifty cities within or near the margins of its known U.S. range. Mosquito abundance results were analyzed alongside travel and socioeconomic factors that are proxies of viral introduction and vulnerability to human-vector contact. Results: Meteorological conditions are largely unsuitable for Aedes aegypti over the U.S. during winter months (December-March), except in southern Florida and south Texas where comparatively warm conditions can sustain low-to-moderate potential mosquito abundance. Meteorological conditions are suitable for Aedes aegypti across all fifty cities during peak summer months (July-September), though the mosquito has not been documented in all cities. Simulations indicate the highest mosquito abundance occurs in the Southeast and south Texas where locally acquired cases of Aedes-transmitted viruses have been reported previously. Cities in southern Florida and south Texas are at the nexus of high seasonal suitability for Aedes aegypti and strong potential for travel-related virus introduction. Higher poverty rates in cities along the U.S.-Mexico border may correlate with factors that increase human exposure to Aedes aegypti. Discussion: Our results can inform baseline risk for local Zika virus transmission in the U.S. and the optimal timing of vector control activities, and underscore the need for enhanced surveillance for Aedes mosquitoes and Aedes-transmitted viruses.

This article describes the outcomes of an international workshop on urban remote sensing. The workshop synthesized the needs of remote sensing scientists to better monitor and analyse urban physical and social dynamics. The workshop was held with urban land use forecasting workshop in April 2011 in Arizona. The four major themes of the jointly held workshops were application, data, scale and case studies. Application refers to how data are used to address urban problems. Data refers to the sources and types of raw data available. Scale is the ever-present concern over data reduction and resolution. Case studies examine a single urban area, typically based on one or two primary themes. One outcome was to integrate multiple case studies to form an urban typology. To respond to this need, this article integrates two case studies on the urban heat island in Atlanta, GA and Phoenix, AZ based on the four themes.

This study provides an introduction to the HyspIRI mission a National Research Council “Decadal Survey” mission that combines a 213 channel visible, near−infrared and shortwave infrared (VSWIR) imaging spectrometer with an 8 channel multispectral thermal infrared (TIR) instrument and evaluates some of its potential in urban science. Potential synergies between VSWIR and TIR data are explored using analogous airborne data acquired over the Santa Barbara metropolitan region in June, 2008. These data were analyzed at both their native spatial resolutions (7.5 m VSWIR and 15 m TIR), and aggregated 60 m spatial resolution similar to HyspIRI. A spectral library of dominant urban materials (e.g., grass, trees, soil, roof types, roads) was developed from field and airborne-measured spectra using Multiple-Endmember Spectral Mixture Analysis (MESMA) and used to map fractions of impervious, soil, green vegetation (GV, e.g., trees, lawn) and non-photosynthetic vegetation (NPV). Land Surface Temperature (LST) and emissivity were also retrieved from the airborne data. Co-located pixels from the VSWIR and TIR airborne data were used to generate reflectance/emissivity spectra for a subset of urban materials. MESMA was used to map GV, NPV, soil and impervious fractions at the different spatial resolutions and compare the fractional estimates across spatial scales. Important surface energy parameters, including albedo, vegetation cover fraction, broadband emissivity and surface temperature were also determined for and evaluated for 14 urban and natural land-cover classes in the region. Fractions were validated using 1 m digital photography. Fractions for GV and NPV were highly correlated with validation fractions at all spatial scales, producing a near 1:1 relationship but with a

Heat waves are one of the largest causes of environmentally-related deaths globally. The intensification of the effects of heat waves by the urban heat island during extended periods of extreme heat, combined with urban demographics, are key elements leading to these disasters and are likely to become more numerous as a result of climate change. Climatological weather station data, moderate resolution thermal imagery from Earth-orbiting satellites, and demographic information for selected cities were use in this research to develop a heat wave risk product as a potential warning tool for public health decision makers and the general public. Keywords: Heat wave risk, Heat wave hazard, Demographics, Urban heat island

Background Urbanization has been correlated with hypertension (HTN) in developing countries undergoing rapid economic and environmental transitions. Objectives We examined the relationships among living environment (urban, suburban, and rural), day/night land surface temperatures (LST), and blood pressure in selected regions from the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. Also, the linking of data on blood pressure from REGARDS with National Aeronautics and Space Administration (NASA) science data is relevant to NASA’s strategic goals and missions, particularly as a primary focus of the agency’s Applied Sciences Program. Methods REGARDS is a national cohort of 30,228 people from the 48 contiguous United States with self-reported and measured blood pressure levels. Four metropolitan regions (Philadelphia, PA; Atlanta, GA; Minneapolis, MN; and Chicago, IL) with varying geographic and health characteristics were selected for study. Satellite remotely sensed data were used to characterize the LST and land cover/land use (LCLU) environment for each area. We developed a method for characterizing participants as living in urban, suburban, or rural living environments, using the LCLU data. These data were compiled on a 1-km grid for each region and linked with the REGARDS data via an algorithm using geocoding information. Results REGARDS participants in urban areas have higher systolic and diastolic blood pressure than do those in suburban or rural areas, and also a higher incidence of HTN. In univariate models, living environment is associated with HTN, but after adjustment for known HTN risk factors, the relationship was no longer present. Conclusion Further study regarding the relationship between HTN and living environment should focus on additional environmental characteristics, such as air pollution. The living environment classification method using remotely sensed data has the potential to facilitate additional research linking environmental variables to public health concerns.

This paper presents a content-based image retrieval process wherein the user identifies a feature of interest using a region quadtree decomposition of the image, spatial statistics and histograms of the grayscale values of the feature definition are calculated, and the result is compared to a database of these same calculations that have been performed on similar images. The sum of squared differences between the indices calculated for the quads that form the feature of interest and corresponding quads in the database yields a ranked list of matching image tiles. In an analysis of Landsat 7 imagery of North Georgia and an IKONOS panchromatic image of Kalamazoo, Michigan, we found that the retrieval success rate depends on the spatial and spectral characteristics of the feature of interest and the configuration of quads used to define the feature.

Studies of the effect of air pollution on cognitive health are often limited to populations living near cities that have air monitoring stations. Little is known about whether the estimates from such studies can be generalized to the U.S. population, or whether the relationship differs between urban and rural areas. To address these questions, we used a satellite-derived estimate of fine particulate matter (PM2.5) concentration to determine whether PM2.5 was associated with incident cognitive impairment in a geographically diverse, biracial US cohort of men and women (n = 20,150). A 1-year mean baseline PM2.5 concentration was estimated for each participant, and cognitive status at the most recent follow-up was assessed over the telephone using the Six-Item Screener (SIS) in a subsample that was cognitively intact at baseline. Logistic regression was used to determine whether PM2.5 was related to the odds of incident cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was not reliably associated with an increased odds of incident impairment, after adjusting for temperature, season, incident stroke, and length of follow-up [OR (95% CI): 1.26 (0.97, 1.64)]. The odds ratio was attenuated towards 1 after adding demographic covariates, behavioral factors, and known comorbidities of cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was slightly associated with incident impairment in urban areas (1.40 [1.06-1.85]), but this relationship was also attenuated after including additional covariates in the model. Evidence is lacking that the effect of PM2.5 on incident cognitive impairment is robust in a heterogeneous US cohort, even in urban areas.

It is estimated that by the year 2025, 80% of the world's population will live in cities. This conversion of the natural landscape vegetation into man-made urban structures such as roads and buildings drastically alter the regional surface energy budgets, hydrology, precipitation patterns, and meteorology. Research studies from many cities have documented that these effects range from decreases in air quality, increased energy consumption, and alteration of regional climate to direct effects on human health.