Urbanization-associated land use and land cover (LULC) changes lead to modifications of surface microclimatic and hydrological conditions, including the formation of urban heat islands and changes in surface runoff pattern. The goal of the paper is to investigate the changes of biophysical variables due to urbanization induced LULC changes in Indianapolis, USA, from 2001 to 2006. The biophysical parameters analyzed included Land Surface Temperature (LST), fractional vegetation cover, Normalized Difference Water Index (NDWI), impervious fractions evaporative fraction, and soil moisture. Land cover classification and changes and impervious fractions were obtained from the National Land Cover Database of 2001 and 2006. The Temperature-Vegetation Index (TVX) space was created to analyze how these satellite-derived biophysical parameters change during urbanization. The results showed that the general trend of pixel migration in response to the LULC changes was from the areas of low temperature, dense vegetation cover, and high surface moisture conditions to the areas of high temperature, sparse vegetation cover, and low surface moisture condition in the TVX space. Analyses of the T-soil moisture and T-NDWI spaces revealed similar changed patterns. The rate of change in LST, vegetation cover, and moisture varied with LULC type and percent imperviousness. Compared to conversion from cultivated to residential land, the change from forest to commercial land altered LST and moisture more intensively. Compared to the area changed from cultivated to residential, the area changed from forest to commercial altered 48% more in fractional OPEN ACCESS Remote Sens. 2015, 7 4881 vegetation cover, 71% more in LST, and 15% more in soil moisture Soil moisture and NDWI were both tested as measures of surface moisture in the urban areas. NDWI was proven to be a useful measure of vegetation liquid water and was more sensitive to the land cover changes comparing to soil moisture. From a change forest to commercial land, the mean soil moisture changed 17%, while the mean NDWI changed 90%.

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Recent years have witnessed an emerging concern of the health impact of heat waves. A common approach to investigate heat waves is to resort to the geostationary thermal infrared imagery, such as those from the Geostationary Operational Environmental Satellite (GOES) and Meteosat Second Generation. However, coarse spatial resolutions of geostationary images cannot meet the need of assessing and monitoring heat waves in complex urban settings. To address the spatial and temporal variability of heat waves in urban areas, this letter presented a study of analyzing heat wave risk in Los Angeles, USA, by the synergistic use of GOES land surface temperature (LST), auxiliary geospatial, and census data within the framework of Crichton's Risk Triangle (i.e., hazard, exposure, and vulnerability). Principal component analysis and regression analysis were employed to downscale the original GOES LST imagery from 4 to 1 km. The resultant subhourly 1-km LST data was used to characterize and quantify heat hazard. The census population represented the exposure, while existing health, socioeconomic, and physical environmental conditions were used to describe the vulnerabilities. The risk map of heat wave was computed using the weighted indices of hazard, exposure, and vulnerability. The map was further overlaid with a zip-code data layer to generate statistics. The derived risk map showed that areas with high risk were identified in the central city, part of western LA County, and the desert area, based on a 10-point scale rank. Index Terms-GeostationaryOperational Environmental Satellite (GOES), heat wave, land surface temperature (LST), risk assessment, thermal downscaling, urban areas. Manuscript

Surface roughness parameters, such as roughness length and displacement height, impact the estimation of surface moisture, and the frontal areas of buildings and trees are two components that contribute to surface roughness in urban areas. Research on tree frontal area has not been conducted in urban areas before, and we hope to fill that gap in the literature with this study by using Terrestrial Light Detection and Ranging (LiDAR) data to estimate tree frontal areas in Warren Township, Indianapolis, IN, USA. We first estimated the frontal areas of individual trees based on their morphology, then calibrated a regression model to estimate the tree frontal area in 30 m pixels using parameters derived from LiDAR data and tree inventory data. The parameters included tree crown base area, height, width, conditions, defects, maintenances, genera, and land use. The validation shows that R 2 yielded values ranging from 0.84 to 0.88, and RMSEs varied with tree category. The tree categories were identified based on the height and broadness of the canopy, which indicated the degree of resistance to air flow. This type of model can be used to empirically determine local roughness values at the tree-level for any city with a complete tree inventory. With the strong correlation between trees' frontal area and crown base area, this model may also be used to determine local roughness value at 30 m resolution with NLCD (National Land Cover Database) tree canopy cover data as a component. A proper tree categorization according to the vertical air resistance, e.g., height and canopy density, was effective to reduce the RMSE in tree frontal area estimation. Geometric parameters, such as height, crown base height, and crown base area extracted from Airborne LiDAR, which demand less storage and computation capacity, may also be sufficient for tree frontal area estimation in the areas where Terrestrial LiDAR is not available.

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