In recent years, building resilient communities to disasters has become one of the core objectives in the field of disaster management globally. Despite being frequently targeted and severely impacted by disasters, the geographical extent in studying disaster resilience of the coastal communities of the United States (US) has been limited. In this study, we developed a composite community disaster resilience index (CCDRI) for the coastal communities of the conterminous US that considers different dimensions of disaster resilience. The resilience variables used to construct the CCDRI were justified by examining their influence on disaster losses using ordinary least squares (OLS) and geographically weighted regression (GWR) models. Results suggest that the CCDRI score ranges from −12.73 (least resilient) to 8.69 (most resilient), and northeastern communities are comparatively more resilient than southeastern communities in the study area. Additionally, resilience components used in this study have statistically significant impact on minimizing disaster losses. The GWR model performs much better in explaining the variances while regressing the disaster property damage against the resilience components (explains 72% variance) than the OLS (explains 32% variance) suggesting that spatial variations of resilience components should be accounted for an effective disaster management program. Moreover, findings from this study could provide local emergency managers and decision-makers with unique insights for enhancing overall community resilience to disasters and minimizing disaster impacts in the study area.

This paper presents the bed topography of Jakobshavn Isbræ, Greenland, and Byrd Glacier, Antarctica, derived from sounding these glaciers with high-sensitivity radars. To understand the processes causing the speed-up and retreat of outlet glaciers, and to enable the development of next-generation ice-sheet models, we need information on bed topography and basal conditions. To this end, we performed measurements with the progressively improved Multichannel Coherent Radar Depth Sounder/Imager (MCoRDS/I). We processed the data from each antenna-array element using synthetic aperture radar algorithms to improve radar sensitivity and reduce along-track surface clutter. We then applied array and image-processing algorithms to extract the weak bed echoes buried in off-vertical scatter (cross-track surface clutter). At Jakobshavn Isbræ, we observed 2.7 km thick ice ~30 km upstream of the calving front and ~850 m thick ice at the calving front. We also observed echoes from multiple interfaces near the bed. We applied the MUSIC algorithm to the data to derive the direction of arrival of the signals. This analysis revealed that clutter is dominated by the ice surface at Jakobshavn Isbræ. At Byrd Glacier, we found ~3.62 km thick ice, as well as a subglacial trench ~3.05 km below sea level. We used ice thickness information derived from radar data in conjunction with surface elevation data to generate bed maps for these two critical glaciers. The performance of current radars must be improved further by ~15 dB to fully sound the deepest part of Byrd Glacier. Unmanned aerial systems equipped with radars that can be flown over lines spaced as close as 5 m apart in the cross-track direction to synthesize a two-dimensional aperture would be ideal for collecting fine-resolution data over glaciers like Jakobshavn near their grounding lines.

Urban expansion is one of the most dramatic forms of land transformation in the world and it is one of the greatest challenges in achieving sustainable development in the 21st century. Previous studies analyzed urbanization patterns in areas with rapid urban expansion while urban areas with low to moderate expansion have been overlooked, especially in developed countries. In this study, we examined the spatiotemporal dynamics of urban expansion patterns in South Florida, United States (US) over the last 25 years (1992–2016) using Remote Sensing and GIS techniques. The main goal of this paper was to investigate the degree and spatiotemporal patterns of urban expansion at different administrative level in the study area and how spatiotemporal variance in different explanatory factors influence urban expansion in this region. More specifically, this research quantifies the rates, types, intensity, and landscape metrics of urban expansion in Miami-Fort Lauderdale-Palm Beach, Florida Metropolitan Statistical Area (Miami MSA) which is the 7th largest MSA and 4th largest urbanized area in the US using remote sensing (satellite imageries) data from National Land Cover Datasets (NLCD) and Coastal Change Analysis Program (C-CAP) at 30 m spatial resolution. We further investigated the urban growth patterns at the county and city areas that are located within this MSA to portray the local ‘picture’ of urban growth in this region. Urban expansion in this region can be divided into two time periods: pre-2001 and post-2001 where the former experienced rapid urban expansion and the later had comparatively slow urban expansion. Results suggest that infilling was the dominant type of urban expansion followed by edge-expansion and outlying. Results from landscape metrics represent that newly developed urban lands became more aggregated and simplified in form as the time progressed in the study region. Also, new urban lands were generated away from the east coast and historic cities which eventually created new urban cores. We also used correlation analysis and multiple linear stepwise regression to address major explanatory factors of spatiotemporal change in urban expansion during the study period. Although the influence of factors on urban expansion varied temporally, Population and Distance to Coast were the strongest variables followed by Distance to Roads and Median Income that influence overall urban expansion in the study area.

The effects of biophysical and meteorological factors on land surface temperature (LST) have been well studied in previous research. However, less attention has been paid to examine how building materials influence the magnitude of LST within an urban environment. This study investigates the interaction of biophysical and building wall materials to influence LST in Harris County, Texas, USA using multiple stepwise linear regression analyses and neighborhood analysis. Working at 1 km grid resolution, LST data is related to impervious surface fraction, albedo, distance to water bodies, and seven major wall types. Ten years of aggregated MODIS (Moderate Resolution Imaging Spectroradiometer) daily LST products were used to calculate the mean LST in January and August for daytime and nighttime conditions. Harris County 2010 parcel level building property data were used to create composition characteristics of the building wall types. Our results demonstrate that both biophysical and building wall characteristics significantly influence the spatiotemporal variations of LST. However, biophysical factors are the dominant explaining factors compared to building wall materials. Impervious surface fraction is the most significant variable to explain the variation of LST, and has positive effects on LST. In contrast, high albedo materials and the presence of open water bodies significantly affect LST and are good candidate variables to mitigate the heat island effect. Furthermore, the building wall variables all increase LST for both daytime and nighttime, but different wall materials have various effects on LST. Brick/veneer and frame/concrete block are the two dominant wall types in Harris County and tend to generate higher LST. These results demonstrate how building materials, in combination with biophysical factors, can be used to mitigate neighborhood-scale LST. This methodology works reasonably well for Houston, but is likely to be more effective in higher density urban settings.

Large amounts of time series of spatial snapshot data have been collected or generated for the monitoring and modeling of environmental systems. Those time series of data also provide the opportunity to study the movements and dynamics of many different natural phenomena. While the snapshot organization is conceptually simple and straightforward, it does not directly capture or represent the dynamic characteristics of the phenomena. This study presents computational methods to identify dynamic events from time series of spatial snapshots. Events are represented as directed spatiotemporal graphs to characterize their initiation, development, movement, and cessation. Graph-based algorithms are used to analyze the dynamics of the events. The method is applied to time series of high-resolution radar reflectivity images during one of the deadliest storm outbreaks that impacted 15 states of southeastern United States between 23 and 29 April 2011. As shown in this case study, convective storm events identified using our methods are consistent with previous studies, and our analysis confirms that the left split/merger occurs more than right split/merger in those convective storm events, which confirms theory, numerical simulations, and other observed case studies. While this study does not differentiate between storm modes, the method shows potential for capturing a more detailed climatology of precipitation characteristics.