The objective of this study is to assess the effectiveness of landscape metrics in quantifying the urbanization-induced land use and land cover (LULC) changes from a landscape ecology perspective using the City of Indianapolis, Indiana, USA as a case study. Two Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, acquired on 3 October 2000 and 13 October 2006, respectively, and two Landsat 5 Thematic Mapper images, acquired on 22 October 1989 and 20 October 2000, respectively, were used for the study. Seven LULC types were identified: urban, agriculture, grasslands, forest, water, barren lands and wetlands. A series of landscape metrics were then computed for each LULC type and these metrics were used to compare the two ASTER-derived LULC maps with the two Landsat-derived maps. Results show that urbanization contributed significantly to LULC changes in the study area. Agricultural lands decreased and forests became more disaggregated. Grassland increased slightly in size and aggregation level and improved in connectedness.

Climate dictates the critical aspects of human environmental conditions. The frequency and intensity of extreme weather conditions due to human-induced climate change have alarmingly increased. Consequently, climate change directly affects environmental sustainability and human mortality in the short term and creates prolonged and complicated long-term indirect grave risks. This paper examines three-level environmental impact risks associated with climate change on human mortality. It proposes a conceptual framework for developing an empirical event-based human mortality database related to climate change and communication strategies to enhance global environmental adaptation, resilience, and sustainability.

Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization.

This study developed a multi-temporal analysis on the relationship between West Nile Virus (WNV) dissemination and environmental variables by using an integrated approach of remote sensing, GIS, and statistical techniques. WNV mosquito cases in seven months (April-October) of the six years (2002–2007) were collected in Indianapolis, USA. Epidemic curves were plotted to identify the temporal outbreaks of WNV. Spatial-temporal analysis and k-mean cluster analysis were further applied to determine the high-risk areas. Finally, the relationship between environmental variables and WNV outbreaks were examined by using Discriminant Analysis.

There is limited research in land surface temperatures (LST) simulation using image fusion techniques, especially studies addressing the downscaling effect of LST image fusion. LST simulation and associated downscaling effect can potentially benefit the thermal studies requiring both high spatial and temporal resolutions. This study simulated LSTs based on observed Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST imagery with Spatial and Temporal Adaptive Reflectance Fusion Model, and investigated the downscaling effect of LST image fusion at 15, 30, 60, 90, 120, 250, 500, and 1000 m spatial resolutions. The study area partially covered the City of Los Angeles, California, USA, and surrounding areas. The reference images (observed ASTER and MODIS LST imagery) were acquired on 04/03/2007 and 07/01/2007, with simulated LSTs produced for 4/28/2007. Three image resampling methods (Cubic Convolution, Bilinear Interpolation, and Nearest Neighbor) were used during the downscaling and upscaling processes, and the resulting LST simulations were compared. Results indicated that the observed ASTER LST and simulated ASTER LST images (date 04/28/2007, spatial resolution 90 m) had high agreement in terms of spatial variations and basic statistics based on a comparison between the observed and simulated ASTER LST maps. Urban developed lands possessed higher LSTs with lighter tones and mountainous areas showed dark tones with lower LSTs. The Cubic Convolution and Bilinear Interpolation resampling methods yielded better results over Nearest Neighbor resampling method across the scales from 15 to 1000 m. The simulated LSTs with image fusion can be used as valuable inputs in heat related studies that require frequent LST measurements with fine spatial resolutions, e.g., seasonal movements of urban heat islands, monthly energy budget assessment, and temperature-driven epidemiology. The observation of scale-independency of the proposed image fusion method can facilitate with image selections of LST studies at various locations.

The geographical concentration of criminal violence is closely associated with the social, demographic, and economic structural characteristics of neighborhoods. However, few studies have investigated homicide patterns and their relationships with neighborhoods in South Asian cities. In this study, the spatial and temporal patterns of homicide incidences in Karachi from 2009 to 2018 were analyzed using the local indicators of spatial association (LISA) method. Generalized linear modeling (GLM) and geographically weighted Poisson regression (GWPR) methods were implemented to examine the relationship between influential factors and the number of homicides during the 2009–2018 period. The results demonstrate that the homicide hotspot or clustered areas with high homicide counts expanded from 2009 to 2013 and decreased from 2013 to 2018. The number of homicides in the 2017–2018 period had a positive relationship with the percentage of the population speaking Balochi. The unplanned areas with low-density residential land use were associated with low homicide counts, and the areas patrolled by police forces had a significant negative relationship with the occurrence of homicide. The GWPR models effectively characterized the varying relationships between homicide and explanatory variables across the study area. The spatio-temporal analysis methods can be adapted to explore violent crime in other cities with a similar social context.