We quantified the spatio-temporal patterns of land cover/land use (LCLU) change to document and evaluate the daytime surface urban heat island (SUHI) for five hot subtropical desert cities (Beer Sheva, Israel; Hotan, China; Jodhpur, India; Kharga, Egypt; and Las Vegas, NV, USA). Sequential Landsat images were acquired and classified into the USGS 24-category Land Use Categories using object-based image analysis with an overall accuracy of 80% to 95.5%. We estimated the land surface temperature (LST) of all available Landsat data from June to August for years 1990, 2000, and 2010 and computed the urban-rural difference in the average LST and Normalized Difference Vegetation Index (NDVI) for each city. Leveraging non-parametric statistical analysis, we also investigated the impacts of city size and population on the urban-rural difference in the summer daytime LST and NDVI. Urban expansion is observed for all five cities, but the urbanization pattern varies widely from city to city. A negative SUHI effect or an oasis effect exists for all the cities across all three years, and the amplitude of the oasis effect tends to increase as the urban-rural NDVI difference increases. A strong oasis effect is observed for Hotan and Kharga with evidently larger NDVI difference than the other cities. Larger cities tend to have a weaker cooling effect while a negative association is identified between NDVI difference and population. Understanding the daytime oasis effect of desert cities is vital for sustainable urban planning and the design of adaptive management, providing valuable guidelines to foster smart desert cities in an era of climate variability, uncertainty, and change.

Background: GoGirlGo! (GGG) is designed to increase girls’ physical activity (PA) using a health behavior and PA-based curriculum and is widely available for free to afterschool programs across the nation. However, GGG has not been formally evaluated. The purpose of this pilot study was to evaluate the effectiveness of the GGG curricula to improve PA, and self-efficacy for and enjoyment of PA in elementary aged girls (i.e., 5-13 years).

Methods: Nine afterschool programs were recruited to participate in the pilot (within subjects repeated measures design). GGG is a 12-week program, with a once a week, one-hour lesson with 30 minutes of education and 30 minutes of PA). Data collection occurred at baseline, mid (twice), post, and at follow-up (3-months after the intervention ended). PA was assessed via accelerometry at each time point. Self-efficacy for and enjoyment of PA was measured using the Self-Efficacy Scale and the Short-PA enjoyment scale and was assessed at baseline, post, and follow-up. Fidelity was assessed at midpoint.

Results: Across all age groups there was a statistically significant increase in PA. Overall, on days GGG was offered girls accumulated an average of 11 minutes of moderate-to-vigorous PA compared to 8 minutes during non-GGG days. There was a statistically significant difference in girls’ self-efficacy for PA reported between baseline and post, which was maintained at follow-up. An improvement in enjoyment of PA for girls was found between baseline and follow-up. According to fidelity assessment, 89% of the activities within the curriculum were completed each lesson. Girls appeared to respond well to the curriculum but girls 5-7 years had difficulties paying attention and understanding discussion questions.

Conclusions: Even though there were statistically significant differences in self-efficacy for PA and enjoyment of PA, minimal increases in girls’ PA were observed. GGG curricula improvements are warranted. Future GGG programming should explore offering GGG every day, modifying activities so that they are moderate-to-vigorous in intensity, and providing additional trainings that allow staff to better implement PA and improve behavior management techniques. With modifications, GGG could provide a promising no-cost curriculum that afterschool programs may implement to help girls achieve recommendations for PA.

Deforestation in Myanmar has recently attracted much attention worldwide. This study examined spatio-temporal patterns of deforestation and forest carbon flux in Myanmar from 2001 to 2010 and environmental impacts at the regional scale using land products of the Moderate Resolution Imaging Spectroradiometer (MODIS). The results suggest that the total deforestation area in Myanmar was 21,178.8 km², with an annual deforestation rate of 0.81%, and that the total forest carbon release was 20.06 million tons, with an annual rate of 0.37%. Mangrove forests had the highest deforestation and carbon release rates, and deciduous forests had both the largest deforestation area and largest amount of carbon release. During the study period, the south and southwestern regions of Myanmar, especially Ayeyarwady and Rakhine, were deforestation hotspots (i.e., the highest deforestation and carbon release rates occurred in these regions). Deforestation caused significant carbon release, reduced evapotranspiration (ET), and increased land surface temperatures (LSTs) in deforested areas in Myanmar during the study period. Constructive policy recommendations are put forward based on these research results.

This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities’ increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.

This study examines the spatial and temporal patterns of the surface urban heat island (SUHI) intensity in the Phoenix metropolitan area and the relationship with land use land cover (LULC) change between 2000 and 2014. The objective is to identify specific regions in Phoenix that have been increasingly heated and cooled to further understand how LULC change influences the SUHI intensity. The data employed include MODerate-resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) 8-day composite June imagery, and classified LULC maps generated using 2000 and 2014 Landsat imagery. Results show that the regions that experienced the most significant LST changes during the study period are primarily on the outskirts of the Phoenix metropolitan area for both daytime and nighttime. The conversion to urban, residential, and impervious surfaces from all other LULC types has been identified as the primary cause of the UHI effect in Phoenix. Vegetation cover has been shown to significantly lower LST for both daytime and nighttime due to its strong cooling effect by producing more latent heat flux and less sensible heat flux. We suggest that urban planners, decision-makers, and city managers formulate new policies and regulations that encourage residential, commercial, and industrial developers to include more vegetation when planning new construction.

The urban heat island (UHI) phenomenon is a significant worldwide problem caused by rapid population growth and associated urbanization. The UHI effect exacerbates heat waves during the summer, increases energy and water consumption, and causes the high risk of heat-related morbidity and mortality. UHI mitigation efforts have increasingly relied on wisely designing the urban residential environment such as using high albedo rooftops, green rooftops, and planting trees and shrubs to provide canopy coverage and shading. Thus, strategically designed residential rooftops and their surrounding landscaping have the potential to translate into significant energy, long-term cost savings, and health benefits. Rooftop albedo, material, color, area, slope, height, aspect and nearby landscaping are factors that potentially contribute. To extract, derive, and analyze these rooftop parameters and outdoor landscaping information, high resolution optical satellite imagery, LIDAR (light detection and ranging) point clouds and thermal imagery are necessary. Using data from the City of Tempe AZ (a 2010 population of 160,000 people), we extracted residential rooftop footprints and rooftop configuration parameters from airborne LIDAR point clouds and QuickBird satellite imagery (2.4 m spatial resolution imagery). Those parameters were analyzed against surface temperature data from the MODIS/ASTER airborne simulator (MASTER). MASTER images provided fine resolution (7 m) surface temperature data for residential areas during daytime and night time. Utilizing these data, ordinary least squares (OLS) regression was used to evaluate the relationships between residential building rooftops and their surface temperature in urban environment. The results showed that daytime rooftop temperature was closely related to rooftop spectral attributes, aspect, slope, and surrounding trees. Night time temperature was only influenced by rooftop spectral attributes and slope.