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Global urbanization drastically reshapes the land–atmosphere interactions, biogeochemical cycles, and ecosystems in the integrated earth system. Urban trees are used extensively for providing desirable ecosystem services, especially to mitigate elevated thermal stress in cities. Urban trees differ markedly from their natural counterparts in the physiology due to their close interactions with anthropogenic stressors. Here we present the cooling capacity of urban trees in response to thermal extremes in cities of the contiguous United States. The cooling capacity is quantified as the surface cooling rate, which is expressed as the negative ratio of land surface temperature (LST) changes to fractional tree cover (FTC) changes, i.e., –ΔLST/ΔFTC. Results show that the surface cooling rate is dominated by plant transpiration up to 1.336 °C per percentage of FTC in heat waves; its temperature dependence remarkably resembles the thermodynamic liquid-water-vapor equilibrium. Urban trees also exert pseudo cooling effect in cold waves in contrast to the anthropogenic heat emission. The average surface cooling rate in cold waves is 0.022 °C per percentage of FTC, which is much smaller than that in heat waves (0.202 °C per percentage of FTC). The enhanced cooling capacity of urban trees will enable their provision of better ecosystem services to the urban environment for projected future increase of extreme heat.

This study aims to examine the spatially varying relationships between social vulnerability factors and COVID-19 cases and deaths in the contiguous United States. County-level COVID-19 data and the Centers for Disease Control and Prevention social vulnerability index (SVI) dataset were analyzed using local Spearman's rank correlation coefficient. Results suggested that SVI and four social vulnerability themes have spatially varying relationships with COVID-19 cases and deaths, which means spatial heterogeneity is an essential factor that influences the relationship, and the strength of association varies significantly across counties. County hot spots that were subject to all four social vulnerability themes during the pandemic were also identified. Local communities and health authorities should pay immediate attention to the most influential social vulnerability factors that are dominant in their region and incorporate measures tailored to the specific groups of people who are under the greatest risk of being affected during the COVID-19 pandemic.

Cities in arid and semi-arid regions have been exploring urban sustainability policies, such as lowering the vegetation coverage to reduce residential outdoor water use. Meanwhile, urban residents express concerns that such policies could potentially impact home prices regardless of the reduced water costs because studies have shown that there is a positive correlation between vegetation coverage and home values. On the other hand, lower vegetation coverage in arid and semi-arid desert regions could increase surface temperatures, and consequently increases energy costs. The question is therefore where the point in which residential outdoor water use can be minimized without overly increasing surface temperatures and negatively impacting home values. This study examines the impacts of spatial composition of different vegetation types on land surface temperature (LST), outdoor water use (OWU), and property sales value (PSV) in 302 local residential communities in the Phoenix metropolitan area, Arizona using remotely sensed data and regression analysis. In addition, the spatial composition of vegetation cover was optimized to achieve a relatively lower LST and OWU and maintain a relatively higher PSV at the same time. We found that drought-tolerant landscaping that is composed of mostly shrubs and trees adapted to the desert environment is the most water efficient way to reduce LST, but grass contributes to a higher PSV. Research findings suggest that different residential landscaping strategies may be better suited for different neighborhoods and goal sets can be used by urban planners and city managers to better design urban residential landscaping for more efficient water conservation and urban heat mitigation for desert cities.

The La Mesa Watershed (LMW) is considered as the ‘lungs’ and the last ecological frontier of the Philippines’ National Capital Region, Metro Manila. It is among the many watersheds in the country that suffered from severe deforestation in the past. Nevertheless, over the past few decades, reforestation programs for the LMW have also been initiated. The spatiotemporal monitoring of landscape pattern (composition and configuration) is needed to inform policy and support forward-looking management planning toward landscape sustainability. However, the changes in the landscape pattern of the LMW, including the extent of forest cover loss and gain over the past decades, have not been quantified; hence, this study. We used remote sensing data (Landsat) to classify the land use/land cover of the LMW in 1988, 2002 and 2016. We subsequently used spatial metrics to quantify the changes in the landscape pattern of the watershed. We found that between 1988 and 2002, a period that largely preceded the start of the LMW’s major rehabilitation (c. 1999), the watershed had a net forest cover loss of 259 ha. From 2002 to 2016, it had a net forest cover gain of 557 ha. The detected increase in forest cover was supported by the percent tree cover change analysis results based on MODIS data. The deforestation of the LMW resulted in landscape fragmentation as indicated by the decrease in the area of forest and mean forest patch size, and the increase in forest patch density, etc. Forest restoration activities have helped improve the watershed’s landscape connectivity as signified by the increase in the area of forest and mean forest patch size, and the decrease in forest patch density, etc. The results also revealed that rapid urbanization has been a major factor driving landscape changes around the LMW, and this requires proactive, forward-looking management planning. Overall, the LMW’s case presents some valuable learning experience and insights regarding public-private partnerships toward watershed and forest-related rehabilitation initiatives. On a national scale, the Philippine government has embarked on a massive national greening program. The findings of this study suggest that such efforts could lead to the enhancement of denuded forest areas, if done properly.

The local climate zone (LCZ) classification scheme is a standardization framework to describe the form and function of cities for urban heat island (UHI) studies. This study classifies and evaluates LCZs for two arid desert cities in the Southwestern United States – Phoenix and Las Vegas – following the World Urban Database and Access Portal Tools (WUDAPT) method. Both cities are classified into seven built type LCZs and seven land-cover type LCZs at 100-m resolution using Google Earth, Saga GIS, and Landsat 8 scenes. Average surface cover properties (building fraction, impervious fraction, pervious fraction) and sky view factors of classified LCZs are then evaluated and compared to pre-defined LCZ representative ranges from the literature, and their implications on the surface UHI (SUHI) effect are explained. Results suggest that observed LCZ properties in arid desert environments do not always match the proposed value ranges from the literature, especially with regard to sky view factor (SVF) upper boundaries. Although the LCZ classification scheme was originally designed to describe local climates with respect to air temperature, our analysis shows that much can be learned from investigating land surface temperature (LST) in these zones. This study serves as a substantial new resource laying a foundation for assessing the SUHI in cities using the LCZ scheme, which could inform climate simulations at local and regional scales.

This study examines the spatio-temporal patterns of urban expansion for Yangon and Nay Pyi Taw, the former and new national capitals of Myanmar, and its impact on the regional environment between 2000 and 2013. The objective is to examine different driving forces of urban expansion for Yangon and Nay Pyi Taw, and their environmental consequences during Myanmar’s transitional economy. Classified time-series Landsat images are used to evaluate urban expansion processes. Environmental parameters being evaluated in this study include five sets of remotely sensed MODIS land products that are land surface temperature (LST), percent tree cover (PTC), evapotranspiration (ET), terrestrial ecosystem net primary productivity (NPP), and aerosol optical depth (AOD). A time-series trend analysis technique is used to examine the environmental consequences. The built-up areas in Nay Pyi Taw and Yangon exhibit exponential and polynomial increase, respectively. A 1% increase of built-up area could potentially cause an increase of daytime LST of 0.7 °C, a PTC loss of 2.3%, a decrease in NPP of 34.3 kg/m2, and an ET decrease of 42.2 mm for Yangon. Similarly, for Nay Pyi Taw, a 1% increase in built-up area could potentially cause a daytime LST increase of 0.3 °C, a nighttime LST increase of 0.06 °C, a PTC loss of 2.5%, a decrease in NPP of 15.1 kg/m2, and a decrease of 19.2 mm ET. No significant change was observed for AOD for either city. Both cities have experienced extensive urban expansion but with different spatial and temporal characteristics, and their effects on the regional environment are different. Urban expansion of Nay Pyi Taw mainly was government-induced municipal infrastructure development. Yangon’s expansion is mainly caused by population pressure and migration from rural areas. The urban expansion in Yangon was mainly due to reconstruction and renovation, as well as infill development during the study period.

Reliable representations of global urban extent remain limited, hindering scientific progress across a range of disciplines that study functionality of sustainable cities. We present an efficient and low-cost machine-learning approach for pixel-based image classification of built-up areas at a large geographic scale using Landsat data. Our methodology combines nighttime-lights data and Landsat 8 and overcomes the lack of extensive ground-reference data. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30 m resolution maps that characterize built-up land cover in three geographically diverse countries: India, Mexico, and the US. Our approach highlights the usefulness of data fusion techniques for studying the built environment and is a first step towards the creation of an accurate global-scale map of urban land cover over time.

Urban areas, with massive built-up landscapes and manmade structures, have different patterns of local microclimate as compared to natural terrains. A better understanding of the surface-air temperature relationship in urban environments is of significant importance in interpreting urban climatic characteristics and solving related environmental problems via sustainable landscape planning strategies. In this study, we analyse the ground-based in-situ measurements as well as remotely sensed thermal dataset in Phoenix, AZ. Prominent hysteresis effect manifests in correlating diurnal cycles of surface and near-surface air temperatures. In particular, a peculiar pattern of “8-shaped” surface-air temperature hysteresis is observed over concrete pavement especially in winters. Pearson’s r values, measuring the strength of surface-air temperature coupling, show strong correlation with incoming solar radiation and wind speed, but are relatively insensitive to humidity. The hysteresis effect diminishes at climatic scale, such that the remotely sensed surface temperature can be approximated as linearly correlated to the near-surface air temperature.

Rapid population growth leading to significant conversion of rural to urban lands requires deep understanding on how the human population interacts with the built-environment. Our research goal is to explore methodologies on how to analyze multidimensional urban change with the consideration of time, space, and landscape patterns. Using NAIP high resolution satellite images and LIDAR data, we were able to derive land cover classification maps and normalized height difference at different time periods. Then we performed the 2D, 3D and landscape pattern change analysis for a case study area. The research results show that a combination of 2D, 3D and landscape pattern change analysis can provide a comprehensive understanding of urban change, and the results will help urban planners and decision makers to better understand the status of urban transformation and design city for the future.

Sweeping across Bangladesh and India, the Sundarbans forest is the world's largest contiguous mangrove forest. Although the human population density is high at the edge, Sundarbans has not encountered significant areal transformation in the last four decades. However, we argue that forest degradation can occur discontinuously within the forest without alteration of the entire forest area. In this paper, we used MODIS land products to compare the spatiotemporal ecological dynamics of the Bangladesh and Indian part of this mangrove forest between 2000 and 2010. We used the following 5 ecological parameters for our analysis: the Percent Tree Cover (PTC), Enhanced Vegetation Index (EVI), Net Primary Productivity (NPP), Leaf Area Index (LAI), and Evapotranspiration (ET). Our pixel-based time-series trend analysis for each MODIS image stack, using an ordinary least square (OLS) regression method, showed that forest degradation is happening in fragmented parcels within the forest. The degradation rate is comparatively higher in the Bangladesh part than in the Indian part of Sundarbans. Compartments 8, 10, 12, and 15 in the Bangladesh part, in particular, show high degradation, while compartment 48 and the southern edge of 45 show slight increases in PTC or EVI. Forest degradation in the Indian part of the forest is evident in the National Park and Reserve Forest blocks; however, no substantial degradation is evident in the western section. We have identified certain anthropogenic stressors (i.e., oil pollution, shrimp farming) and natural stressors (i.e., increased salinity, cyclones, forest fire) which might be responsible for the observed degradation. We have provided sustainable planning options and policy transformation alternatives for those areas under pressure from these stressors. We anticipate that our analysis of forest degradation will help management agencies, conservators, and policy makers achieve better management of this world's largest mangrove forest for a sustainable future.

In this study, an empirical model for predicting urban evapotranspiration (ET) is examined for the Phoenix metropolitan area that is in a subtropical desert climate using in situ ET measurements from a local flux tower and remotely sensed moderate-resolution imaging spectroradiometer land products. Annual ET maps of Phoenix are then created for the period from 2001 to 2015 using the empirical model developed. A time-series trend analysis is finally performed using predicted ET maps to discover the spatio-temporal patterns of ET changes during the study period. Results suggest that blue-sky albedo and land surface temperature are two statistically significant variables explanatory to model urban ET for Phoenix. Areas that have experienced significant increases of ET are highly spatially clustered, and are mainly found on the outskirts of the city, while areas of decreasing ET are generally associated with highly developed areas, such as downtown Phoenix.

Myanmar is one of the mangrove-richest countries in the world, providing valuable ecosystem services to people. However, due to deforestation driven primarily by agricultural expansion, Myanmar's mangrove forest cover has declined dramatically over the past few decades, while what remains is still under pressure. To support management planning, accurate quantification of mangrove forest cover changes on a national scale is needed. In this study, we quantified Myanmar's mangrove forest cover changes between 2000 and 2014 using remotely sensed data, examined the environmental impacts of such changes, and estimated the changes in the economic values of mangrove ecosystem services in the country. Results indicate that Myanmar had a net mangrove loss of 191,122 ha over the study period. Since 2000, Myanmar has been losing mangrove forest cover at an alarming rate of 14,619 ha/year (2.2%/year). The loss was predominant in Rakhine and Ayeyarwady. The observed mangrove forest cover loss has resulted in decreased evapotranspiration, carbon stock, and tree cover percentage. Due to deforestation, Myanmar also suffered a net loss of 2,397 million US$/year in its mangrove ecosystem service value (i.e. 28.7% decrease from 2000), in which maintenance of fisheries nursery populations and habitat and coastal protection were among those services that were greatly affected. We suggest that intensive reforestation and mangrove protection programs be implemented immediately. Agroforestry and community forestry programs are encouraged in areas that are under immense pressure from paddy field expansion, fuelwood extraction, charcoal production, and fish and shrimp farming activities. Potential alternative sustainable solutions should include intensive government-led private forest plantations or community-owned forest plantations to be developed with care by local farmers, nongovernmental organizations, and business owners.

Elevated concentration of atmospheric aerosols during severe urban air pollution episodes necessitates a deep understanding of the underlying determinants for a sustainable urban environment. The 15-year (2001-2015) Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) data for the Phoenix and Los Angeles Metropolitan Areas were applied to examine the spatio-temporal patterns and dynamics of urban aerosols. The strongly correlated temporal trends of AOD were observed due to the similar seasonal pattern of aerosol emissions and potential synoptic connections between two areas. Relatively higher mean value and lower decreasing trend of AOD were found in the PMA. Correlations reveal that topography is the predominant factor affecting the spatial pattern of AOD, as compared to the urban land use and vegetation. The effect of urbanization on air pollution varies with preexisting landscape, which apparently alleviates aerosol concentration in the PMA. Vegetation mitigates air pollution despite its emission of fine mode aerosols. As a cross-validation, the ground-measured concentrations of particulate matters (PM2.5 and PM10) were compared against AOD. The abnormal weak positive or strong negative AOD-PM2.5 associations result from the relatively small portion of anthropogenic aerosols and the changing atmospheric boundary layer height.

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.

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.