Nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National ...Polar-orbiting Partnership satellite provide a great opportunity for monitoring human activities from regional to global scales. Despite the valuable records of nightscape from DMSP (1992-2013) and VIIRS (2012-2018), the potential of the historical archive of NTL observations has not been fully explored because of the severe inconsistency between DMSP and VIIRS. In this study, we generated an integrated and consistent NTL dataset at the global scale by harmonizing the inter-calibrated NTL observations from the DMSP data and the simulated DMSP-like NTL observations from the VIIRS data. The generated global DMSP NTL time-series data (1992-2018) show consistent temporal trends. This temporally extended DMSP NTL dataset provides valuable support for various studies related to human activities such as electricity consumption and urban extent dynamics.
Urban configuration can influence the local thermal environment by altering energy balances. However, previous studies have found that either sprawling urban or compact urban development could ...intensify urban heat island (UHI) intensity. How urban configurations can mitigate the UHI intensity has drawn full attention. In this study, we quantified the diurnal and seasonal UHI intensities in 36 cities in China and investigated their response to urban configurations. In each city, urban land cover maps were classified from Landsat 8 and UHIs were quantified using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST). Results show that the average UHI intensities of 36 cities vary temporally with a sequence of summer day > summer night > winter night > winter day. Moreover, whether the daytime UHI is higher or lower than the nighttime UHI significantly depends on climatic zones and seasons. Besides, we found that UHIs significantly correlate with urban configurations in two ways. First, for the spatial structure among built-up patches, a lower UHI located in the smaller built-up area with dispersed distribution when compared to the larger built-up patches, if the total built-up area holds constant. Second, for the spatial structure of a single patch, the single patch with more complex shape would mitigate the UHI intensities. Overall, urban configuration and other control variables (e.g., urban characteristics and climatic condition) can explain 41% and 51% of the variance in UHI in summer day and night, respectively. Therefore, the design of urban configuration can serve as an essential practice to mitigate UHI intensity. Considering the difficulties of altering the urban configuration in the urbanized area, planting vegetation might be a great choice to change the urban contiguity and shape complexity with providing an extra cooling effect.
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•We examined the effect of urban configuration on urban heat island intensities in 36 China mega-cities.•The relative relationship between daytime UHI and nighttime UHI depends on climatic zones and seasons.•UHI variations were significantly influenced by the division and shape complexity of the built-up area.•This study provides essential implication and perspectives of heat mitigation.
Urban heat island (UHI) could have significant impacts on building energy consumption by increasing space cooling demand and decreasing space heating demand. However, the impacts of UHI on building ...energy consumption were understudied due to challenges associated with quantifying UHI-induced temperature change and evaluating building energy consumption. In this study, we reviewed existing literature for improving the understanding of UHI impacts on building energy consumption. It was found that UHI could result in a median increase of 19.0% in cooling energy consumption and a median decrease of 18.7% in heating energy consumption. The reported UHI impacts showed strong intercity variations with an increase of cooling energy consumption from 10% to 120% and a decrease of heating energy consumption from 3% to 45%. The UHI impacts also showed clear intra-city variations with stronger impacts in urban center than that in urban periphery. There were significant differences in the method and the data used to evaluate the UHI impacts in previous studies. Four future research focuses were recommended to better understand the UHI impacts on building energy consumption.
•The literature of UHI impacts on building energy consumption was reviewed.•UHI could lead to a median of 19% increase in building cooling energy consumption.•UHI could lead to a median of 18.7% decrease in building heating energy consumption.•UHI impacts showed strong spatial variations within and among cities.•Four future research focuses were recommended for better understanding of UHI impacts.
Urbanization has transformed the world’s landscapes, resulting in a series of ecological and environmental problems. To assess urbanization impacts and improve sustainability, one of the first ...questions that we must address is: how much of the world’s land has been urbanized? Unfortunately, the estimates of the global urban land reported in the literature vary widely from less than 1–3 % primarily because different definitions of urban land were used. To evade confusion, here we propose a hierarchical framework for representing and communicating the spatial extent of the world’s urbanized land at the global, regional, and more local levels. The hierarchical framework consists of three spatially nested definitions: “urban area” that is delineated by administrative boundaries, “built-up area” that is dominated by artificial surfaces, and “impervious surface area” that is devoid of life. These are really three different measures of urbanization. In 2010, the global urban land was close to 3 %, the global built-up area was about 0.65 %, and the global impervious surface area was merely 0.45 %, of the word’s total land area (excluding Antarctica and Greenland). We argue that this hierarchy of urban land measures, in particular the ratios between them, can also facilitate better understanding the biophysical and socioeconomic processes and impacts of urbanization.
Urban heat island (UHI), the phenomenon that urban areas experience higher temperatures compared to their surrounding rural areas, has significant socioeconomic and environmental impacts. With ...current and anticipated rapid urbanization, improved understanding of the response of UHI to urbanization is important for developing effective adaptation measures and mitigation strategies. Current studies mainly focus on a single or a few big cities and knowledge on the response of UHI to urbanization for large areas is limited. As a major indicator of urbanization, urban area size lends itself well for representation in prognostic models. However, we have little knowledge on how UHI responds to urban area size increase and its spatial and temporal variation over large areas. In this study, we investigated the relationship between surface UHI (SUHI) and urban area size in the climate and ecological context, and its spatial and temporal variations, based on a panel analysis of about 5000 urban areas of 10km2 or larger, in the conterminous U.S. We found statistically significant positive relationship between SUHI and urban area size, and doubling the urban area size led to a SUHI increase as high as 0.7°C. The response of SUHI to the increase of urban area size shows spatial and temporal variations, with stronger SUHI increase in Northern U.S., and during daytime and summer. Urban area size alone can explain as much as 87% of the variance of SUHI among cities studied, but with large spatial and temporal variations. Urban area size shows higher association with SUHI in regions where the thermal characteristics of land cover surrounding the urban area are more homogeneous, such as in Eastern U.S., and in the summer months. This study provides a practical approach for large-scale assessment and modeling of the impact of urbanization on SUHI, both spatially and temporally.
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•We studied relationship between SUHI and urban area size and their spatial and temporal variation in the conterminous U.S.•SUHI increases nonlinearly with the increase of urban area size in a log-linear form.•Doubling urban size increases SUHI as high as 0.7 °C, with larger increase in high latitude areas and in summer and daytime.•Urban area size explains as much as 87% of SUHI variation, with higher value in regions covered by homogenous land cover.
The Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable nighttime light (NTL) data provide a wide range of potentials for studying global and regional dynamics, ...such as urban sprawl and electricity consumption. However, due to the lack of on-board calibration, it requires inter-annual calibration for these practical applications. In this study, we proposed a stepwise calibration approach to generate a temporally consistent NTL time series from 1992 to 2013. First, the temporal inconsistencies in the original NTL time series were identified. Then, a stepwise calibration scheme was developed to systematically improve the over- and under- estimation of NTL images derived from particular satellites and years, by making full use of the temporally neighbored image as a reference for calibration. After the stepwise calibration, the raw NTL series were improved with a temporally more consistent trend. Meanwhile, the magnitude of the global sum of NTL is maximally maintained in our results, as compared to the raw data, which outperforms previous conventional calibration approaches. The normalized difference index indicates that our approach can achieve a good agreement between two satellites in the same year. In addition, the analysis between the calibrated NTL time series and other socioeconomic indicators (e.g., gross domestic product and electricity consumption) confirms the good performance of the proposed stepwise calibration. The calibrated NTL time series can serve as useful inputs for NTL related dynamic studies, such as global urban extent change and energy consumption.
Reliable quantification of urban heat island (UHI) can contribute to the effective evaluation of potential heat risk. Traditional methods for the quantification of UHI intensity (UHII) using ...pairs-measurements are sensitive to the choice of stations or grids. In order to get rid of the limitation of urban/rural divisions, this paper proposes a new approach to quantify surface UHII (SUHII) using the relationship between MODIS land surface temperature (LST) and impervious surface areas (ISA). Given the footprint of LST measurement, the ISA was regionalized to include the information of neighborhood pixels using a Kernel Density Estimation (KDE) method. Considering the footprint improves the LST-ISA relationship. The LST shows highly positive correlation with the KDE regionalized ISA (ISAKDE). The linear functions of LST are well fitted by the ISAKDE in both annual and daily scales for the city of Berlin. The slope of the linear function represents the increase in LST from the natural surface in rural regions to the impervious surface in urban regions, and is defined as SUHII in this study. The calculated SUHII show high values in summer and during the day than in winter and at night. The new method is also verified using finer resolution Landset data, and the results further prove its reliability.
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•Quantifying surface urban heat island intensity using the relationship between LST and Impervious Surface Areas.•The impervious surface areas was regionalized within the footprint of remote sensing observation using a Kernel Density Estimation method.•Linear functions of LST were well fitted using the regionalized impervious surface areas.•Slope of the linear function of LST was defined as the surface urban heat island intensity.
Reliable quantification of urban heat island intensity (UHII) is crucial for the evaluation of extreme heat waves and the related heat stress. As a powerful approach for the study of urban climate, ...numerical models can simulate urban heat island (UHI) in both high spatial and temporal resolutions. However, accurate quantification of UHII using modelling grid data is still a challenge at present, due to the different criterions for the selection of urban/rural grids. This study simulates the high-resolution UHI in the city of Berlin using the Weather Research and Forecasting Model coupled with Urban Canopy Module. A new method to quantify UHII, which is based on the fitted linear functions of simulated 2-m air temperature (T2m) using the impervious surface area in WRF grids (ISAWRF), was adopted and evaluated. The simulated T2m matches the observations well, with a correlation coefficient of 0.95 (P < 0.01) and RMSE of 1.76 °C. The study area shows a strong UHI at nighttime. The simulated nighttime T2m increases with the increase in the ISAWRF. The linear functions of simulated nighttime T2m against ISAWRF are well fitted. The UHII is calculated as the products of the slopes of fitted functions and the largest ISAWRF. The derived UHII shows U-shaped diurnal variations, with high values at nighttime. The difference of simulated surface temperature and sensible heat flux between the impervious surface and the vegetation surface jointly determines the derived UHII. The large difference of surface temperature and the small difference of sensible heat flux between the impervious and the vegetation surface generate the high UHII at nighttime and vice versa during the daytime. The method of ISAWRF-based function of T2m overcomes the problems of traditional methods in arbitrary selecting urban/rural grids. It can be used easily to quantify UHII and to do the comparison study of UHII between different cities.
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•High-resolution urban heat island was simulated using WRF/UCM.•The linear function of simulated air temperature against impervious surface area was used to quantify UHI intensity.•The physical mechanism of the derived UHI intensity was examined.
Climate change will affect the energy system in a number of ways, one of which is through changes in demands for heating and cooling in buildings. Understanding the potential effect of climate change ...on heating and cooling demands requires taking into account not only the manner in which the building sector might evolve over time, but also important uncertainty about the nature of climate change itself. In this study, we explore the uncertainty in climate change impacts on heating and cooling requirement by constructing estimates of heating and cooling degree days (HDD/CDDs) for both reference (no-policy) and 550 ppmv CO
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concentration pathways built from three different Global Climate Models (GCMs) output and three scenarios of gridded population distribution. The implications that changing climate and population distribution might have for building energy consumption in the U.S. and China are then explored by using the results of HDD/CDDs as inputs to a detailed, building energy model, nested in the long-term global integrated assessment framework, Global Change Assessment Model (GCAM). The results across the modeled changes in climate and population distributions indicate that unabated climate change would cause building sector’s final energy consumption to decrease modestly (6 % decrease or less depending on climate models) in both the U.S. and China by the end of the century as decreased heating consumption more than offsets increased cooling using primarily electricity. However, global climate change virtually has negligible effect on total CO
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emissions in the buildings sector in both countries. The results also indicate more substantial implications for the fuel mix with increases in electricity and decreases in other fuels, which may be consistent with climate mitigation goals. The variation in results across all scenarios due to variation of population distribution is smaller than variation due to the use of different climate models.
The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST ...data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.
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