Urban Surface Characteristics Research Articles

Spatial and Temporal Changes and Influencing Factors of Capital Cities in Five Provinces of the Underdeveloped Regions of Northwest China

In recent years, China’s economy has experienced rapid development, and its cities have undergone rapid expansion; however, the development of cities in the northwest region has been relatively slow due to various geographical and economic constraints. Studying the urban expansion in these regions is of significant importance for regional planning and development. This study selected the provincial capitals of five underdeveloped provinces in northwestern China as the research sample and used Landsat TM/OLI remote-sensing imagery as the primary data, supplemented by Digital Elevation Model (DEM), meteorological, and socio-economic data, the study extracted urban impervious surfaces using the ENDISI and MNDWI indices. It analyzed the spatial and temporal characteristics of urban impervious surfaces from 1990 to 2020 using indicators such as urban expansion intensity, compactness and fractal dimension, centroid migration, and standard deviation ellipse. Furthermore, the study quantified the influencing factors using Geodetectors. The findings reveal the following: (1) From 1990 to 2020, impervious surfaces in the five cities continued to expand, with Xi’an experiencing the largest expansion area at 549.94 km2 and Xining the smallest at only 132.83 km2, with an expansion intensity of merely 2.99%. However, significant disparities existed in expansion intensity and area across different periods. (2) Overall, the compactness of the cities decreased by 47.6% while the overall fractal dimension increased by 2.85%, indicating a trend towards more dispersed and complex urban forms. (3) Expansion directions varied among the cities, with Xi’an and Urumqi expanding towards the northwest, Lanzhou towards the north, Yinchuan primarily towards the east, and Xining mainly towards the west. (4) Economic, demographic, and investment factors were identified as the primary influencers of urban expansion, exhibiting changes over different periods. Analyzing the similarities and differences in city development can offer valuable insights into urban construction and sustainable development in underdeveloped areas.

The Concurrent Effect of Building Height Diversity and Cool Pavement Materials on Air Temperature Near the Surface of an Urban Facade: A Case Study of Shahriar Street In Esfahan, Iran

The rise in urban temperatures is a significant threat to the urban heat island effect, increasing building energy demand. This increase is worrisome because the supply of renewable energy is a big challenge. The approach to improving the urban microclimate offers a promising solution. This research investigates the concurrent effect of urban morphological parameters and physical characteristics of urban surfaces, such as cool materials, on the urban microclimate near the building's facades in an urban street. This evaluation was conducted by ENVI-met(v4). The results show that the concurrent effects of increasing building height diversity and by using cool pavement materials are more helpful in reducing the average air temperature of an urban street. Because the amount of shade and wind speed increased as building height variety increased, the absorption of solar radiation decreased as pavement material albedo increased. As a result, these two parameters reduced air temperature by 0.8 °C. Also in the combined scenario of increasing building height diversity and by using cool pavement materials, the air temperature near the building's facades was reduced by 1°C on the first and second floors and by approximately 0.5°C on the upper floors.

Impact of Urban Heat Island on Formation of Precipitation in Indian Western Coastal Cities

Rapid urbanization is leading to a drastic hike in anthropogenic activities and urban surface alterations. As a result, there are many repercussions, one of them being higher temperatures in urban areas when compared to rural areas. This phenomenon is termed Urban Heat Island (UHI). The impacts of urban surface characteristics, climate, and population density on UHI have been extensively studied. However, the influence of UHI on the local climate remains elusive. Relatively few studies demonstrate interrelation between UHI, population density, and unanticipated precipitation events. Therefore, it is important to comprehend the connection as it can impact extreme temperature events like heat waves and unanticipated precipitation events like flash flooding. The objective of this study is to investigate the association between UHI, population density, and precipitation in the summer and winter seasons in Indian Western Coastal Cities. To comprehend this association, a hypothesis test employing the Spearman rank correlation coefficient is conducted for 1991, 2001, 2011, and 2021. From the study, it is found that in summer, the surface temperature is directly proportional to population density and inversely proportional to precipitation. In winter the contrary relation is observed. This study also provides the seasonal variation and temporal evolution of the correlation among the parameters. This research will aid in making informed decisions for urban planning and addressing climate change.

Regulatory Effect Evaluation of Warming and Cooling Factors on Urban Land Surface Temperature Based on Multi-Source Satellite Data

Various physical characteristics of urban impervious surfaces (ISAs) and urban green spaces (UGSs) collectively regulate environmental temperatures through heating and cooling processes. However, current research often analyzes each regulating factor as an independent variable when examining its relationship with land surface temperature (LST), with limited studies considering the combined contribution weights of all regulating factors. Based on multi-source remote sensing data and ground observations from the near summers of 2014, 2016, 2017, and 2018 in the built-up area of Xuzhou City, numerical values and spatial distributions of 15 regulating factors, including ISA density (fi), land surface albedo (Albedo), population density (Population), anthropogenic heat flux (AHF), maximum ISA patch index (LPIISA), natural connectivity of ISA patches (COHESIONISA), aggregation index of ISA patches (AIISA), average shape index of ISA patches (SHAPE_MNISA), UGS density (fv), evapotranspiration (ET), UGS shading index (UGSSI), maximum UGS patch index (LPIUGS), natural connectivity of UGS patches (COHESIONUGS), aggregation index of UGS patches (AIUGS), and average shape index of UGS patches (SHAPE_MNUGS), were separately extracted within the study area. Using geographically weighted regression models and bivariate spatial autocorrelation models, we separately obtained the quantitative and spatial correlations between the 15 regulating factors and LST. The results revealed that all selected regulating factors exhibited high goodness-of-fit and significant spatial correlations with LST, which led to their categorization into eight warming factors and seven cooling factors. The factor detection of the Geographic Detector further reveals the combined contribution of all regulating factors to LST. The results indicate that cooling factors collectively have higher explanatory power for LST compared to warming factors, with UGSSI contributing the most to LST, while Population contributed the least. Furthermore, the interaction detection results of the Geographic Detector have highlighted variations in the explanatory power of different factor combinations on LST. Ultimately, it has identified factor combinations that have proven to be most effective in mitigating the urban heat environment across three scenarios: warming factors alone, cooling factors alone, and a combination of both warming and cooling factors. The suggested factor combinations are as follows: fi ∩ Albedo, fi ∩ LPIISA, UGSSI ∩ fv, UGSSI ∩ LPIUGS, fi ∩ UGSSI, and Albedo ∩ UGSSI. Therefore, our findings hold the potential to provide a valuable reference for urban planning and climate governance. Tailoring factor combinations to the local context and selecting the most effective ones can enable cost-effective mitigation of the urban heat environment.

Analysis of changes in the spatiotemporal characteristics of impervious surfaces and their influencing factors in the Central Plains Urban Agglomeration of China from 2000 to 2018

The change of the impervious surface area (ISA) is an important feature of urban spatial expansion. Understanding the spatial and temporal change characteristics of urban impervious surfaces and their influencing factors is of great significance to the planning, management, and urbanization development of cities. This paper adopts a global artificial impervious surface dataset, with a resolution of 30 m, calculates and processes the data based on the ArcGIS platform, adopts the MK trend test method, introduces the dynamic degree, qualitatively and quantitatively analyses the changing characteristics of the ISA in the Central Plains Urban Agglomeration (CPUA), and analyzes the influencing factors of ISA changes using GeoDetector. The results show that the ISA dynamic degree was significantly enhanced from 2000 to 2018, which increased 1.86 times, indicating the accelerated outward expansion of the CPUA and the rapid level of urban development during that period. The explanatory power of the greenery coverage area on the change of the ISA in the CPUA during 2000–2018 was the strongest; the same factor has different explanatory powers for the ISA in different periods. The influencing factors have an enhanced relationship between two and two, including two-factor enhancement and nonlinear enhancement, in 2000–2009, during which the interaction level of the greenery coverage area and the GDP per capita was strong, while from 2009 to 2018, the interaction between transportation and other factors was significantly strong.

Appraisal of stormwater-induced runoff quality influenced by site-specific land use patterns in the south-eastern region of Bangladesh.

Urban stormwater runoff is considered as one of the major contributors to nonpoint source that contributes to the pollution of all water resources in the surrounding environment. Pollutant concentrations of urban stormwater runoff are directly or indirectly linked with land use types in a catchment that is quite different in different places, and hence, site-specific studies are necessary, unless otherwise the modelling of runoff quality using modelling tools may not be rationally reflected the actual field scenarios. This paper portrays the influence of land use types on stormwater runoff physicochemical quality in Chattogram city, where land use's demarcation is often complicated due to the different natural and human-induced anthropogenic factors. The stormwater runoff samples were collected from the residential, commercial, institutional, and industrial land use types, in the city of Chattogram, Bangladesh, during the period from July to September 2020. The standard laboratory protocol for elemental concentrations and principal component analysis was performed in addition to basic statistics to identify the influence of urban surface characteristics on the quality of stormwater runoff. In general, pollutant concentrations were identified by analysing key physical and chemical quality parameters including colour, temperature, turbidity, total suspended solids (TSS), total dissolved solids (TDS), electrical conductivity (EC), salinity, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD5), chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) and exhibited 2 to 3 times higher concentrations than reported elsewhere. Furthermore, the present study reported the greater concentrations of few pollutants, such as TSS, BOD5, and EC, derived from the residential land uses compared to other land use types that are surprising; however, it confirmed the distinct complexity of unplanned land use patterns that should not be overlooked rather discussed. The strong correlation between land use types and stormwater runoff quality indicates the site-specific influences of runoff quality. The outcomes of this study would be particularly helpful in calibrating water quality models considering different land use types. Additionally, datasets and information obtained from this research will assist engineers and practitioners in developing decision-making tools for effective stormwater management.

The impacts of urban anthropogenic heat and surface albedo change on boundary layer meteorology and air pollutants in the Beijing-Tianjin-Hebei region

In this study, present and future anthropogenic heat flux (AH) and surface albedo (SA) in Beijing are estimated and incorporated into the WRF-Chem/UCM model. A series of nested model simulations are conducted at 9 km and 27 km horizontal grid resolution with 30 layers in the vertical to explore the sensitivity of model results to urban surface characteristics during the study period from 23 February to 13 March 2014, and the impacts of anthropogenic heat and surface albedo changes in the future on urban meteorology and air pollutants in Beijing. Model validation demonstrates that the model with the up-to-date urban surface parameters apparently improves predictions for both meteorological variables and chemical species compared with model results with default urban parameters. The combined effect of SA increase and AH decrease leads to consistent decreases in T2 (air temperature at 2 m above ground), WS10 (wind speed at 10 m) and PBLH (planetary boundary layer height) by up to 1.2 °C, 0.4 m s−1 and 200 m (33%), respectively, in Beijing averaged over the study period. In response to the meteorological changes, near surface PM2.5 (particulate matter with aerodynamic equivalent diameter below 2.5 μm) concentration increases by up to 15.6 μg m−3 (8.6%), whereas O3 concentration decreases by 2.6 ppb (25.1%), respectively. In daytime, near surface cooling due to SA increase and AH decrease causes a strong downdraft over Beijing urban areas, a wind divergence near the surface and an upslope wind over the western mountain, resulting in aerosol transport from urban district to western mountain slope. At nighttime, surface cooling is weaker than that in daytime, leading to a weaker downdraft over Beijing and a slight downslope wind in the mountain. The combined changes in SA and AH lead to a decrease in T2 by 1.2 °C, a decrease of O3 concentration by approximately 20%, and increases in PM2.5 and its components by 10–17% in daytime averaged over the urban areas of Beijing during the study period, and the above changes are weaker during haze days than those in clean days. The changes in urban surface parameters in Beijing in the future may be benefit for mitigating heat wave and O3 pollution risks, but could increase fine aerosol level and human health risk, which require an optimal strategy for co-benefits of climate change mitigation and air pollution control in future city planning.

Harmonized gap-filled datasets from 20 urban flux tower sites

Abstract. A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling. Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022).

Assessing the synergistic modulation of evapotranspiration by global impervious surface and vegetation changes

The different characteristics of impervious urban surfaces and natural underlying surfaces affect water and heat balances, with consequent impacts on evapotranspiration (ET). The global impervious surface area in 1982 was only 34% of that in 2015, motivating an assessment of the net effect of impervious surface changes on ET under rapid impervious surface expansion. In this study, we incorporated the van Dijk model's interception evaporation (Ei) algorithm into the PT-JPL (Priestley-Taylor Jet Propulsion Laboratory) model, and added an impervious surface evaporation (Eu) module, to enable urban ecosystem simulations. This improved model (named the PT-JPLim model) was validated against annual mean ET calculated by P-Q in 8,205 catchments, and against ET measured at 203 flux towers in natural ecosystems and 11 flux towers in urban ecosystems. Calibration showed that the PT-JPLim model performed well (catchments: R = 0.36; natural ecosystems: R = 0.78, RMSE = 13.17 mm; urban ecosystems: R = 0.79, RMSE = 11.74 mm). Additionally, we examined the reliability of modelled ET and its partitioning, by comparison with observed soil evaporation (Eb), Ei, and satellite-based products. By controlling for the impervious surface fraction in the model, it was concluded that the net increase in Eu due to increased impervious surface changes was 0.23 mm/yr, with larger increases in eastern China, India, Europe and southeastern North America than in other regions. The effects of impervious surface and vegetation changes on ET were then analyzed for global impervious surface, with a ratio of 1/4.1 for the effects of impervious surfaces to vegetated surfaces on ET. The contributions of transpiration (Et), Eu, and Eb changes to ET changes on impervious surfaces were 65%, 27% and 8% respectively. These results indicate that synergistic modulation of ET by impervious surface changes and vegetation changes should not be ignored when studying urban eco-hydrological processes.

Background climate modulates the impact of land cover on urban surface temperature

Cities with different background climates experience different thermal environments. Many studies have investigated land cover effects on surface urban heat in individual cities. However, a quantitative understanding of how background climates modify the thermal impact of urban land covers remains elusive. Here, we characterise land cover and their impacts on land surface temperature (LST) for 54 highly populated cities using Landsat-8 imagery. Results show that urban surface characteristics and their thermal response are distinctly different across various climate regimes, with the largest difference for cities in arid climates. Cold cities show the largest seasonal variability, with the least seasonality in tropical and arid cities. In tropical, temperate, and cold climates, normalised difference built-up index (NDBI) is the strongest contributor to LST variability during warm months followed by normalised difference vegetation index (NDVI), while normalised difference bareness index (NDBaI) is the most important factor in arid climates. These findings provide a climate-sensitive basis for future land cover planning oriented at mitigating local surface warming.

The potential impact of increased urbanization on land surface temperature over South-West Nigeria

Due to the negative consequences of urban heat islands (UHI) on urban ecology, Identifying the regional variation of land surface temperature (LST) offers a clear picture of the UHI phenomena and will aid in the introduction of appropriate mitigation techniques to address the UHI's advancing impact. Delineating the impact of UHI on land cover changes and land surface temperature (LST) is of uttermost importance due to its dynamic nature. This study makes use of Landsat Images TM, ETM and OLI sensors over the southwestern region of Nigeria, consisting of Ekiti, Osun, Ondo, Oyo, Ogun and Lagos. A supervised classification was performed by creating a training sample from four classifications consisting of vegetation, built-up, water body, and bare surface. Our results during the observed period (1986, 2002, and 2007) shows that the distribution pattern has changed as a result of changing urban surface characteristics and the influence of anthropogenic activities. The result of the research revealed that Southwest Nigeria has gone through rapid urban expansion that is expected to continue in the future, 20% decline in vegetal coverage 15% increase in bare soil amounting to a total of 35% change caused by human activities during the investigation period. The result further reveals the existence of an inverse connection between LST and NDVI with correlation coefficients of −0.8738, −0.8594, and −0.8546 respectively, which suggests that the growth of vegetation in any geographical location reduces the intensity of the land surface temperature over the area, which might be used to minimize the impacts of Urban Heat Island. In addition, thermal and evapotranspiration capacity of water also contributed to the comparatively high temperature. Hence, to achieve the sustainable environment in this region, urban planning strategies that include embracing green initiatives such as planting of trees to improve urban forestry should be adopted.

Prediction modeling of skid resistance and texture depth on flexible pavement for urban roads

Due to the urbanization and vehicle growth in India, the accident rate was very higher especially in urban cities of India. In India on the year 2019 the country has reported around 1,51,000 deaths due to road accidents. The cause of road accidents may be due number of factors such as Road surface characteristics, geometric characteristics, human factors, vehicle characteristics and weather condition etc. Among the above factors the road surface characteristics such as macro and micro texture contributes to the vehicle skidding and causes of serious accidents, thus the above characteristics should be monitored and maintained in the road surface. In reality, monitoring the skid resistance of all roads and maintain the required skid resistance is very difficult. Hence in this study, the prediction model was developed for skid resistance and texture depth using regression technique for the optimum maintenance of urban roads surface characteristics such as skid resistance which mainly depends on the macro and micro texture of the pavement surface. In this study the vast network of roads with varying traffic characteristics, age and surfacing etc., the ease in data collection prompted to take the city of Chennai as the study area for the development of Skid resistance and texture depth prediction model. Models developed for using regression technique is found to be highly satisfactory. It is found out that factors influencing the progression of skid resistance are texture depth, traffic condition, age, variation of bitumen content from optimum and abrasion value, among which, the texture depth has greater influence on the skid resistance value. The model developed in this study can be utilized for the optimum maintenance of urban roads.

An Application of the LCZ Approach in Surface Urban Heat Island Mapping in Sofia, Bulgaria

This article presents the results of the thermal survey of the capital of Bulgaria (Sofia) carried out in August 2019, with the application of an unmanned aerial system (UAS). The study is based on the concept of local climate zones (LCZs), taking into account the influence of the features of land use/land cover and urban morphology on the urban climate. The basic spatial units used in the study are presented in the form of a regular grid consisting of 3299 cells with sides of 250 × 250 m. A total of 13 types of LCZs were identified, of which LCZs 6, 5, 8, 4, D, and A form the largest share. In the thermal imaging of the surface, a stratified sampling scheme was applied, which allowed us to select 74 cells, which are interpreted as representative of all cells belonging to the corresponding LCZ in the urban space. The performed statistical analysis of the thermal data allowed us to identify both the most thermally loaded zones (LCZs 9, 4, and 5) and the cells forming Urban Cool Islands (mainly in LCZs D and C). The average surface temperature in Sofia during the study period (in the time interval between 8:00 p.m. and 10:00 p.m.) was estimated at 20.9 °C, and between the different zones it varied in the range 17.2–25.1 °C. The highest maximum values of LST (27.9–30.6 °C) were registered in LCZ 4 and LCZ 5. The relation between the spatial structure of the urban thermal patterns and urban surface characteristics was also analyzed. Regression analysis confirmed the hypothesis that as the proportion of green areas increases, surface temperatures decrease, and, vice versa, as the proportion of built-up and impermeable areas increases, surface temperatures increase. A heat load map (via applying a z-transformation to standardize the temperature values), a map of the average surface temperature, and a map of the average intensity of the heat island on the surface were generated in the GIS environment. The results of the study adequately reflect the complex spatial model of the studied phenomenon, which gives grounds to conclude that the research approach used is applicable to similar studies in other cities.

Effectiveness of urban surface characteristics as mitigation strategies for the excessive summer heat in cities

Extreme urban heat, influenced by surface characteristics, negatively affects human thermal comfort. Understanding the thermal behaviour of different urban surface parameters (USPs) at large spatial scales is essential for strategic heat mitigation. This study evaluates USPs from several perspectives to assess their comparative effectiveness as heat mitigation strategies. The Air Pollution Model (TAPM) is used to simulate the urban climate of Melbourne for summer 2019 – Australia's warmest year. Fifty-two simulations are tested to represent changes in USPs as in vegetation type, built-cover ratio, building height, green roofs and cool roofs. The results of each simulation are compared with the baseline in terms of heat indices. Roofs with high albedos are found to be the best heat mitigation for reducing daytime temperatures (0.85 albedo; −1.29 °C) while green roofs show the best nighttime efficacy (100 %, −1.15 °C). Vegetation ratio, green and cool roofs show near-linear negative relationships with heat. Cooling is found to be more effective with trees when distributed in both canyon and urban parks than only planted in street canyon. These findings underscore the significance of USP characteristics in heat mitigation that can inform strategic urban planning with spatial arrangements of green infrastructure.

Spectral library creation and analysis of urban built-up surfaces and materials using field spectrometry

Hyperspectral remote sensing is useful for the study of urban environment due to its ability to examine the comprehensive spectral characteristics of urban built-up surfaces and materials. This study deals with the creation of a spectral library and statistical analysis of spectral signatures of urban built-up surfaces and materials. Field measurement has been carried out using a spectroradiometer over the wavelength range of 350 to 2500 nm. Further, this study investigates the unique spectral characteristics and complexity of heterogeneous urban environments using mean spectral signatures of major built-up surfaces and materials. The study area belongs to the Udaipur, Rajasthan, India, which is an amalgamation of urban built-up and non-built-up surfaces. The results of the analysis suggest that various built-up surfaces and materials can be identified by investigating different absorption features in different spectral regions of a particular urban built-up class.

The influence of urban surface type and characteristics on runoff water quality

Untreated runoff was collected over multiple rain events from 19 impermeable urban surfaces, including nine roofs, six roads and four carparks, to quantify the differences in water quality due to surface type, age, condition and location. All 19 sites were exposed to the same climatic conditions. Samples were analysed for key urban pollutants of concern, namely total suspended solids and total and dissolved copper and zinc. Results showed uncoated zinc-based roofs produced zinc concentrations (up to 55 mg/L) several orders of magnitude higher than receiving environment water quality guidelines in New Zealand, of which the vast majority was in dissolved form. Even non-metallic roofs with zinc-based guttering produced zinc concentrations over ten times higher than the same roof material without zinc-based guttering. Older zinc-based roofs had approximately five times higher zinc concentrations, demonstrating a substantial age effect on the untreated runoff quality. Similarly, copper roofs produced more than an order of magnitude higher copper concentrations (up to 7.8 mg/L) above the next highest copper-producing surfaces: higher trafficked roads and carparks. Regardless of traffic volume or function, all roads and carparks produced high TSS concentrations. Dissolved metal concentrations were high across the dataset confirming that metal partitioning is an important consideration for effective pollutant control as different removal processes need to be used for dissolved versus particulate metals. This dataset provides an important benchmark of untreated runoff quality across different impermeable surface types within the same geographical area and clearly shows the influence of surface characteristics on water quality runoff regardless of the local differences in land use. These findings provide valuable guidance to stormwater managers in identifying priority surfaces and selection of appropriate treatment strategies for effective stormwater management for total suspended solids, zinc and copper.

Night and day: The influence and relative importance of urban characteristics on remotely sensed land surface temperature

The characteristics of urban land surfaces contribute to the urban heat island, and, in turn, can exacerbate the severity of heat wave impacts. However, the mechanisms and complex interactions in urban areas underlying land surface temperature are still being understood. Understanding these mechanisms is necessary to design strategies that mitigate land temperatures in our cities. Using the recently available night-time moderate-resolution thermal satellite imagery and employing advanced nonlinear statistical models, we seek to answer the question “What is the influence and relative importance of urban characteristics on land surface temperature, during both the day and night?” To answer this question, we analyze urban land surface temperature in four cities across the United States. We devise techniques for training and validating nonlinear statistical models on geostatistical data and use these models to assess the interdependent effects of urban characteristics on urban surface temperature. Our results suggest that vegetation and impervious surfaces are the most important urban characteristics associated with land surface temperature. While this may be expected, this is the first study to quantify this relationship for Landsat-resolution nighttime temperature estimates. Our results also demonstrate the potential for using nonlinear statistical analysis to investigate land surface temperature and its relationships with urban characteristics. Improved understanding of these relationships influencing both night and day land surface temperature will assist planners undertaking climate change adaptation and heat wave mitigation.

Impacts of town characteristics on the changing urban climate in Vantaa

In this work, the climatic impacts of modifying urban surface characteristics are examined for the medium-sized city of Vantaa, Finland, in the current climate and in a projected future climate of 2040–2069. In simulations with the SURFEX air-surface interaction model with a horizontal resolution of 500 m, the fraction of green spaces and relatively sparsely built suburban-type land use was increased at the expense of more densely built commercial and industrial areas. The influence of this land use intervention was found to be rather modest but comparable to the effects of the expected climate change under the RCP8.5 greenhouse gas scenario. For temperature, the climate change is the dominating effect, while wind speed is mainly controlled by surface characteristics. For relative humidity, climate change and the imposed intervention are of comparable importance. The results of this sensitivity study are intended to support policy makers by assessing the potential impact of altering the urban layout in order to improve thermal comfort or as a countermeasure to climate warming in a high-latitude city.

MODELLING URBAN HEAT ISLAND USING REMOTE SENSING AND CITY MORPHOLOGICAL PARAMETERS

Abstract. The aim of this study was to model the surface urban heat island (SUHI) based on remote sensing data, urban morphology parameters and partial least square (PLS) regression using Santa Rosa, California, USA as a case study. Night-time Land surface temperature (LST) was estimated for all the available Landsat 8 night-time data from august to November for the year 2013. Urban morphology parameters such as Building Volume (BV) and Sky View Factor (SVF) were calculated using available LIDAR data of the study area and Normalized Difference Vegetation (NDVI), Index based Built-up Index (IBI) were calculated using Landsat 8 data sets and were used to identify the impact of urban surface characteristics on land surface temperature. Partial least square (PLS) regression analysis was used to assess the correlation and statistically significance of the variables on LST and model the night time LST of the study area. The results of the analysis showed that the LST has a strong positive relationship with IBI and BV and negative relationship with SVF and NDVI and also at night-time, results showed that SVF has a stronger impact on LST differences than NDVI where areas with high density trees had higher temperature than obstacle free vegetated areas. The result of night time LST modelling of the study area was R-square with value of 0.81 between estimated and predicted LST and RMSE with value of 1.02 °C.

Local climate zone classification of surface energy flux distribution within an urban area of a hot-humid tropical city

In this study, the surface energy flux distribution within Akure city, Nigeria, was assessed using a remote sensing-based model. The Surface Energy Balance over Land (SEBAL) model was used to estimate the net radiation, soil heat flux, sensible and latent heat flux over the city. The estimated fluxes were extracted based on Local Climate Zones (LCZ) and the intensity of each component was assessed. Results showed that LCZ_3 and LCZ_6 with the highest impervious surface cover (above 50%) had higher sensible heat flux intensities, while other zones with lower percentage of impervious surface cover such as LCZ_6c and LCZ_9 had lower sensible heat flux intensities. The derived mean sensible heat flux at the built-up zones ranged between 196 W/m2 and 349 W/m2 while the latent heat flux ranged between 78 W/m2 and 295 W/m2. The latent heat flux was lower over the built-up zones compared to the zones with intense vegetation which recorded as high as 415 W/m2. The urban surface characteristics were shown to have impact on the surface energy flux distribution within the city as the components were altered differently and at dissimilar intensities at each zone.