Impervious Surface Fraction Research Articles

Urban Morphology and Surface Urban Heat Island Relationship During Heat Waves: A Study of Milan and Lecce (Italy)

The urban heat island (UHI) effect, marked by higher temperatures in urban areas compared to rural ones, is a key indicator of human-driven environmental changes. This study aims to identify the key morphological parameters that primarily contribute to the development of surface urban heat island intensity (SUHII) and investigates the relationship between SUHII and urban morphology using land surface temperature (LST) data from the Sentinel-3 satellite. The research focuses on Milan and Lecce, analyzing how urban geometry affects SUHII. Factors such as building height, aspect ratio, sky visibility, and surface cover are examined using approximately 1000 satellite images from 2022 and 2023. The study highlights seasonal and diurnal variations in SUHII, with particular emphasis on HW periods. Through multicollinearity and multiple regression analyses, the study identifies the main morphological drivers influencing SUHII in the two cities, specifically the Impervious Surface Fraction (ISF) and Mean Building Height (HM). Milan consistently exhibits higher SUHII, particularly during HWs, while Lecce experiences a negative SUHII, especially during the summer, due to lower urban density, more vegetation, and the low soil moisture around the urban area. Both cities show positive SUHII values at night, which are slightly elevated during HWs. The heat wave analysis reveals the areas most susceptible to overheating, typically characterized by high urban density, with ISF and HM values in some cases above the 90th percentile (0.8 and 13.0 m, respectively) compared to the overall distribution, particularly for Milan. The research emphasizes the importance of urban morphology in influencing SUHII, suggesting that detailed morphological analysis is crucial for developing climate adaptation and urban planning strategies to reduce urban overheating and improve urban resilience to climate change.

A WRF-UCM-SOLWEIG framework for mapping thermal comfort and quantifying urban climate drivers: Advancing spatial and temporal resolutions at city scale

The capability to evaluate city-scale outdoor thermal comfort is crucial for understanding the public's experience of heat stress, especially during heat waves. Nevertheless, there remains a methodological gap in accurately mapping thermal comfort with high spatial and temporal resolutions in complex urban environment and in quantifying the contributions of various urban climate drivers, such as sky view factors and tree canopy fraction. To bridge this gap, we propose an innovative WRF-UCM-SOLWEIG framework that couples a mesoscale model with an urban microclimate model. Specifically, it integrates the Weather Research and Forecasting model coupled with the urban canopy model (WRF-UCM) and the Solar and Longwave Environmental Irradiance Geometry model (SOLWEIG). This framework is then applied to a densely populated tropical city in China, to quantify the impact of urban climate drivers on the Universal Thermal Climate Index (UTCI). The results reveal a significant diurnal variation in the impact of urban morphology. Notably, the most significant drivers affecting daytime UTCI are the impervious surface fraction and the tree canopy fraction, with maximum Pearson's correlation coefficients of 0.80 and -0.70, respectively. These findings suggest that urban heat mitigation strategies should prioritize on reducing impervious surfaces and enhancing shade management, alongside expanding urban forestry measures.

Separation of the Urbanization Effects on Thermal Environment

Rapid urbanization leads to changes in the thermal characteristics of urban areas. Despite cities serving as natural laboratories to investigate responses to global change, it remains unclear how urbanization affects the thermal environment during global warming. Urbanization effects can be categorized as either the effect of urban climate (UCE) or land cover change (LCCE). This study proposes a conceptual framework for separating the urbanization effects on the thermal environment in 29 major Chinese cities. Results show that land surface temperature increases with impervious surface fractions in the urban area and decreases with vegetation fractions in rural areas, resulting in significant UCE and LCCE across cities, with LCCE approximately 1.3 to 5.7 times greater than UCE. These findings provide a comprehensive understanding of the thermal environment change caused by urbanization. To mitigate the negative effects of urbanization on the thermal environment, reducing land cover change would be more effective.

Spatial effect of urban morphology on land surface tempature from the perspective of local climate zone

Urban expansion results in the intrusion of artificial surfaces into natural environments, giving rise to phenomena such as urban heat islands and heightened temperatures within inhabited areas. This study examined the dynamic evolution of urban morphology and land surface temperature through the lens of local climate zone. Furthermore, the spatial relationship between urban morphology indices and land surface temperature was analyzed within the context of loops. The investigation yielded several noteworthy findings: (1) LCZ8, LCZ6, and LCZD were the most significant transfer-in and transfer-out classes within the study area. (2) The heat contribution of residential LCZs (LCZ 2,3,5) decreased over time, ranging from 0.415 to 0.171, while that of industrial LCZs (LCZ 8,10) increased within the range of 0.348–0.553. (3) Urban heat island mitigation can be achieved by regulating building height and surface fraction. Establishing a judicious threshold for reducing LST is imperative for sky view factor and impervious surface fraction factors. Surface albedo exhibited a substantial inhibitory effect in 2020. The observation of urban morphological changes and long-term local climate zone considerations integrate the responsibility of mitigating urban heat islands into urban planning.

How do the 3D urban morphological characteristics spatiotemporally affect the urban thermal environment? A case study of San Antonio

Urban landscapes, characterized by intricate interactions within a three-dimensional (3D) framework, give rise to complex mechanisms that underlie the Surface Urban Heat Island phenomenon. While global case studies have emphasized the impact of 3D urban morphological components on urban thermal environments, conducting quantitative assessments to identify spatial and seasonal variations in these relationships poses significant challenges. Based on Lidar and land cover dataset, this study first investigated the 3D urban morphology characteristics across nine components, and then assessed their spatial and seasonal impact on the urban thermal environment through Geographically Weighted Regression (GWR). We found that Impervious Surface Fraction (ISF) has the greatest warming effect, whereas the Vegetation Coverage Fraction (VCF) exerts the strongest cooling influence on the thermal environment across all seasons. Building Height (BH) and Street Aspect Ratio (SAR) exhibit spatially varying cooling effects, while Building Number (BN) and Standard Deviation of Building Height (BH_SD) have minimal warming impacts. Additionally, the influence of BH on the thermal environment is significant primarily in areas characterized by higher building coverage. VCF has a stronger cooling effect in central regions with limited vegetation during spring and winter, but this effect diminishes compared to suburban ones in summer and autumn. These findings provide valuable insights into developing effective heat mitigation strategies at both city and community levels, ultimately fostering sustainable urban development.

Urban Morphology Influencing the Urban Heat Island in the High-Density City of Xi’an Based on the Local Climate Zone

Urban form plays a critical role in enhancing urban climate resilience amidst the challenges of escalating global climate change and recurrent high-temperature heatwaves. Therefore, it is crucial to study the correlation between urban spatial form factors and land surface temperature (LST). This study utilized Landsat 8 remote sensing data to estimate LST. Random forest nonlinear analysis was employed to investigate the interaction between the urban heat island (UHI) and six urban morphological factors: building density (BD), floor area ratio (FAR), building height (BH), fractional vegetation coverage (FVC), sky view factor (SVF), and impervious surface fraction (ISF), within the framework of local climate zones (LCZs). Key findings revealed that Xi’an exhibited a significant urban heat island effect, with over 10% of the study area experiencing temperatures exceeding 40 °C. Notably, the average LST of building-class LCZs (1-6) was 3.5 °C higher than that of land cover-class LCZs (A-C). Specifically, compact LCZs (1-3) had an average LST 3.02 °C higher than open LCZs (4-6). FVC contributed the most to the variation in LST, while FAR contributed the least. ISF and BD were found to have a positive impact on LST, while FVC and BH had a negative influence. Moreover, SVF was observed to positively influence LST in the compact classes (LCZ2-3) and open low-rise class (LCZ6). In the open mid-rise class (LCZ5), SVF and LST showed a U-shaped relationship. There is an inverted U-shaped relationship between FAR and LST, with the inflection point occurring at 1.5. The results of nonlinear analysis were beneficial in illustrating the complex relationships between LST and its driving factors. The study’s results highlight the effectiveness of utilizing LCZ as a detailed approach to explore the relationship between urban morphology and urban heat islands. Recommendations for enhancing urban climate resilience include strategies such as increasing vegetation coverage, regulating building heights, organizing buildings in compact LCZs in an “L” or “I” shape, and adopting an “O” or “C” configuration for buildings in open LCZs to aid planners in developing sustainable urban environments.

Spatial analysis of dynamic groundwater potential maps of Nagpur district of Maharashtra using the AHP and RS-GIS tools

ABSTRACT Identification of groundwater potential (GWP) is essential for groundwater management. Recently some areas of Nagpur district have faced water scarcity with severe groundwater level fluctuation (GWLF). The study incorporates the dynamic behaviour of rainfall, land use/cover, fractional impervious surface (FIS), and GWLF from 2017 to 2022 along with topographical wetness index (TWI), topographical ruggedness index (TRI), lineament density, drainage density, slope, soil, and geomorphology. The study employs the geographical information system (GIS)-based multi-criteria decision-making approach, analytical hierarchy process, and remote sensing for spatiotemporal GWP mapping. The weighted overlay tool of ArcGIS 10.5 was used to derive final GWP maps. Critically, the northwestern part of the study area experienced major shifts in GWP, 448 km2 area has increased under the poor GWP category representing a decline in recharge probability. The safe GWP category diminished by nearly 531 km2 which exacerbates the problem whereas, high GWP showed very less changes. The most sensitive parameters are identified using an area-sensitivity approach, which reveals that TRI, slope, soil, geomorphology, rainfall, and TWI cause 35, 29, 20, 19, 11, and 11% area changes, respectively, while maximizing their weights. The validation of GWP maps shows good agreement with pre- and post-monsoon well data. The methodology and results may serve for GWP appraisal of similar regions.

Urban microclimate differences in continental zone of China

In modern cities, the block geometry and land cover feature directly affect the heat transfer between ground surface and atmosphere, resulting in significant differences of local microclimate. Clarifying this difference will help to accurately predict regional building energy demand and optimize regional layout design. However, existing microclimate research mainly focuses on tropical, arid, and temperate climate regions, leaving continental areas virtually unexplored. To address this problem and clarify the interactions of the urban blocks and microclimate, the combination of the local climate zone (LCZ) scheme and fixed-point weather station monitoring is employed to investigate the variation characteristics of the thermal and humidity conditions of typical urban blocks in continental zone of China. Firstly, sixty-seven typical urban blocks with self-built, national and regional meteorological stations are selected as the object of study, and classified into twelve LCZ categories, according to their inherent features. Secondly, the spatial and temporal variation patterns of the thermal environment indexes of the twelve LCZ types are analyzed and compared. Additionally, correlation analyses and regression fittings are performed between the thermal environment indexes and the urban morphological parameters. The results indicate significant differences in thermal environment indexes among different LCZ types. The mean temperature of built types is 0.72 °C higher than land cover types. The temperature and humidity exhibit a parabolic relationship with height of roughness elements and building surface fraction respectively, while the humidity has strong negatively correlations with the impervious surface fraction and the height of roughness elements.

Synergies or trade-offs between surface urban heat island and hot extreme: Distinct responses in urban environments

Hot extremes are among the most damaging climate extremes to human society, with urban areas being particularly vulnerable due to the surface urban heat island (SUHI). Despite the growing frequency and intensity of surface hot extreme (SHX), a comprehensive understanding of the patterns and drivers of SUHI responsiveness to SHX (difference in SUHI intensity [δSUHII] between SHX and non-hot extreme conditions [NSHX]) remains obscure. This study aims to shed light on the spatial-temporal pattern of SHX and the distinct SUHI responses to SHX throughout climatically diverse Chinese urban clusters using the seamless daily land surface temperature (LST) dataset. Our results provided evidence that Chinese urban clusters have experienced increasingly frequent, prolonged, and intensive SHX events regardless of climatic context, with stronger occurrences in summer and fall. Spatially, urban clusters in the northern regions have been subjected to more intensified SHX episodes compared to their southern counterparts. Furthermore, our findings revealed distinct δSUHII responses to SHX across China, demonstrating synergies between SUHI and SHX when SHX events constrained to urban areas whereas trade-offs when SHX isolated within rural settings. The magnitude of δSUHII depended upon the relative intensity of SHX between urban and rural settings, and heightened δSUHII generally associated with more intensified SHX events in urban than rural areas. Additionally, the explainable machine learning-based driver exploration showed that δSUHII was largely controlled by disparities in evaporative cooling (δET) between SHX and NSHX during the daytime, whereas during nighttime, it was predominantly governed by changes in surface heat storage, including urban-rural disparities in surface albedo (δABD) and impervious surface fraction (ISF). Facing the intertwined challenges posed by climate change and urbanization, it is imperative for cities to develop effective cooling strategies that emphasize enhanced evaporative cooling and minimized heat storage. These strategies are essential for safeguarding urban residents from potential synergistic effects between SUHI and SHX towards sustainable cities and human settlements.

Influence of urban extent discrepancy on the estimation of surface urban heat island intensity: A global-scale assessment in 892 cities

The estimation of surface urban heat island intensity (SUHII) is crucial for studying the urban thermal environment, which is influenced not only by the commonly known definition of rural reference but also by the delineation of urban extent. Existing studies relies on various urban extent products defined in different ways, and the influence of urban extent discrepancy (UED) on SUHII estimates still remains unclear. In this study, we collected five open-source global urban extent products (GUEPs) for the year 2015 and corresponding daily land surface temperature (LST) observations (MYD11A1). Based on these products, we quantified the UED-induced uncertainty in SUHII estimates by comparing absolute difference (ΔSUHIIAD) and relative difference (ΔSUHIIRD) in SUHII among GUEPs across 892 global cities. Besides, we introduced an ISF-constrained (ISF–C) method to reduce SUHII differences among GUEPs by constraining the impervious surface fraction (ISF) within urban and rural extents. The results show that urban extents delineated by different GUEPs are not consistent, leading to their difference in ISF as well as LST, which in turn causes uncertainties in the estimated SUHII. On average for global cities, the annual daytime and nighttime ΔSUHIIAD are 0.46 ± 0.02 °C (mean ± 95% confidence interval) and 0.24 ± 0.01 °C, with corresponding ΔSUHIIRD of 42.0 ± 2.7% and 35.2 ± 2.3%, respectively. The UED-induced uncertainty in SUHII estimates varies among climate zones, and the annual daytime ΔSUHIIRD averaged for cities located in the arid zone reaches up to 60.8 ± 6.6%, which is nearly twice as high as that in other climate zones. More importantly, both ΔSUHIIAD and ΔSUHIIRD show lower values when using the ISF-C method, implying the effectiveness of this method. This study highlights the non-negligible impact of UED on the estimation of SUHII, which requires more attention due to the inconsistency of urban extents among current products.

Synergistic effects of heat and carbon on sustainable urban development: Case study of the Wuhan Urban Agglomeration

The rapid pace of urbanization has resulted in pressing climatic challenges, notably an escalation in carbon emissions and rising urban temperatures. Urban expansion frequently coincides with heightened carbon emissions, releasing substantial quantities of greenhouse gases such as CO2. This exacerbates the urban heat island (UHI) effect and poses a threat to urban living conditions. To shed light on the intricate relationship between land surface temperature (LST) and carbon emissions and provide novel insights into the synergistic regulation of “heat” and “carbon” in sustainable urban development, we conducted an in-depth investigation of the Wuhan Urban Agglomeration spanning from 2003 to 2018. Employing data on impervious surface fraction (ISF), LST, and carbon emissions, we harnessed weighted center and standard deviation ellipse analyses to explore the spatiotemporal expansion patterns of ISF, LST, and carbon emissions. We examined the relationship between LST and carbon emissions using spatial correlation analysis. The results revealed that regions with high carbon emissions and elevated LST were predominantly concentrated in the central urban areas of each city, aligned with the distribution of high-density impervious surfaces. The weighted centers of ISF, LST, and carbon emissions exhibited a progressive shift toward the central region of Wuhan, intensifying spatial clustering effects. The global Moran's I index for LST and carbon emissions increased from 0.33 in 2003 to 0.48 in 2018, signifying a strong positive correlation between LST and carbon emissions. In the clustering maps of LST and carbon emissions, the high-high clustering trend persistently expanded, signifying a shift from “dispersion” to “agglomeration” in urban heat and carbon effects. In regions marked by consistent carbon emission growth, the correlation coefficient (R) between carbon emissions and LST was 0.4, and the Pearson coefficient was 0.64, both higher than those in areas with gradual carbon emission growth. Within stable carbon emission growth areas, every 0.1-ton increase in carbon emissions has a 40% probability of a 1.33 °C temperature rise. As the rate of carbon emissions increased, the positive synergistic effect between carbon emissions and LST continued to increase. This study provides empirical support and academic groundwork to reduce urban emissions and mitigate the effects of UHIs.

Four-decade response of land surface temperature to urban expansion in Beijing

Urban expansion with increased impervious surface area (ISA) can affect the thermal regimes by altering the water and energy balance. Previous studies have indicated that ISA is the primary surrogate of rising land surface temperature (LST). However, urban-induced LST changes are not yet well quantified because of the temporal limitations of the LST datasets and the uncertainties from climate variabilities. In this study, we integrated multiple satellite-based products (vegetation coverage, albedo and radiation forcing) into a physically-based land surface model i.e., Variable Infiltration Capacity (VIC), to detect multi-temporal scale response of LST to the increasing ISA in Beijing. Heat transfer-related parameters in the model were updated along with urbanization processes. The integrated modeling presented high performance for simulating LST, as evaluated with ground-based observations and the MODerate-resolution Imaging Spectroradiometer (MODIS) LST product. We found that the average LST over Beijing increased from 10.3 °C in 1980–1990 to 11.2 °C in 2010–2020, with nearly 33% contributed by the urban growth. The urban-induced thermal effect was particularly strong in summer daytime and winter nighttime. The frequency of heat days (the day with the maximum LST over 40 °C) in a year for Beijing appeared to linearly correlate with the impervious surface fraction (ISF) when ISF > 25%. For the urbanizing area, moreover, the four-decade LST estimates indicated that the annual overheating duration was obviously extended with a rate of about 5 days per decade. We therefore conclude that the urban expansion in Beijing not only amplified heat stress but also altered heat phenology, which have implications for urban planning and the construction of heat mitigation facilities.

Assessing the synergies between heat waves and urban heat islands of different local climate zones in Guangzhou, China

Urban heat island (UHI) effects and heat waves (HWs) increase, posing a threat to human health and urban ecological systems. However, the synergistic mechanisms of UHI and HWs are yet to be fully elucidated. This study aims to assess the characteristics of UHI under HWs in different Local Climate Zones (LCZs) using temperature observations from Guangzhou, China. Fixed-point measurements were employed to simultaneously collect air temperature from nine LCZs in 2021. The results showed a localised interplay between HWs and UHI. During HWs, the UHI effect was significantly amplified in built-type LCZ, where the UHI intensity (UHII) was amplified by 0.1–0.5 °C throughout the day, 0.4–1 °C at night, but slightly reduced by 0–0.4 °C during daytime. The UHII was significantly reduced in land-cover type LCZ, and the urban cold effect was amplified during HWs. Furthermore, with strong solar radiation and low wind speed in HWs, the daily maximum UHII of LCZs increased 0.4–1.1 °C. Finally, correlations between UHII and morphological characteristics of LCZs are examined in HWs and non-heat waves (NHWs). During HWs, the increase in impervious surface fraction (ISF) and building surface fraction (BSF) and the decrease in sky view factor (SVF) exacerbate UHII, particularly at night. The augment in surface vegetation fraction (Fveg), does not significantly contribute to reducing the UHI and is less effective than NHWs at night. Therefore, different measures should be taken in different LCZs to mitigate UHII amplification during HWs. This study can serve as a reference for preventing responses to climate extremes.

Groundwater potential mapping in Jashore, Bangladesh

The global groundwater crisis is a perplexing issue, and for its resolution, it is of the utmost importance for delineating groundwater potential zones. This research aims to create a precise groundwater potential map for Bangladesh's Jashore district by combining geospatial approach and an analytical hierarchy process. Fourteen parameters, namely, lineament density, drainage density, land use and land cover, slope, curvature, topographic position index, topographic wetness index, rainfall, geology, roughness, fractional impervious surface, topsoil texture, soil permeability, and general soil types, were considered for the study after an extensive literature review. The weights of these parameters were determined using an analytical hierarchy process, and the scores of each sub-parameter were assigned based on published literature. The final groundwater potential map was then generated using the weighted overlay analysis tool in ArcGIS 10.3 and categorized into five classes. The analysis reveals that very high, high, moderate, low, and very low groundwater potential zones cover 3.96 km2 (0.16%), 444.75 km2 (17.72%), 1615.51 km2 (64.37%), 441.79 km2 (17.60%), and 3.59 km2 (0.14%) of the study area, respectively. The map removal sensitivity analysis shows that geology is the most significant element in groundwater potential zoning, followed by land use and land cover (LULC), slope, and topsoil texture as moderately sensitive elements. Since the groundwater potentiality zones of the study region are clearly delineated, this research may be valuable for implementing an appropriate water resource management strategy.

City-wide, high-resolution mapping of evapotranspiration to guide climate-resilient planning

The impacts of global change, including extreme heat and water scarcity, are threatening an ever-growing urban world population. Evapotranspiration (ET) mitigates the urban heat island, reducing the effect of heat waves. It can also be used as a proxy for vegetation water use, making it a crucial tool to plan resilient green cities. To optimize the trade-off between urban greening and water security, reliable and up-to-date maps of ET for cities are urgently needed. Despite its importance, few studies have mapped urban ET accurately for an entire city in high spatial and temporal resolution. We mapped the ET of Berlin, Germany in high spatial (10-m) and temporal (hourly) resolution for the year of 2019. A novel machine learning (ML) approach combining Sentinel-2 time series, open geodata, and flux footprint modeling was applied. Two eddy flux towers with contrasting surrounding land cover provided the training and testing data. Flux footprint modeling allowed us to incorporate comprehensive land cover types in training the ML models. Open remote sensing and geodata used as model inputs included Normalized Difference Vegetation Index (NDVI) from Sentinel-2, building height, impervious surface fraction, vegetation fraction, and vegetation height. NDVI was used to indicate vegetation phenology and health, as plant transpiration contributes to the majority of terrestrial ET. Hourly reference ET (RET) was calculated and used as input to capture the temporal dynamics of the meteorological conditions. Predictions were carried out using Random Forest (RF) regression. Weighted averages extracted from hourly ET maps using flux footprints were compared to measured ET from the two flux towers. Validation showed that the approach is reliable for mapping urban ET, with a mean R2 of 0.76 and 0.56 and a mean RMSE of 0.0289 mm and 0.0171 mm at the more vegetated site and the city-center site, respectively. Lastly, the variation of ET between Local Climate Zones (LCZs) was analyzed to support urban planning. This study demonstrated the capacity to map urban ET at an unprecedented high spatial and temporal resolution with a novel methodology, which can be used to support the sustainable management of green infrastructure and water resources in an urbanizing world facing climate change.

HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020

Abstract. Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generated the first high spatial resolution (1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. In this collection, 12 commonly used thermal indices were generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface temperature, topography, land cover, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on the observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable accuracies, with the mean R2, root mean square error, and mean absolute error of 0.996, 0.693 ∘C, and 0.512 ∘C, respectively, by averaging the 12 indices. Moreover, the data exhibit high agreements with the observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. A comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) studies. Further investigation reveals that nearly all thermal indices exhibit increasing trends in most parts of China during 2003–2020. The increase is especially significant in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, during spring and summer. The HiTIC-Monthly dataset is publicly available from Zenodo at https://doi.org/10.5281/zenodo.6895533 (Zhang et al., 2022a).

Linear spectral mixture analysis of land cover for assessment level sub-pixel using PlanetScope imagery in Dieng Plateau

Information on the land cover is essential as thematic information for planning, controlling, and spatial planning to create sustainable development. Remote sensing is a tool used to solve a problem related to space, environment, and zoning. One of the obstacles in using remote sensing data is the presence of mixed pixels. This research was conducted in the Dieng Plateau. This research aims to calculate accuracy assessment and land cover map fraction using PlanetScope imagery in Dieng Plateau. The remote sensing image is PlanetScope, recorded November 19, 2020. Topographic correction is carried out using the cosine correction method. The Linear Spectral Mixture Analysis (LSMA) method determines each endmember percentage land cover fraction. The field survey was conducted with 74 samples for accuracy testing. Each sample plot measures 10 m x 10 m. The topographic correction results can significantly affect land cover classification results. The result of the LSMA method classification used PlanetScope images to determine the percentage land cover map fraction of bare soil, impervious surface, vegetation, and water in one pixel with an accuracy of MAE 99.76%.

The impact of urban morphology on the spatiotemporal dimension of estate-level air temperature: A case study in the tropics

This research evaluated the spatiotemporal dimension of the thermal environment of urban microclimate system in Singapore through a case study. To provide a holistic assessment of air temperature due to urban morphology, for the spatial dimension, correlations between different urban morphology variables and estate-level air temperature were examined through machine learning models to test nonlinear assumption. Different urban morphologies were measured and calculated including shading coefficient of buildings, shading coefficient of greenery, green plot ratio, building height, building cover ratio, building wall area, impervious ground surface fraction, and ground albedo. The temporal dimension of air temperature changes was examined at hourly resolution. The test score of hourly temperature predictions using nonlinear algorithm ranged from 0.24 °C to 0.67 °C, with RMSE above 0.5 °C occurring at T8-T17. Furthermore, this research aggregated hourly temperature prediction into an average cumulative temperature increase (ACTI). A heatmap was created showing ACTI on a university campus with a maximum temperature difference of 2 K between cold and hot spots. Based on the investigation, this study further proposed guidelines for low carbon urban planning for greenery, painting or pavement material, and active system.

Applying a diagnostic equation for maximum urban heat island intensity based on local climate zones for Guangzhou, China

The Urban Heat Island (UHI) is a widely studied phenomenon, characterized by unique spatiotemporal variations depending on urban structure. Seeking a simple and rapid method to monitor the Urban Heat Island Intensity (UHII) for local urban area with multiple inner morphology is essential for energy saving, citizen health, and urban planning. Hence, this study proposed a diagnostic equation for daily maximum urban heat island intensity (UHIImax) based on routine meteorological data and basic urban properties. The applicability of equation, previously proposed by European scholars, was evaluated based on Local Climate Zone (LCZ) scheme by a long-term temperature observation experiment conducted in Guangzhou. Overall, the underestimation of UHIImax was caused by LCZ1,2,3,4, in which the morphological parameters were outside the application range of the original equation. Then, a revised equation was proposed by adding the impervious surface fraction (ISF) in morphological parameters based on the spatiotemporal variance of UHII for different LCZ. The revised equation was evaluated against a year dataset and revealed a higher accuracy than the original one with a decrease of RMSE and MEAE at 0.4K, 0.15K, an increase of dr at 0.1. Moreover, the efficiency of the equations for all the seasons and LCZs were elucidated. In summary, the results can be used as a tool for monitoring the development of UHI in outdoor temperature studies.

Spatial and temporal characteristics of near‐surface air temperature across local climate zones in a tropical city

AbstractThe present study examines the temporal and spatial variability of near‐surface air temperature and the canopy layer urban heat island (UHI) in Singapore. Observations collected at 20 locations across the island city‐state and during 6 years make this one of the most extensive studies carried out in a tropical city. Local climate zones (LCZs), defined as urban built and rural land cover types which produce a unique air temperature response, are used to standardize intersite comparison. The results show that the choice of the rural reference can affect the night‐time UHI magnitude by up to 2°C under “ideal” (dry, clear, calm) conditions. The most frequently observed median UHI magnitude increases from 2.8 during all‐weather to 3.7°C during “ideal” conditions, respectively. A seasonality is present with lowest (highest) mean all‐weather values of ~2.0°C (~3.3°C), observed during the wet (dry) December–January (April–October) period. Mean night‐time UHI intensity across seven built‐type LCZs ranges between 1.8 and 3.5°C (2.5 and 4.3°C) for all‐weather (“ideal”) conditions. Corresponding daily values are 1.1–2.3°C (1.5–2.7°C). The lowest (highest) night‐time magnitudes are associated with open low‐rise LCZ 6 (compact high‐rise LCZ 1) built type. In the middle of the day LCZ 1 can experience a cool island effect. The average UHI values presented here give an indication of the extra warmth experienced in the built‐up spaces of Singapore relative to one possible rural reference land cover type (scattered trees LCZ B). Highest daytime heat exposure is observed in LCZs 3 and 8 which are characterized by low building heights, high impervious surface fraction and lack of vegetation. The present results can be used to support the development and evaluation of urban climate models, develop urban planning policies and improve local weather forecasts and the delivery of integrated urban services.