Local Climate Zones Research Articles

How urban spatial characteristics impact surface urban heat island in subtropical high-density cities based on LCZs: A case study of Macau

Although numerous studies have explored the influence of urban spatial characteristics (USC) on surface urban heat island intensity (SUHII) from the perspective of local climate zone (LCZ), the spatio-temporal heterogeneity within and between LCZs has not been demonstrated. In this study, a total of 1540 grid units in Macau across 7 years were screened as the research samples. USC, SUHII and LCZ were calculated and derived from multi-source data. GWR and GTWR models were applied to detect the spatio-temporal non-stationarity between USC and SUHII, and Geodetector was used for ranking the interpretation strength of driving factors in each LCZ class. Results show that SUHII in LCZ 7 and LCZ 8 is the highest. Besides, the GWR model had the best fitting degree in accounting for thermal variations due to urban form changes, and the regression performance was generally different among LCZ classes, especially between the compact-types and the open-types. Furthermore, strategies related to urban form optimization were finally proposed in each LCZ class based on the contribution of driving factors. This study sheds new light on the spatio-temporal variations of “USC-SUHII-LCZ” linkage and provides specific planning recommendations for thermal environment improvement, especially in subtropical highdensity cities.

A combined Remote Sensing and GIS-based method for Local Climate Zone mapping using PRISMA and Sentinel-2 imagery

In the last decade, several methods have been developed for Local Climate Zone (LCZ) mapping, encompassing Remote Sensing and Geographic Information Systems (GIS) −based procedures. Combined approaches have also been proposed to compensate for intrinsic limitations that characterized their separate application. Recent work has disclosed the potential of hyperspectral satellite imagery for improving LCZ identification. However, the use of hyperspectral data for LCZ mapping is yet to be fully unfolded. A combined Remote Sensing and GIS-based method for LCZ mapping is proposed to exploit the integration of hyperspectral PRISMA and multispectral Sentinel-2 images with ancillary urban canopy parameter layers. Random Forest algorithm is applied to the feature sets to obtain the LCZ classification. The method is tested on the Metropolitan City of Milan (Italy), for the period from February to August 2023. A spectral separability analysis is carried out to investigate the improvement in LCZ identification using PRISMA in comparison to Sentinel-2 data, as well as improvements in LCZ spectral separability on PRISMA pan-sharpened images. The resulting maps’ quality is evaluated by extracting accuracy metrics and performing inter-comparisons with maps computed from the LCZ Generator benchmark tool. Inter-comparisons yield promising results with a mean Overall Accuracy increase of 16% using PRISMA for each LCZ class. Furthermore, we find that PRISMA improves the detection of LCZs compared to Sentinel-2, with a mean Overall Accuracy increase of 5%, in line with the higher spectral separability of PRISMA spectral signatures computed on the training samples.

Spatio-temporal analysis of LST, NDVI and SUHI in a coastal temperate city using local climate zone

Extreme heat due to changing climate poses a new challenge for temperate climates. The challenge is further aggravated by inadequate research, policy, or preparedness to effectively respond and recover from its impacts. While urban morphology is crucial in mitigating urban heat, it has received limited attention in urban planning, highlighting the need for further exploration, particularly in temperate regions. To illustrate the challenge and its potential mitigations, we use the example of the coastal temperate city of Cardiff. To establish the interrelations between urban morphology and urban heat island patterns, we explored the spatiotemporal variations in land surface temperature (LST), normalised difference vegetation index (NDVI), and surface urban heat island (SUHI) to local climate zone (LCZ) classification for Cardiff. Results showed a significant variation in SUHI in the LCZ zones. Both LST and NDVI land were found to vary significantly across the LCZ zones demonstrating their association with the urban form and morphology of the locality. For built-up areas, a more compact built-environment with smaller vegetation cover and larger building density was 2.0 °C warmer than the open built-environment when comparing the mean summer LSTs. On average, the natural classes exhibit a LST that is 8.0 °C lower than the compact built-environment and 6.0 °C lower than the open built-environment. Consequently, the high-density, built-up LCZs have a greater SUHI effect compared to the natural classes. Therefore, temperate climate cities will benefit from incorporating an open built-environment that has sufficient greenery and open spaces. These findings help determine the optimal urban form for temperate climates and develop heat mitigation strategies while planning, designing, or improving the new and existing urban areas. In addition, the LCZ map applied in this study for Cardiff will enable international comparison and testing of proven climate change adaptation and mitigation techniques for similar urban areas.

Assessment of Urban Thermal Environments Using a Combination of the Local Climate Zone and Landscape Ecological Metrics in Taipei City

Assessment of Urban Thermal Environments Using a Combination of the Local Climate Zone and Landscape Ecological Metrics in Taipei City

Fundamental LCZ Mapping with ArcGIS-Based Spatial and Correlational Analyses to Study the Impacts of Infrastructural Area- Maximum Volume (A-MV) Attributes on OTC – An Urban Aw Summer Case

The aim of this study is to map the study location stretches into 100mx100m local climate zones (LCZs) on the basis of primary observations and secondary photographic data for spatial analysis of various urban infrastructural elements (grey(G)-built structures, blue(B)-water bodies and green (Gr)-natural vegetation) and impact assessment of their estimated area (EA) and maximum volume (EMV)) on the air temperature (Ta) that characterizes the outdoor thermal comfort (OTC). Climate Modification by Urban Infrastructure, Fundamental Concepts of Urban Infrastructure Mensuration Parameters, Fundamental Concepts of Urban Grey Blue Green Infrastructure (UGBGI) and Concepts Of OTC, Koppen Classification and LCZs are the linked areas. Study locations are based on socio-economic and geophysical attributes. Initial 200 pilot survey responses regarding thermal sensation votes and other subjective details were used. Preferred values of Physiological Equivalent Temperature (PET) and optimum Ta have been used. After the mapping in ArcGIS and polygonal delineation of the areas, EA values were compiled in tabular format. Based on primary measurements and secondary studies, a maximum height (hmax) value was assigned to each sub-category for EMV evaluation. Statistically, the EA and estimated perimeter (EP) values were analyzed using QQ, scatter plots, histogram and generate near table tools in ArcGIS. EA and EMV values were correlated with mean Ta values by linear regression. The R-square values generated gave a clear idea about the extent of their impacts on Ta. From the established relationships and preferred PET and optimum Ta values, optimum EA, EMV and hmax values of the broader categories(G,B,Gr) required for preferred OTC levels were finally evaluated.

Investigating Stagnant Air Conditions in Almaty: A WRF Modeling Approach

This study investigates stagnant atmospheric conditions in Almaty, Kazakhstan, a city nestled within a complex terrain. These conditions, characterized by weak local winds and inversion layers, trap pollutants within the city, particularly during winter. The Weather Research & Forecasting (WRF) model was employed to simulate atmospheric conditions using Local Climate Zone data. Verification of the model’s accuracy was achieved through comparisons with data from weather stations and the Landsat-9 satellite. The model successfully reproduced the observed daily temperature variations and weak winds during the testing period (13–23 January 2023). Comparisons with radiosonde data revealed good agreement for morning temperature profiles, while underestimating the complexity of the evening atmospheric structure. The analysis focused on key air quality factors, revealing cyclical patterns of ground-level and elevated inversions linked to mountain-valley circulation. The model effectively captured anabatic and katabatic flows. The study further examined the urban heat island (UHI) using a virtual rural method. The UHI exhibited daily variations in size and temperature, with heat transported by prevailing winds and katabatic flows. Statistical analysis of temperature and wind patterns under unfavorable synoptic situations revealed poor ventilation in Almaty. Data from three Januaries (2022/2023/2024) were used to create maps showing average daytime and nighttime air temperatures, wind speed, and frequency of calm winds.

Differences in LCZ composition according to urban planning and impacts on urban thermal environment

As a climate-aware and standardized classification scheme, the Local Climate Zone (LCZ) can be a research framework for analysis of effects of urban development on urban forms and Urban Heat Island (UHI) phenomenon in relation to urban planning. In this study, two new towns built with different urban planning in South Korea are selected as the study areas. In respect of the growing popularity of deep learning and advancements deep learning-based LCZ classification, we build a deep convolutional neural network architecture using a modified SE-ResNext50 backbone. As input data, we designed six schemes using different combinations of Sentinel-1 SAR and Sentinel-2 MSI imagery and thermal band from Landsat 9 is used for SUHI magnitude estimation and heat vulnerability. In addition, robust and quantitative data sampling using building surface fraction data and building height data was performed. The results indicate that combining SAR and multispectral data could increase LCZ classification accuracy and outline the capacity of polarimetric decomposition components. In addition, it was suggested that urban planning causes differences in the LCZ distribution of each new town, resulting in differences in SUHI magnitude and heat vulnerability. The research findings can help guide future urban development by considering the urban form and thermal environment according to the LCZ composition within the new plannedcity.

The Effect of Green Stormwater Infrastructures on Urban-Tier Human Thermal Comfort—A Case Study in High-Density Urban Blocks

Green stormwater infrastructure (GSI) is a key approach to greening and cooling high-density blocks. Previous studies have focused on the impact of a single GSI on thermal comfort on sunny days, ignoring rainwater’s role and GSI combinations. Therefore, based on measured data of a real urban area in Nanjing, China, this study utilized 45 single-GSI and combination simulation scenarios, as well as three local climate zone (LCZ) baseline scenarios to compare and analyze three high-density blocks within the city. Among the 32 simulations specifically conducted in LCZ1 and LCZ2, 2 of them were dedicated to baseline scenario simulations, whereas the remaining 30 simulations were evenly distributed across LCZ1 and LCZ2, with 15 simulations allocated to each zone. The physiological equivalent temperature (PET) was calculated using the ENVI-met specification to evaluate outdoor thermal comfort. The objective of this research was to determine the optimal GSI combinations for different LCZs, their impact on pedestrian thermal comfort, GSI response to rainwater, and the effect of GSI on pedestrian recreation areas. Results showed that GSI combinations are crucial for improving thermal comfort in compact high-rise and mid-rise areas, while a single GSI suffices in low-rise areas. In extreme heat, rainfall is vital for GSI’s effectiveness, and complex GSI can extend the thermal comfort improvement time following rainfall by more than 1 h. Adding shading and trees to GSI combinations maximizes thermal comfort in potential crowd activity areas, achieving up to 54.23% improvement. Future GSI construction in high-density blocks should focus on different combinations of GSI based on different LCZs, offering insights for GSI planning in Southeast Asia.

PRISMA Hyperspectral Satellite Imagery Application to Local Climate Zones Mapping

Abstract. The urban heat island effect exacerbates the vulnerability of cities to climate change, emphasizing the need for sustainable urban planning driven by data evidence. In the last decade, the Local Climate Zone (LCZ) model emerged as a key tool for categorizing urban landscapes, aiding in the development of urban temperature mitigation strategies. In this work, the contribution of hyperspectral satellite imagery to LCZ mapping, leveraging the Italian Space Agency (ASI)’s PRISMA satellite, is investigated. Mapping performances are compared with traditional multispectral-based LCZ mapping using Sentinel-2 satellite imagery. The Random Forest algorithm is utilized for LCZ classification, with evaluation conducted through spectral separability analysis and accuracy assessment between PRISMA and Sentinel-2 derived LCZ maps as well as with the benchmark LCZ Generator mapping tool. An initial experiment on the effect of PRISMA image pan-sharpening on LCZ spectral separability is also presented. Results obtained for Milan (Northern Italy) demonstrate the potential of hyperspectral imagery in enhancing LCZ identification compared to multispectral data, with promising improvements in LCZ maps overall accuracy. Finally, air temperature patterns within each LCZ class are explored, qualitatively confirming the influence of urban morphology on thermal comfort.

A Scene Unmixing Deep Learning Network for Local Climate Zone Mapping and Analysis Using Very High Resolution Remote Sensing Imagery

Abstract. With the acceleration of urbanization, the environment and climate necessary for our survival have gradually deteriorated, leading to the increasing prominence of the Urban Heat Island (UHI) effect. Local Climate Zone (LCZ) classification, as a standard of urban morphology, has become an essential tool for monitoring the UHI effect and conducting temperature studies. Deep Learning (DL) models have the ability to represent high-level semantic features. Therefore, this paper proposes amixed scene unmixing DL framework for LCZ mapping and analysis using Very High Resolution (VHR) remote sensing images. This framework consists of a two-stream deep network, including a pure scene classification network (PS-Net) and a mixed scene unmixing network (MSU-Net). We conducted random sampling tests in Wuhan, China in the experiment A. The results show that this model achieved a satisfactory accuracy with the Overall Accuracies (OAs) is 96.78% and a mixed scene unmixing Mean Absolute Error (MAE) of 0.0495. Furthermore, we applied the proposed model to generate LCZ map for five districts in Wuhan in the experiment B. The test accuracy between two experiments differs very slightly. These results demonstrate the applicability and potential of our model for LCZ mapping and urban climate analysis.

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.

Numerical Investigation of Interventions to Mitigate Heat Stress: A Case Study in Dubai

Urbanization and changes in microclimate have negative impacts on outdoor thermal comfort, making urban design more important. This study aims to improve outdoor thermal comfort in a local climate zone (LCZ) in Dubai using computational fluid dynamics (CFD) methods. This study evaluates cooling interventions, such as vegetation, architectural, and pavement material, using Reynolds-averaged Navier–Stokes (RANS) equations and the SIMPLE scheme. The results show that a combination of cooling interventions affects the average temperature between 4.44 °C and 6.14 °C. Light-colored ground material has a 5.4 °C cooling effect in the LCZ compared with dark-colored materials. The predicted mean vote (PMV) method is used to compare outdoor thermal comfort and the results show that thermal sensation in the LCZ improves from warm to slightly cool. Lastly, the most effective cooling interventions are, in order, shade structures, trees, chimneys, and bushes.

Biophysical Drivers of Seasonal Hysteresis of Urban Heat Islands Across Climates and Urban Landscapes

AbstractThe phenomenon of seasonal hysteresis between urban heat islands (UHIs) and background surface temperature was reported in many studies, but a unifying explanation for the causes of its magnitude, shape and looping direction remained less clear, and the impacts of urban landscapes and background climate on such hysteresis were also the subject of enquiry. Here we performed a systematic study based on a long‐term offline simulation for surface energy budgets, surface air and skin temperature of the urban‐biosphere system in East China. It was carried out with an up‐to‐date land surface model — the CLM5‐LCZs (the Community Land Model version 5 (CLM5) coupled with a newly developed urban canopy model representing diverse urban landscapes with the local climate zones classification scheme). To isolate the causes of UHIs hysteresis, a simplified framework was constructed by combining a mathematical representation of grid/subgrid model outputs, the two‐resistance mechanism method and the time‐lagged cross correlation analysis. The results exhibited distinct hysteresis patterns of UHIs such as twisted, concave‐down, concave‐up and convex‐up curve across diverse background climates and urban landscapes. The basic shapes of hysteresis depict a gradual transition from concave/convex‐up to concave/convex‐down by characterized by a larger winter UHIs from a wetter and warmer climate to a drier and colder climate. Magnitude and seasonality of surface UHIs are separately dominated by the urban‐rural contrast of evapotranspiration and heat storage at daytime and nighttime, whereas across‐climate variations of daytime and nighttime surface UHIs hysteresis are regulated by the phase shift of convection efficiency and anthropogenic heat. Urban landscapes affect the hysteresis mainly by altering the amplitude rather than the shape of looping curves. Our work could provide a fuller picture of how to mitigate urban warming considering inter‐seasonal tradeoffs over a broader region.

Surface urban heat island analysis based on local climate zones using ECOSTRESS and Landsat data: A case study of Valencia city (Spain)

Extreme heat events in cities are becoming more intense, frequent and prolonged. The spatiotemporal dynamics of surface temperature are closely related to land cover and atmospheric conditions, especially for urban areas with extensive surface heterogeneity. Two high spatial resolution satellites, ECOSTRESS and Landsat, provide land surface temperature (LST) for a typical Mediterranean climate city, Valencia, Spain. In total of 17 images throughout two heatwaves in the period of July and August from 2022 to 2023 were selected to monitor LST and surface urban heat island (SUHI). The Local Climate Zone (LCZ) scheme and hotspot analysis were applied to analyze the patterns of LST and SUHI during and after the heatwaves. Daytime images show LCZ 2/3/6/8 and LCZ C/E/F exhibit elevated SUHI intensity, meanwhile the LCZ 4, A/B and G have lower SUHI. The SUHI intensity of compact buildings (LCZ 1/2/3), LCZ 8/E/G are significantly higher at night, especially on heatwave days, the SUHI of all types increased by 0.1–0.9 °C. Furthermore, the meteorological parameters were introduced trying to explain the obvious diurnal difference of SUHI during heatwaves. This research serves as a proxy for understanding the spatiotemporal dynamics of SUHI during heatwave events, offering valuable insights for city planners and policymakers to enhance thermal comfortable level and effectively cope with extreme weather.

Downscaled numerical weather predictions can improve forecasts of sugarcane irrigation indices

Efficient irrigation reduces energy and water costs, increases profit margins and delivers better environmental outcomes. Whilst many growers rely on weather forecasts to make decisions, few studies have sought to incorporate weather forecast uncertainty into the optimisation of irrigation management or to evaluate weather forecasts through the lens of irrigation indices. Therefore, in this study, we seek to generate and evaluate ensemble forecasts of irrigation indices produced by coupling numerical weather prediction (NWP) forecasts with a biophysical process model (APSIM). We investigate a case study application for sugarcane in northeastern Australia. As a first step, three and a half years of forecasts from the Australian Bureau of Meteorology’s ACCESS-G3 model are statistically post-processed to generate 7-day forecasts that are downscaled and calibrated to local climate zones. In addition, the forecast post-processor converts the deterministic forecasts into an ensemble, thus quantifying forecast uncertainty. The generated forecasts are then used as forcing for the APSIM crop model to produce ensemble forecasts of soil water deficit (SWD), crop water use (CWU) and crop stress (Stress) for a simulated sugarcane crop. Through cross-validation, the post-processed weather forecasts demonstrate improved forecast accuracy compared to naive climatology and raw NWP forecasts for daily rainfall, maximum and minimum temperature and solar radiation; with the added benefit of providing a reliable uncertainty estimate. Improvement to an even greater degree is observed for the derived irrigation indices, particularly CWU and Stress, for which the forecasts are also reliable. The developed irrigation indices based on NWP can be used directly for decision making or, alternatively, may be used further in machine learning for optimisation of irrigation schedules in conjunction with other remotely sensed variables.

Assessment of daytime and nighttime surface urban heat islands across local climate zones – A case study in Florianópolis, Brazil

Affected air movement, artificial heat production, increased solar absorption and suppressed vegetation cause significant temperature differences between urban and suburban/rural regions, forming Surface Urban Heat Islands (SUHI). Here, we propose a methodological framework combining daytime and nighttime Landsat 8 data with comprehensive statistical evaluation based on the retrieved information. To quantify SUHI, we selected a pair of daytime and nighttime Landsat 8 scenes, calculated the Urban Thermal Field Variance Index and applied a comprehensive statistical approach to assess differences in SUHI behaviour across Local Climate Zones and between day and night, examining its relationships with albedo, elevation, land surface emissivity and vegetation cover. Open urban typologies were characterised by milder day temperatures, especially over open areas with higher buildings, while compact low-rise areas presented the highest SUHI intensity. Higher buildings presented more intense SUHI at night, with milder temperatures over open and low-rise regions. These results confirm the SUHI dynamics and its strong association with the urban structure and the presence of vegetation. The proposed complex methodological framework can be, with minor adjustments, applied to other regions as well, which can improve the comparability among studies on SUHI and promote our understanding of its causes and possible mitigation measures.

Artificial intelligence for predicting urban heat island effect and optimising land use/land cover for mitigation: Prospects and recent advancements

Rocketing global urbanisation has caused an increase in the Urban Heat Island (UHI) effect, resulting in various negative implications for the urban environment. Quantifying the Surface UHI (SUHI) effect using Land Surface Temperature (LST), Local Climate Zones (LCZ), and deep learning algorithms such as Convolutional Neural Networks (CNN) and pix2pix have prospects in aiding sustainable city planning and modification. Most research on mitigating SUHI promotes greenery as a solution, allowing LCZ optimisation to be explored. Using Heat Vulnerability Index (HVI) and evolutionary algorithms like Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO) show promise in achieving high-quality optimisation solutions. This short communication explores the potential of these artificial intelligence technologies to combat the UHI effect and enhance urban sustainability.

Tropospheric Ozone levels in and around southern city Bengaluru, India

The diurnal to seasonal ozone (O3) pre-cursors (NO, NO2, CO, VOCs) trends over local climatic zones (LCZs) with the changes in weather patterns with respect to land cover classes determines the O3 trends and useful for air quality management. In this work, diurnal to seasonal patterns of O3 concentrations are analysed across the different LCZs in Bengaluru region. The Central/State Pollution Control Boards (CPCB/SPCB) operate more than 500 continuous air quality and weather monitoring stations across the India. Hourly O3 concentration data were collected from CPCB for 5 different LCZs within the Bengaluru region over a period of four years (2019-2022). Time series analyses were conducted to examine the temporal variations in O3 levels. The results showed significant fluctuations in diurnal trends of O3 due to the influence of air temperature and relative humidity variations across the five LCZs within Bengaluru region. There were distinct seasonal trends in O3 concentrations with maximum (60-65 ppb) observed during the winter and summer seasons and that of minimum concentration in the range 20-25 ppb during the monsoon months. The observed maximum ozone levels (60 -65 ppb) found to be beyond the threshold national ambient air quality standard (NAAQ) and the accumulation exposure over threshold of 40 ppb (AOT 40) during the winter and summer. The O3 concentration trends are in line with the published research on premature mortality about 1200 annually for Karnataka state for chronic obstructive pulmonary disease owing to population exposure to O3 beyond its threshold level of NAAQ. Also, the annual rice yield loss is about 2% (~0.1 Metric Tonne) with the AOT 40 to paddy crop for Karnataka state. Thus, O3 diurnal to seasonal trends serve the first-hand information to local authorities for the action plan of air quality management taking into consideration O3 precursors, wind speed, and wind direction.

Pune City’s Microclimate: An Assessment of Selected Local Climate Zones Using ENVI-Met

The urban fabric of Pune city has undergone massive changes due to diversifying urban functionalities. The study attempts to identify different urban morphological landscapes within the city based on Local Climate Zones (LCZs) configurations. After identification of urban morphology in accordance with the LCZ scheme, the microclimatic simulations for the performance and attenuation of the meteorological parameters that is air temperature (Ta), relative humidity (RH), mean radiant temperature (Tmrt), and wind speed (WS) was accomplished through ENVI-met during the warmest month for the city. The urban landscapes characteristics similar to the proposed LCZ configuration and to collect field data for the chosen meteorological parameters ground survey was done. In addition to field observations, climatological normals and long-term trends for the meteorological parameters were also analyzed. The simulation results revealed that barren lands are warmer (by ~18℃) than the compact city core due to the heat sink effect. For Ta and RH the simulation is more reliable in case of open LCZs (R2~0.9) compared to compact LCZs (R2~0.5). With changing LCZs, there is no significant change in the simulation except continuous underestimation of the Tmrt and the role of wind flow in modifying Tmrt was noticed.

Using deep learning to unravel the structural evolution of block-scale green spaces in urban renewal

Most urban green space (UGS) studies focus on city-level analysis, which is not conducive to urban renewal. We developed a new deep learning approach for characterising the structural evolution of block-scale UGS in urban renewal scenarios. Taking Beijing and Shanghai as models, we first segmented the study areas into block-scale grids and trained a random forest model based on a local climate zone framework to identify the spatial morphological classes of the grids. Second, we established a VGG16-based deep learning model to identify UGS structural types. Finally, we examined the associated patterns of grid spatial morphology and UGS structure under different renewal scenarios. We found that six distinct types of UGS structure are associated with different urban morphological classes. Spatial and temporal variations of UGS structures were observed in terms of direction of conversion, activity level, and extent of improvement to service level. Holistic and micro-renewal approaches drove positive evolution of block-scale UGS structures mainly by reducing building density and via precise optimization, respectively. Our results provide a reference for urban renewal policies and planning guidelines for block-scale UGSs in high-density cities.