Spatiotemporal Evolution Characteristics of Urban Land Surface Temperature Based on Local Climate Zones in Xi’an Metropolitan,China

Local climate zones(LCZs)are an effective nexus linking internal urban structures to the local climate and have been widely used to study urban thermal environment.However,few studies considered how much the temperature changed due to LCZs transformation and their synergy.This paper quantified the change of urban land surface temperature(LST)in LCZs transformation process by combining the land use transfer matrix with zonal statistics method during 2000–2019 in the Xi’an metropolitan.The results show that,firstly,both LCZs and LST had significant spatiotemporal variations and synchrony.The period when the most LCZs were converted was also the LST rose the fastest,and the spatial growth of the LST coincided with the spatial expansion of the built type LCZs.Secondly,the LST difference between land cover type LCZs and built type LCZs gradually widened.And LST rose more in both built type LCZs transferred in and out.Finally,the Xi’an-Xianyang profile showed that the maximum temperature difference between the peaks and valleys of the LST increased by 4.39℃,indicating that localized high temperature phenomena and fluctuations in the urban thermal environment became more pronounced from 2000 to 2019.

Impact of Local Climate Zones on the Urban Heat and Dry Islands in Beijing:Spatial Heterogeneity and Relative Contributions

Based on the building height and density data on a 100-m resolution,hourly 2-m temperature and humidity data at83 automatic weather stations,and gridded local climate zone(LCZ)data on a 120-m resolution in urban Beijing in2020,this study first employs the semivariogram combined with building parameters to calculate spatial correlations and has identified an LCZ grid resolution of 500 m suitable for best usage of the available observation data.Then,how the spatially heterogeneous LCZs affect and contribute to the canopy urban heat island intensity(UHII)and urban dry island intensity(UDII)are quantitatively investigated.It is found that UHII is high in winter and low in summer with a unimodal diurnal variation while UDI is low in winter but high in summer with a bimodal diurnal variation.The LCZ with compact mid-rise(open high-rise)buildings exhibits the highest UHII(UDII),followed by the compact high-rise(compact low-rise),while the LCZ of scattered trees presents both the lowest UHII and the lowest UDII.The most significant difference in the UHII(UDII)among the nine LCZ types in the urban area of Beijing is2.62℃(1.1 g kg^(-1)).Area-weighted averaging analysis reveals that the open mid-rise LCZ is the most significant contributor to the UHII(UDII),immediately followed by compact mid-rise(open low-rise),with the least contribution from bare rock or paved(scattered trees).The results also indicate that beyond the intrinsic physical properties of the LCZs of a city,their area proportions cannot be overlooked in evaluating their impact on the UHI and UDI.These quantitatively findings could help urban planners to create a livable urban climate and environment by adjusting the relevant land use.

Mapping urban carbon emissions in relation to local climate zones:Case of the building sector in Bangkok Metropolitan Administration,Thailand

This study focuses on carbon emissions of the building sector in relation to local climate zone(LCZ)classification,concentrating on two major parts.First,we estimated carbon emissions in the building sector,which were cal-culated for weekdays and weekends real-time daily energy consumption patterns.The estimations were divided into direct(from petroleum products consumption)and indirect emissions(from electricity consumption).Sec-ond,we examined urban carbon emissions mapping in relation to LCZ.Bangkok Metropolitan Administration(BMA)was used as the case study and 2016 as the base year for examination.The results illustrate that indirect emissions in Bangkok can be up to ten times higher than direct emissions.The analysis indicates that LCZ,such as compact high-rise,large low-rise,light industry,and warehouse zones had a relatively higher carbon emission intensity than others.Additionally,we identified that the compact high-rise zone has the highest indirect emission intensity,while the light industry and warehouse zone have the greatest direct emission intensity.These results provide insights into the dynamics of carbon emission characteristics in the building sector and the methodology purported here can be used to support low carbon city planning and policymaking in Bangkok.

Local climate zone mapping using remote sensing:a synergetic use of daytime multi-view Ziyuan-3 stereo imageries and Luojia-1 nighttime lightdata

The local climate zone(LCZ)scheme has been widely utilized in regional climate modeling,urban planning,and thermal comfort investigations.However,existing LCz classification methods face challenges in characterizing complex urban structures and human activities involving local climatic environments.In this study,we proposed a novel LCZ mapping method that fully uses space-borne multi-view and diurnal observations,i.e.daytime Ziyuan-3 stereo imageries(2.1 m)and Luojia-1 nighttime light(NTL)data(130 m).Firstly,we performed land cover classification using multiple machine learning methods(i.e.random forest(RF)and XGBoost algorithms)and various features(i.e.spectral,textural,multi-view features,3D urban structure parameters(USPs),and NTL).In addition,we developed a set of new cumulative elevation indexes to improve building roughness assessments.The indexes can estimate building roughness directly from fused point clouds generated by both along-and across-track modes.Finally,based on the land cover and building roughness results,we extracted 2D and 3D USPs for different land covers and used multi-classifiers to perform LCZ mapping.The results for Beijing,China,show that our method yielded satisfactory accuracy for LCZ mapping,with an overall accuracy(OA)of 90.46%.The overall accuracy of land cover classification using 3D USPs generated from both along-and across-track modes increased by 4.66%,compared to that of using the single along-track mode.Additionally,the OA value of LCZ mapping using 2D and 3D USPs(88.18%)achieved a better result than using only 2D USPs(83.83%).The use of NTL data increased the classification accuracy of LCZs E(bare rock or paved)and F(bare soil or sand)by 6.54%and 3.94%,respectively.The refined LCZ classification achieved through this study will not only contribute to more accurate regional climate modeling but also provide valuable guidance for urban planning initiatives aimed at enhancing thermal comfort and overall livabillity in urban areas.Ultimately,this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates.

Enhanced geographic information system-based mapping of local climate zones in Beijing, China

The vague urban-rural dichotomy severely restricts effective comparisons and communications among urban heat island studies.A local climate zone(LCZ) scheme has therefore been developed to classify urban and natural landscapes in a standardized and universal manner. Despite LCZ mapping efforts in worldwide cities, this study attempts to propose an enhanced geographic information system-based workflow to enable the hierarchical classification of LCZs with fewer indicators but higher accuracies while considering supplementary classes and subclasses. Specifically, five morphological and coverage indicators that were easily obtained and well differentiated among LCZs were derived from a city street map and satellite images, and 25 LCZs(including 16 standard, 3 supplementary, and 6 sub-classified zones) were determined at a block-level according to the indicator hierarchy and criteria. The method was performed over Beijing, China, and evaluations by field surveys and google earth images showed a high accuracy with little noise and sharp boundaries, outperforming the widely-used remote sensing-based method of the World Urban Database and Access Portal Tools, particularly in terms of building height and heavy industry. Results also demonstrate that the Beijing core was dominated by open(including extremely open) mid-rise buildings(28.7%) and open lowrise buildings(12.8%), forming an inner-low-middle-high-outer-low annular building-height pattern. Significant land surface temperature differences were detected among the LCZs, where the low-rise and compact LCZs had higher temperatures than the mid-/high-rise and open LCZs during daytime, and subclasses LCZ XB/C/D(LCZ XE/F) generated lower(higher) temperatures than their parent classes in May. This method was proposed to augment the LCZ mapping system and further support applications(e.g., urban planning/management and climate/weather modeling) in high-density cities similar to Beijing.

Mapping and analyzing the local climate zones in China’s 32 major cities using Landsat imagery based on a novel convolutional neural network

The Local Climate Zone(LCZ)scheme provides researchers with a standard method to monitor the Urban Heat Island(UHI)effect and conduct temperature studies.How to generate reliable LCZ maps has therefore become a research focus.In recent years,researchers have attempted to use Landsat imagery to delineate LCZs and generate maps worldwide based on the World Urban Database and Access Portal Tools(WUDAPT).However,the mapping results obtained by the WUDAPT method are not satisfactory.In this paper,to generate more accurate LCZ maps,we propose a novel Convolutional Neural Network(CNN)model(namely,LCZ-CNN),which is designed to cope with the issues of LCZ classification using Landsat imagery.Furthermore,in this study,we applied the LCZ-CNN model to generate LCZ mapping results for China’s 32 major cities distributed in various climatic zones,achieving a significantly better accuracy than the traditional classification strategies and a satisfactory computational efficiency.The pro-posed LCZ-CNN model achieved satisfactory classification accuracies in all 32 cities,and the Overall Accuracies(OAs)of more than half of the cities were higher than 80%.We also designed a series of experiments to comprehensively analyze the proposed LCZ-CNN model,with regard to the transferability of the network and the effectiveness of multiseasonal information.It was found that the first convolutional stage,corresponding to low-level features,shows better transferability than the second and third convolutional stages,which extract high-level and more image-or task-oriented features.It was also confirmed that the multi-seasonal information can improve the accuracy of LCZ classifica-tion.The thermal characteristics of the different LCZ classes were also analyzed based on the mapping results for China’s 32 major cities,and the experimental results confirmed the close relationship between the LCZ classes and the magnitude of the Land Surface Temperature(LST).

Deep learning-based local climate zone classification using Sentinel-1 SAR and Sentinel-2 multispectral imagery

As a newly developed classification system,the LCZ scheme provides a research framework for Urban Heat Island(UHI)studies and standardizes the worldwide urban temperature observa-tions.With the growing popularity of deep learning,deep learning-based approaches have shown great potential in LCZ mapping.Three major cities in China are selected as the study areas.In this study,we design a deep convolutional neural network architecture,named Residual combined Squeeze-and-Excitation and Non-local Network(RSNNet),that consists of the Squeeze-and-Excitation(SE)block and non-local block to classify LCZ using freely available Sentinel-1 SAR and Sentinel-2 multispectral imagery.Overall Accuracy(OA)of 0.9202,0.9524 and 0.9004 for three selected cities are obtained by applying RSNNet and training data of individual city,and OA of 0.9328 is obtained by training RSNNet with data from all three cities.RSNNet outperforms other popular Convolutional Neural Networks(CNNs)in terms of LCZ mapping accuracy.We further design a series of experiments to investigate the effect of different characteristics of Sentinel-1 SAR data on the performance of RSNNet in LCZ mapping.The results suggest that the combination of SAR and multispectral data can improve the accuracy of LCZ classification.The proposed RSNNet achieves an OA of 0.9425 when integrat-ing the three decomposed components with Sentinel-2 multispectral images,2.44%higher than using Sentinel-2 images alone.

Changing Characteristics of Urban Heat Island Effect in Weihai City

Based on the local climate zoning theory and the observation data of hourly temperature of 22 automatic weather stations from 2012 to 2021, K-means clustering algorithm was used to analyze the daily, monthly, seasonal, annual and spatial variation characteristics of urban heat island effect in Weihai City in the past 10 years. The results showed that in recent 10 years, the average urban heat island intensity was 1.24 ℃, showing a gradual weakening trend of -0.169 3 ℃/10 a;the summer average heat island intensity was 0.86 ℃, showing a gradual weakening trend of -0.047 5 ℃/10 a. The heat island intensity had obvious diurnal variation characteristics, that is, "it was weak in the day and strong at night". In terms of seasonal variation, heat island effect was the weakest in summer, stronger in spring and autumn, and the strongest in winter. The diurnal, seasonal and annual changes of heat island intensity showed a reverse trend to those of temperature. The high-value area of urban heat island was highly consistent with human residential activity areas and industrial and commercial intensive areas, and the extension trend of heat island intensity was the same as the direction of urban development and construction. The "cold island phenomenon" in some offshore areas was related to the geographical location, terrain and the southeast monsoon trend in summer. The adverse effects of urban heat island effect can be reduced by optimizing the types and distribution of vegetation communities, rationally planning and constructing urban water body, promoting green building materials and adjusting shape design, etc.

Mapping the urban natural ventilation potential by hydrological simulation

Urban wind environments are closely related to air pollution and outdoor human comfort.The urban natural ventilation potential(NVP)is an important factor in urban planning and design.However,for ventilation studies on urban scales,neither macroscale numerical simulations(i.e.,WRF,MM5,etc.)nor microscale computational fluid dynamics(CFD)simulations can conduct efficient analyses.Based on the similarity between water flows and airflows,an efficient approach is proposed in this paper to map the urban NVP.Through integrating the urban terrain model,urban form model,and prevailing wind pressure model,an airflow digital elevation model(AF-DEM),which represents the resistance to airflow and can be used for a hydrological simulation,is generated and applied to evaluate the urban airflow patterns under different terrain,urban form and ambient wind conditions.The objective was to develop a simulation platform that can efficiently predict the distribution of natural ventilation corridor and NVP.The stream network calculated through the simulation is regarded as potential ventilation corridors within the city,and an index calculated from the coverage rate of wind corridors(CRW)is proposed for evaluating the relative NVP.Taking Nanjing as a case study,8 AF-DEMs based on different wind directions and wind speed conditions are generated,and their corresponding ventilation corridor maps are constructed.The results are in good agreement with the empirical evidence,indicating that the hydrological model,though a rudimentary approximation of the actual airflows,was effective in revealing the natural ventilation corridor and characterize the relative NVP.Moreover,the implementation of this novel method is simple and convenient,and it has great application potential and value in urban design and management.

Effect of Using Land Use Data with Building Characteristics on Urban Weather Simulations:A High Temperature Event in Shanghai

Land use data with building characteristics are important for modeling the impacts of urban morphology on local climate.In this study,an extreme heat event in Shanghai,China,was simulated by using a WRF/BEP+BEM(Weather Research and Forecasting/Building Effect Parameterization+Building Energy Model)model.We incorporated local climate zone(LCZ)land use data that resolved urban morphology using 10 classes of building parameters.The simulation was compared to a control case based on MODIS(Moderate-resolution Imaging Spectroradiometer)land use data.The findings are as follows:(1)the LCZ data performed better than the MODIS data for simulating 10-m wind speed.An increase in building height led to the wind speed to decrease by 0.6-1.4 m s^(-1)in the daytime and by 0.2-0.7 m s^(-1)at nighttime.(2)High-rise buildings warmed the air by trapping radiation in the urban canyon.This warming effect was partially offset by the cooling effect of building shadows in the day.As a result,the 2-m temperature increased by 0.8℃ at night but only by 0.4℃ during the day.(3)Heterogeneous urban surfaces increased the 50-m turbulent kinetic energy by 0.4 m^(2) s^(-2),decreased the 10-m wind speed by 1.8 m s^(-1)in the daytime,increased the surface net radiation by 45.1 W m^(2)-,and increased the 2-m temperature by 1.5℃ at nighttime.(4)The LCZ data modified the atmospheric circulation between land and ocean.The shadowing effect reduced the air temperature differences between land and ocean and weakened the sea breeze.Moreover,high-rise buildings obstructed sea breezes,restricting their impact to a smaller portion(10 km along the wind direction)of inland areas compared to that with MODIS.