Local Climate Zone Mapping Research Articles

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

ABSTRACT 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 livability in urban areas. Ultimately, this study paves the way for more comprehensive and effective strategies in addressing the challenges posed by urban microclimates.

Local Climate Zone Classification Using Daytime Zhuhai-1 Hyperspectral Imagery and Nighttime Light Data

The tremendous advancement of cities has caused changes to the urban subsurface. Urban climate problems have become increasingly prominent, especially with regard to the intensification of the urban heat island (UHI) effect. The local climate zone (LCZ) is a new quantitative method for analyzing urban climate that is based on the kind of urban surface and can effectively deal with the problem of the hazy distinction between urban and rural areas in UHI effect research. LCZs are widely used in regional climate modeling, urban planning, and thermal comfort surveys. Existing large-scale LCZ classification methods usually use visual features of optical images, such as spectral and textural features. There are many problems with hyperspectral LCZ extraction over large areas. LCZ is an integrated concept that includes features of the geography, society, and economy. Consequently, it makes sense to consider the characteristics of human activity and the visual features of the images to interpret them accurately. ALOS_DEM data can depict the city’s physical characteristics; however, images of nighttime lights are crucial indicators of human activity. These three datasets can be used in combination to portray the urban environment. Therefore, this study proposes a method for fusing daytime and nighttime data for LCZ mapping, i.e., fusing daytime Zhuhai-1 hyperspectral images and their derived feature indices, ALOS_DEM data, and nighttime light data from Luojia-1. By combining daytime and nighttime information, the proposed approach captures the temporal dynamics of urban areas, providing a more complete representation of their characteristics. The integration of the data allows for a more refined identification and characterization of urban land cover. It comprehensively integrates daytime and nighttime data, exploits synergistic information from multiple sources, and provides higher accuracy and resolution for LCZ mapping. First, we extracted various features, namely spectral, red-edge, and textural features, from the Zhuhai-1 images, ALOS_DEM data, and nighttime light data from Luojia-1. Random forest (RF) and XGBoost classifiers were used, and the average impurity reduction method was employed to assess the significance of the variables. All the input variables were optimized to select the best combination of variables. The results from a study of the 5th ring road area of Beijing, China, revealed that the technique achieved LCZ mapping with good precision, with a total accuracy of 87.34%. In addition, to examine and contrast the effects of various feature indices on the LCZ classification accuracy, feature combination methods were used. The results of the study showed that the accuracies of LCZ classification in terms of spectral and textural were improved by 2.33% and 2.19% using the RF classifier, respectively. The radiation brightness value (RBV) (GI value = 0.0212) attained the classification’s highest variable importance value; the DEM also produced a high GI value (0.0159), indicating that night lighting and landform features strongly influence LCZ classification.

Developing a CNN-based, block-scale oriented Local Climate Zone mapping approach: A case study in Guangzhou

The limited available urban land in megacities is represented by large areas of impervious underlying surfaces and buildings, which are expressed by the basic urban fabric unit of the “city block.” The concept of the Local Climate Zone (LCZ) scheme is capable of quantifying the combined influences that different urban elements have on block-scale local climates, rendering block-scale LCZ classification and block-scale-oriented LCZ map production crucial for climate-conscious urban planning and design. Taking the city of Guangzhou as a case study, deep learning was combined with the Convolutional Neural Network (CNN) to perform effective image recognition, vector cutting, and automatic area statistics based on multilevel road networks. Road network division, block area processing, characteristic parameter calculation, and LCZ classification were integrated to automatically generate block-scale-oriented LCZ maps, which was achieved via threshold contrast using the recommended parameter intervals of the calculated characteristic parameters. Basic land surface temperature (LST) data for summer to winter in Guangzhou were then applied, and correlation analysis was conducted between the block-scale LST vector and LCZ characteristic parameters. The results demonstrate the applicability and potential of the CNN algorithm for urban climate analysis. The developed CNN-based LCZ mapping approach prioritizes “block” scale analysis and typical characteristic parameters in each block are obtained with high calculation accuracy. This study contributes to guiding parameterized climate-sensitive urban planning.

Multiscale mapping of local climate zones in Tokyo using airborne LiDAR data, GIS vectors, and Sentinel-2 imagery

ABSTRACT Multisource remote sensing and geographic information system (GIS) data have contributed powerfully to the large-scale automated mapping of local climate zones (LCZs). However, the accessibility of high-resolution height data, the applicability of standard thresholds to local contexts, and the dependence of mapping scales have limited LCZ classification studies. In this study, we combined airborne LiDAR data, Sentinel-2 imagery, and GIS vector (buildings and roads) data to develop a multiscale automated LCZ classification scheme in the 23 special wards of Tokyo. Based on the optimized thresholds of seven LCZ properties, GIS-based LCZ mapping was implemented using fuzzy logic classifiers at the block scale and at different grid-cell scales (100 m–1000 m). In addition to assessing accuracy using reference samples, multidate thermal infrared data (Landsat-8 and ASTER data) were used to understand the LCZ-LST (land surface temperature) relationship at multiple scales. The results showed that the overall accuracies of LCZs could be significantly increased by threshold optimization at all scales. Significant differences in LCZs and LSTs among different mapping units were observed. The highest overall accuracy was greater than 80% at the 100-m grid-cell scale. As the size of grid cells increased, the overall accuracy of LCZ classification decreased. For each LCZ, the mean daytime/nighttime LST exhibited more variation by date than by scale. This study provides a promising picture of GIS-based LCZ mapping and LCZ-LST relationships at multiple scales.

Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data

Accurate, rapid, and automatic local climate zone (LCZ) mapping is essential for urban climatology and studies in terms of urban heat islands. Remotely sensed imageries incorporated with machine learning algorithms are widely utilized in LCZ labeling. Nevertheless, large-scale LCZ mapping is still challenging due to the complex vertical structure of underlying urban surfaces. This study proposed a new method of LCZ labeling that uses a random forest classifier and multi-source remotely sensed data, including Sentinel 1A Synthetic Aperture Radar (SAR), Sentinel 2 Multispectral Instrument, and Luojia1-01 night-time light data. In particular, leaf-on and -off imageries and surface thermal dynamics were utilized to enhance LCZ labeling. Additionally, we systematically evaluated how daytime and night-time features influence the performance of the classification procedure. Upon examination, the results for Beijing, China, were confirmed to be robust and refined; the Overall Accuracy (OA) value of the proposed method was 88.86%. The accuracy of LCZs 1–9 was considerably increased when using the land surface temperature feature. Among these, the Producer Accuracy (PA) value of LCZ 3 (compact low-rise) significantly increased by 16.10%. Notably, it was found that NTL largely contributed to the classification concerning LCZ 3 (compact low-rise) and LCZ A/B (dense trees). The performance of integrating leaf-on and -off imageries for LCZ labeling was better than merely uses of leaf-on or -off imageries (the OA value increased by 4.75% compared with the single use of leaf-on imagery and by 3.62% with that of leaf-off imagery). Future studies that use social media big data and Very-High-Resolution imageries are required for LCZ mapping. This study shows that combining multispectral, SAR, and night-time light data can improve the performance of the random forest classifier in general, as these data sources capture significant information about surface roughness, surface thermal feature, and night-time features. Moreover, it is found that incorporating both leaf-on and leaf-off remotely sensed imageries can improve LCZ mapping.

Integrating satellite and street-level images for local climate zone mapping

Timely and accurate local climate zone (LCZ) classification maps are valuable for urban climate studies. The integration of remote sensing and street-level images is promising to produce high-quality LCZ maps, since the former can efficiently capture the information of landscapes on a large-scale while the latter include ground-level details. However, due to their significant differences in spatial distributions and capture views, as well as existing sampling issues of street-level images, how to fuse them effectively is challenging and remains an uncharted research area. To address these issues and fill the gap, this study proposes an effective method to integrate satellite and street-level images for LCZ mapping. Additionally, a simple yet effective street-level image sampling method is proposed. Extensive experiments have been performed and the results demonstrate the effectiveness of the proposed data fusion method and also confirm the usefulness of fusing street-level images with satellite images in enhancing the performance of LCZ mapping. Moreover, the proposed sampling method can increase data representativeness and avoid data redundancy, thus significantly reducing the number of required images while retaining high classification accuracy. To the best of our knowledge, this study is the first attempt to integrate cross-view satellite and street-level images for LCZ mapping. The study and proposed methods can contribute to the development of multi-source data fusion for LCZ map production and further benefit urban climatic research.

Investigating the Spatial Heterogeneity of Urban Heat Island Responses to Climate Change Based on Local Climate Zones

Global warming and the urban heat island (UHI) phenomenon have significant impacts on human activities, against which it is necessary to develop effective coping strategies. Based on the local climate zone (LCZ) system, this study used the land-cover and surface temperature data on the Chang–Zhu–Tan (CZT) urban agglomeration in China in 2006, 2010, 2016, and 2020 to analyze the impact of climate change on the land surface temperature (LST) under different land-cover types. The results illustrate that the LCZ map generated on the basis of the improved World Urban Database and Access Portal Tools (WUDAPT) is more accurate and efficient than the traditional method. The accuracy is increased by more than 15%. From 2006 to 2020, the main built-up types in the CZT urban agglomeration were the sparsely built, the large low-rise, and the compact mid-rise types. The low-plant type represents the most significant proportion of the natural types, followed by the water and the dense-tree types. The built-up types in the CZT urban agglomeration tend to be the high-rise, dense, and industrial types. Urban construction land is taken mainly from the sparsely built type of land. The average LST of the large low-rise and heavy-industry zones is significantly higher than the average LST of the three cities. The average LST values for the water and dense-tree zones are significantly lower than the other average LST values. The LST is stable in each LCZ, showing little correlation with the size of the LCZ area. Compact low-rise land use is ineffective against climate warming and inhibits economic growth. Compact high-rise and open high-rise land can not only effectively deal with climate warming but can also significantly stimulate economic growth. This paper helps us to understand the effect of land cover on climate warming and the economic benefits of LCZs in the CZT urban agglomeration and provides strategies to optimize the use of land resources.

Towards better exploiting object-based image analysis paradigm for local climate zones mapping

Local Climate Zones (LCZs) have demonstrated significant promise in urban climate research. Although an LCZ consists of diverse geographic objects, most object-based LCZ mapping research encounters difficulty and seems to have no advantage due to the huge intra-class differences of objects within an LCZ. To address this problem, we propose a new object-based LCZ mapping framework based on multi-level features, which consist of information from pixel and region, bridging adjacent objects through region-level features while preserving original image features. As only using remote sensing imagery makes it hard to fully depict urban form, we modified urban morphological parameters (UMPs) to help with mapping, including sky view factor (SVF), building surface fraction (BSF), and permeable surface fraction (PSF). In addition, a refined sampling strategy is proposed for object-based LCZ mapping to avoid unrepresentative objects. Experiments were conducted in three cities (Hong Kong, Nanjing, and Nanchang), and the results showed that the accuracy of built LCZ types (OAbu) with modified UMPs and region-level features performed best, since they performed about 20% better than those without UMPs and region-level features. Furthermore, compared with patch-based methods, it is demonstrated that the object-based strategy can more appropriately depict the boundary of an LCZ. With further analysis of results at different scales and with features extracted from different region sizes, we found that built types benefit more than natural types at fine scales, and the region size of 17 × 17 is satisfied. In fact, we expect this study would directly reverse the unfavorable situation of object-based image analysis (OBIA) paradigm in LCZ mapping.

Impacts of Urban Development between 2002 and 2022 on the Effects of Sea Breezes in Sendai, Japan—Analyzing Heat Balance Mechanism in Urban Space

Sea breezes are important in a coastal urban climate; however, the impact of urban development on the effects of sea breezes, which decrease air temperature and increase humidity, has not been understood quantitatively. To quantitatively evaluate this impact in Sendai, Japan over the past twenty years, this study analyzed the heat balance mechanisms in urban spaces based on the simulation results of the Weather Research and Forecasting (WRF) model coupled with Local Climate Zone (LCZ) maps. Compared to the observation data on air temperature, specific humidity, and wind in August 2002, results of the numerical simulation, using the 2002 LCZ map and the meteorological conditions of August 2002, confirmed that the WRF model could reproduce meteorological factors well. Thereafter, two numerical simulations using the LCZ maps from 2002 and 2022 were conducted based on the same meteorological condition, from 25 July to 1 September 2008, to extract the impact of urban development on the effects of sea breeze. Consequently, when land use changed from urban built-up land to natural land cover, both the effects of sea breeze—decreasing air temperature and increasing humidity—decreased. Additionally, increases in LCZ 3 (compact low rise), mainly from LCZ 6 (open low rise) and LCZ 9 (sparsely built), decreased the effects of sea breeze (decreasing air temperature and increasing humidity) by 5% and 10%, respectively, in areas around Sendai Station. This was because the consumption of the sea breeze’s potential to decrease air temperature and increase humidity increased and the wind speed of sea breezes decreased in the windward areas of Sendai Station. These results provide new insights into the impact of urban development on the effects of sea breeze and quantitatively reveal changes in the effects of sea breeze.

Downscaling mapping method for local climate zones from the perspective of deep learning

Increased attention has been paid to refining Local Climate Zone (LCZ) maps. However, the mapping process is difficult because of image resolution limitations and the lack of detailed planning data. To our knowledge for the first time, to promote the application of refined LCZ maps in urban climate research, we have proposed the downscaling mapping process by by integrating GIS workflow and remote sensing workflow. The results have shown that: (1) By adjusting the scale and input of the deep learning model, the LCZ basemap achieved the highest overall accuracy of 83.64% and the highest data matching degree of 0.71. (2) Visual interpretation showed that the overall accuracy of LCZ map at all scales is >70%, and the highest overall accuracy of 84.44% is achieved at 20 m scale. (3) Usefulness assessment showed that the LST levels of LCZs significantly differ at all scales (p < 0.01). The 160 m scale was the most suitable for analysis of the thermal characteristics of urban landscapes. This downscaling mapping method will help urban planners in developing urban climate models to determine the impact mechanisms of urban development and support progress in sustainable urban development efforts.

Local Climate Zones to Identify Surface Urban Heat Islands: A Systematic Review

The Land Surface Temperature (LST) obtained by remote sensing data is an essential indicator for analyzing the Surface Urban Heat Island (SUHI). A factor contributing to its occurrence is the reflections of the different materials in urban and rural areas, which significantly affect the energy balance near the surface. Therefore, recent studies have increasingly used the Local Climate Zones (LCZs) classification system to discriminate those urban areas. Therefore, our study aims to perform a systematic review using the PRISMA method of LCZ classification applied to understand the LST and the SUHI phenomenon. At first, 10,403 related articles were found in the scientific literature which, after passing through filtering stages, resulted in 51 that were further analyzed. Our results showed that these articles were very recent, beginning in 2016, and present an increasing trend. Even though all articles reviewed somehow try to understand the Urban Heat Island phenomenon, in more than half of them, the SUHI calculation method was not mentioned. China was the country with the most studies. Images from Landsat and TERRA/AQUA sensors appeared in 82% of the studies. The method that appears the most to LCZ definitions is from the World Urban Database. Finally, we conclude that LCZ mapping methodologies were accurate enough for SUHI studies considering the current climatic changes. Therefore, this systematic review can help new studies on SUHI identification through LCZ in different world areas using remote sensing data to estimate the LST.

Insights into the Effect of Urban Morphology and Land Cover on Land Surface and Air Temperatures in the Metropolitan City of Milan (Italy) Using Satellite Imagery and In Situ Measurements

With a concentration of people, activities, and infrastructures, urban areas are particularly vulnerable to the negative effects of climate change. Among others, the intensification of the Urban Heat Island (UHI) effect is leading to an increased impact on citizen health and the urban ecosystem. In this context, this study aims to investigate the effect of urban morphology and land cover composition—which are established by exploiting the Local Climate Zone (LCZ) classification system—on two urban climate indicators, i.e., Land Surface Temperature (LST) and air temperature. The study area is the Metropolitan City of Milan (northern Italy). LCZ and LST maps are derived by leveraging satellite imagery and building height datasets. Both authoritative and crowdsourced in situ measurements are used for the analysis of air temperature. Several experiments are run to investigate the mutual relation between LCZ, LST, and air temperature by measuring LST and air temperature patterns in different LCZs and periods. Besides a strong temporal correlation between LST and air temperature, results point out vegetation and natural areas as major mitigating factors of both variables. On the other hand, higher buildings turn out to increase local air temperature while buffering LST values. A way lower influence of building density is measured, with compact building areas experiencing slightly higher air temperature yet no significant differences in terms of LST. These outcomes provide valuable tools to urban planners and stakeholders for implementing evidence-based UHI mitigation strategies.

Investigating urban heat-related health risks based on local climate zones: A case study of Changzhou in China

Assessing heat-related health risks is important for sustainable urban development. Although fine-scale information (e.g., at the community/neighborhood or city block level) is ideal for identifying and mitigating these risks, previous studies have preferred to work at the administrative unit level. High-resolution Local Climate Zone (LCZ) maps, i.e., maps of urban “zones” with different microclimates, could help to standardize the analyzing units. In this study, we proposed an LCZ-based risk assessment approach for this purpose. First, an LCZ map of the study site (Changzhou, China) was generated using multisource big data and machine-learning techniques. Next, Crichton's Risk Triangle framework, based on the hazard-exposure-vulnerability risk components, was employed to estimate heat-related health risks. Finally, the relationship between LCZ types and heat-related health risk levels was quantitatively analyzed in detail. The results indicated that at least 60% of LCZ1-5 (compact high-/mid-/low-rise, open high-/mid-rise areas) were designated as high-risk areas, while heat hazard mitigation and climate adaptation strategies in urban planning would benefit more from LCZ 6 (open low-rise). This study, based on the LCZ concept, shows the risk difference at the community level, and can be used for informing and implementing area-level urban planning strategies. It could contribute to global heat-related health risk analysis, since the LCZ is a globally consistent system for urban microclimate analysis.

SAR and Multi-Spectral Data Fusion for Local Climate Zone Classification with Multi-Branch Convolutional Neural Network

The local climate zone (LCZ) scheme is of great value for urban heat island (UHI) effect studies by providing a standard classification framework to describe the local physical structure at a global scale. In recent years, with the rapid development of satellite imaging techniques, both multi-spectral (MS) and synthetic aperture radar (SAR) data have been widely used in LCZ classification tasks. However, the fusion of MS and SAR data still faces the challenges of the different imaging mechanisms and the feature heterogeneity. In this study, to fully exploit and utilize the features of SAR and MS data, a data-grouping method was firstly proposed to divide multi-source data into several band groups according to the spectral characteristics of different bands. Then, a novel network architecture, namely Multi-source data Fusion Network for Local Climate Zone (MsF-LCZ-Net), was introduced to achieve high-precision LCZ classification, which contains a multi-branch CNN for multi-modal feature extraction and fusion, followed by a classifier for LCZ prediction. In the proposed multi-branch structure, a split–fusion-aggregate strategy was adopted to capture multi-level information and enhance the feature representation. In addition, a self channel attention (SCA) block was introduced to establish long-range spatial and inter-channel dependencies, which made the network pay more attention to informative features. Experiments were conducted on the So2Sat LCZ42 dataset, and the results show the superiority of our proposed method when compared with state-of-the-art methods. Moreover, the LCZ maps of three main cities in China were generated and analyzed to demonstrate the effectiveness of our proposed method.

Multicascaded Feature Fusion-Based Deep Learning Network for Local Climate Zone Classification Based on the So2Sat LCZ42 Benchmark Dataset

A detailed investigation of the microclimate is beneficial for optimizing the urban inner/spatial pattern to enhance thermal comfort or even reduce heatwave disasters, whereas accurately classifying local climate zones (LCZs) severely restricts analysis of thermal characterization. Generally, deep learning-based approaches are effective for adaptive LCZ mapping, yet they often have poor accuracy because inadequate cascade feature extraction patterns may not adapt to the fuzzy LCZ boundaries produced by intricate urban textures, especially when using large-scale datasets. To address these issues, we propose a novel CNN model in which we design a strategy that incorporates a triple feature fusion pattern to enhance LCZ recognition based on the So2sat LCZ 42 large-scale annotated dataset. The approach connects multilayer cascaded information to participate in category judgment, which avoids the loss of effective feature information via continuous cascade transformation as much as possible. The results show that the overall accuracy and kappa coefficient of the proposed model reach 0.70 and 0.68, respectively, manifesting an improvement of approximately 4.47% and 6.25% over advanced LCZ classification approaches. In particular, the accuracy of the proposed approach improves even further after the fusion structure or layer depth is partially removed or reduced, respectively. Finally, we discuss several items, including the effectiveness of different parameters and cascaded feature fusion patterns, the superiority of multilayer cascade feature fusion, the mapping impact of seasons and cloud cover, and even some other issues in LCZ mapping. This article will facilitate improvements in the research precision of urban thermal environments.

Exploring How Street-Level Images Help Enhance Remote-Sensing-Based Local Climate Zone Mapping

Exploring How Street-Level Images Help Enhance Remote-Sensing-Based Local Climate Zone Mapping

Urban Heat Island Mapping Based on a Local Climate Zone Classification : a Case Study in Strasbourg City, France

The Urban Heat Island (UHI) effect is characterised by higher temperatures in cities than in rural surroundings. This phenomenon leads to increased health risks in urban dwellers, particularly in the context of global climate change. It is essential to consider its spatial variability to propose efficient UHI mitigation strategies. The Local Climate Zone (LCZ) scheme is a climate-based classification that can accurately capture UHI intensities according to the urban area characteristics. In this study, an LCZ classification has been established for the city of Strasbourg by using a vector-based method that relies on a large vector database composed of land cover and cadastral parcels data. LCZ polygons are digitized from cadastral maps, then the different LCZ parameters are calculated for each of them. Six of the ten LCZ parameters proposed in the literature have been obtained. New criteria have been added to improve the classification, i.e. a vegetation parameter (VgSF) and a compactness index (CI). A given LCZ class is then assigned to each polygon using a trapezoidal fuzzy logic model, which is completed by a decision tree. The acquired final LCZ map shows that the developed vector-based method permits obtaining relevant LCZ classification. The LCZ parameters values are subsequently used to determine a multiple linear regression (MLR) aiming to get a UHI intensity for each LCZ polygon. The resulting UHI map of the Eurométropole de Strasbourg (EMS) accurately illustrates the strong spatial heterogeneity of the phenomenon.

Spatial and temporal characteristics of nighttime UHII based on local climate zone scheme using mobile measurement-A case study of Changsha

The local climate varies with time and space because of diversified urban patterns across the complex urban region. Achieving accurate local climatic characteristics by efficient field survey method will contribute to the theoretical and technical reference for urban heat island intensity (UHII) alleviation and eco-friendly environment construction. This study takes the main urban area of Changsha as an example, and focuses on the local-scale UHII characteristics under the Local Climate Zone (LCZ) scheme. An efficient method integrating both the mobile measurement and the GIS-based method is proposed and applied. By applying temporal corrections to the obtained mobile data, the local UHII along the mobile route are expressed. Then based on the UHII values along the mobile route and the LCZ map, a stepwise multiple regression model was built to predict the spatial distributions of UHII within the whole main urban area of Changsha. Results indicated that the built-up LCZs accounted for 42.07% and the land cover LCZs took up 57.93%. The monthly average UHII of Changsha ranged from 1 °C to 4 °C and the core UHII overlayed the city center. The mean UHII of different LCZs ranged from 0.6 °C to 4.8 °C, and the high UHII was more likely to be observed in the compact LCZs. The proposed methodology could be useful for supporting local scale UHII adaptation and mitigation efforts, thus encouraging a more sustainable built environment.

Impact of LCZs spatial pattern on urban heat island: A case study in Wuhan, China

Based on World Urban Database and Access Portal Tools (WUDAPT), this study mapped Local Climate Zones (LCZs) in Wuhan in 2020 and detected surface urban heat island intensity (SUHII) within LCZs in day/night of all seasons through Moderate Resolution Imaging Spectroradiometer (MODIS) land products. Four landscape metrics were used to explore spatial heterogeneous effects of LCZs on SUHII by Geographically weighted regression (GWR) model. Results showed that LCZs map can be used to describe the thermal environment in Wuhan. SUHII difference among LCZs was stronger in spring and summer, while less in winter. SUHII difference of the same LCZ was found in three districts in Wuhan. SUHII of most other built classes in Hankou were higher than Wuchang and Hanyang. SUHII within LCZ G in Wuchang was the lowest in daytime of all seasons but the highest at night of all seasons. LCZ 2, LCZ 4, LCZ 5, LCZ 8 and LCZ H had strong positive correlation with SUHII and LCZ D and LCZ G had significant negative correlation with SUHII. Percentage of landscape (PLAND) of LCZs had stronger impact on SUHII than Contagion index (CONTAG), Shannon's diversity index (SHDI) and Area-weighted mean shape index (SHAPE_AM) of LCZs. PLAND of LCZ 8 had strong impact on increasing SUHII in daytime of all seasons, while PLAND of LCZ 2, LCZ 4 and LCZ 5 showed obvious influence on increasing SUHII at night of all seasons. LCZ D had strong impacts on decreasing SUHII in daytime of all seasons and less cooling impacts at night of all seasons. LCZ G showed strong cooling impacts on SUHII in day/night time of all seasons except winter nighttime. SHDI richness of LCZs in analysis unit helped alleviate SUHII. High CONTAG of LCZ with high LST increase SUHII, while High CONTAG of LCZ with low LST decrease SUHII. Very regular shape of LCZ built classes increased the SUHII, while less fragmented of LCZ land cover classes decreased the SUHII.

Impact of urban parameterization and integration of WUDAPT on the severe convection

Amplified rates of urban convective systems pose a severe peril to the life and property of the inhabitants over urban regions, requiring a reliable urban weather forecasting system. However, the city scale's accurate rainfall forecast has constantly been a challenge, as they are significantly affected by land use/ land cover changes (LULCC). Therefore, an attempt has been made to improve the forecast of the severe convective event by employing the comprehensive urban LULC map using Local Climate Zone (LCZ) classification from the World Urban Database and Access Portal Tools (WUDAPT) over the tropical city of Bhubaneswar in the eastern coast of India. These LCZs denote specific land cover classes based on urban morphology characteristics. It can be used in the Advanced Research version of the Weather Research and Forecasting (ARW) model, which also encapsulates the Building Effect Parameterization (BEP) scheme. The BEP scheme considers the buildings' 3D structure and allows complex land–atmosphere interaction for an urban area. The temple city Bhubaneswar, the capital of eastern state Odisha, possesses significant rapid urbanization during the recent decade. The LCZs are generated at 500 m grids using supervised classification and are ingested into the ARW model. Two different LULC dataset, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and WUDAPT derived LCZs and initial, and boundary conditions from NCEP GFS 6-h interval are used for two pre-monsoon severe convective events of the year 2016. The results from WUDAPT based LCZ have shown an improvement in spatial variability and reduction in overall BIAS over MODIS LULC experiments. The WUDAPT based LCZ map enhances high-resolution forecast from ARW by incorporating the details of building height, terrain roughness, and urban fraction.