Local Climate Zone Types Research Articles

Is LCZ Enough? Physical Properties, Thermal Environments and Cooling Effects of Green Roofs in High-Density Urban Industrial Blocks

With rapid worldwide urbanization, the urban heat island (UHI) effect is becoming more and more serious. The UHI effect is more intense in industrial areas. Green roofs are an effective way to mitigate UHIs in high-density cities, which calls for thorough examination. This study explored the associations between the block characteristics and block thermal environment in high-density industrial areas based on the widely accepted Local Climate Zone (LCZ) scheme. The pedestrian air temperature comparisons before and after virtual green roof installations presented the cooling effects of green roofs. Thirty-six typical industrial blocks were involved in the study and the simulations were conducted utilizing ENVI-met. The results showed that (1) the air temperature in LCZ4 is significantly lower than those in LCZ2 and LCZ6, but no significant differences were identified between other pairs of LCZ types; (2) the cooling effect of green roofs significantly differs among LCZs, and is associated with sky view factor (SVF), average building area (ABA) and average building shape index (ABSI); (3) in high-density urban areas, additional functional parameters and building-volume indices should be included to better address the physical characteristics, thermal environment, and green roof cooling effect of industrial blocks. This study could improve the validity of LCZ classification for high-density industrial blocks and may provide direct implications for green roof planning.

Defining and Verifying New Local Climate Zones with Three-Dimensional Built Environments and Urban Metabolism

The urban heat island (UHI) effect, where the temperature in an urban area is higher than in the surrounding rural areas, is becoming a major concern. The concept of a Local Climate Zone (LCZ) system was devised to provide an objective framework for UHI research, which allows for a microscale definition of the UHI effect within urban areas by considering ‘urban’ and ‘rural’ as a continuum versus a dichotomy. However, most LCZ types are classified only by surface structure and coverings, which seem irrelevant to climatological and microscale concepts. In addition, microclimate is influenced by urban metabolism related to human activities as well as structural effects, but the LCZ-classification system does not incorporate these functional concepts. Therefore, this study proposes a novel urban-classification system that addresses the limitations of the LCZ concept by quantifying structural and functional elements of the city at the pedestrian level using S-DoT sensors and semantic segmentation techniques. This study holds significance as it suggests a New-LCZ (N-LCZ) system to support the classification framework of highly valid urban types and follow-up studies related to the UHI. Moreover, the N-LCZ offers a regional urban-planning strategy for sustainable development through a more valid classification system.

Sensitivity of Local Climate Zones and Urban Functional Zones to Multi-Scenario Surface Urban Heat Islands

Local climate zones (LCZs) and urban functional zones (UFZs) can intricately depict the multidimensional spatial elements of cities, offering a comprehensive perspective for understanding the surface urban heat island (SUHI) effect. In this study, we retrieved two types of land surface temperature (LST) data and constructed 12 SUHI scenarios over the Guangdong–Hong Kong–Macao Greater Bay Area Central region using six SUHI identification methods. It compared the SUHI sensitivity differences among different types of LCZ and UFZ to analyze the global and local sensitivity differences of influencing factors in the 12 SUHI scenarios by utilizing the spatial gradient boosting trees, geographically weighted regression, and the coefficient of variation model. Results showed the following: (1) The sensitivity of different LCZ and UFZ types to multi-scenario SUHI was significantly affected by differences in SUHI identification methods and non-urban references. (2) In the morning, the shading effect of building clusters reduced the surface urban heat island intensity (SUHII) of some built environment types (such as LCZ 1 (compact high-rise zone) to LCZ 5 (open midrise zone)). The SUHIIs of LCZ E (bare rock or paved zone) and LCZ 10 (industry zone) were 4.22 °C and 3.87 °C, respectively, and both are classified as highly sensitive to SUHI. (3) The sensitivity of SUHI influencing factors exhibited regional variability, with importance differences in the sensitivity of importance for factors such as the impervious surface ratio, elevation, average building height, vegetation coverage, and average building volume between LCZs and UFZs. Amongst the 12 SUHI scenarios, an average of 87.43% and 89.97% of areas in LCZs and UFZs, respectively, were found to have low spatial sensitivity types. Overall, this study helps urban planners and managers gain a more comprehensive understanding of the complexity of the SUHI effect in high-density cities, providing a scientific basis for future urban climate adaptability planning.

Development of an expanded local climate zone scheme to accommodate diversified urban morphological evolution: A case study of Shanghai, China

The local climate zone (LCZ) scheme efficiently associates urban forms with thermal environments. However, confronted with diversified urban fabrics, the initial LCZ scheme has encountered challenges of its restricted classes and intraclass temperature differences. This study aimed to develop an expanded LCZ scheme to accommodate the diversified urban morphological evolution. Thirty-two block prototypes were developed according to the building footprint ratios (BFRs), mean building heights (BHs), and mean sky view factors (SVFs) of the blocks in Shanghai, China. Then, the block prototypes were classified based on their morphological and temperature indices by k-means clustering, an unsupervised clustering method that maximizes the distance between the clusters and minimizes the distance within the cluster. Results indicated that the block prototypes were clustered into 14 classes, the high-rise LCZs were subdivided, and blocks within six classes were not covered by the initial LCZ scheme. Accordingly, an expanded LCZ scheme was developed by introducing four novel LCZ types and adjusting and subdividing the initial LCZ types, which covered 35% more blocks than the initial LCZ scheme. The mean SVF demonstrated the most significant correlation with the temperature of LCZs, whereas the effects of BFR and BH on the temperature depended on LCZ types.

Assessing spatiotemporal population density dynamics from 2000 to 2020 in megacities using urban and rural morphologies

Rapid urbanization has resulted in the substantial population growth in metropolitan areas. However, existing research on population change of the cities predominantly draws on grid statistical data at the administrative level, overlooking the intra-urban variegation of population change. Particularly, there is a lack of attention given to the spatio-temporal change of population across different urban forms and functions. This paper therefore fills in the lacuna by clarifying the spatio-temporal characteristics of population growth in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2020 through the methods of local climate zone (LCZ) scheme and urban–rural gradients. The results showed that: (1) High population density was observed in the compact high-rise (LCZ 1) areas, with a noticeable decline along urban–rural gradients. (2) The city centers of GBA experienced the most significant population growth, while certain urban fringes and rural areas witnessed significant population shrinkage. (3) The rate of growth tended to slow down after 2010, but the uneven development of population-based urbanization was also noticeable, as urbanization and industrialization varied across different LCZ types and cities in GBA. This paper therefore contributes to a deeper understanding of population change and urbanization by clarifying their spatio-temporal contingences at landscape level.

How urban heat island magnifies hot day exposure: Global unevenness derived from differences in built landscape

Urban heat-islands reportedly expose densely populated areas to higher temperatures. However, the magnitude of the impact of extra hot-day exposure (EHDE) and its association with the effects of urbanization on a global scale remain unclear. As local climate zones (LCZs) refine the impact of differences in urban built-type on heat-island effects, this study aimed to quantify the global EHDE caused by the urban heat-island effect based on LCZs and explored the joint impacts of low gross-domestic product and an increasing vulnerable-age population on EHDE. The results showed that EHDE accounted for 48.01 % of overall hot-day exposure. Additionally, despite a significant geographic differentiation among LCZ types with the highest EHDE intensity, they are almost typically building-intensive LCZs. Furthermore, our study revealed regional differences in the structure of the EHDE share in LCZs, which support the adoption of targeted EHDE mitigation strategies.

Quantifying the effect of surrounding spatial heterogeneity on land surface temperature based on local climate zones using mutual information

Local climate zone (LCZ), a landscape classification scheme that segments urban region into 17 distinct zones, has become the standard for analyzing urban thermal environments since 2012. The characteristic features of each LCZ decide the standard regime for their Land Surface Temperature (LST). In heterogeneous cities, the variation in LST is observed for similar but spatially dispersed LCZs and has not yet been analysed by the researchers. The surrounding spatial heterogeneity could be partially responsible for such variation. Hence, we have presented a framework to analyze the LST variation of similar but spatially dispersed LCZs by considering the surrounding LCZ pattern11LCZ pattern represents the group of LCZs surrounding any considered LCZ.details in the study area of Nagpur (India). The framework uses machine learning techniques like random forest (RF), Xgboost, deep learning to confirm that the LST variation is due to surrounding LCZ pattern. Later it indentifies the factors responsible for LST variation in each LCZ type using our proposed mutual information based Adjacent LCZ preference Estimator (ALPE). Deep learning model captures 90–92 % of the neighbourhood heterogeneity responsible for LST variation as compared to RF (0.83- 0.79) and Xgboost (0.86–0.81). The reduction in bias (by 1.05 °C -1.39 °C) is observed while estimating LST incorporating surrounding LCZ pattern. This confirms that the external heterogeneity affects the LST of the corresponding LCZ. Further, the result analysis of ALPE suggests that characteristics of open LCZ types (when they are present in surrounding) highly influence the LST of compact and open LCZs.

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.

A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities

Abstract. Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is nowadays seen as a standard approach for classifying any zone according to a set of urban canopy parameters. While many methods already exist to map the LCZ, only few tools are openly and freely available. This paper presents the algorithm implemented in the GeoClimate software to identify the LCZ of any place in the world based on vector data. Six types of information are needed as input: the building footprint, road and rail networks, water, vegetation, and impervious surfaces. First, the territory is partitioned into reference spatial units (RSUs) using the road and rail network, as well as the boundaries of large vegetation and water patches. Then 14 urban canopy parameters are calculated for each RSU. Their values are used to classify each unit to a given LCZ type according to a set of rules. GeoClimate can automatically prepare the inputs and calculate the LCZ for two datasets, namely OpenStreetMap (OSM, available worldwide) and the BD TOPO® v2.2 (BDT, a French dataset produced by the national mapping agency). The LCZ are calculated for 22 French communes using these two datasets in order to evaluate the effect of the dataset on the results. About 55 % of all areas have obtained the same LCZ type, with large differences when differentiating this result by city (from 30 % to 82 %). The agreement is good for large patches of forest and water, as well as for compact mid-rise and open low-rise LCZ types. It is lower for open mid-rise and open high-rise, mainly due to the height underestimation of OSM buildings located in open areas. Through its simplicity of use, GeoClimate has great potential for new collaboration in the LCZ field. The software (and its source code) used to produce the LCZ data is freely available at https://doi.org/10.5281/zenodo.6372337 (Bocher et al., 2022); the scripts and data used for the purpose of this article can be freely accessed at https://doi.org/10.5281/zenodo.7687911 (Bernard et al., 2023) and are based on the R package available at https://doi.org/10.5281/zenodo.7646866 (Gousseff, 2023).

Comparing Characteristics of the Urban Thermal Environment Based on the Local Climate Zone in Three Chinese Metropolises

Urban landscape has important effects on urban climate, and the local climate zone (LCZ) framework has been widely applied in related studies. However, few studies have compared the relative contributions of LCZ on the urban thermal environment across different cities. Therefore, Beijing, Shanghai, and Shenzhen in China were selected to conduct a comparative study to explore the relationship between LCZ and land surface temperature (LST). The results showed that (1) both the composition and spatial configuration of LCZ had obvious differences among the three cities. Beijing had a higher area proportion of compact mid-rise and low-rise LCZ types. The spatial pattern of LCZ in Shenzhen was especially quite different from those of Beijing and Shanghai. (2) Shenzhen had the strongest summer surface urban heat island (UHI) intensity and the largest UHI region area. However, the proportion of urban cooling island areas was still the highest in Shenzhen. (3) Different LCZs showed significant LST differences. The largest LST difference between the LCZs reached 5.57 °C, 4.50 °C, and 12.08 °C in Beijing, Shanghai, and Shenzhen, respectively. Built-up LCZs had higher LSTs than other LCZ types. (4) The dominant driving LCZs on LST were different among these cities. The LST in Beijing was easily influenced by built-up LCZ types, while the cooling effects generated by LCZ G(water) were much stronger than built-up LCZs’ warming effects in Shanghai. These results indicated that the effect of the LCZ on LST had significant differences among LCZ types and across cities, and the dominant LCZs should be given more priority in future urban planning.

Dynamic Impact of Urban Built Environment on Land Surface Temperature Considering Spatio-Temporal Heterogeneity: A Perspective of Local Climate Zone

Thermal environment deterioration has seriously threatened urban habitat quality and urban sustainable development. The evolution of the urban built environment (UBE) is an important cause for urban thermal environment variation. However, the dynamic effect of the UBE on the land surface temperature (LST) is rarely studied by combining the local climate zone (LCZ) theory and spatio-temporal heterogeneity. Based on a case study of Beilin District in Xi’an, China, this paper identified LCZ types of Beilin District in 2010, 2015, and 2020 using the GIS method. It also analyzed the spatial–temporal characteristics of the LST in summer based on the remote sensing retrieval method and explored the effects of the built environment on the LST by Geodetector and geographically weighted regression (GWR). The results showed the following: (1) The area share of dense building zones in Beilin District was greater than that of open building zones and natural surface zones, while the share of mid- and high-rise dense building zones continued to increase and the share of low-rise dense building zones continued to decrease during the study period. (2) The LST of different LCZ types in Beilin District was obviously different, and the LST of dense building zones was generally higher than that of open building zones and natural surface zones. Meanwhile, the LST of mid- and low-rise dense building zones increased gradually, and the LST of high-rise open building zones decreased gradually, but the overall warming area was obviously more than the cooling area. (3) The effects of the UBE factors on the LST varied greatly, with their interaction having an enhancement effect. The direct and interactive influence of the two-dimensional (2D) UBE indicators on the LST were greater than those of the three-dimensional (3D) indicators, but there was a gradual decrease in the force of the 2D indicators and a simultaneous diminution, enhancement, and invariance of the force of the 3D indicators. (4) Vegetation cover (VC) and floor area ratio (FAR) acted negatively, and the building height (BH) was changing from a positive to a negative role, with the average action intensity of VC changing from −0.27 to −0.15, FAR from −0.20 to −0.16, and BH from 0.05 to −0.04. The impervious surface area (ISA), building area (BA), and space congestion (SC) acted positively, with the average action intensity of the ISA changing from 0.12 to 0.20, BA from 0.12 to 0.19, and SC was stable at 0.04. The framework enables a deeper portrayal of LST changes in different LCZs, reflecting the direct and interactive effects of different UBE indicators on LST, as well as local variations in the impact effects and provides a basis for urban managers or planners to improve urban heat resilience.

Heat risk assessment and response to green infrastructure based on local climate zones

The escalation of thermal risks is attributed to accelerating pace of urbanization. However, assessment and response to green infrastructure with respect to heat risk under different climate and function have been inadequate. This study intends to address these gaps by focusing on local climate zones (LCZs). Firstly, spatial characteristics of heat risk indexes (HRIs) constructed based on heat hazard-exposure-vulnerability for three large cities in 2010, 2015 and 2020 were explored. Secondly, whether HRI cross LCZs have significant differences was examined. Third, proportion and heat contribution of different HRI classes under different LCZs were quantified. Finally, effects of green infrastructure under different LCZs on HRI were analyzed. The results revealed consistent upward trends in the prevalence of sub-high and high HRIs from 2010 to 2015. HRIs exhibited significant spatial aggregation characteristics. Importantly, more than 95.83% of HRIs cross LCZs exhibited significant variations. The HRI for open building type was lower when compared to the compact LCZ types. Additionally, Normalized Difference Vegetation Index (NDVI) had a more pronounced mitigating effect on HRIs in compact high-rise (LCZ 1), compact mid-rise (LCZ 2), open high-rise (LCZ 4) and open mid-rise (LCZ 5). Updating the compact LCZ types to open LCZ types, avoiding configuration of LCZ 1 and 2, and prioritizing the configuration of NDVI enhancement in the existing LCZ 1, 2, 4 and 5, and increasing the amount of greenery by upgrading mono-structures to composite structures consisting of trees, shrubs and grasses and by implementing greening of façade are suggested to alleviate heat risk.

Assessment of Urban Local High-Temperature Disaster Risk and the Spatially Heterogeneous Impacts of Blue-Green Space

Urban high-temperature disasters have gradually emerged as a significant threat to human society. Therefore, it is crucial to assess and identify areas at risk of such disasters and implement urban planning measures aimed at mitigating their impact. Additionally, a multitude of studies have demonstrated the significant cooling effect of urban blue-green spaces (UGBS), which play a pivotal role in urban environments. Incorporating a UBGS layout into planning and evaluation processes has substantial potential for mitigating high-temperature disasters. This paper presents the construction of a set of assessment processes for mitigating urban high-temperature disaster risk using a UBGS structure layout specifically for the main urban area of Harbin, China. We employed GIS and multi-source remote sensing imagery to develop local climate zone (LCZ) maps applicable to the designated study area. The differentiated impact of UBGS factors on high-temperature disaster risk was determined using the multi-scale geographical weighted regression model (MGWR). The results showed the following: (a) There was an overall low risk level, with 19.61% of the high-risk areas concentrated within the second ring road, forming a spatial pattern characterized by “one line, one cluster”. (b) The risk of the building category LCZs was generally higher than that of the natural category LCZs. The risk of the architectural LCZs could be summarized as the risk of low-density LCZs being smaller than that of the high-density LCZs, except LCZ 5. The mean value of the LCZ 2 and LCZ 5 types was the highest. (c) Through indicator screening, AREA_MN, SHAPE_MN, PD, and NP were found to be significant determinants influencing the risk, and the effectiveness and spatial differentiation of these main factors exhibited notable disparities. (d) By comparing different LCZ types, we concluded that the mitigation effect of these factors on risk may be interfered with by building height (BH); NP may be positively interfered with by BH; and PD and SHAPE_MN may be negatively interfered with by BH. The research results provided a new perspective and practical scientific basis for high-temperature disaster risk-mitigation planning based on UBGSs under LCZ classification.

Projecting high resolution population distribution using Local Climate Zones and multi-source big data

High precision and easily updatable data on population estimates are critical for urban planning, disaster risk assessment, and public health campaigns. However, traditional administrative census data often faces limitations in spatial resolution, making it difficult for further applications. In this study, we construct a fast and efficient population estimation model using Local Climate Zones (LCZ) products at a fine spatial scale. The population totals are estimated by LCZ units, based on a robust linkage between built LCZ types and population density. Validated by city- and county-level census data in 21 cities in China, the new model exhibits a very good fit with R2 values of 0.77 and 0.73 respectively, which confirms the effectiveness of LCZ-based population estimates. To our knowledge, this is the first study that directly estimates the population via LCZ maps. In addition, it was proven that population density can be used as a new property for LCZ type definition. As LCZ products are convenient to obtain, this work provides a simple, economical, and reliable population estimation method that can complement the traditional census.

Identifying Major Diurnal Patterns and Drivers of Surface Urban Heat Island Intensities across Local Climate Zones

Deepening the understanding of diurnal characteristics and driving mechanisms of surface urban heat islands (SUHIs) across different local climate zones (LCZs) and time scales is of great significance for guiding urban surface heat mitigation. However, a comprehensive investigation of SUHIs from the diurnal, local, multi-seasonal, and interactive perspectives remains a large gap. Here, we generalized major diurnal patterns of LCZ-based SUHI intensities (SUHIIs) throughout 2020 over the urban area of Beijing, China, based on diurnal temperature cycle modeling, block-level LCZ mapping, and hierarchical clustering. A geographical detector was then employed to explore the individual and interactive impacts of 10 morphological, socioeconomic, and meteorological factors on the multi-temporal spatial differentiations of SUHIIs. Results indicate six prevalent diurnal SUHII patterns with distinct features among built LCZ types. LCZs 4 and 5 (open high- and mid-rise buildings) predominantly display patterns one, two, and five, characterized by an afternoon increase and persistently higher values during the night. Conversely, LCZs 6, 8, and 9 (open, large, and sparsely built low-rise buildings) mainly exhibit patterns three, four, and six, with a decrease in SUHII during the afternoon and lower intensities at night. The maximum/minimum SUHIIs occur in the afternoon–evening/morning for patterns 1–3 but in the morning/afternoon for patterns 5–6. In all four seasons, the enhanced vegetation index (EVI) and gross domestic product (GDP) have the top two individual effects for daytime spatial differentiations of SUHIIs, while the air temperature (TEM) has the largest explanatory power for nighttime differentiations of SUHIIs. All factor interactions are categorized as two-factor or nonlinear enhancements, where nighttime interactions exhibit notably greater explanatory powers than daytime ones. The strongest interactions are EVI ∩ GDP (q = 0.80) during the day and TEM ∩ EVI (q = 0.86) at night. The findings of this study contribute to an improved interpretation of the diurnal continuous dynamics of local SUHIIs in response to various environmental conditions.

Identifying the Effects of Vegetation on Urban Surface Temperatures Based on Urban–Rural Local Climate Zones in a Subtropical Metropolis

Many studies have observed the crucial role of vegetated local climate zone (LCZ) types in mitigating the surface urban heat island (SUHI) effect. However, research analyzing the spatial variations in land surface temperature (LST) in a metropolis based on an urban–rural LCZ scheme and exploring the cooling effects of different vegetation types is still lacking. Here, our study focuses on the Guangzhou–Foshan metropolis and aims to elucidate the spatial variations in LST in subtropical cities and the regulating effect of vegetation on LST changes. We used a normalized difference vegetation index (NDVI) and LST data from space-borne MODIS products for the years 2000, 2009, and 2019, as well as LCZ maps, urban–rural gradient data, and land use and land cover (LULC) maps. Urban–rural, seasonal, daytime, nighttime, and diurnal comparative analyses were conducted using logarithmic regression, Pearson partial correlation, and comparison analysis. The results showed that LST values for built LCZ types were generally higher than those of land cover LCZ types, showing a positive correlation with building density and height. The LST decreased logarithmically across the urban–rural gradients, with a rapid decrease initially in the near-gradient urban area, followed by a flattening trend in farther-gradient suburban and rural areas. Regarding vegetated LCZ types, the NDVI metrics showed a significant negative correlation with the LST during the daytime but a positive correlation during the nighttime. The cooling effect of vegetated LCZ types was evident, with an average cooling amplitude of 1.92 °C over the three investigated years. In conclusion, urban LST changes are closely associated with LCZ types, urban–rural gradients, NDVI values, and vegetation types. The cooling ability of vegetation exhibited seasonal and diurnal variations, with a special emphasis on the cooling effect of dense evergreen broadleaf forests. Our findings offer valuable insights and can guide urban ecological construction and management by comprehensively assessing the impact of vegetation on urban surface temperatures.

General air temperature and humidity features of local climate zones: A multi-city observational study in eastern China

The local climate zone (LCZ) scheme has been widely applied in urban climate studies. However, most field observations were conducted in individual cities and focused solely on air temperature. Few investigations have focused on the general climatic features of LCZs. This study aimed to obtain a reliable understanding of the air temperature and humidity features of LCZs by conducting multi-city observations. Towards this goal, fixed stations were established using temperature/humidity loggers deployed at 17 pairs of urban and rural sites in 15 cities of eastern China for one year. All the rural reference sites were LCZ D (low plants). Differences in air temperature (ΔT) and humidity ratio (ΔW) between the urban and rural sites (LCZ X − LCZ D) were analyzed on multiple time scales (diurnal, seasonal, and annual). In addition, we assessed urban-rural differences in the frequency and duration of heat and cold waves and the effects of local urban climates on building energy demands and human thermal stress. The results showed that: (1) the nocturnal ΔT values for ‘ideal’ days (calm and clear days) exhibited distinguishable differences among different LCZ types, but approximately similar magnitudes for urban sites belonging to the same LCZ type; (2) the analysis of ΔW revealed that moisture deficits and excesses were common occurrences in urban sites across cities; and there was no discernible pattern in ΔW among LCZs; (3) compared to their rural counterparts, all urban sites exhibited remarkable increases in heat-wave incidence, cooling energy demand, and heat exposure risk. These findings will help better understand the general climatic features of LCZs and develop adaption strategies.

Study on the Spatial and Temporal Distribution of Urban Vegetation Phenology by Local Climate Zone and Urban–Rural Gradient Approach

Understanding variations in the temporal and spatial distribution of vegetation phenology is essential for adapting to and mitigating future climate change and urbanization. However, there have been limited vegetation phenology studies within small-scale areas such as urban environments over the past decades. Therefore, the present study focuses on Jinan city, Shandong Province, China as the study area and employs a more refined local climate zone (LCZ) approach to investigate spatial and temporal variations in vegetation phenology. The three phenological indicators used in this study from 2007 to 2018, namely, the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS), were provided by MODIS satellite data. The SOS, EOS, and LOS were superimposed on the LCZ and urban–rural gradient to analyze the changes in vegetation phenology, and the applicability of these two analysis methods in the study of urban vegetation phenology was compared by the honest significant difference test. We found that the SOS, EOS, and LOS of vegetation in the study area generally showed an advance, delay, and extension trend, respectively. The means of the SOS and EOS along different LCZ types varied noticeably more than those along urban–rural gradients. In 2016, 77.5%, 80.0%, and 75.8% of LCZ pairs indicated statistically significant differences for SOS, EOS, and LOS, respectively. This study provides a new perspective for the study of urban vegetation phenology which can help in management of urban-scale environments, identification of areas rich in biodiversity, and conservation and restoration of biodiversity in urban areas.

Exploring Spatiotemporal Variations in Land Surface Temperature Based on Local Climate Zones in Shanghai from 2008 to 2020

Understanding changes in urban internal structure and land surface temperature (LST) is essential. The local climate zone (LCZ) scheme has been extensively applied to characterize urban spatial structure, which has potential for urban climate research. We combined optical imagery and synthetic aperture radar (SAR) data (Landsat-5 and PALSAR for 2008; Sentinel-2 and PALSAR-2 for 2020) to map the LCZs in Shanghai, China. The results showed that the areas of open high-rise and open mid-rise buildings significantly increased from 2008 to 2020. Then, we investigated the spatiotemporal variations in LST based on the LCZ data from 2008 to 2020 using the grid method. The mean daytime LST (obtained from Landsat-5 and Landsat-8) was higher in 2020 than in 2008 for each LCZ type in spring. The mean daytime LSTs of compact mid-rise, compact low-rise, large low-rise and heavy industry zones were higher than those of other LCZ types in spring and summer. The mean nighttime LST (obtained from ASTER) in the downtown area was higher than that in the suburbs in summer. Furthermore, the mean nighttime LST of the built types was also generally higher than that of the natural types in summer. A comparison of the mean daytime LSTs in 2008 and 2020 revealed that the expansion trend of the higher LST areas in spring and summer is consistent with the expansion areas of the mid-rise and high-rise built types.

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.