Local Climate Zone Classification Research Articles

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.

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.

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.

Urban growth’s implications on land surface temperature in a medium-sized European city based on LCZ classification

The study determined the influence of changes in land use and land cover (LULC) on land surface temperature (LST) over a 33-year period based on a medium-sized European city (Poznań, Poland). The LST was estimated from Landsat 5, 8 and Terra (MOD11A2v6) satellites. The local estimation of climate patterns was based on the Local Climate Zones (LCZ) classification utilised with the methodology proposed by the World Urban Database and Access Portal Tools (WUDAPT). Moreover, the Copernicus’ imperviousness density product (IMD) was used. Between 2006 and 2018 the area with IMD of 41–100% increased by 6.95 km2, 0–20% decreased by 7.03 km2. The contribution of built-up LCZs increased by 7.4% (19.21 km2) between 1988 and 2021 reaching 13% (34 km2) within open mid-rise LCZ. Due to urbanisation and reforestation, low plants LCZ shrunk by 12.7%. For every 10% increase in IMD, LST increases by up to 0.14 °C. Between 1988 and 2021 the LSTm in specific LCZs rose from 1.52 up to 2.97 °C. As per LST models LCZ change from natural to built-up led up to 1.19 °C LST rise. The increase of the LSTm was registered even when the LCZ remained unchanged.

Local Climate Zone Classification via Semi-Supervised Multimodal Multiscale Transformer

Local Climate Zone Classification via Semi-Supervised Multimodal Multiscale Transformer

Classifying heterogeneous urban form into local climate zones using supervised learning and greedy clustering incorporating Landsat dataset

The concept of Local climate zone (LCZ) is used to generate LCZ maps worldwide as a part of World Urban Database and Access Portal Tools (WUDAPT) project utilizing Landsat imagery. Though WUDAPT has become the standard for LCZ mapping, it has certain limitations: 1) its outcome for heterogeneous cities is less accurate. 2) Due to fine granularity and huge-sized imagery, validating WUDAPT outcome becomes difficult for an urban expert. To overcome these, we have presented the LCZ classification and greedy clustering framework for heterogeneous cities (Nagpur (India)). Three classification schemes based on random forest, deep neural network, 1D-convolutional neural network and, subset ensemble are proposed. The overall accuracies are respectively 88%, 86%, 82%, 88%. Contextual information (mean, minimum, maximum, median, 25th, and 75th quantile values) at 90 m resolution is adopted while classification to handle spatial heterogeneity. Adding contextual information increases the overall accuracy by 4–6%, urban and mixed classes' accuracy by 4–7% and, 3–4% respectively. Further, clustering groups the spatially connected but similar pixels (classified Landsat) using breadth-first search concept and deque data structure creating LCZ map. Such LCZ map is helpful for an urban expert to validate correctness, select suitable level of granularity, visualize heterogeneity and suggest improvements.

Numerical analysis of extreme heat in Nagpur city using heat stress indices, all-cause mortality and local climate zone classification

Spatio-temporal investigation of heat-related impacts forms the basis for planning resources and formulating possible countermeasures. The present study aims to examine the effect of temperature extremes on all-cause mortality in Nagpur, a tropical Indian city and investigates (i) long-term (1969–2015) daily air temperature trend (ii) long-term (1985–2015) heat stress pattern using heat stress indices (iii) relationship between heat stress indices and all-cause mortality, and (iv) role of local climate zones to estimate intra-urban variation in mortality. The findings reveal a steady rise in maximum temperature, heat wave, severe heat wave, and hot nights. It was observed that during 1985–2015, heat index and humidex increased by 4 °C and 1 °C respectively. A cubic spline regression revealed a strong influence (R2 > 0.75) between heat stress indices and all-cause mortality. The results indicate that large low-rise and sparsely built areas (i.e., local climate zones 8, 9 and 93) present higher levels of heat stress indicating greater susceptibility to mortality. In contrast, compact/open low-rise developments with vegetation cover showed lower susceptibility to heat stress. The findings provide potential evidence to develop hyperlocal thresholds, and strengthen early warning systems by selecting appropriate indices for a given climate zone.

Seasonal Cooling Effect of Vegetation and Albedo Applied to the LCZ Classification of Three Chinese Megacities

The urban heat island effect poses a growing threat to human society, especially in densely populated and developed megacities. With the introduction of the Local Climate Zones (LCZ) framework, new perspectives and findings have been brought to urban heat island studies. This study investigated the cooling effect of vegetation and albedo on the surface urban heat island (SUHI) in the classification system of LCZ during different seasons, using three Chinese megacities as case study areas. Single-factor linear regression and Pearson’s correlation coefficient were applied to analyze the seasonal cooling effect of both albedo and the NDVI on the SUHI within different LCZs. The results show that (1) the variability of the SUHI is reflected in its dominance and intensity within certain LCZs in different cities and in the efficiency of cooling factors; (2) the cooling effect of vegetation is dominant in each season, and the cooling effect produced by albedo within specific seasons can be differentiated by LCZs. This study provides valuable information for the mitigation of the SUHI magnitude in specific regions and at specific times of the year.

Mapping Local Climate Zones in Lausanne (Switzerland) with Sentinel-2 and PRISMA imagery: comparison of classification performance using different band combinations and building height data

ABSTRACT The Local Climate Zone (LCZ) concept has emerged as a valuable classification system for climate-related studies. The World Urban Database and Access Portal Tools (WUDAPT) protocol provides a framework for generating a LCZ segmentation which relies on the supervised classification of multispectral imagery. However, since LCZ is based on the physical and thermal properties of the urban surfaces, more insightful information on the surface reflectivity characteristics – which is provided by hyperspectral sensors – may be beneficial for improving the LCZ classification. This assumption is investigated in this study by comparing the classification performance of a supervised algorithm applied to multispectral (Sentinel-2) and hyperspectral (PRISMA) satellite imagery. The study area is the city of Lausanne (Switzerland). Experiments are performed considering these sensors and different band combinations, including the building height layer as an additional band. Preliminary outcomes show that PRISMA imagery yields satisfying results in terms of classification accuracy while not outperforming Sentinel-2. An improvement is achieved by leveraging the first 10 PRISMA Principal Components which allows to retain the uncorrelated information out of the original bands. These first results will be validated in future investigations by improving image pre-processing and exploiting a larger number of seasonal PRISMA acquisitions.

Urban WRF-Chem evaluation over a high-altitude tropical city

Morphology and grid resolution are important aspects that need to be considered in urban modeling applications, since together with buildings they induce a direct effect on wind and dispersion of pollutants over urban areas. In this study, we evaluate high-resolution simulations of a multi-layer urban canopy model (UCM) based on a local climate zone (LCZ) classification coupled to the Weather Research and Forecasting model with Chemistry (WRF-Chem), in the local meteorological conditions and air quality pollutants of a highly urbanized megacity. This modeling system, known as Building Effect Parameterization (BEP) considers the effects of buildings’ vertical and horizontal surfaces on the momentum that considerably impacts the lower part of the urban boundary layer (UBL). Simulations of the urbanized model (WRFu) were compared against a Noah land surface model (Noah LSM) with no urban physics (WRF) for the same period. It was observed that the LCZ classification and urban parameterization coupled to the model have a direct influence in meteorological parameters and pollutant concentrations. Urban simulations of temperature and wind speed showed higher sensitivity to initial and boundary conditions, increasing the correlation with observations and reducing the bias error. An important observation is that emissions drive air quality concentrations despite the improvements in local meteorology.

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.

Satellite images interpretation for health studies of urban areas

Every year a variety of vector-borne infectious diseases claims the lives of millions of people worldwide. The study of the favorable conditions for their vectors and hosts is a particularly important task for understanding the patterns of the distribution with the focus on the urban environment, characterizing by a high population density and rapid transmission of the diseases. The existing methodology of Local Climate Zones (LCZ), which are areas with homogeneous land surface coverage, structure, and a specific nature of human activity was the first attempt to standardize urban environmental studies and has become an international standard for the analysis of urban morphology. The article provides an algorithm for adapting the methodology of identifying LCZ accounting vegetation and water areas for the tasks of medical geographical zoning and assessment of epidemiological risks and using the geographic information technology. The examples of the outbreaks of vivax malaria in the Moscow region in 1999–2003 and West Nile fever in the Volgograd region in 2010–2011 were used. As a result, a methodology of medical geographical zoning based on the idea of fragmenting the classification of LCZ using the normalized difference water index as indicator of the favorability for vector habitats was developed. The use of the methodology made it possible to reveal that the areas of various LCZs change after outbreaks, which may reflect changes in conditions and an increase in the favorability for vectors. Thus, LCZ can be used as indicators of changes in the natural and man-made environment that can provoke disease outbreaks.

The spatial configuration of local climate zones reveals effects on wayfinding in human walking.

The importance of easy wayfinding in complex urban settings has been recognized in spatial planning. Empirical measurement and explicit representation of wayfinding, however, have been limited in deciding spatial configurations. Our study proposed and tested an approach to improving wayfinding by incorporating spatial analysis of urban forms in the Guangdong-Hong Kong-Macau Great Bay Area in China. Wayfinding was measured by an indicator of intelligibility using spatial design network analysis. Urban spatial configurations were quantified using landscape metrics to describe the spatial layouts of local climate zones (LCZs) as standardized urban forms. The statistical analysis demonstrated the significant associations between urban spatial configurations and wayfinding. These findings suggested, to improve wayfinding, 1) dispersing LCZ 1 (compact high-rise) and LCZ 2 (compact mid-rise) and 2) agglomerating LCZ 3 (compact low-rise), LCZ 5 (open mid-rise), LCZ 6 (open low-rise), and LCZ 9 (sparsely built). To our knowledge, this study is the first to incorporate the LCZ classification system into the wayfinding field, clearly providing empirically-supported solutions for dispersing and agglomerating spatial configurations. Our findings also provide insights for human-centered spatial planning by spatial co-development at local, urban, and regional levels.

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.

Spatial Association Rules and Thermal Environment Differentiation Evaluation of Local Climate Zone and Urban Functional Zone

Urban heat islands (UHIs) caused by urbanization have become a major issue affecting the sustainable development of the ecological environment. The distribution of UHIs is mainly affected by the reflection and transmission of heat radiation caused by differences in urban spaces, and the anthropogenic heat emissions caused by social activities. At present, the research on the urban thermal environment involves two spatial classification systems: local climate zone (LCZ), based on urban morphology and spatial patterns; and urban functional zone (UFZ), based on socio-economic activities. It is not clear whether there are association rules between these two systems in different cities. Against this background, this study explores the association rules between the UFZ and LCZ classification systems using the selected Chinese cities in different regions as typical examples. Our results confirm that there are common association rules from UFZ to LCZ, as the form of buildings is greatly influenced by the types of functional areas in urban construction. Specifically, the medical zone corresponds to the compact mid-rise zone (LCZ2); the business service area and the office area also correspond to the compact zone (LCZ1-LCZ3); and the industrial area corresponds to the compact low-rise zone (LCZ3). These functional zones have the same association rules in different cities. The cross-regional mining of the relationship between different urban functional systems will help to coordinate different planning departments and carry out the integration of multiple spatial plans. Furthermore, we found that LCZ has a better differentiation effect on the surface temperature through our comparison research, which makes it more suitable as a reference for research on the thermal environment and the heat island effect.

Effects of Sky View Factor on Thermal Environment in Different Local Climate Zoning Building Scenarios—A Case Study of Beijing, China

As an essential structural indicator of buildings, sky view factor (SVF) is one of the most critical factors affecting the urban thermal environment. However, the relationship between SVF and the thermal environment at the neighborhood scale has not been adequately studied. Therefore, this paper investigates the relationship between SVF and air temperature in different building scenarios based on the Local Climate Zone (LCZ) classification framework. Firstly, the study is based on multi-source urban data and the Open Street Map (OSM) to map the local climate zones in Beijing. Then, a simulation model with different LCZs was constructed based on realistic scenarios using the microclimate simulation software ENVI-met, and the thermal environment was simulated in 24 h on a single day in summer. Finally, the SVF and air temperature relationship under different LCZ scenarios was calculated and analyzed. The results show that (1) the SVF values of LCZ 1, LCZ 2, and LCZ 5 show a more apparent positive correlation with air temperature than other categories, and the SVF values of LCZ 6–9 show a negative and then positive correlation with air temperature; (2) in the morning, the dense building areas show a weak correlation with air temperature, and the differences in air temperature corresponding to the SVF values in different zones are greater; (3) in the morning, the air temperature in the dense building areas showed a weak correlation, the difference between the SVF values and the air temperature in different intervals was different, and when the SVF was larger or smaller, the air temperature change was smaller and concentrated, and the correlation between the air temperature and the SVF in the open building areas was not obvious; (4) with 12:00 as the dividing line, the SVF and the air temperature in all categories showed a weak positive correlation after this time. This study can provide guidance on optimizing building layouts and mitigating the impacts of urban heat on human health.

Observed neighborhood-scale meteorology and air quality characteristics in downtown area of Nanjing

Neighborhood-scale meteorology and air quality characteristics based on dense observation are valuable to understand the urban environment. Local climate zone (LCZ) has established a universal correlation between urban heterogeneity and meteorology, but the impact of urban heterogeneity on air quality is more complicated, which can be investigated with neighborhood-scale observation. This study collects data from 152 stations in Nanjing downtown, China. With the LCZ classification and the analysis of station's meteorology, it is found that compactness and surroundings are representative contributors. From the LCZ and observation data, five landscape compositions' meteorology and air quality are analyzed. Lakeside green space station has the best thermal comfort in the daytime due to its high wind and heat capacity. The local characteristic of ozone and NO2 is related to the local traffic demand index (determined by main road and parking lot density). Fragmented green space stations have better resistance to serious ozone pollution compared to compact and open buildings. This study shows an observed correlation of urban heterogeneity and air quality, which is instructive for the further mitigation of neighborhood-scale air pollution.

Comparative Analysis of the Surface Urban Heat Island (SUHI) Effect Based on the Local Climate Zone (LCZ) Classification Scheme for Two Japanese Cities, Hiroshima, and Sapporo

The Local Climate Zone (LCZ) classification system is used in this study to analyze the impacts of urban morphology on a surface urban heat island (SUHI). Our study involved a comparative analysis of SUHI effects in two Japanese cities, Sapporo and Hiroshima, between 2000 to 2022. We used geographical-information-system (GIS) mapping techniques to measure temporal LST changes using Landsat 7 and 8 images during the summer’s hottest month (August) and classified the study area into LCZ classes using The World Urban Database and Access Portal Tools (WUDAPT) method with Google Earth Pro. The urban thermal field variance index (UTFVI) is used to examine each LCZ’s thermal comfort level, and the SUHI heat spots (HS) in each LCZ classes are identified. The research findings indicate that the mean LST in Sapporo only experienced a 0.5 °C increase over the time, while the mean LST increased by 1.8 °C in Hiroshima City between 2000 and 2022. In 2000, open low-rise (LCZ 6) areas in Sapporo were the hottest, but by 2022, heavy industry (LCZ 10) became the hottest. In Hiroshima, compact mid-rise (LCZ 2) areas were the hottest in 2000, but by 2022, heavy-industry areas took the lead. The study found that LCZ 10, LCZ 8, LCZ E, and LCZ 3 areas in both Dfa and Cfa climate classifications had unfavorable UTFVI conditions. This was attributed to factors such as a high concentration of heat-absorbing materials, impervious surfaces, and limited green spaces. The majority of the SUHI HS and areas with the highest surface temperatures were situated near industrial zones and large low-rise urban forms in both cities. The study offers valuable insights into the potential long-term effects of various urban forms on the SUHI phenomenon.

Investigating surface UHI using local climate zones (LCZs), the case study of Cairo’s River Islands

The spatial configuration of the built environment influences the thermal behaviour at the local level and may generate the urban heat island (UHI) phenomenon. This impact has been investigated through several physical and climatological approaches. In this respect, the local climate zones (LCZs) scheme is recently adopted to facilitate the spatial study and understanding of UHIs. This research investigates the formation of surface UHIs in the case of the Nile River Islands, Cairo. It adopts the classification scheme of LCZs for selecting and comparing samples and conducting analyses of the distribution of land surface temperature (LST) for the area of interest. The adopted workflow aims at defining the homogenous zones of LCZs by three main steps: (i) the classification of LCZs based on supervised pixel-based classification of geospatial data, using the web-based tool of LCZ Generator, (ii) the detection of UHIs through the estimation of LST, and (iii) spatial statistical tests of Global Moran’s Index and Hotspot analysis for studying the significance of LST distribution within the study area. The adopted workflow successfully assists in estimating the LST and mapping UHI, and it contributes to the understanding of the physical and thermal characteristics of the selected urban areas. The analyses of outputs show reliable results with the recent studies of spatial relations between LCZs and LST.

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.