Detecting Cooling Effect of Landscape from Composition and Configuration: An Urban Heat Island Study on Hangzhou

Abstract. Global warming is widely regarded as one of the most critical environmental issue in the 21st century. It is known to exacerbate urban heat island effects, a phenomenon that could affect the livability of cities and the lives and comfort of more than half of the world population who are now living in urban areas. The issue on how to mitigate the impacts of this phenomenon and help urban dwellers adapt to it, has been, and continues to be, an important topic in the context of landscape design and urban planning. This study sought to examine the cooling effects of different land cover types from the perspectives of landscape composition and configuration. Landsat-8 OLI/TIRS data was employed to derive both landscape composition and land surface temperature (Figure 1). The objectives of this study are to: (1) detect the spatial pattern of LST in Hangzhou; (2) examine the cooling effect of land cover resources; and (3) understand the correlation between LST and land cover from the perspectives of composition and configuration. Its goal is to reflect on optimal landscape patterns from the perspective of mitigating UHI effects and building more comfortable and livable urban environments. Various spatial and statistical approaches, including urban-rural gradient, grid-based, landscape metrics, and scatter plot, were employed to analyze the derived land cover and land surface temperature (LST) maps. An urban-rural gradient analysis was performed to examine the difference in LST between the urban and rural areas. First, we created a multiple ring buffer zone from the city center of Hangzhou with a distance interval of 1 km. Second, we determined the mean LST and the density of all the land cover types (LCTs) in each ring buffer zone. Finally, the results were plotted along the urban-rural gradient for analysis (Figure 2). The results showed that forest and water were the cooling land cover types in Hangzhou during daytime. The heat core in Hangzhou was situated close to the central business district and the variance in LST in urban and rural recorded at 2.94 °C. The correlation of forest density with mean LST could be detected in both large (690 m) and small (210 m) grids, whereas the correlation of water density with mean LST was only observed in small grids. In general, forest with high aggregation and complex shape behave best in cooling LST while huge and complete water bodies had better cooling effect than scattered water patches. Besides, the minimum cooling scale of forest and water were 150 m and 120 m, respectively. The findings were considered valuable for urban planners to spatially and architecturally build and arrange the cooling land cover resources, eventually to fulfill the Chinese government’s new plan of building “livable cities”.

Land surface temperature (LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change (LCC) on LST has been assessed using Landsat TM 5, Landsat 8 TIRS/OLI and Digital Elevation Model (ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha (30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha (605.87%) and 45 ha (0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha (23.92%) and 88,589 (14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11°C in 1990 but it rose by 2°C and reached to 13°C in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6°C, 9°C, 1°C, and 7°C, respectively. Further, relationships between LST, Normalized Difference Snow Index (NDSI), and Normalised Difference Vegetation Index (NDVI) were established using Linear Regression.

Inter-local climate zone differentiation of land surface temperatures for Management of Urban Heat in Nairobi City, Kenya

Urban areas are generally several degrees hotter than surrounding rural areas, which is referred to as the urban heat island effect. Understanding the spatial pattern of land surface temperature (LST) in cities is important for urban planning, heat mitigation, and air pollution studies. This study developed five models to compare how land cover classes, normalized difference vegetation index (NDVI), social factors, and the combinations of social factors with land cover classes and NDVI perform in estimating LST at the census block group level. We found that all three groups of variables contribute significantly to understanding the spatial heterogeneity in LST. Land cover and NDVI explained LST to a similar extent (approx. 70%). Social factors alone explained 53% of LST's variance. Adding social factors in the combined models added little to the explanatory power (4.0%) compared to using land cover classes or NDVI along as independent variables.

We live in an urbanizing world. Since 2008, more than half of humanity lives in cities, both large and small, and old and new.[...]

城市热环境是城市局部气候与环境的综合表现，它与土地利用格局密切相关，但是相应的机制研究还不充分。以深圳市为例，首先基于2018年LANDSAT 8遥感影像，采用支持向量机方法和线性光谱混合模型提取土地利用与覆盖度信息，分析了城市土地覆盖对地表温度以及热量收支状况的影响。基于2003-2018的MODIS地表温度数据，进一步研究了深圳市城市热岛现象的时空变化，从地表能量的角度分析城市热岛变化背后的形成机制。结果表明，深圳市地表温度从西北到东南逐渐降低，城市不透水面温度显著高于植被覆盖区域，城市热岛效应明显。不透水面和城市植被共同影响深圳市的地表温度与热量收支状况，不透水地表与感热具有较好的相关性，城市植被与潜热具有较好的相关性。长时间序列分析表明深圳的城市热岛现象在夏季较高而冬季较低，月均热岛强度为2.14℃；对于年际变化，深圳在2003-2018表现出显著的下降趋势。归因分析显示感热通量的影响在深圳起主导作用，这一模式在全年和季节上都较为明显。结果表明深圳市经过高速扩张阶段，目前发展方向是提高建成区的利用效率，该现象强调了热传输在加强城市热岛效应过程中对近地面湍流的干扰作用。本研究可以为缓解热岛效应与景观格局优化研究提供借鉴。;Urban thermal environment is a comprehensive expression of local climate and environment. While it is closely related to land use patterns, its underlying mechanisms are largely unexplored. Urban heat island affects not only regional climate, vegetation growth and air quality, but also human health. Therefore, the urban thermal environment has been regarded as a critical variable. It is important to characterize the spatial and temporal patterns of urban thermal environments and quantify the response of the associated influencing factors. This study uses the city of Shenzhen as an example where rapid urbanization has occurred. Based on LANDSAT 8 remote sensing images collected in 2018, we first extract land cover types and coverage information using support vector machine and multiple linear spectral analysis models. The accuracies of the estimated land covers are acceptable, which provides confidence for further analyses. The impacts of urban land cover on land surface temperature and heat energy component are then investigated. Using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature dataset obtained from 2003 to 2018, this study further explores the spatiotemporal variations of urban heat island in Shenzhen, and analyzes its potential formation mechanisms from the perspective of surface energy balance. Our results show that the land surface temperature gradually decreases from northwest to Southeast of Shenzhen. This is largely attributed to the spatial distribution of impervious surfaces and the urban vegetation. Specifically, the land surface temperature of impervious surfaces is substantially higher than that of vegetation-covered area, implying that the urban heat island effect is obvious in Shenzhen. Impervious surface and urban vegetation jointly affect the surface temperature and heat budget through, for example, latent heat flux and sensible heat flux. Impervious surface is more correlated with sensible heat flux while urban vegetation is more correlated with latent heat flux. Obvious differences occur in sensible heat and latent heat among land cover types, which provides implications for the formation and elimination of urban heat island. Time series analysis regarding the period of 2003-2018 illustrates that the urban heat island effect in Shenzhen is stronger during summer season whereas relatively weaker during winter season. The urban heat island effect is remarkably exhibited using the time series dataset, with an average monthly intensity of 2.14 ℃. Regarding the internal variation, the downward trend in urban heat island intensity is shown from 2003 to 2018. This negative trend is significantly present during all seasons except spring, with a change rate of approximately 0.1 ℃/annual. Attribution analysis based on MODIS dataset and FLDAS flux dataset reveals that sensible heat flux plays a more important role in the urban heat island of Shenzhen in comparison to latent heat flux. This is consistent in the both annual and seasonal scales, and the relative contribution of latent heat flux and sensible heat flux is 42% and 58%, respectively. Results indicate that after a rapid urban expansion period in Shenzhen, the current development focus is to improve the utilization efficiency of built-up areas. This emphasizes the interference of heat transfer with the near-surface turbulence that strengthens the urban heat island effect. Our study provides reference values for mitigating the urban heat island effect and optimizing landscape patterns.

Understanding the relationship between urban blue infrastructure and land surface temperature

Background Urban Heat Islands (UHIs) are an escalating environmental and public health concern in rapidly urbanising cities, particularly in the Global South. Durban, a major coastal city in South Africa, has undergone significant land-use and land-cover transformation over recent decades, potentially intensifying surface temperature dynamics. Understanding long-term UHI trends is essential for supporting climate-resilient urban planning, reducing heat-related risks, and promoting sustainable city development. Methods This study adopted a longitudinal ecological remote sensing design to assess UHI dynamics in Durban between 2010 and 2025. Multi-temporal Landsat and Sentinel satellite imagery were used to derive land surface temperature (LST) and classify land-cover types. Spatial analysis techniques were applied to examine temperature distribution patterns, while temporal trend analysis and correlation statistics were used to evaluate associations between LST and land-cover changes, including built-up expansion, vegetation loss, and coastal proximity. Results Findings indicate a consistent intensification of UHI patterns over the 15-year period. Mean LST in densely built-up areas increased from 28.4 °C in 2010 to 31.2 °C in 2025 (+2.8 °C), while vegetated zones recorded a smaller increase of +1.2 °C. Impervious surface expansion (approximately 20%) showed a strong positive correlation with elevated LST values (r = 0.76, p < 0.01). Inland urban zones demonstrated a statistically significant warming trend (β = 0.18 °C per year, p < 0.05), whereas areas with high vegetation cover and coastal influence exhibited cooling effects of up to 2 °C relative to industrial zones. Recommendations Urban planners and policymakers should prioritise green infrastructure development, protect and restore vegetated areas, and integrate heat mitigation strategies into land-use planning frameworks. Institutionalising remote sensing-based monitoring systems is essential to support evidence-based climate adaptation and sustainable urban management in Durban.

Based on the land surface temperature (LST), the land cover classification map, vegetation coverage, and surface evapotranspiration derived from EOS-MODIS satellite data, and by the use of GIS spatial analytic technique and multivariate statistical analysis method, the urban heat island (UHI) spatial distribution of the diurnal and seasonal variabilities and its driving forces are studied in Beijing city and surrounding areas in 2001. The relationships among UHI distribution and landcover categories, topographic factor, vegetation greenness, and surface evapotranspiration are analyzed. The results indicate that: (i) The significant UHI occur in Beijing city areas in the four seasons due to high heat capacity and multi-reflection of compression building, as well as with special topographic features of its three sides surrounded by mountains, especially in the summer. The UHI spatial distribution is corresponding with the urban geometry structure profile. The LST difference is approximately 4-6℃between Beijing city and suburb areas, comparatively is 8- 10℃between Beijing city area and outer suburb area in northwestern regions. (ii) The UHI distribution and intensity in daytime are different from nighttime in Beijing city area, the nighttime UHI is obvious. However, in the daytime, the significant UHI mainly appears in the summer, the autumn takes second place, and the UHI in the winter and the spring seem not obvious. The surface evapotranspiration in suburb areas is larger than that in urban areas in the summer, and high latent heat exchange is evident, which leads to LST difference between city area and suburb area. (iii) The reflection of surface landcover categories is sensitive to the UHI, the correlation between vegetation greenness and UHI shows obviously negative. The scatterplot shows that there is the negative correlation between NDVI and LST (R2 = 0.6481). The results demonstrate that the vegetation greenness is an important factor for reducing the UHI, and large-scale construction of greenbelts can considerably reduce the UHI effect.

In the context of urban warming associated with rapid urbanization, the relationship between urban landscape patterns and land surface temperature (LST) has been paid much attention. However, few studies have comprehensively explored the effects of two/three-dimensional (2D/3D) building patterns on LST, particularly by comparing their relative contribution to the spatial variety of LST. This study adopted the ordinary least squares regression, spatial autoregression and variance partitioning methods to investigate the relationship between 2D/3D building patterns and summertime LST across 2016–2017 in Shanghai. The 2D and 3D building patterns in this study were quantified by four 2D and six 3D metrics. The results showed that: (1) During the daytime, 2D/3D building metrics had significant correlation with LST. However, 3D building patterns played a significant role in predicting LST. They explained 51.0% and 10.2% of the variance in LST, respectively. (2) The building coverage ratio, building density, mean building projection area, the standard deviation of building height, and mean building height highly correlated with LST. Specifically, the building coverage ratio was the main predictor, which was obviously positively correlated with LST. The correlation of building density and average projected area with LST was positive and significant, while the correlation of building height standard deviation and average building height with LST was negative. The increase in average height and standard deviation of buildings and the decrease in building coverage ratio, average projected area, and density of buildings, can effectively improve the urban thermal environment at the census tract level. (3) Spatial autocorrelation analysis can elaborate the spatial relationship between building patterns and LST. The findings from our research will provide important insights for urban planners and decision makers to mitigate urban heat island problems through urban planning and building design.

Impervious surface area (ISA) from National Land Cover Database 2001 and land surface temperature (LST) from MODIS averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) signature on LST amplitude and its relationship to development intensity, size, and ecological setting for 323 urban settlements over continental USA. Development intensity zones based on fractional ISA are defined for each urban area emanating outward from the urban core to the nearby non-urban rural areas and used to stratify sampling for LST. We find that the ecological context and settlement size significantly influence the amplitude of summer daytime UHI. In desert environments, the LST's response to ISA presents an uncharacteristic "U-shaped" horizontal gradient and points to a possible heat sink effect. For all cities combined, ISA is the primary driver for increase in temperature explaining 72% of the total variance in LST.

Spatio-temporal dynamics of land cover and land surface temperature in Ganges-Brahmaputra delta: A comparative analysis between India and Bangladesh

The Urban Heat Island (UHI) effect has been extensively studied as a global issue. The urbanization process has been proved to be the main reason for this phenomenon. Over the past 20 years, the built-up area of Zhengzhou city has grown five times larger, and the UHI effect has become increasingly pressing for the city’s inhabitants. Therefore, mitigating the UHI effect is an important research focus of the expanding capital city of the Henan province. In this study, the Landsat 8 image of July 2019 was selected from Landsat collection to obtain Land Surface Temperature (LST) by using Radiative Transfer Equation (RTE) method, and present land cover information by using spectral indices. Additionally, high-resolution Google Earth images were used to select 123 parks, grouped in five categories, to explore the impact factors on park cooling effect. Park Cooling Intensity (PCI) has been chosen as an indicator of the park cooling effect which will quantify its relation to park patch metrics. The results show that: (1) Among the five studied park types, the theme park category has the largest cooling effect while the linear park category has the lowest cooling effect; (2) The mean park LST and PCI of the samples are positively correlated with the Fractional Vegetation Cover (FVC) and with Normalized Difference Water Index (NDWI), but these are negatively correlated with the Normalized Difference Impervious Surface Index (NDISI). We can suppose that the increase of vegetation cover rate within water areas as well as the decrease of impervious surface in landscape planning and design will make future parks colder. (3) There is a correlation between the PCI and the park characteristics. The UHI effect could be mitigated by increasing of park size and reducing park fractal dimension (Frac_Dim) and perimeter-area ratio (Patario). (4) The PCI is influenced by the park itself and its surrounding area. These results will provide an important reference for future urban planning and urban park design to mitigate the urban heat island effect.

Investigating land use land cover changes and their effects on land surface temperature and urban heat islands in Sharqiyah Governorate, Egypt

A study of urban heat island effects using remote sensing and GIS techniques in Kancheepuram, Tamil Nadu, India