Quantifying the Relationship between 2D/3D Building Patterns and Land Surface Temperature: Study on the Metropolitan Shanghai

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

Characteristics of daytime land surface temperature in wind corridor: A case study of a hot summer and warm winter city

Effects of 'Inhaling' and 'Exhaling' of buildings in three-dimensional built environment on Land Surface Temperature

With the acceleration of urbanization, the heat island effect, as a prominent feature of urban climate, has attracted more attention. Differences in urban landscape patterns have an essential impact on the urban thermal environment. The objective of the study is to examine the impact of urban landscape types and patterns on surface temperature. Taking Zhengzhou City, China, as an example, using Google Earth remote sensing images, an urban landscape type map was created, and landscape indices were calculated. The land surface temperature (LST) of the study area was retrieved by the Landsat-8 thermal infrared band. Correlation analysis indicated that the relationships between urban landscape patterns and the thermal environment were as follows: (i) The scale indices (percentage of landscape (PLAND), largest patch index (LPI), edge density (ED), patch density (PD)) of urban landscape types with cooling effect (water body, riverfront area, park, high-rise building) were significantly negative correlated with mean LST of each partition. (ii) Conversely, there were significant positive correlations between the PLAND and LPI of landscape types with warming effect (block, development land, railway land) and the LST of the partition. (iii) The DIVISION index of the four kinds of landscapes with cooling effect was highly positively correlated with LST, and the DIVISION and SPLIT indices of the three kinds of landscapes with warming effect displayed a remarkable negative relationship with LST. Therefore, under the condition of scale control, integrated distribution of landscape with cooling effect, scattered distribution of landscape with warming effect, and reduced connectivity of landscape with warming effect will contribute to effectively alleviating the formation of urban heat islands.

Effect of land cover composition and building configuration on land surface temperature in an urban-sprawl city, case study in Bangkok Metropolitan Area, Thailand

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”.

The inappropriate thermal conditions resulting from increasingly severe climate issues have led to numerous complications for urban residents, decreased urban settlement comfort, and increased average and peak energy demands in built environments. Existing studies have demonstrated the significant influence of urban morphology (UM) on the urban thermal environment (UTE); however, at the meso-scale and macro-scale, UTE is often simplified to land surface temperature (LST) and building surface temperatures. To investigate the impact of UM on UTE, we developed an evaluation framework consisting of thermal sensing feedback (TSF) and LST. We employed the seven-level TSF scale to evaluate TSF data obtained from the Internet, emphasizing individualized thermal perceptions of urban spaces and reorienting UTE research towards a human-centric perspective. Using a regression model, we examined the relationships between two-dimensional and three-dimensional UM variables and UTE at the meso-scale in the central urban area of Shanghai, China, during August and December 2024. The results indicated the following: (1) The normalized difference vegetation index (NDVI), building density (BD), floor area ratio (FAR), impervious surface index (ISI), building height (BH), average building volume (ABV), sky view fraction (SVF), and building shape (BSsh) effectively explained TSF. However, area weighted mean shape index (SHAPEAM), aggregation index (AI), edge density (ED), elevation, building spacing (BSsp), and spatial congestion degree (SCD) showed no significant correlation with TSF. (2) Significant variables, including NDVI, FAR, ISI, UM, BD, and BH, exhibited opposite effects on cold perception in winter compared to heat perception in summer, indicating a consistent influence on thermal perception across seasons. (3) In summer, the significant variables SVF, BSsh, and ISI showed opposite effects on TSF and LST, while in winter, FAR demonstrated contrasting impacts on TSF and LST. The results of this study advance understanding of the mechanisms through which UM influences UTE, providing valuable insights for the development of sustainable, thermally comfortable urban environments.

Building height is one of the most important determinants of streetscapes. According to zoning regulations in countries such as Japan, building heights are indirectly controlled by the floor area ratio (FAR). The maximum FAR and the building coverage ratio (BCR) represent the main tools of zoning regulations, which legally regulate building shapes and volumes based on plot sizes at the district scale. If plot sizes and shapes are uniform due to zoning regulations, uniform building shapes (both their plane shapes and heights) may emerge. However, in Japan, plot sizes and shapes are not only non-uniform but irregular. Furthermore, the plot is a conceptually and analytically ambiguous concept and data regarding plot shapes and building heights are unavailable. Therefore, it is difficult to understand the relationship between variations in building heights and zoning regulations. These issues prompt the following research question: How can we estimate building height distribution under zoning regulations at the district scale? To answer this question, plot shapes are objectively defined as area Voronoi cells whose generators are building polygons and road networks. Then, the following two analyses are carried out in downtown districts of the Tokyo metropolitan region. First, the relationship between plot sizes, BCR and building heights is analysed. It is found that building heights scale sub-linearly to plot sizes. Second, following this relationship, the probability density function of building heights under zoning regulations is theoretically derived as a log-normal distribution, primarily estimated from building density, road network density, average road width, BCR and FAR. This is compared with the empirical distribution of discretised building heights and the difference is investigated. Furthermore, how to harmonise variations in building heights is discussed by considering their opportunities and external costs. These findings are expected to provide urban planners with a theoretical basis for creating harmonious streetscapes.

Urban expansion results in landscape pattern changes and associated changes in land surface temperature (LST) intensity. Spatial patterns of urban LST are affected by urban landscape pattern changes and seasonal variations. Instead of using LST change data, this study analysed the variation of LST aggregation which was evaluated by hotspot analysis to measure the spatial dependence for each LST pixel, indicating the relative magnitudes of the LST values in the neighbourhood of the LST pixel and the area proportion of the hotspot area to gain new insights into the thermal effects of increasing impervious surface area (ISA) caused by urbanization in Fuzhou, China. The spatio-temporal relationship between urban landscape patterns, hotspot locations reflecting urban land cover change in space and the thermal environment were analysed in different sectors. The linear spectral unmixing method of fully constrained least squares (FCLS) was used to unmix the bi-temporal Landsat TM/OLI imagery to derive subpixel ISA and the accuracy of the percent ISA was assessed. Then, a minimum change threshold was chosen to remove random noise, and the change of ISA between 2000 and 2016 was analysed. The urban area was divided into three circular consecutive urban zones in the cardinal directions from the city centre and each circular zone was further divided into eight segments; thus, a total of 24 spatial sectors were derived. The LST aggregation was analysed in different directions and urban segments and hotspot density was further calculated based on area proportion of hotspot areas in each sector. Finally, variations of mean normalized LST (NLST), area proportion of ISA, area proportion of ISA with high LST, and area proportion of hotspot area were quantified for all sectors for 2000 and 2016. The four levels of hotspot density were classified for all urban sectors by proportional ranges of 0%–25%, 25%–50%, 50%–75% and 75%–100% for low-, medium-, sub-high, and high density, and the spatial dynamics of hotspot density between the two dates showed that urbanization mainly dominated in sectors south–southeast 2 (SSE2), south–southwest 2 (SSW2), west–southwest 2 (WSW2), west–northwest 2 (WNW2), north–southwest 2 (NSW2), south–southeast 3 (SSE3) and south–southwest 3 (SSW3). This paper suggests a methodology for characterizing the urban thermal environment and a scientific basis for sustainable urban development.

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.

Coupling effects of building-vegetation-land on seasonal land surface temperature on street-level: A study from a campus in Beijing

Quantifying the effects of 2D/3D urban landscape patterns on land surface temperature: A perspective from cities of different sizes

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.

PDF HTML阅读 XML下载 导出引用 引用提醒 城市公园景观秋冬季动态热效应 DOI: 10.5846/stxb201407151437 作者: 作者单位: 西南大学地理科学学院,中国科学院生态环境研究中心 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目(41471150;41230633); 城市与区域生态国家重点实验室自主项目(SKLURE2013-1-02) Diurnal variations of landscape thermal effect in city parks from the later autumn to early winter Author: Affiliation: College of Geography Science, Southwest University,research center for Eco-Environmental Sciences, Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在北京市3个公园中选择裸地、道路、林地、草地4种主要景观类型,通过2013年10月、11月、12月的实地监测数据,定量揭示不同景观类型地表温度、近地表空气温度和湿度的日间变化特征,并分析日间温湿度的均值、幅度大小、空气温湿度的匹配程度。4种景观类型日间地表和空气温度均呈单峰变化趋势,裸地、道路、林地、草地的地表温度最高值分别出现在14:00、12:00、14:00、12:00,空气温度最高值则都在14:00,相对湿度最大值出现在8:00,最小值在14:00。从日间均值来看,地表温度为裸地>道路>草地、林地,裸地的日均空气温度最高,4种景观的相对湿度差异不显著。从日间变化幅度来看,所有景观类型的地表温度均明显大于空气温度,尤其是裸地和道路的日间变化比林地和草地更加明显。通过定量分析4种景观类型的日间热环境动态变化,可以为城市热环境改善、景观设计和生态规划提供科学依据。 Abstract:The urban landscape pattern is increasing changed as cities replace their natural land cover with impervious surfaces, buildings, and other infrastructure. The impacts of landscape changes have become a growing concern particularly for the ecosystem service in recent decades. It is well-known that city parks could provide multiple ecosystem services including temperature regulation services in different seasons. Some thermal characteristics of city parks may directly contribute to the degree of comforts for the mankind, such as the land surface temperature (LST), near-surface air temperature (NAT), and relative humidity (RH). Generally, landscape types and pattern in parks are responsible for the extent and degree of local thermal effects. In this study, three city parks were selected to analysis the diurnal variations of thermal effects in Beijing. We measured LST, NAT, and RH in the grassland, forest, road, and bare land for three months from the later autumn to early winter. The study aims to assess the variations of LST, NAT, and RH as well as their relationships among different landscape types. The results showed that: (1) the lowest LST and NAT are at 8:00 whereas the highest values are different according to the types of landscape. The highest values of LST and NAT are at 14:00,12:00,14:00,12:00, respectively. The relative humidity reaches its lowest value at 14:00 (2) In the daytime, the decreasing orders of mean LST, NAT, and RH are bare land > road > grassland≈forest, bare land > forest≈grassland=road, grassland≈road≈forest≈bare land, respectively. The LST and NAT are increasing with the decreasing RH for all landscape types. (3) The maximum difference in LST, NAT, and RH decrease as: bare land (13.5℃) > road (10.0℃) > grassland (7.5℃) > forest (5.9℃), bare land (7.4℃) ≈ road (7.3℃) > forest (6.3℃) > grassland (5.7℃), bare land (20.8%) = road (20.8%) ≈grassland (19.5%)≈forest (18.8%), respectively. (4) The relationship between LST and NAT are complex due to the various solar radiations in a day. Specifically, the daily variation of LST is greater than that of NAT for all the landscapes. The daily variation of LST and NAT is low in forest compared with the high variations in temperature of the grassland, bare land, and road. (5) The combination of temperature and humidity is represented by the difference between NAT and dew-point temperature. The hydrothermal environment reaches the driest and hottest conditions at 14:00. By quantifying thermal effects of different landscape types in city parks, this study can provide useful implication on the urban management to improve the thermal environment and related ecosystem services. 参考文献 相似文献 引证文献

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