On Urban Heat Island of Beijng Based on Landsat TM Data

IntroductionThe scientists have begun to retrieve land sur-face temperature (LST) fromsatellite data sincethe launch of TIROS-Ⅱin 60s of the 20th centu-ry . With the development of remote sensingtechnology and its application, more and moreLST retrieval …     

On urban heat island of Beijing based on landsat TM data

Land surface temperature (LST) of Beijing area was retrieved from Landsat TM thermal band data utilizing a radiative transfer equation and the urban heat island (HUI) effects of Beijing and its relationship with land cover and normalized difference vegetation index (NDVI) were discussed. The result of LST showed that the urban LST was evidently higher than the suburban one. The average urban LST was found to 4. 5°C and 9°C higher than the suburban and outer suburban temperature, respectively, which demonstrated the prominent UHI effects in Beijing. Prominent negative correlation between LST and NDVI was found in the urban area, which suggested the low percent vegetation cover in the urban area was the main cause of the urban heat island.     

Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan,China

Urban heat islands (UHIs) have attracted attention around the world because they profoundly affect biological diversity and human life. Assessing the effects of the spatial structure of land use on UHIs is essential to better understanding and improving the ecological consequences of urbanization. This paper presents the radius fractal dimension to quantify the spatial variation of different land use types around the hot centers. By integrating remote sensing images from the newly launched HJ-1B satellite system, vegetation indexes, landscape metrics and fractal dimension, the effects of land use patterns on the urban thermal environment in Wuhan were comprehensively explored. The vegetation indexes and landscape metrics of the HJ-1B and other remote sensing satellites were compared and analyzed to validate the performance of the HJ-1B. The results have showed that land surface temperature (LST) is negatively related to only positive normalized difference vegetation index (NDVI) but to Fv across the entire range of values, which indicates that fractional vegetation (Fv) is an appropriate predictor of LST more than NDVI in forest areas. Furthermore, the mean LST is highly correlated with four class-based metrics and three landscape-based metrics, which suggests that the landscape composition and the spatial configuration both influence UHIs. All of them demonstrate that the HJ-1B satellite has a comparable capacity for UHI studies as other commonly used remote sensing satellites. The results of the fractal analysis show that the density of built-up areas sharply decreases from the hot centers to the edges of these areas, while the densities of water, forest and cropland increase. These relationships reveal that water, like forest and cropland, has a significant effect in mitigating UHIs in Wuhan due to its large spatial extent and homogeneous spatial distribution. These findings not only confirm the applicability and effectiveness of the HJ-1B satellite system for studying UHIs but also reveal the impacts of the spatial structure of land use on UHIs, which is helpful for improving the planning and management of the urban environment.     

Bi-temporal characterization of land surface temperature in relation to impervious surface area,NDVI and NDBI,using a sub-pixel image analysis

As more than 50% of the human population are situated in cities of the world, urbanization has become an important contributor to global warming due to remarkable urban heat island (UHI) effect. UHI effect has been linked to the regional climate, environment, and socio-economic development. In this study, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery, respectively acquired in 1989 and 2001, were utilized to assess urban area thermal characteristics in Fuzhou, the capital city of Fujian province in south-eastern China. As a key indicator for the assessment of urban environments, sub-pixel impervious surface area (ISA) was mapped to quantitatively determine urban land-use extents and urban surface thermal patterns. In order to accurately estimate urban surface types, high-resolution imagery was utilized to generate the proportion of impervious surface areas. Urban thermal characteristics was further analysed by investigating the relationships between the land surface temperature (LST), percent impervious surface area, and two indices, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The results show that correlations between NDVI and LST are rather weak, but there is a strong positive correlation between percent ISA, NDBI and LST. This suggests that percent ISA, combined with LST, and NDBI, can quantitatively describe the spatial distribution and temporal variation of urban thermal patterns and associated land-use/land-cover (LULC) conditions.     

光谱指数趋势面的城市地表温度降尺度转换

针对城市及周边区域建造区和自然地表交织分布的特点,探讨了利用归一化植被指数(NDVI)和归一化建造指数(NDBI)构造趋势面的地表温度(LST)降尺度方法,以北京市市区及周边较平坦区域为例实现了LST自960 m向120 m的降尺度转换。分析了LST空间分布特征及NDVI、NDBI对地物的指示性特征;以北京市四至六环为界分析NDVI、NDBI趋势面对地表温度的拟合程度及各自的适用区域;在120 m、240 m、480 m和960 m 4个尺度上评价了NDVI、NDBI和NDVI+NDBI趋势面对LST的拟合程度和趋势面转换函数的尺度效应;对NDVI、NDBI和NDVI NDBI等3种方法的降尺度结果分覆盖类型、分区域对比评价。实验结果表明结合两种光谱指数的NDVI NDBI方法降尺度转换精度有所改善,改善程度取决于地表覆盖类型组合。     

Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects

The urban heat island (UHI) is increasingly recognized as a serious, worldwide problem because of urbanization and climate change. Urban vegetation is capable of alleviating UHI and improving urban environment by shading together with evapotranspiration. While the impacts of abundance and spatial configuration of vegetation on land surface temperature (LST) have been widely examined, very little attention has been paid to the role of vertical structure of vegetation in regulating LST. In this study, we investigated the relationships between horizontal/vertical structure characteristics of urban tree canopy and LST as well as diurnal divergence in Nanjing City, China, with the help of high resolution vegetation map, Light Detection and Ranging (LiDAR) data and various statistical analysis methods. The results indicated that composition, configuration and vertical structure of tree canopy were all significantly related to both daytime LST and nighttime LST. Tree canopy showed stronger influence on LST during the day than at night. Note that the contribution of composition of tree canopy to explaining spatial heterogeneity of LST, regardless of day and night, was the highest, followed by vertical structure and configuration. Combining composition, configuration and vertical structure of tree canopy can take advantage of their respective advantages, and best explain variation in both daytime LST and nighttime LST. As for the independent importance of factors affecting spatial variation of LST, percent cover of tree canopy (PLAND), mean tree canopy height (TH_Mean), amplitude of tree canopy height (TA) and patch cohesion index (COHESION) were the most influential during the day, while the most important variables were PLAND, maximum height of tree canopy (TH_Max), variance of tree canopy height (TH_SD) and COHESION at night. This research extends our understanding of the impacts of urban trees on the UHI effect from the horizontal to three-dimensional space. In addition, it may offer sustainable and effective strategies for urban designers and planners to cope with increasing temperature.     

基于Landsat数据地表特征参数与地表温度关系

以成都市为研究区,定量分析了各地表特征参数与地表温度之间的线性关系。通过对地表温度与归一化植被指数(NDVI)、归一化建筑指数(NDBI)、归一化水汽指数(NDMI)进行局部区域逐像元分析和总体区域统计分析,结果表明NDVI,NDMI,NDBI与地表温度间都存在明显的线性关系,可用于说明地表温度的动态变化,在3月份,NDMI与地温的相关性更优于NDVI。对传统城市热现象研究中,NDMI与NDBI能够用来以NDVI作为分析地表温度随季节而变化的互补的度量标准。     

基于城市地表温度与归一化植被指数对北京市建成区热环境分析

针对研究城市热环境的过程中,利用归一化植被指数(NDVI)进行地表温度(LST)反演,再将LST和NDVI结合说明地物变迁与城市热环境的影响的现状,利用Landsat-8多时相遥感影像、高分辨率影像、公开GIS等多源数据,通过人工交互判读和量化统计分析,实现了2013—2017年北京建成区NDVI变化及其对地表热环境影响分析,再对分析结果进行差值拟合评价。对NDVI阈值分割按照大小为LC1、LC2、LC3、LC4,对LST分为高温区(TH)、常温区(TR)和低温区(TL)。结果表明,2013年至2017年:1)建成区的平均NDVI增加0.03,其中LC1增加1.0%,LC2减少11.6%,LC3区域减少1.7%,LC4区域增加12.3%。2)建成区平均LST增加2.55 K,TH百分比增加0.6%;TR百分比减少1.1%,TL百分比增加0.5%。3)NDVI相对增加,地表温度相对下降以及NDVI相对减少,地表温度相对上升占60%,NDVI相对下降,地表温度相对下降以及NDVI相对增加,地表温度相对上升的占40%。     

Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST)

Land cover changes associated with urbanisation modify microclimate, leading to urban heat islands, whereby cities are warmer than the surrounding countryside. Understanding the factors causing this phenomenon could help urban areas adapt to climate change and improve living conditions of inhabitants. In this study, land surface temperatures (LST) of Aarhus, a city in the high latitudes, are estimated from the reflectance of a thermal band (TIRS1; Band 10; 10.60–11.19 μm) of Landsat 8 on five dates in the summer months (one in 2015, and four in 2018). Spectral indices, modelled on the normalised difference vegetation index (NDVI), using all combinations of the first seven bands of Landsat 8 are calculated and their relationships with LST, analysed. Land cover characteristics, in terms of the percentages of tree cover, building cover and overall vegetation cover are estimated from airborne LiDAR data, building footprints and 4-band aerial imagery, respectively. The correlations between LST, the spectral indices and land cover are estimated.The difference in mean temperature between the rural and urban parts of Aarhus is up to 3.96 °C, while the difference between the warmer and colder zones (based on the mean and SD of LST) is up to 13.26 °C. The spectral index using the near infrared band (NIR; Band 5; 0.85-0.88 μm) and a short-wave infrared band (SWIR2; Band 7; 2.11–2.29 μm) has the strongest correlations (r: 0.62 to 0.89) with LST for the whole study area. This index is the inverse of normalised burn ratio (NBR), which has been used for mapping burnt areas. Spectral indices using different combinations of the infrared bands have stronger correlations with LST than the more widely used vegetation indices such as NDVI. The percentage of tree cover has a higher negative correlation (Pearson’s r: -0.68 to -0.75) with LST than overall vegetation cover (r: -0.45 to -0.63). Tree cover and building cover (r: 0.53 to 0.71) together explain up to 68 % of the variation in LST. Modification of tree and building cover may therefore have the potential to regulate urban LST.     

Area Delineation and Spatial-Temporal Dynamics of Urban Heat Island in Lanzhou City,China Using Remote Sensing Imagery

One of the key impacts of rapid urbanization on the environment is the effect of urban heat island (UHI). By using the Landsat TM/ETM+ thermal infrared remote sensing data of 1993, 2001 and 2011 to retrieve the land surface temperature (LST) of Lanzhou City, and by adopting object-oriented fractal net evolution approach (FNEA) to make image segmentation of the LST, the UHI elements were extracted. The G* index spatial aggregation analysis was made to calculate the urban heat island ratio index (URI), and the landscape metrics were used to quantify the changes of the spatial pattern of the UHI from the aspects of quantity, shape and structure. The impervious surface distribution and vegetation coverage were extracted by a constrained linear spectral mixture model to explore the relationships of the impervious surface distribution and vegetation coverage with the UHI. The information of urban built-up area was extracted by using UBI (NDBI-NDVI) index, and the effects of urban expansion on city thermal environment were quantitatively analyzed, with the URI and the LST grade maps built. In recent 20 years, the UHI effect in Lanzhou City was strengthened, with the URI increased by 1.4 times. The urban expansion had a spatiotemporal consistency with the UHI expansion. The patch number and density of the UHI landscape were increased, the patch shape and the whole landscape tended to be complex, the landscape became more fragmented, and the landscape connectivity was decreased. The heat island strength had a negative linear correlation with the urban vegetation coverage, and a positive logarithmic correlation with the urban impervious surface coverage.     

Land surface temperature and its impact factors in Western Sichuan Plateau,China

The understanding influence of multiple factors variations on land surface temperature (LST) remains elusive. LST was retrieved by the atmospheric correction algorithms. Based on the correlation coefficients, stepwise regression analysis was developed to examine how multiple factors variability led to LST variations. The differences in LST between impact factors vary depending on time in a day. The elevation and land use types significantly affect the LST in sunny slope or shadow areas has a significantly quadratic curve correlation or a negative linear correlation with it, the influence of slope and aspect is not very significant. LST for forestland, grassland and bare land in the sunny slope and shadow area was the cubic polynomial related to its elevation. Normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) effectively express LST in mountainous. LST and NDMI or NDVI have a significantly negative correlation, NDMI is more effective and more applicable for the expression of LST.     

An integrated methodology for soil moisture analysis using multispectral data in Mongolia

Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.     

Landsat ETM＋数据的武汉市地表温度反演研究

以Landsat ETM＋为主要遥感数据源,辅助以气象数据资料,利用NDVI计算出植被覆盖率,继而计算出地表比辐射率,并利用单窗算法精确反演得到了武汉市2002年夏季地表温度。研究表明,武汉城市热岛由武昌、汉口、汉阳三大城区热岛构成,且几乎集中在城区;城区大热岛区中还存在多数热岛效应更为严重的＂岛中岛＂;武汉热岛分布与...     

Land surface temperature and emissivity estimation for Urban Heat Island assessment using medium- and low-resolution space-borne sensors: A review

The Urban Heat Island (UHI) phenomenon, a typical characteristic on urban landscapes, has been recognised as a key driver to the transformation of local climate. Reliable retrieval of urban and intra-urban thermal characteristics using satellite thermal data depends on accurate removal of the effects of atmospheric attenuations, angular and land surface emissivity. Several techniques have been proposed to retrieve land surface temperature (LST) from coarse resolution sensors. Medium spatial resolution sensors like the Advanced Space-borne Thermal Emission and Reflection Radiometer and the Landsat series offer a viable option for assessing LST within urban landscapes. This paper reviews the theoretical background of LST estimates from the thermal infrared part of the electromagnetic spectrum, LST retrieval algorithms applicable to each of the commonly used medium-resolution sensors and required variables for each algorithm. The paper also highlights LST validation techniques and concludes by stipulating the requirements for LST temporal and spatial configuration.     

Land surface temperature retrieval and its validation using NOAA AVHRR thermal data

The retrieval of land (soil-vegetation complex) surface temperature (LST) was carried out over semi-arid mixed agriculture landscape of Gujarat using thermal bands (channel 4 and 5) and ground emissivity from atmospherically corrected NDVI of NOAA AVHRR LAC images. The atmospheric correction of Visible and NIR band reflectance was done using SMAC model. The LST computed from split-window method and subsequently corrected with fractional vegetation cover were then compared with near synchronous ground observations of soil and air temperatures made during 13–17 January and April, 1997 at five Land Surface Processes Experiment (LASPEX) sites of Anand, Sanand, Derol, Arnej and Khandha covering 100 km x 100 km. The fractional vegetation cover corrected LST at noon hrs. varied from 301.6 – 311.9K in January and from 315.8 – 325.6K in April. The LST_corr were found to lie in the mid way between AT and ST during January. But in April, LST were found to be more close to ST which may be due to relatively poor vegetation growth as indicated by lower NDVI values in April indicating more contribution to LST from exposed soil surface.     

Satellite-based analysis of the role of land use/land cover and vegetation density on surface temperature regime of Delhi,India

The knowledge of the surface temperature is important to a range of issues and themes in earth sciences central to urban climatology, global environmental change and human-environment interactions. The study analyses land surface temperature (LST) estimation using temporal ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) datasets (day time and night time) over National Capital Territory Delhi using the surface emissivity information at pixel level. The spatial variations of LST over different land use/land cover (LU/LC) at day time and night time were analysed and relationship between the spatial distribution of LU/LC and vegetation density with LST was developed. Minimum noise fraction (MNF) was used for LU/LC classification which gave better accuracy than classification with original bands. The satellite derived emissivity values were found to be in good agreement with literature and field measured values. It was observed that fallow land, waste land/bare soil, commercial/industrial and high dense built-up area have high surface temperature values during day time, compared to those over water bodies, agricultural cropland, and dense vegetation. During night time high surface temperature values are found over high dense built-up, water bodies, commercial/industrial and low dense built-up than over fallow land, dense vegetation and agricultural cropland. It was found that there is a strong negative correlation between surface temperature and NDVI over dense vegetation, sparse vegetation and low dense built-up area while with fraction vegetation cover, it indicates a moderate negative correlation. The results suggest that the methodology is feasible to estimate NDVI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban area. The analysis also indicates that the relationship between the spatial distribution of LU/LC and vegetation density is closely related to the development of urban heat islands (UHI).     

Analysing spatio-temporal footprints of urbanization on environment of Surat city using satellite-derived bio-physical parameters

Our study examines the relationships among various environmental variables in Surat city using remote sensing. Landsat Thematic Mapper satellite data were used in conjugation with geospatial techniques to study urbanization and correlation among satellite-derived biophysical parameters namely, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), normalized difference bareness index (NDBaI) and land surface temperature (LST). A modified NDWI (MNDWI) was used for extracting areas under water. Land use/land cover classification was performed using hierarchical decision tree classification technique using ERDAS IMAGINE Expert classifier with an accuracy of 90.4% for 1990 and 85% for 2009. It was found that city has expanded over 42.75 sq.km within two decades. Built-up, fallow and sediment land use classes exhibited high dynamics with increase of nearly 200% and 50% and decrease of 55% respectively from 1990 to 2009. Vegetation and water classes were less dynamic with 20% decrease and 15% increase. The transformation of land parcels from vegetation to built-up, vegetation to fallow and fallow to built-up has resulted in increase of LST by 5.5 ± 2.6°C, 6.7 ± 3°C and 3.5 ± 2.9°C, respectively.     

卫星遥感地表温度降尺度的光谱归一化指数法

针对卫星遥感技术监测地表温度(land surface temperature,LST)存在时空分辨率矛盾这一难题,以TsHARP温度降尺度算法为基础,根据地表覆盖类型的不同,分别选择与LST相关性更好的光谱指数(归一化植被指数,NDVI;归一化建造指数,NDBI;改进的归一化水体指数,MNDWI;增强型裸土指数,EBSI)提出了新的转换模型,并从定性和定量两个角度评价了TsHARP法和新模型的降尺度精度。结果表明:两种模型在提高LST空间分辨率的同时又能较好地保持MODIS LST影像热特征的空间分布格局,消除了原始1km影像中的马赛克效应,两种模型均能够达到较好的降尺度效果;全局尺度分析表明,不管是在降尺度结果的空间变异性还是精度方面,本文提出的模型(RMSE:1.635℃)均要优于TsHARP法(RMSE:2.736℃);TsHARP法在水体、裸地和建筑用地这些低植被覆盖区表现出较差的降尺度结果,尤其对于裸地和建筑用地更为明显(|MBE|3℃),新模型提高了低植被覆盖区地物的降尺度精度;不同季节的降尺度结果表明,两种模型都是夏、秋季的降尺度结果优于春、冬季,新模型的降尺度结果四季均好于TsHARP法,其中春、冬季的降尺度精度提升效果要优于夏、秋季。     

北京市热环境时空分异与区划

城市热环境空间区划是采用分区管理的思路来缓解城市社会经济发展与热环境之间矛盾的技术基础。本文构建城市热环境区划模型的思路为:(1)将不同时相的MODIS地表温度数据产品进行正规化、分级,分析2008年北京城市热环境时空分布特征。(2)构建城市热环境影响因素评价体系,并通过空间主成分分析计算得到热环境影响主成分因子。(3)通过自组织映射神经网络,利用热环境影响主因子,进一步对热环境进行空间区划。结果表明,北京夜间较白天城市热岛分布层次感明显,夏季白天较其他季节高温区聚合程度高。区域下垫面组成要素直接影响热环境,北京城市热环境的主成分因子依次为植被覆盖、地形地貌、城市下垫面建设规模和人为热排放,并依此将北京划为7个热环境区域,根据各个分区热环境成因机制差异分别提出热环境改善和调控措施。     

基于NPP-VIIRS夜间灯光数据和Landsat-8数据的城镇建筑用地提取方法改进——以广州市为例

以广州市为例,将Landsat-8-OLS数据与NPP-VIIRS夜间灯光数据相结合提取城镇建筑用地,在常规的NDBI指数、ULI指数方法的基础上进行改进,以获得更加精确的城镇建筑用地信息。首先,利用NPP-VIIRS夜间灯光数据得到城镇建筑用地的大致分布范围,基于Landsat-8-OLS数据提取NDBI、NDVI指数;然后通过分析夜间灯光建筑用地范围、NDVI指数,NDBI指数三者的特性,分别对三者图像进行二值化、求交运算来提取城镇建筑用地;最后将提取结果与传统的NDBI指数、ULI指数方法的提取结果进行对比,利用Google Earth高空间分辨率影像数据进行精度验证。结果表明,结合NPP-VIIRS数据和Landsat-8-OLS数据的多指数提取相交运算,可有效剔除提取结果中包含的裸土信息,与NDBI等指数方法相比,能得到更高的精度。