基于遥感数据的城市地表温度与土地覆盖定量研究

利用Landsat TM数据,以徐州市为研究区,采用单窗算法反演地表温度,通过混合像元分解和V-I-S(植被—不透水面层—土壤)模型将土地覆盖类型分解为对城市热环境具有重要影响的植被、土壤、不透水面层3个分量,最后利用得到的3种地物比例、直接分类后的土地覆盖类型和地表温度对研究区城市热岛的空间分布特征、地表温度与土地覆盖类型以及各种影响因子之间的关系进行定量研究。研究成果能够有效地应用于城市人居环境研究和生态环境过程分析中。     

超大城市地表特征参数估算及其对城市热环境的影响研究

近年来超大城市的下垫面环境发生了显著变化,并对区域生态系统造成了明显的影响。因此有必要定量化下垫面特征参数并研究它们之间的相互关系对城市热环境的影响。以北京市为例,采用2009年6月2日Landsat\|5 TM卫星影像提取城市不透水层百分比、地表温度、土地利用/土地覆盖和植被指数这些典型地表特征参数,并分析它们之间的定量关系。研究结果表明:随着城市化进程加快,北京市高不透水面扩展到六环,六环以内地表温度保持在40 ℃以上,特别是商业区等特高不透水面区域地表温度甚至高达45 ℃,处于城市高温区域,六环以内区域平均温度波动幅度不大。另外,森林和农业用地的降温作用明显,最高降温幅度达到6 ℃,而且夏季裸土地表温度接近高密度居民区地表温度。     

基于ASTER遥感的杭州城市热/冷岛的景观特征分析

以杭州市中心城区为研究对象,基于ASTER热红外遥感数据反演杭州市中心城区地表温度、提取热环境边界,结合Landsat8OLI数据进行土地利用分类来提取不透水面和植被信息。在此基础上主要进行以下两方面研究:通过均值—标准差法提取杭州市热岛和冷岛边界,对比分析热岛和冷岛区内的景观模式差异,识别对杭州市中心城区地表温度影响最大的地物;使用空间梯度分析法来定量揭示杭州市地表温度与植被、不透水面的关系,并分析杭州市地表温度的空间分布特征。结果表明:(1)热岛和冷岛区域内存在较大的景观模式差异,热岛区域内不透水面对温度变化影响最大,而冷岛区域内植被对杭州市中心城区地表温度影响最为明显,且降温效果大于水体;(2)随着与市中心距离的增加,地表温度平均值与不透水面密度趋势线走向基本一致(正相关),与植被密度趋势线走向大致相反(负相关),且不透水面对地表温度的增温效应大于植被的冷却效应。     

广州市地表温度反演与土地利用覆盖变化关系研究

利用环境星1A/1B遥感影像,运用Jiménez-Munoz & Sobrino's普适性单通道算法定量反演广州市的地表温度(Land Surface Temperature,LST) ,结合MNF主成分分析和支持向量机获取的不透水面分布格局,利用面向对象分类方法获得了土地利用覆盖情况,重点研究广州市不透水面、土地覆盖和植被指数与城市热环境的定量关系。研究结果显示:基于大气水汽含量实测数据的JM&S普适性单通道算法反演结果更精确;广州市2009~2011年的不透水面面积和土地覆盖与平均地表温度相关性分析表明:广州市连续3 a呈现城市扩张的现象,城市热效应显著加剧;城市平均地表温度与不透水面面积呈现正相关,与城市的植被指数和裸土指数呈现负相关。     

基于ETM+热红外遥感的武汉市城市地温反演研究

利用ERDAS及ArcGIS软件,通过影像预处理、影像解译,最终提取城区土地分类信息;由单窗算法,以大气辐射传输方程简化的单波段地表温度反演算法为基础,对武汉市2002年ETM+热红外遥感影像及相关气象资料进行地面亮温反演和不同土地利用类型的热效应定量评价研究.研究中采用了基于归一化差值植被指数和基于地表分类相结合的方法确定地表发射率;地温反演引入了热效应贡献度和区域热单元权重指数,对不同地表类型的热贡献度以及植被覆盖与地表温度的关系进行分析.     

山地新城市化进程的地表热通量时空演变——以悦来新城为例

山地城市通常因为地形复杂、气候多变使得局地的地表热通量分布规律与平原城市有较大区别。为探求山地新开发区城市化进程中的地表热通量时空演变规律,利用卫星遥感影像资料和LUMPS、SEBS模型,对代表重庆市未来发展窗口的悦来新城不同土地利用类型的热平衡过程和城市化前、中、后3期的各热通量过程时空演变规律进行了分析,并探讨了土地利用/植被覆盖对各地表热通量的影响,结果表明：（1）悦来新城各土地利用类型的净辐射通量及差值在7月份最大,1月份最小,植被覆盖度是不同土地利用类型显热通量存在差异的因素之一,潜热通量依次为：林地>农田>未利用土地>居民用地,土壤热通量依次为：未利用土地>居民用地>林地>农田;（2）城市化进程使得悦来新城的净辐射低值区域增多,显热通量呈增加趋势且在能量输出中占比最多,潜热通量的低值区有向南北逐渐扩张的趋势,在潜热通量较低的地区土壤热通量和显热通量较高;土壤热通量与显热的分布规律基本一致,均呈增大趋势;（3）土壤热通量与土地利用面积的相关关系是各能量输出因子中最好的;植被覆盖度对各热通量的影响远大于土地利用面积,居民组合用地与植被覆盖度关...     

利用AMSR-E遥感数据反演地表温度——以2008年广东省春季寒害为例

基于2008年1月25日至2008年2月5日期间的AMSR-E/Aqua L2A微波亮度温度数据,以广东省为研究对象,依据微波极化差异指数（MPDI）、归一化植被指数（NDVI）和比率植被指数（RVI）等3种植被指数,将广东省地表植被覆盖情况分为裸地、草地、灌木林、针叶林和阔叶林等5种类型,利用逐步回归分析方法,建立了基于不同植被覆盖类型的微波亮度温度与地面气象温度多元回归模型。同步地面气象温度数据验证表明,本文建立的基于地表植被覆盖分类的多波段地表温度反演模型,地表温度反演精度基本可达到3.0℃,其中有大约86%的地区地表温度反演精度可以控制在2.5℃以内,为广东省作物寒害预测提供了微波遥感技术支持。     

阳江市绿地空间分布及其热环境分析

鉴于Landsat-8热红外数据在城市绿地热环境方面研究的不足,提出一种参数修正后的单通道地表温度算法,可直接用于城市绿地热环境分析。研究以阳江市建成区为例,以Landsat-8的热红外波段为数据源,利用单通道算法反演地表温度,结合土地利用矢量数据计算得到的景观指数,将地表平均温度与绿地景观进行相关性分析。结果表明:Landsat-8能够提供较高空间分辨率的地表温度信息,适合用来研究区域性的城市热环境;绿地景观的组成和空间配置对城市热环境有一定的影响,其中植被覆盖度是地表温度的重要影响因子,以阳江市建成区实验数据为例,植被覆盖度增加10%,地表温度降低0.35℃;绿地斑块相近的地区,平均地表温度会随着斑块密度的增大而升高;不同绿地斑块对周围环境的改善能力有较大差异。     

河谷型城市土地利用类型及格局的热环境效应遥感分析——以兰州市为例

以河谷型城市兰州为例,采用Landsat ETM+遥感影像为基本数据源,定量反演了地表温度（LST）和植被指数（NDVI）,利用GIS空间分析方法,分析了LST和NDVI 在不同土地利用类型之间的差异以及二者之间的定量关系,并引入多样性和聚集度指数,讨论了在不同土地利用的空间组合下,LST和NDVI 的空间差异及相互关系。结果显示:LST和NDVI具有明显的相关性,中心城区LST表现出热岛效应,而NDVI则为低谷效应;土地利用斑块和类型两种尺度水平上LST和NDVI均具有明显负相关的线性关系,城市内部不同土地类型所产生的热环境效应不同;土地利用多样性越丰富、聚集度越小的区域,其温度对地表植被覆盖的敏感性越弱。     

基于Landsat 8影像和能量平衡的西安市热场格局研究!

基于2014年8月15日的Landsat 8影像,通过劈窗算法反演西安中心城区地表温度,定量测算热岛中心范围。估算多种地表能量分量,分析热环境格局与地表能量分量的关系。结果表明:(1)西安中心城区城市热岛集中分布在人口、居住、商业密集区、经济技术开发区以及植被覆盖较差的区域;(2)感热、波文比与地表温度呈正相关,人为热与温度呈不显著正相关,净辐射、潜热与地表温度呈显著负相关;(3)城市热岛的地表能量结构中感热与潜热差异是构成城市热岛差异的主要原因。     

Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval

Data from three thermal sensors with different spatial resolution were assessed for urban surface temperature retrieval over the Yokohama City, Japan. The sensors are Thermal Airborne Broadband Imager (TABI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODerate resolution Imaging Spectroradiometer (MODIS). Two algorithms were developed for land surface temperature (LST) retrieval from TABI image and ASTER thermal infrared (TIR) channels 13 and 14. In addition, ASTER LST and MODIS LST products were also collected. All the LST images were assessed by analyzing the relationship between LST and normalized difference vegetation index (NDVI) and by spatial distributions of LST profiles, derived from typical transects over the LST images. In this study, a strong negative relationship between LST and NDVI has been demonstrated although the degree of correlation between NDVI and LST varies slightly among the different LST images. Cross-validation among the LST images retrieved from the three thermal sensors of different spatial resolutions indicates that the LST images retrieved from the 2 channel ASTER data and a single band TABI thermal image using our developed algorithms are reliable. The LST images retrieved from the three sensors should have different potential to urban environmental studies. The MODIS thermal sensor can be used for the synoptic overview of an urban area and for studying urban thermal environment. The ASTER, with its TIR subsystem of 90-m resolution, allows for a more accurate determination of thermal patterns and properties of urban land use/land cover types, and hence, a more accurate determination of the LST. In consideration of the high heterogeneity of urban environment, the TABI thermal image, with a high spatial resolution of 2 m, can be used for rendering and assessing complex urban thermal patterns and detailed distribution of LST at the individual house level more accurately.     

Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies

Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)-vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth's surface that determine LST.     

Establishing the relationship between urban land-cover configuration and night time land-surface temperature using spatial regression

Studies suggest that urban form can influence microclimate regulation. Remote sensing studies have contributed to these findings through analysis of high-resolution land cover maps, landscape ecology metrics, and thermal imagery. Collectively, these have been referred to as land cover configuration studies. There are three objectives to this study. The first is to assess the relationship between nighttime land surface temperatures (LST) and land cover configuration and composition. The second objective is to outline a comprehensive methodology that includes ordinary least squares (OLS), spatial regression, variable selection, and multicollinearity analysis. Our last objective is to test three hypotheses about the relationship between LST and land cover, which can briefly be described as: 1) the importance of land-use regimes in modeling LST from land cover composition and configuration variables; 2) the strength of the correlation between LST and roads, buildings, and vegetation; and 3) the improved quality of models using landscape metrics in modeling the relationship between LST and land cover. Based on 16 different models (8 OLS, 8 spatial regression) we could confirm the above hypotheses, but we found that the configuration of buildings, roads, and vegetation have a complex relationship with LST. Our interpretation of this complexity, combined with the strength of composition variables, is that parsimonious models, for now, are more useful to urban planners because they are more generalizable. Finally, spatial regression models of land cover configuration and LST demonstrated an improvement over non-spatial linear models (OLS). Spatial regression models reduced heteroskedasticity and clusters of residuals, and tempered coefficients, suggesting that the OLS models could be biased. OLS models were still found to be a valuable tool for exploratory analysis.     

The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data

The relationship between land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) associated with urban land‐use type and land‐use pattern is discussed in the City of Shanghai, China using data collected by the Enhanced Thematic Mapper Plus (ETM+) and aerial photographic remote sensing system. There is an apparent correlation between LST and NDVI from the visual interpretation of LST and NDVI contrasts. Mean LST and NDVI values associated with different land‐use types are significantly different. Multiple comparisons of mean LST and NDVI values associated with pairings of each land‐use type are also shown to be significantly different. The result of a regressive analysis shows an inverse correlation relationship between LST and NDVI within all land‐use polygons, the same to each land‐use type, but correlation coefficients associated with land‐use types are different. An analysis on the relationship between LST, NDVI and Shannon Diversity Index (SHDI) shows a positive correlation between LST and SHDI and a negative correlation between NDVI and SHDI. According to the above results, LST, SHDI and NDVI can be considered to be three basic indices to study the urban ecological environment and to contribute to further validation of the applicability of relatively low cost, moderate spatial resolution satellite imagery in evaluating environmental impacts of urban land function zoning, then to examine the impact of urban land‐use on the urban environment in Shanghai City. This provides an effective tool in evaluating the environmental influences of zoning in urban ecosystems with remote sensing and geographical information systems.     

Characterizing fractional vegetation cover and land surface temperature based on sub-pixel fractional impervious surfaces from Landsat TM/ETM+

Estimating the distribution of impervious surfaces and vegetation is important for analysing urban landscapes and their thermal environment. The application of a crisp classification of land-cover types to analyse urban landscape patterns and land surface temperature (LST) in detail presents a challenge, mainly due to the complex characteristics of urban landscapes. In this article, sub-pixel percentage impervious surface areas (ISAs) and fractional vegetation cover (FVC) were extracted from bitemporal Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data by linear spectral mixture analysis (LSMA). Their accuracy was assessed with proportional area estimates of the impervious surface and vegetation extracted from high-resolution data. A range approach was used to classify percentage ISA into different categories by setting thresholds of fractional values and these were compared for their LST patterns. For each ISA category, FVC, LST, and percentage ISA were used to quantify the urban thermal characteristics of different developed areas in the city of Fuzhou, China. Urban LST scenarios in different seasons and ISA categories were simulated to analyse the seasonal variations and the impact of urban landscape pattern changes on the thermal environment. The results show that FVC and LST based on percentage ISA can be used to quantitatively analyse the process of urban expansion and its impacts on the spatial–temporal distribution patterns of the urban thermal environment. This analysis can support urban planning by providing knowledge on the climate adaptation potential of specific urban spatial patterns.     

The influence of land-cover type on the relationship between NDVI–LST and LST-Tair

Air temperature (T_2m or T_air) measurements from 20 ground weather stations in Berlin were used to estimate the relationship between air temperature and the remotely sensed land surface temperature (LST) measured by Moderate Resolution Imaging Spectroradiometer over different land-cover types (LCT). Knowing this relationship enables a better understanding of the magnitude and pattern of Urban Heat Island (UHI), by considering the contribution of land cover in the formation of UHI. In order to understand the seasonal behaviour of this relationship, the influence of the normalized difference vegetation index (NDVI) as an indicator of degree of vegetation on LST over different LCT was investigated. In order to evaluate the influence of LCT, a regression analysis between LST and NDVI was made. The results demonstrate that the slope of regression depends on the LCT. It depicts a negative correlation between LST and NDVI over all LCTs. Our analysis indicates that the strength of correlations between LST and NDVI depends on the season, time of day, and land cover. This statistical analysis can also be used to assess the variation of the LST–T_2m relationship during day- and night-time over different land covers. The results show that LST_Day and LST_Night are correlated significantly (p = 0.0001) with T_2mDay (daytime air temperature) and T_2mNight (night-time air temperature). The correlation (r) between LST_Day and T_Day is higher in cold seasons than in warm seasons. Moreover, during cold seasons over every LCT, a higher correlation was observed during daytime than during night-time. In contrast, a reverse relationship was observed during warm seasons. It was found that in most cases, during daytime and in cold seasons, LST is lower than T_2m. In warm seasons, however, a reverse relationship was observed over all land-cover types. In every season, LST_Night was lower than or close to T_2mNight.     

基于RS和GIS的地面温度和土地利用/覆被关系研究进展

对地面温度和土地利用／覆被关系的研究，不仅能够深入理解土地利用／覆被变化下城市热环境变化的空间特征和动态变化，而且可为防暑降温、市政建设及土地合理规划和利用提供重要的科学依据。本文从遥感数据的获取、地面温度的反演、GIS技术的运用以及定量研究等方面详细论述了国内外此领域的研究进展。最后文章提出此研究领域存在的一些问题和今后工作所要继续努力的方向，如实现地面温度的精确反演，获取多时段的地面温度数据，加强RS和GIS技术的结合，进一步深入两者关系的定量研究。