基于MODIS 数据的南京市夏季城市热岛分析

城市热岛效应是当前城市环境与气候主要研究内容之一。地表温度与气温之间有紧密的联系, 通过遥感反演地表温度已成为研究城市热岛的有效手段。利用MODIS 数据, 获取地表比辐射率与大气透过率2 个基本参数, 运用劈窗算法反演南京市夏季地表温度。基于不同时相的MODIS数据, 对4 幅南京市地表温度反演图像作对比分析, 较好地显示了南京市城市热岛的空间分布、热岛范围和城市热岛强度, 结果表明南京市夏季热岛问题较为严重。     

利用Landsat-8影像的南昌地区热岛效应研究

针对南昌地区热岛效应问题,提出采用改进的单窗算法反演地表温度,在利用实测数据验证了本算法可靠性的同时,分析了南昌地区热岛效应的空间分布与季节变化特征及其与植被覆盖度、土地利用分类、水体等影响因子之间的关系。结果表明,该文所用单窗算法是有效的,南昌地区热岛时空现象明显,秋季的热岛效应强度最强,冬春季次之,夏季最弱。不同土地利用类型对热岛效应影响不同;地表温度与植被覆盖度之间存在明显的负线性相关;水体对降低地表温度效果显著。     

一带一路沿海超大城市热岛时空特征遥感分析

城市热岛不仅影响城市局地及区域气候,而且对城市空气质量、能源消耗、居民健康等有显著的负面作用。利用长时序遥感数据,系统地分析各超大城市热岛的时空特征,能够为城市热岛效应减缓政策的制定提供参考,对带路城市可持续发展具有重要意义。基于2001~2017年MODIS地表温度产品和Landsat土地利用分类数据,以城市热岛强度（Surface Urban Heat Island Intensity, SUHII）作为指标,从季节和年际的角度分析一带一路沿海超大城市2001~2017年热岛效应时空格局的变化。研究结果表明：①2001~2017年期间各超大城市的核心区存在扩张趋势,高强度热岛主要分布在人口活动密集的城市核心区;②年均城市热岛强度最大的城市是卡拉奇,多年SUHII平均值为3.02 ℃,热岛强度显著上升的是金奈（0.07 ℃/a,P<0.1）;③各城市热岛强度存在季节性差异,其中夏季城市热岛强度最大的城市是伊斯坦布尔,SUHII平均值为2.88 ℃,冬季城市热岛强度最大的城市是卡拉奇,SUHII平均值为4.45 ℃。     

多源参数在晋江城市热岛分析中的差异性

城市热岛遥感监测分析中,不同参数与指标得到的热岛时空分布结果不同。针对过去研究仅利用遥感反演地表温度或亮度温度监测城市热岛现象,以福建省晋江市为研究区,选择2010年与2014年夏季Landsat数据,结合数字高程数据和气象站点实测数据,反演晋江市域内地表温度与近地表气温。在此基础上,利用多源参数计算多种城市热岛监测指标,并对比分析这些指标用于晋江城市热岛研究的差异性。结果表明:1)2014年较2010年的城市热岛范围扩大,区域内热岛比例显著上升,热岛斑块面积增多且热岛等级增强;2)相对于地表温度,近地表气温用于评价城市热岛空间分布更为合理;3)不同热岛监测指标作用不同,热岛强度、归一化热场强度可以在空间上直接表明热岛空间分布及强弱,热岛比例指数则在数值上反映了不同区域热岛现象的发生概率,热岛源汇指数结合地表热参数和土地覆盖类型,反映了土地利用变化对区域内热岛效应加剧或减缓的贡献程度。     

HJ-1B卫星遥感数据在重庆市城市热环境监测的应用分析

本文以MODIS反演大气透射率,以HJ-1B/CCD分类结果反演地表比辐射率,并基于单窗算法,利用HJ-1B/IRS4数据反演地表温度.在此基础上,提取研究区的热场变异指数来分析重庆热岛空间分布特征,并就NDVI与NDBI对热岛效应的影响进行了分析.其结果如下:1)重庆城市热岛大致位于中梁山、铜锣山之间,呈东北、西南走向分布;2)热岛中心不在市中心,而是集中在大渡口工业园区、江北机场这些能耗大、人口密集区域,热岛强度范围在5?C-10?C之间;3)接近长江、嘉陵江水域的建筑用地密集区域,其热岛效应并不明显;4)NDVI与热岛强度呈负相关关系,NDBI与热岛强度呈现较为明显的正相关关系,二者对热岛都有重要影响,而NDBI的影响更大.因此,利用HJ-1B数据监测城市热环境,能较好地揭示重庆城市热岛空间分布特征,为城市环境监测与改善提供参考.     

基于遥感的长沙市城市热岛效应时空分析

利用TM/ETM+数据,以长沙市为例,反演了地表温度,并针对不同时相的遥感数据,利用城市热岛强度来反映热岛效应强弱的变化。结果表明,长沙市的热岛空间分布与城市建成区的轮廓相吻合。城市热岛的范围随着城市建设、新建开发区以及道路交通网的发展不断增大,且在东南方向为主要增长方向。根据热岛强度鉴别并提取了强热岛区与正常区,发现长沙市强热岛区的面积已经从1993年的13.18km2扩大至2008年的68.16km2。最后,对长沙市热岛未来的发展趋势进行了预测。     

乌海矿区AOD时空变化特征及影响因素

针对大气低空间分辨卫星遥感数据不能准确反映长时间序列矿区小尺度区域的大气环境问题,利用MODIS MAIAC高分辨率数据,分析2003—2018年乌海矿区气溶胶光学厚度(aerosol optical depth,AOD)时空变化特征,采用Theil-Sen median趋势分析、Mann-Kendall检验方法、Pearson相关系数法,分析像元尺度AOD空间变化趋势和AOD与6个影响因子的相关性。结果表明,乌海矿区年际和季度AOD空间分布有明显的差异;乌海矿区年际AOD均值整体呈下降趋势,季度AOD变化秋季最高,春季和冬季较高,夏季最低;大尺度AOD与人为因子呈正相关关系,与自然因子无显著相关性;像元尺度AOD与人为因子和气温因子呈正相关性分布,在中部煤矿开采区及工业园区,与风速和降水因子呈强负相关性分布,在南部,与植被指数的相关性呈分散分布。     

城市群热岛时空特征与地表生物物理参数的关系研究

以长株潭城市群区域为例,利用2005年3个不同季节的TERRA/MODIS数据提取的地表温度、归一化植被指数(NDVI)和归一化建筑指数(NDBI),分析了城市热岛效应及其随季节的变化,采用归一化植被指数(NDVI)和归一化建筑指数(NDBI)作为反映地表生物物理特征的参数,分析了城市热岛时空特征与地表生物物理参数的关系。研究结果表明,研究区域城市热岛效应的季相变化明显,一年中夏季与春季的城市热岛效应相对显著,城市地表温度高出周边的郊区达8~10℃;而冬季城市热岛效应相对不太明显,城市地表温度高出周边的郊区4℃。地表温度与归一化植被指数(NDVI)的相关性随季节变化较为明显,说明通常将归一化植被指数(NDVI)作为城市地表温度或城市热岛的代用指标是不适宜的;然而,地表温度与归一化建筑指数(NDBI)在不同季节都呈显著的线性关系,而且地表温度与NDBI线性关系的斜率和截距能够很好地指示不同季节城市热岛的强度,这就为定量分析不同季节城市地表温度的变化提供了物理指数,也为利用遥感研究城市热岛效应提供了新的方法与途径。     

遥感城市热岛提取的影响因素分析

随着城市化进程的加快,热岛效应成为当今社会热点问题,研究主要集中于热岛强度变化和景观格局影响分析。遥感热岛提取结果受到多方面因素的影响,因此影像选择尤为关键。以石家庄地区为例,选取不同季节、相同季节但植被状态不同的Landsat影像以及ASTER夜间影像,分析季节、农田生长状态、昼夜等因素对遥感热岛提取结果的影响。研究表明：农田作物生长茂盛、平均温度高的季节,遥感热岛提取效果较好且热岛强度较大;农田作物收获后,土地裸露,为保证遥感热岛有效提取应选择夜间数据。实验结果可为热岛研究过程中遥感数据选择和分析提供参考。     

长春市城市扩张及其微气候响应

针对目前城市化和热岛效应研究多为单一定性评价和半定量化研究,缺乏耦合分析的问题,提出了一种可行的定量耦合分析方法。分析了长春市建设用地扩张和热岛效应的时空演变特征及其关系。首先收集遥感数据,采用归一化差分复合指数阈值分类法提取建设用地,之后采用辐射传输法反演地表温度,以此为基础计算扩张强度、热岛强度等指数,进而将城市扩张与微气候变化进行相关性分析。结果表明,长春市建设用地1990—2016年间增加了608.85 km^2,2000年后陡增,扩张模式由面状扩张变为沿道路辐射状扩张、飞地式扩张。城市扩张初期,相对热岛效应显著,随着建设用地的扩大,热岛面积增大,但是强度下降。相关性分析表明,单位格网内城市用地所占面积高于40%时地表温度上升明显。     

Monitoring of urban heat island effect in Beijing combining ASTER and TM data

This paper focuses on the monitoring of the urban heat island (UHI) effect with temporal and spatial variation, combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Thematic Mapper (TM) data. Our study area is located in the central urban area of Beijing, which mainly refers to the areas within the fifth ring road. For detecting UHI changes over the years 2002–2006, three ASTER images in the summers of 2003, 2004 and 2006 and two TM datasets in the summers of 2002 and 2005 were collected. For monitoring UHI changes with the seasons, three ASTER images and one TM image in 2004 in winter, spring, summer and autumn, respectively, were employed. To calculate the urban heat island intensity, the land surface temperatures were retrieved iteratively for ASTER data and using a generalized single-channel method for the TM image. Four separated regions located in four directions outside the fifth ring road were selected as representing rural comparative regions. Their averaged land surface temperature was regarded as the rural comparative temperature. The UHI intensity was computed by the difference between the pixel urban land surface temperature in the urban area and the comparative temperature in the rural area. Detection of the UHI effect over 2002 to 2006 indicated that most of the areas with high UHI effect were the industrial land use regions and the areas having a high density of buildings, roads, transportations and residents; and the areas without UHI effect were located around the regions with large areas of grassland, trees and water bodies. Our results also showed that the UHI effect was not proportional to urbanization over time. Statistical UHI data during 20 July to 20 September in 2003–2008 also support this point. The monitoring of the UHI effect over seasons (winter, spring, summer and autumn) showed that the urban area of Beijing city had a high UHI effect except in winter, when the urban area of Beijing was in an urban heat sink; the UHI effect increased in spring, summer and autumn.     

基于TM影像的城市热岛效应监测与预测分析

近些年,随着城市建设的高速发展,热岛效应也越来越被人们所重视。采用1999年、2005年、2009年的TM遥感影像,分析了东营市的热岛效应时空演化规律并应用马尔科夫模型对其未来发展趋势进行了预测。研究发现：东营市热岛效应明显且稳定存在,热岛中心主要分布在西城区、东城区人口密度大以及工业区聚集的地方,植被和水体有减弱热岛效应的作用。随着城市的扩展,热岛区分布变广,呈现小而广的分布状态,且随着城市注重环境治理和注重绿化,未来10年东营市热岛效应有减弱的趋势,但是速度较慢。     

Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures

The term urban heat island describes the phenomenon of altered temperatures in urban areas compared to their rural hinterlands. A surface urban heat island encompasses the patterns of land surface temperatures in urban areas. The classical indicator to describe a surface urban heat island is the difference between urban and rural surface temperatures. However, several other indicators for this purpose have been suggested in the literature. In this study, we compared the eleven different indicators for quantifying surface urban heat islands that were most frequently used in recent publications on remote sensing-based urban heat island assessments. The dataset used here consists of 263 European cities with monthly mean temperatures from MODIS data products for July 2002, January 2003 and July 2003. We found that (i) the indicators individually reveal diurnal and seasonal patterns but show rather low correlations over time, and (ii) for single points in time, the different indicators show only weak correlations, although they are supposed to quantify the same phenomenon. Differentiating cities according to thermal climate zones increased the relationships between the indicators. Thus, we can identify temporal aspects and indicator selection as important factors determining the estimation of urban heat islands. We conclude that research should take into account the differences and instabilities of the indicators chosen for quantifying surface urban heat islands and should use several indicators in parallel for describing the surface urban heat island of a city.     

Remote-sensing image-based analysis of the patterns of urban heat islands in rapidly urbanizing Jinan,China

According to the UN Population Reference Bureau, 1.4 billion more people will have settled in urban areas by 2030. One of the key environmental effects of rapid urbanization is the urban heat island (UHI) effect. Understanding the mechanism of surface UHIs associated with land-use/land-cover (LULC) change patterns is important for improving the ecology and sustainability of cities. In this article, time series Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) data were used to extract LULC data and land surface temperature (LST) data for the city of Jinan, China, from 1987 to 2011, a period during which the city experienced rapid urbanization. With the aid of a geographical information system (GIS) and remote sensing (RS) approach, the changes in this urban area’s LULC were explored, and the impact of these changes on the spatiotemporal patterns and underlying driving forces of the surface UHI effect were further quantitatively characterized. The results show that significant changes in land use and land cover occurred over the study period, with loss of farmland, forest, and shrub vegetation to urban use, leading to spatial growth of impervious surfaces. Consequently, the land surface characteristics and spatiotemporal patterns of the UHI have changed drastically. According to the seasonal and inter-annual variations in intensity of UHIs, mean differences in UHI intensity between city centre, peri-urban, and nearby rural areas were stronger during summer and spring and weaker during winter and autumn. Spatially, there were significant LST gradients from the city centre to surrounding rural areas. The city centre exhibited higher LSTs and remarkable variation in LSTs, while the surrounding rural areas exhibited lower LSTs and lower variation in LSTs. Moreover, the analysis of LSTs and indices showed that great differences of temperature even existed in a LULC type except for variations between different LULC types. In addition, a local-level analysis revealed that the intensity of the UHI effect is proportional to the size of the urban area, the population density, and the frequent occurrence of certain activities.     

北京市热岛演变遥感研究

利用1987年9月26日，1994年8月28日和2001年8月31日3期TM图像对北京市的热岛状况进行了研究。发现从1987到1994年和从1994到2001年，北京的热岛比例一直在增加，但第二阶段与第一阶段比。增加速度有所减缓。     

上海城市热场与植被覆盖的关系研究

以TM和ETM+遥感数据,反演了自20世纪80年代以来的6个特定年份的上海市地表温度,并以此来分析上海城市热岛扩展的时空演变格局,结果表明:上海城市热岛范围不断扩大,强度不断增加,分布格局逐渐由集中分布呈现片状破碎化分布;上海市建成区的扩展是导致城市热岛范围扩大、强度加大的最直接,也是最根本的原因之一。相关分析和回归分析的结果表明:植被覆盖与地表温度具有明显的负相关关系,植被分布面积的增加对城市热岛强度的降低具有非常积极的 作用。