以上海市中心城区为例，首先采用SPOT影象，人工解译出城市土地利用的类型，同时利用Landsat7卫星，ETM＋影象的热红外波段反演每个像元内的陆地表面温度，分析陆地表面温度在不同土地利用类型之间的差异，进一步分析不同土地空间分布格局与地面温度之间的关系。研究结果显示：上海市地表温度具有明显中心城区高、郊区低的热岛效应存在；在城市不同土地利用类型上的表面温度具有显著性差异，两两之间的比较揭示，城市地面温度在大多数土地利用类型之间的差异是显著的。城市内部不同土地类型所产生的热环境效应不同；城市土地类型在空间布局上越复杂，其产生的热岛效应越明显。

This paper compares the normalized difference vegetation index (NDVI) and percent impervious surface as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the land surface temperature (LST), percent impervious surface area (%ISA), and the NDVI. Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data were used to estimate the LST from four different seasons for the Twin Cities, Minnesota, metropolitan area. A map of percent impervious surface with a standard error of 7.95% was generated using a normalized spectral mixture analysis of July 2002 Landsat TM imagery. Our analysis indicates there is a strong linear relationship between LST and percent impervious surface for all seasons, whereas the relationship between LST and NDVI is much less strong and varies by season. This result suggests percent impervious surface provides a complementary metric to the traditionally applied NDVI for analyzing LST quantitatively over the seasons for surface urban heat island studies using thermal infrared remote sensing in an urbanized environment.

在ArcGIS支撑下,基于1982—2010年8 km分辨率的AVHRR NDVI及气温和降水数据,应用最小二乘法和地理加权回归方法,构建中国NDVI与气候因子的地理加权回归模型,定量分析中国NDVI与气温和降水的相互关系,获取各个回归参数的空间格局,并将模拟结果与全局性回归结果进行对比。结果表明,与线性回归模型相比,地理加权回归模型的拟合效果显著提高,拟合优度从0.3提高到0.6。气候因子对NDVI的影响具有空间异质性：从北到南,气候因子对NDVI的影响逐渐减小;西北内陆等干旱荒漠地带,气候因子对NDVI的影响较大。对中国大部分地区而言,气温对NDVI的影响超过降水。各区NDVI与主导气候因子发生作用的特征尺度不同。

植被覆盖动态监测及与气候变化的响应,是陆地生态系统研究的重要内容。本文以2001-2013年间京津冀地区MOD13A 3月合成NDVI数据,结合生长季的降水和气温资料,运用偏相关和复相关分析、趋势分析方法,研究了该区域NDVI的变化特征和空间分布,以及其区域植被覆盖变化的气候驱动力。结果表明,该区域NDVI最大值在13a间缓慢增加,植被覆盖呈现改善趋势;NDVI和生长季降雨量及平均气温的平均偏相关系数分别为0.20和-0.14,表明在年际变化水平上,京津冀地区NDVI总体与降水量呈正相关,与平均气温呈负相关,且降水对NDVI的影响大于温度对NDVI的影响。对植被覆盖驱动分区得出,降水和气温驱动型占区域面积的5.68%;单独降水驱动型和气温驱动型分别占4.51%、0.18%;区域内植被覆盖变化主要受非气候因子驱动型为主,所占比例为89.63%,表明人类活动对植被变化的影响巨大。

Urban surface temperature distribution is closely linked to its ground vegetation cover. Determining the correlation between the urban surface temperature and its ground vegetation has an important role in the urban surface temperature improvement and the urban environment optimization. This paper selects the TM data in the summer of 2009 to obtain the normalized difference vegetation index (NDVI) and selects 129 samples in the seasons acquired by the images to do the actual measurement of the surface temperature. Then it analyzes the quantitative relationship between the surface measured temperature and normalized difference vegetation index finding out that there exists a negative correlation; Their regression fits to obtain the type of relationship: Ts = 37-14.64 * NDVI. The identification of the correlation between the urban temperature and vegetation index provides a relatively reliable theoretical basis and technical support to the urban planning, and urban ecological environment construction.

The coupling relationship between urban vegetation and land surface temperature (LST) has been heatedly debated in a variety of environmental studies. This paper studies the urban vegetation information and LST by utilizing a series of remote sensing imagery covering the period from 1990 to 2007. Their coupling relationship is analyzed, in order to provide the basis for ecological planning and environment protection. The results show that the normalized difference vegetation index (NDVI), urban vegetation abundance (UVA) and urban forest abundance (UFA) are negatively correlated with LST, which means that both urban vegetation and urban forest are capable in decreasing LST. The apparent influence of urban vegetation and urban forest on LST varies with the spatial resolution of the imagery, and peaks at the resolutions ranging from 90 m to 120 m.

Abstract There is now ample evidence of the ecological impacts of recent climate change, from polar terrestrial to tropical marine environments. The responses of both flora and fauna span an array of ecosystems and organizational hierarchies, from the species to the community levels. Despite continued uncertainty as to community and ecosystem trajectories under global change, our review exposes a coherent pattern of ecological change across systems. Although we are only at an early stage in the projected trends of global warming, ecological responses to recent climate change are already clearly visible.

This article analysed the spatio-temporal changes in vegetation cover in the Beijing–Tianjin sandstorm source region in China and related these changes to vegetation types based on the Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data set from 1982 to 2006. The annual maximum NDVI and peak time were identified. The different periods (1–12 months) of accumulated precipitation before the peak time were then calculated at the grid scale for each year. On this basis, the NDVI–rainfall relationship and the temporal responses in this area were studied by calculating the correlation coefficient between the annual maximum NDVI and different periods (1–12 months) of accumulated precipitation before the occurrence of the annual maximum NDVI for each pixel. The results show an upward trend in regional vegetation, a significant recovery efficiency for grassland, and the evident degradation of cropland. Peak plant growth is significantly related to precipitation and is strongly positively correlated with precipitation in the previous period (1 month) regardless of vegetation type. The regions showing the strongest correlations between peak plant growth and 1 month cumulative rainfall are the western desert grassland, grassland to forest in the transitional hill regions, the mountains of Yanshan, and the Greater Hinggan Mountains.

地表温度和归一化植被指数是全球环境变化重要的指示因子,对生态系统的研究具有重要意义.利用经过Savitzky-Golay滤波平滑处理的四川省MODIS地表温度和归一化植被指数数据,研究2001年～2010年全省地表温度和NDVI变化规律,以及高程和坡度对两者的影响.研究结果表明:四川省地表温度和NDVI在年时间序列都有较大的波动,地表温度轻微降低,NDVI小幅增加,二者之间存在一定的负相关性;地表温度和NDVI线性回归斜率在空间上同样也存在较大的负相关性,回归斜率平均值分别是-0.006和0.0025;NDVI与高程和坡度的相关性呈现分段式特征,而地表温度随高程的升高而平缓降低,随着坡度的增加而递减.

Vegetation coverage has a significant influence on the land surface temperature (LST) distribution. In the field of urban heat islands (UHIs) based on remote sensing, vegetation indexes are widely used to estimate the LST-vegetation relationship. This paper devises two objectives. The first analyzes the correlation between vegetation parameters/indicators and LST. The subsequent computes the occurrence of vegetation parameter, which defines the distribution of LST (for quantitative analysis of urban heat island) in Kalaburagi (formerly Gulbarga) City. However, estimation work has been done on the valuation of the relationship between different vegetation indexes and LST. In addition to the correlation between LST and the normalized difference vegetation index (NDVI), the normalized difference build-up index (NDBI) is attempted to explore the impacts of the green land to the build-up land on the urban heat island by calculating the evaluation index of sub-urban areas. The results indicated that the effect of urban heat island in Kalaburagi city is mainly located in the sub-urban areas or Rurban area especially in the South-Eastern and North-Western part of the city. The correlation between LST and NDVI, indicates the negative correlation. The NDVI suggests that the green land can weaken the effect on urban heat island, while we perceived the positive correlation between LST and NDBI, which infers that the built-up land can strengthen the effect of urban heat island in our case study. Although satellite data (e.g., Landsat TM thermal bands data) has been applied to test the distribution of urban heat islands, but the method still needs to be refined with in situ measurements of LST in future studies.

The interaction between vegetation and the atmosphere is important in geosciences and has become a research focus in recent years. As a sensitive indicator of surface vegetation coverage and vegetation growth status, the Normalized Difference Vegetation Index (NDVI) has been widely used in environmental, ecological, and agricultural studies. With the time lag correlation method, characteristics of spatial and temporal response of NDVI to variations in air temperature and precipitation in East China and its surrounding areas were comprehensively examined based on SPOT VGT-NDVI data, monthly air temperature, and precipitation data at 135 meteorological stations during the period 1998-2011. Results indicate that across the entire study region, both air temperature and precipitation have an influence on NDVI, but air temperature plays a more prominent role. In general, the correlation between NDVI and air temperature is stronger in summer and autumn, and the correlation between NDVI and precipitation is stronger in autumn and spring. In winter, the correlation between NDVI and air temperature and precipitation seem to be low. The lag time of the maximum NDVI in response to air temperature is shorter in spring and autumn. The lag time of the maximum NDVI in response to precipitation is shorter in winter. In summer, the lag time of the maximum NDVI in response to air temperature and precipitation is longer. Spatial distribution of the maximum NDVI-temperature and NDVI-precipitation correlation coefficients varies slightly between the northern and southern parts of the study region in winter, spring and autumn, but in summer, it shows a marked difference between the northern and southern parts. The lag time of the maximum NDVI in response to temperature exhibits an obvious difference between the northern and southern parts of the study region in spring, summer, and autumn, but the lag time of the maximum NDVI in response to precipitation has a little difference between the northern and southern parts in all seasons except summer. Characteristics of response of NDVI to variations in air temperature and precipitation are closely related to the monsoon climate and the features of farming systems in East China and its surrounding areas. In the northern and middle parts of the study region, temperature rises in spring and drops in autumn quickly, and the precipitation is mainly concentrated in summer. In addition, cropland occupies a larger percentage in East China and its surrounding areas, with relatively consistent sowing- and harvest-time. Both may result in seasonal variations in NDVI and differences in the lag time of the maximum NDVI in response to temperature and precipitation.

基于4个季节的MODIS影像，计算长株潭地区的地表温度、NDBI和NDVI，比较NDBI和NDVI与地表温度之间关系，对地表城市热岛效应研究的指标NDBI和NDVI进行对比分析。结果表明，NDBI与4个季节的地表温度间都存在明显的线性关系，而NDVI与地表温度间关系并不明显且随季节发生变化，说明NDBI是地表城市热岛效应研究的有效指标，在地表城市热岛效应的季节变化研究中NDBI可作为NDVI的一个附加指标。

热岛效应是一种城市化进程中所产生的特有环境问题,是一个地区的气温高于周围地区的现象。为了揭示快速城市化地区热岛的时空变化特点,利用2014年的Landsat8 oli遥感数据,通过遥感算法反演合肥市地表温度,并对合肥市热岛分布及成因加以分析,同时分析了归一化植被指数（NDVI）、归一化建筑指数（NDBI）与热岛分布的关系,以及城市下垫面对热岛效应的影响,并对城市热场进行生态评价分析。结果表明,合肥市四季均存在热岛现象,热岛强度表现为夏季最强,最高温度达57.86℃,秋季次之,春季、冬季较弱。春、夏、秋3个季节热岛多集中在主城区,冬季热岛多分布在乡镇及裸土区,城区热岛强度较弱。热岛效应多集中在不透水面和裸土区,城市冷岛多出现在水体位置。城市热岛分布与归一化植被指数呈负相关关系,与归一化建筑指数呈正相关关系。改进半径法可以较好区分城市建成区,建成区与郊区温度分布存在明显差异。

本文利用2008-2011年HJ-1B/CCD可见光-近红外数据,以及HJ-1B/IRS热红外数据,采用遥感算法反演北京市地表温度,并用MODIS地表温度产品对反演结果进行了初步验证。同时分析了北京市热岛效应的年际、年内变化趋势。另利用热场变异指数分析其空间分布特征,以及NDVI、NDBI与城市下垫面对热岛效应的影响。结果表明:(1)2008-2010年北京市热岛强度总体呈上升趋势,2011年有所缓解,4年热岛强度分别为:5.2℃、5.2℃、9.2℃、8.2℃;(2)北京市2010年四季存在明显热岛现象,夏季最强,春、秋次之,冬季最弱,四季热岛强度分别为8.2℃、9.4℃、9.2℃、4.3℃;(3)2008-2011年北京市热岛空间分布特征表明,房山区和大兴区的南部热岛效应逐年缓解,2011年昌平区热岛效应比前3年明显,植被和水体形成城市冷岛;(4)地表温度与NDVI呈明显负相关,与ND-BI呈正相关,城市热岛效应与下垫面类型存在明显相关性。

The formation and occurrence of urban heat island (UHI) is a result of rapid urbanization and associated concretization. Due to intensification of heat combined with high pollution levels, urban areas expose humans to unexpected health risks. In this context, the study aims at comparing the UHI in the two largest metropolitan cities of India, i.e., Delhi and Mumbai. The presence of surface UHI is analyzed using the Landsat 5 TM image of 5 May 2010 for Delhi and the 17 April 2010 image for Mumbai. The validation of the heat island is done in relation to the Normalized Difference Vegetation Index (NDVI) patterns. The study reveals that built-up and fallow lands record high temperatures, whereas the vegetated areas and water bodies exhibit lower temperatures. Delhi, an inland city, possesses mixed land use and the presence of substantial tree cover along roads; the Delhi Ridge forests and River Yamuna cutting across the city have a high influence in moderating the surface temperatures. The temperature reaches a maximum of 35 °C in West Delhi and a minimum of 24 °C in the east at the River Yamuna. Maximum temperature in East Delhi goes to 30 °C, except the border areas. North, Central and south Delhi have low temperatures (28 °C–31 °C), but the peripheral areas have high temperatures (36 °C–37 °C). The UHI is not very prominent in the case of Delhi. This is proven by the correlations of surface temperature with NDVI. South Delhi, New Delhi and areas close to River Yamuna have high NDVI and, therefore, record low temperatures. Mumbai, on the other hand, is a coastal city with lower tree cover than Delhi. The Borivilli National Park (BNP) is in the midst of dense horizontal and vertical growth of buildings. The UHI is much stronger where the heat is trapped that is, the built-up zones. There are four small rivers in Mumbai, which have low carrying capacity. In Mumbai suburban district, the areas adjoining the creeks, sea and the lakes act as heat sinks. The coastal areas in South Mumbai record temperatures of 28 °C–31 °C; the Bandra-Kurla Complex has a high range of temperature i.e., 31 °C–36 °C. The temperature witnessed at Chattrapati Shivaji International Airport is as high as 38 °C. The temperature is nearly 37 °C–38 °C in the Dorai region in the Mumbai suburban district. The BNP has varied vegetation density, and therefore, the temperature ranges from 27 °C–31 °C. Powai Lake, Tulsi Lake and other water bodies record the lowest temperatures (24 °C–26 °C). There exists a strong negative correlation between NDVI and UHI of Mumbai, owing to less coverage of green and vegetation areas.

位于珠江三角洲的广州市在城市化进程的推动下,城市空间快速扩展。本文利用1990年、2000年、2008年的Landsat遥感影像对广州市的城市扩展及其热环境效应进行了分析,采用IBI建筑指数和NDVI植被指数分别获取了建筑用地和植被信息,然后讨论了城市热环境与这些地表参数的定量关系。结果发现广州市建筑用地与地表温度呈指数型正相关关系,高比例建筑用地地区的升温要比低比例建筑用地地区大0.3℃;而植被则对地表温度起降温作用。总的看来,尽管1990年~2008年间广州城市建成区的面积持续扩张,但是其城市热岛效应并不是一直在增强,而是呈现出先增强后减弱的趋势。

[1] This paper presents results of validating the Collection 4 Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product using LAI data collected in a 3 3 km agricultural (grasses and cereal crops) area near Avignon, France, and 30 m resolution Enhanced Thematic Mapper (ETM+) image. Estimates of the accuracy, precision, and uncertainty with which the ETM+ data convey information about LAI underlie the derivation of a 30 m resolution reference LAI map by accounting for both field measurement and satellite observation errors. The 30 m reference LAI was then extrapolated from sampling points to a 58 km2 area without loss in the quality and was degraded to a 1 km resolution LAI map. The latter was taken as a reference to assess the quality of the MODIS LAI product. Comparison of the reference and corresponding MODIS retrievals suggests that Collection 4 MODIS LAI is accurate to within an accuracy of 0.3 with a precision and uncertainty of 0.23 and 0.38, respectively. It was found that the Collection 3 MODIS land cover product, input to the Collection 4 operational LAI algorithm, misclassified the 58 km2 area as broadleaf crops. The use of correct biome type in the operational processing improves the accuracy in LAI by a factor of 2 with an almost unchanged precision and uncertainty. Our results also indicate that the retrieval of LAI from satellite data is an ill-posed problem; that is, small variations in input due to observation errors result in a very low precision of the desired parameter. Any retrieval technique based on a simple model inversion or empirical relationships is unable to generate stable retrievals. The use of information on input errors in the retrieval technique is necessary to generate solutions to the ill-posed problem. The MODIS operational LAI algorithm meets this requirement.