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。     

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。     

结合光谱响应函数的Landsat-8影像大气校正研究

针对受大气吸收与散射影响,遥感器得到的测量值与目标物的真实值间存在误差,给反演地表反射率/反照率和地表温度等关键参数带来较大误差,影响图像分析精度的问题,该文利用Landsat-8的光谱响应函数,对OLI多光谱数据进行大气辐射校正和反射率反演,对校正前后的地物光谱曲线和归一化植被指数(Normalized Difference Vegtation Index,NDVI)的变化进行了对比。研究表明:OLI大气校正后较好地恢复各类地物光谱的典型特征;大气校正后NDVI增幅明显;类似的基于光谱响应函数的FLAASH大气校正方法可以为其他的高级陆地成像仪等传感器校正提供依据。     

城市热岛效应评价的植被指数法

研究中将固定气象观测点遥感观测的植被指数和地面辐射温度的日值及周合成值与地面观测的最低气温日值及周平均值作了比较，证明标准偏差植被指数（NDVI）的周合成值和地面辐射温度的周会成值都与地面观测的最低气温有明显关系。不过研究表明，城市与乡村在植被指数（NDVI）方面的差异看来是区分城市与乡村两种环境间地表特征（如蒸发和热贮量）差异的主要标志。     

利用NDVI估算云覆盖地区的植被表面温度研究

干旱监测等实际应用都需要全面掌握地表温度(LST)的空间分布,而云覆盖是这种应用的重要阻碍。试图根据地表温度变化与地表植被之间的相互关系,研究遥感影像中云覆盖区域植被表面温度的估算方法。由于植被的蒸腾作用,植被茂密程度对其表面温度的空间分布有较大影响。这种影响不仅在晴朗无云区域存在,同样适用于云覆盖区域。因此,首先分析云覆盖区域周边无云植被像元的LST与植被指数NDVI之间的关系,建立方程式,然后再利用NDVI在短时间内相对稳定的特点用另一幅图像来获取云覆盖区域的NDVI值,最后根据NDVI与LST之间的关系估计云覆盖植被像元的表面温度。将这一方法应用到山东省聊城市的Landsat ETM+图像,结果表明：当云覆盖范围≤2 000个像元(约1.72 km²)时,通过NDVI来估计云覆盖区域植被表面温度的平均绝对误差<0.7 ℃,均方根误差<1.2 ℃。为了验证其实用性,又将该方法应用于安徽省蚌埠地区的TM图像,云覆盖范围在300个像元以下时,平均绝对误差小于0.1 ℃。因此,可以认为,当云覆盖范围不是很大时,利用NDVI估算云覆盖地区的植被表面温度,具有一定的可行性。     

基于缨帽变换分析地表温度变化

利用缨帽变换提取土壤亮度指数、绿度植被指数、湿度指数等地表参数,利用模型提取归一化植被指数NDVI、比值植被指数RVI、修改型土壤调整指数MSAVI等植被指数和水体指数MNDWI,利用Artis单窗算法估算热红外波段像元尺度地表温度,将地表温度的影响因素作为BP神经网络输入估算30m空间分辨率的亚像元地表温度,分析1989~2006年桂林城区土地利用变化、缨帽变换特征分量变化、植被参数变化、水体指数变化对地表温度的影响机理。     

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.     

不同辐射校正水平下水稻植被指数监测对比分析

归一化植被指数(NDVI)是反映植被长势特征的重要参数之一。获取准确的植被指数对揭示植被长势变化等定量遥感分析至关重要。基于不同辐射校正水平(辐射定标与大气校正),分别利用Landsat ETM+影像的灰度值(DN)、表观(TOA)反射率与地表(Surface)反射率计算相应NDVI,并根据鄱阳湖区野外定点观测数据,从农田、景观尺度揭示不同辐射校正水平下水稻生育期内NDVI动态变化特征。结果表明,根据DN、TOA反射率与Surface反射率提取的NDVI基本上可以反映出年内水稻不同熟制种植信息变化特征,即移栽期NDVI处于谷值,孕穗抽穗期NDVI达到峰值。但相应NDVI逐渐增加,且波动范围逐渐增大。就不同熟制水稻生育期而言,根据DN值计算并构建的NDVI曲线差异较小,而根据TOA反射率与Surface反射率反演的NDVI曲线差异明显。在植被定量遥感研究中,通过大气校正反演地表反射率计算植被指数相对客观准确。     

基于多时相光谱和物候特征的陕西省神木县地物遥感分类研究

开展了时间序列Landsat TM/ETM遥感影像定量化处理与相对辐射校正,提取了陕西神木县不同地物光谱和NDVI物候特征,结合时间序列NDVI物候特征和多时相光谱信息,采用了地表覆盖的决策树分类算法,实现了陕西神木县地物的高精度遥感分类,包括水体、沙地、城镇、耕地、林地、草地及灌丛等7类地物,分类总体精度达95.77%,Kappa系数达0.93。研究结果表明,基于多时相光谱和物候特征的决策树分类算法能够有效集成多时相、多光谱信息,从而克服了单时相影像分类的缺陷,实现了地物的分类。论文研究方法和结果能够为三北防护林区域的生态环境监测与评估提供技术支持。     

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。     

Downscaling MODIS LST in the East African mountains using elevation gradient and land-cover information

Land-surface temperature (LST) is strongly affected by altitude and surface albedo. In mountain regions where steep slopes and heterogeneous land cover are predominant, LST can vary significantly within short distances. Although remote sensing currently provides opportunities for monitoring LST in inaccessible regions, the coarse resolution of some sensors may result in large uncertainties at sub-pixel scales. This study aimed to develop a simple methodology for downscaling 1 km Moderate Resolution Spectroradiometer (MODIS) LST pixels, by accounting for sub-pixel LST variation associated with altitude and land-cover spatial changes. The approach was tested in Mount Kilimanjaro, Tanzania, where changes in altitude and vegetation can take place over short distances. Daytime and night-time MODIS LST estimates were considered separately. A digital elevation model (DEM) and normalized difference vegetation index (NDVI), both at 250 m spatial resolution, were used to assess altitude and land-cover changes, respectively. Simple linear regressions and multivariate regressions were used to quantify the relationship between LST and the independent variables, altitude and NDVI. The results show that, in Kilimanjaro, altitude variation within the area covered by a 1 km MODIS LST pixel can be up to ±300 m. These altitude changes can cause sub-pixel variation of up to ±2.13°C for night-time and ±2.88°C for daytime LST. NDVI variation within 1 km pixels ranged between –0.2 and 0.2. For night-time measurements, altitude explained up to 97% of LST variation, while daytime LST was strongly affected by land cover. Using multivariate regressions, the combination of altitude and NDVI explained up to 94% of daytime LST variation in Kilimanjaro. Finally, the downscaling approach proposed in this study allowed an improved representation of the influence of landscape features on local-scale LST patterns.     

气温与陆地表面温度和光谱植被指关系的研究

Prihodko 和Goward (1997) 在假设气温与浓密植被冠层温度近似的基础上, 利用空间分辨率为1 km NOAA 影像13×13 像素窗口的植被指数和LST 的线性外推求得NDVI= 0. 86 时LST, 作为中间像素气温的估计值, 这种方法假设了两个前提, 即13×13 像元窗口的植被指数和LST 呈负线性相关; 高植被覆盖条件下的LST 与气温相等。 根据Parton 和Logan (1981) 提出的气温时间尺度转换模型将297 个气象观测站获得的最高和最低气温资料计算MODIS Terra 卫星过境时刻的气温, 利用MODIS 每天陆地表面温度(LST ) 产品、16 d 合成植被指数产品, 探讨气温、植被指数和LST 之间的关系, 对Prihodko & Gow ard 法的两个前提进行调研。结果表明: ①在晚上,LST 与植被指数之间相关性很小; 方差分析的结果表明晚上LST 与晚上气温差异不显著, 因此晚上的气温基本可以由LST 代替; ②在白天, 在地形平坦的平原地区, 植被覆盖度范围较大的情况下,LST 与植被指数呈负相关关系, 但是在地形复杂的青藏高原地区和植被覆盖度范围较小(如在沙漠地区) 的情况下, 植被指数与LST 的关系很不明确; ③在白天,LST 与气温的关系随着植被生长状况差异而不同, 在稀疏植被覆盖条件下,LST 大于气温;当植被指数> 0. 7 时, 获得的LST 与气温差异不显著, 这与前人研究成果一致。根据结果②和③, 我们认为Prihodok & Goward 模型应用于区域尺度上计算白天气温存在一定局限性, 特别是应用于我国地形复杂的青藏高原地区和植被稀少的西北荒漠地区。     

Scaling of impervious surface area and vegetation as indicators to urban land surface temperature using satellite data

Vegetation and impervious surface as indicators of urban land surface temperature (LST) across a spatial resolution from 30 to 960 m were investigated in this study. Enhanced thematic mapper plus (ETM+) data were used to retrieve LST in Nanjing, China. A land cover map was generated using a decision tree method from IKONOS imagery. Taking the normalized difference vegetation index (NDVI) and percent vegetation area (V) to present vegetated cover, and the normalized difference building index (NDBI) and percent impervious surface area (I) to present impervious surface, the correlation coefficients and linear regression models between the LST and the indicators were simulated. Comparison results indicated that vegetation had stronger correlation with the LST than the impervious surface at 30 and 60 m, a similar magnitude of correlation at 120 and 240 m, and a much lower correlation at 480 and 960 m. In total, the impervious surface area was a slightly better indicator to the LST than the vegetation because all of the correlation coefficients were relatively high (>0.5000) across the spatial resolution from 30 to 960 m. The indicators of LST, V and I are slightly better than the NDVI and NDBI, respectively, based on the correlation coefficients between the LST and the four indices. The strongest correlation of the LST and vegetation at the resolution of 120 m, and the strongest correlation between the LST and impervious surface at 120, 480 and 960 m, denoted the operational scales of LST variations.     

The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region

ABSTRACT

Land surface temperature (LST) plays a significant role in surface water circulation and energy balance at both global and regional scales. Thermal disaggregation technique, which relies on vegetation indices, has been widely used due to its advantage in producing relatively high resolution LST data. However, the spatial enhancement of satellite LST using soil moisture delineated vegetation indices has not gained enough attention. Here we compared the performances of temperature vegetation dryness index (TVDI), normalized difference vegetation index (NDVI), and fractional vegetation coverage (FVC), in disaggregating LST over the humid agriculture region. The random forest (RF) regression was used to depict the relationship between LST and vegetation indices in implementing thermal disaggregating. To improve the model performance, we used the thin plate spline (TPS) approach to calibrate the RF residual estimation. Results suggested that the models based on TVDI performed better than those based on NDVI and FVC, with a reduced average root mean square error and mean absolute error of 0.20 K and 0.16 K, respectively. Moreover, based on the surface energy balance model, we found the surface evapotranspiration (ET) derived with the TVDI disaggregated LST as inputs achieved higher accuracy than those derived with NDVI and FVC disaggregated LST. It is indicated that TVDI, a soil moisture delineated vegetation indices, can improve the performance of LST enhancement and ET estimation over the humid agriculture region, when combining random forest regression and TPS calibration. This work is valuable for terrestrial hydrology related research.     

Evaluation of LST downscaling algorithms on seasonal thermal data in humid subtropical regions of India

Thermal image downscaling algorithms use a unique relationship between land surface temperature (LST) and vegetation indices (e.g. normalized difference vegetation index (NDVI)). The LST–NDVI correlation and regression parameters vary in different seasons depending on land-use practices. Such relationships are dynamic in humid subtropical regions due to inter-seasonal changes in biophysical parameters. The present study evaluates three downscaling algorithms, namely disaggregation of radiometric surface temperature (DisTrad), sharpening thermal imagery (TsHARP), and local model using seasonal (25 February 2010, 14 April 2010, and 26 October 2011) thermal images. The aggregated Landsat LST of 960 m resolution is downscaled to 480, 360, 240, and 120 m using DisTrad, TsHARP, and the local model and validated with aggregated Landsat LSTs of a similar resolution. The results illustrate that the seasonal variability of the LST–NDVI relationship affects the accuracy of the downscaling model. For example, the accuracy of all algorithms is higher for the growing seasons (February and October) unlike the harvesting season (April). The root mean square error of the downscaled LST increases from 480 to 120 m spatial resolution in all seasons. The models are least suitable in water body and dry-river bed sand areas. However, the downscaling accuracy is higher for NDVI > 0.3. The present study is useful to understand the applicability of the downscaling models in seasonally varied landscapes and different NDVI ranges.     

Disaggregation of LST over India: comparative analysis of different vegetation indices

The non-availability of high-spatial-resolution thermal data from satellites on a consistent basis led to the development of different models for sharpening coarse-spatial-resolution thermal data. Thermal sharpening models that are based on the relationship between land-surface temperature (LST) and a vegetation index (VI) such as the normalized difference vegetation index (NDVI) or fraction vegetation cover (FVC) have gained much attention due to their simplicity, physical basis, and operational capability. However, there are hardly any studies in the literature examining comprehensively various VIs apart from NDVI and FVC, which may be better suited for thermal sharpening over agricultural and natural landscapes. The aim of this study is to compare the relative performance of five different VIs, namely NDVI, FVC, the normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI), for thermal sharpening using the DisTrad thermal sharpening model over agricultural and natural landscapes in India. Multi-temporal LST data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors obtained over two different agro-climatic grids in India were disaggregated from 960 m to 120 m spatial resolution. The sharpened LST was compared with the reference LST estimated from the Landsat data at 120 m spatial resolution. In addition to this, MODIS LST was disaggregated from 960 m to 480 m and compared with ground measurements at five sites in India. It was found that NDVI and FVC performed better only under wet conditions, whereas under drier conditions, the performance of NDWI was superior to other indices and produced accurate results. SAVI and MSAVI always produced poorer results compared with NDVI/FVC and NDWI for wet and dry cases, respectively.     

Mapping urban land surface temperature using remote sensing techniques and artificial neural network modelling

The objective of the present study is to monitor and predict the changes in land surface temperature (LST) in the North of Jordan during the Period 2000 to 2016. Due to political instability in the nearby countries Syria and Iraq, Jordan has witnessed increased influxes of refugees, starting from the year 2003, which has been led to the urban expansion in the area that reflected on the climatic conditions and affected the LST values. Satellite images were used for providing LST, the acquired images represented two seasons of each year, namely summer and winter. Simulation and prediction of LST values for the next 10 years were carried out using nonlinear autoregressive exogenous (NARX) artificial neural network (ANN) model. The inputs to the model consist of meteorological data collected from eight stations in the study area, population, and land use and land cover (LULC). In fact, LULC was expressed in terms of normalized difference building index (NDBI) and normalized difference vegetation index (NDVI) that were obtained from satellite images. The model showed a high correlation between these parameters and the values of simulated LST, where the correlation coefficient for the training set, validation set, testing set and for the entire data ranged from 0.91 to 0.92. Based on the predicted LST values, LST maps for the next 10 years were developed and compared with the present actual LST maps for the year 2016. The comparison has shown an average increase of 1.1 ^°C in the average LST values, which is considered a significant increase compared with previous studies.     

MODIS-informed greenness responses to daytime land surface temperature fluctuations and wildfire disturbances in the Alaskan Yukon River Basin

Pronounced climate warming and increased wildfire disturbances are known to modify forest composition and control the evolution of the boreal ecosystem over the Yukon River Basin (YRB) in interior Alaska. In this study, we evaluate the post-fire green-up rate using the normalized difference vegetation index (NDVI) derived from 250 m 7 day eMODIS (an alternative and application-ready type of Moderate Resolution Imaging Spectroradiometer (MODIS) data) acquired between 2000 and 2009. Our analyses indicate measureable effects on NDVI values from vegetation type, burn severity, post-fire time, and climatic variables. The NDVI observations from both fire scars and unburned areas across the Alaskan YRB showed a tendency of an earlier start to the growing season (GS); the annual variations in NDVI were significantly correlated to daytime land surface temperature (LST) fluctuations; and the rate of post-fire green-up depended mainly on burn severity and the time of post-fire succession. The higher average NDVI values for the study period in the fire scars than in the unburned areas between 1950 and 2000 suggest that wildfires enhance post-fire greenness due to an increase in post-fire evergreen and deciduous species components.     

Stratification of forest density and its validation by NDVI analysis in a part of western Himalaya,India using Remote sensing and GIS techniques

This study deals with the assessment of the status of forest density in the Kangra district of Himachal Pradesh, western Himalaya. A classified forest map of the area with an accuracy of 88.17% was produced using the hybrid classification method in Erdas Imagine. An IRS 1D LISS III satellite image was used for mapping and classification. Forest density was calculated in the ArcGIS environment by overlaying a mesh of uniform resolution cells (500 m×500 m) on a classified forest map. The forest density value of each cell was later used for the preparation of forest density contours. The forest density output was verified by Normalized Difference Vegetation Index (NDVI) analyses. The forest cover of the study area was found to be 34.3%. Baroh area had the highest (45.87%) forest density and Baijnath (18.65%) the lowest. Central and western regions of the district showed high‐value forest density contours (>50%). The derived NDVI values were compared against the forest density classes for assessing the accuracy of the results obtained. A positive correlation (r = 0.99) between NDVI values and forest density confirms the accuracy of the results.     

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.