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。     

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).     

Exploring relation of land surface temperature with selected variables using geographically weighted regression and ordinary least square methods in Manipur State,India

In the present study, relationship between Land surface temperature and selected indices, vegetation index (VARI), built-up index (BUI) and elevation (DEM) is investigated. Ordinary least square method and geographically weighted regression are used to analyse the spatial correlation between the indices with surface temperature. Subsequently, temporal trends (2001–2015) in surface temperature and vegetation are explored after every two years of interval. LANDSAT image and ASTER DEM are used to extract LST and additional indices. The selected variables (Built-up, vegetation and topography) explain 69% of the variation in surface temperature. The OLS and GWR revealed that topography and vegetation are the significant factor of LST in Manipur State. Topography being a constant parameter, its effect is constant over time. The changing scenario of vegetation is significantly contributing to LST. The surface temperature over a period of 15 years show increasing trend and is negatively and strongly correlated to vegetation cover.     

基于遥感的长沙市城市热岛与土地利用/覆盖变化研究

基于多时相Landsat TM/ETM+影像,首先计算长沙市地表亮度温度,然后利用NDVI(归一化植被指数)、MNDWI(改进 的归一化水体指数)、NDBI(归一化建筑指数)和NDBaI(归一化裸土指数)4个指数,采用决策树分类方法对长沙市影像进行 土地利用/覆盖分类。在此基础上,对长沙市城市热岛的空间分布特征、时空演变特征以及城市热岛与土地利用/覆盖变化和各种影 响因子之间的关系进行研究。结果表明,随着长沙市城区范围的不断扩张,城市热岛范围也不断增大; 土地利用/覆盖类型的变化 会改变地表温度的空间分布,城市用地和裸地是城市热岛强度的主要贡献因素,水体和林地具有较好的降温作用。地表温度与4种 归一化指数的回归分析表明,它们之间存在明显的相关性,不同土地利用/覆盖类型的地表温度存在较大差异。     

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.     

藏东南冰川地区250 m空间分辨率全天候地表温度生成

遥感全天候地表温度产品在多云雾地区意义重大,对冰川泥石流多发的藏东南地区极具应用价值,但遥感全天候地表温度空间分辨率不足限制了其在精细化灾害监测中的应用。以藏东南冰川地区为研究区,采用高程、坡度、坡向、地表覆盖类型、植被指数、地表反射率、积雪指数作为全天候地表温度的影响因子,结合移动窗口,进行多种地表温度降尺度方法的对比,进而使用最优的降尺度方法将现有的遥感全天候地表温度产品（TRIMS LST）的空间分辨率从1 km提升至250 m。利用地面站点实测数据的评价结果表明,基于梯度提升决策树（LightGBM）的降尺度方法得到的250 m空间分辨率全天候地表温度的均方根误差在白天/夜间为2.25 K/2.15 K,优于基于多元线性回归和随机森林的降尺度方法,且比原始1 km分辨率全天候地表温度的精度高0.25 K左右。基于Q指数与SIFI指数的图像质量评价结果表明,降尺度得到的250 m地表温度不仅在空间格局和幅值上与原始1 km遥感全天候地表温度一致,而且补充了大量的地表温度空间细节信息。生成得到的250 m分辨率的地表温度对于藏东南冰川地区的灾害分析具有积极的意义。     

Analysis of urban built-up areas and surface urban heat island using downscaled MODIS derived land surface temperature data

Regional scale urban built-up areas and surface urban heat islands (SUHI) are important for urban planning and policy formation. Owing to coarse spatial resolution (1000 m), it is difficult to use Moderate Resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) products for mapping urban areas and visualization, and SUHI-related studies. To overcome this problem, the present study downscaled MODIS (1000 m resolution)-derived LST to 250 m resolution to map and visualize the urban areas and identify the basic components of SUHI over 12 districts of Punjab, India. The results are compared through visual interpretation and statistical procedure based on similarity analysis. The increased entropy value in the downscaled LST signifies higher information content. The temperature variation within the built-up and its environs is due to difference in land use and is depicted better in the downscaled LST. The SUHI intensity analysis of four cities (Ludhiana, Patiala, Moga and Vatinda) indicates that mean temperature in urban built-up core is higher (38.87 °C) as compared to suburban (35.85 °C) and rural (32.41 °C) areas. The downscaling techniques demonstrated in this paper enhance the usage of open-source wide swath MODIS LST for continuous monitoring of SUHI and urban area mapping, visualisation and analysis at regional scale. Such initiatives are useful for the scientific community and the decision-makers.     

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.     

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.     

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。     

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.     

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 …     

利用Landsat TM影像进行地表温度像元分解

提出了一种基于Landsat TM的地表温度二次像元分解方法,将地表温度的空间分辨率从120 m提高到30 m。首先,利用地表类型的线性统计模型（E-DisTrad）获取初次分解子像元的地表温度,计算得到初次分解子像元的辐亮度;然后,利用面向对象的图像分割方法获取二次分解子像元的权重,实现对地表温度的二次分解;最后,采用升尺度再分解的验证方法进行精度分析,并选取了北京市TM影像进行实例分析。实验结果表明,二次像元分解模型不仅能有效地提高地表温度的空间分辨率,反映出不同地表类型地表温度的空间差异性,而且保证了像元分解前后能量值的一致性,非常适合于复杂地表覆盖地区的热红外波段遥感影像数据的降尺度处理。     

Time series evaluation of landscape dynamics using annual Landsat imagery and spatial statistical modeling: Evidence from the Phoenix metropolitan region

Urbanization is a natural and social process involving simultaneous changes to the Earth’s land systems, energy flow, demographics, and the economy. Understanding the spatiotemporal pattern of urbanization is increasingly important for policy formulation, decision making, and natural resource management. A combination of satellite remote sensing and patch-based models has been widely adopted to characterize landscape changes at various spatial and temporal scales. Nevertheless, the validity of this type of framework in identifying long-term changes, especially subtle or gradual land modifications is seriously challenged. In this paper, we integrate annual image time series, continuous spatial indices, and non-parametric trend analysis into a spatiotemporal study of landscape dynamics over the Phoenix metropolitan area from 1991 to 2010. We harness local indicators of spatial dependence and modified Mann-Kendall test to describe the monotonic trends in the quantity and spatial arrangement of two important land use land cover types: vegetation and built-up areas. Results suggest that declines in vegetation and increases in built-up areas are the two prevalent types of changes across the region. Vegetation increases mostly occur at the outskirts where new residential areas are developed from natural desert. A sizable proportion of vegetation declines and built-up increases are seen in the central and southeast part. Extensive land conversion from agricultural fields into urban land use is one important driver of vegetation declines. The xeriscaping practice also contributes to part of vegetation loss and an increasingly heterogeneous landscape. The quantitative framework proposed in this study provides a pathway to effective landscape mapping and change monitoring from a spatial statistical perspective.     

Built-up and vegetation extraction and density mapping using WorldView-II

This study demonstrates the use of high resolution WorldView-II satellite data in extraction of built-up land and vegetation using normalized index techniques. The PCA 1 and NIR 2 bands-based built-up index was proposed for extracting built-up land, which exhibit high accuracy. The normalized difference vegetation index based on Red Edge and NIR 2 bands of WorldView-II produced high accuracy inthe estimation of vegetation compared to the use of Red and NIR bands. The grid technique used in estimating built-up and vegetation density from precisely classified images provided better and accurate assessment of built-up and vegetation density in heterogeneous landscape of urban areas. This shows areas of very high to high built-up density are located in the central, western and southern parts, which are primarily devoid of vegetation. This study indicates possibilities of utilizing high resolution satellite data in urban landscape characterization using a grid-based technique.     

Urban Built-up Area Assessment of Ranchi Township Using Cartosat-I Stereopairs Satellite Images

In the present study, the Cartosat-I digital elevation model (DEM) was utilized to deduce the vertical characteristics of Ranchi urban area and its relation to long term built-up expansion (1927–2010). The DEM represents moderate variation in terrain relief ranging from 595 m to 754 m with majority of area exhibiting upto 3° of slope and 3° to 6° indicating flat to undulating nature of terrain in Ranchi township. The DEM was used to generate location of sinks within urban area, which are generally delineated along the drainage channels, adjacent to high-rise built-up land and along the elevated road network. The pattern of urban sprawl over the eight decades (1927–2010) were examined with reference to terrain relief zones, which indicated that the built-up growth was mainly taken place over the elevation range of moderate (620–660 m) (67.0%) and high relief (660–680 m) (19.8%) zones. Although earlier preference for built-up development was more in high elevation zones (660–680 m), the low elevation zones (<600–620 m) are now preferred for multistoried built-up land development where better groundwater availability occur. The spatial pattern of vertical growth of built-up land was assessed using contour density obtained from Cartosat-I DEM. The results show that the high density contours predominately correspond to hilly area and high-rise buildings at majority of locations. The urban sprawl pattern and population trend exhibited rapid increase in vertical built-up growth after 1996 indicating beginning of urban densification in Ranchi township.     

A Method to Determine Appropriate Spatial Resolution for Hard Image Classification

This study analyzed the relationship between the spatial resolution and the hard classification effect based on pixel-based image classification, and then discussed how to determine appropriate spatial resolution. Thematic maps of winter wheat derived from 250 m MODIS image, 19.5 m China-Brazil Earth Resources Satellite (CBERS) image, and 2.44 m QuickBird image were used to examine the classification effect as a case study. It indicated that the “Pareto Boundaries” and the “within-class variability” could be used to determine the coarsest and the highest resolution for hard classification, respectively. The methods proposed in this study should be useful to guide how to select appropriate spatial resolution for land cover mapping.     

A simple retrieval method of land surface temperature from AMSR-E passive microwave data—A case study over Southern China during the strong snow disaster of 2008

The analysis of the passive microwave radiance transfer equation certifies that there is a linear relationship between satellite-generated brightness temperatures (BT) and in situ observation temperature and that land surface temperature (LST) is largely influenced by vegetation cover conditions. Microwave polarization difference index (MPDI) is an effective indicator for characterizing the land surface vegetation cover density. Based on the analysis of LST models from AMSR-E BT with 6.9 GHz MPDI intervals at 0.04, 0.02 and 0.01, respectively, this paper developed a simplified LST regression model with MPDI-based five land cover types, combining observation temperatures from 86 meteorological observation stations. The study shows that smaller MPDI intervals can obtain higher accuracy of AMSR-E LST simulation, and that the combination of HDF Explorer and ArcGIS software was useful for automatically processing the pixel latitude, longitude and BT information from the AMSR-E HDF imagery files. The RMSE of the five LST simulation algorithms is between 1.47 and 1.92 °C, with an average LST retrieval error of 0.91–1.30 °C. Besides, only 7 polarization bands and 5 land surface types are required by the proposed simplified model. The new LST simulation models appears to be more effective for producing LST compared to past most studies, of which the accuracy used to be more than 2 °C. This study is one of the rare applications that combine the meteorological observation temperature with MPDI to produce the LST regression analysis algorithms with less RMSE from AMSR-E data. The results can be referred to similar areas of the world for LST retrieval or land surface process research, in particular under extreme bad weather conditions.     

Resolution vs. image quality in pre-tsunami imagery used for tsunami impact models in Aceh,Indonesia

Land cover roughness coefficients (LCRs) have been used in multivariate spatial models to test the mitigation potential of coastal vegetation to reduce impacts of the 2004 tsunami in Aceh, Indonesia. Previously, a Landsat 2002 satellite imagery was employed to derive land cover maps, which were then combined with vegetation characteristics, i.e., stand height, stem diameter and planting density to obtain LCRs. The present study tested LCRs extracted from 2003 and 2004 Landsat (30 m) images as well as a combination of 2003 and 2004 higher spatial resolution SPOT (10 m) imagery, while keeping the previous vegetation characteristics. Transects along the coast were used to extract land cover, whenever availability and visibility allowed. These new LCRs applied in previously developed tsunami impact models on wave outreach, casualties and damages confirmed previous findings regarding distance to the shoreline as a main factor reducing tsunami impacts. Nevertheless, the models using the new LCRs did not perform better than the original one. Particularly casualties models using 2002 LCRs performed better (δAIC > 2) than the more recent Landsat and SPOT counterparts. Cloud cover at image acquisition for Landsat and low area coverage for SPOT images decreased statistical predictive power (fewer observations). Due to the large spatial heterogeneity of tsunami characteristics as well as topographic and land-use features, it was more important to cover a larger area. Nevertheless, if more land cover classes would be referenced and high resolution imagery with low cloud cover would be available, the full benefits of higher spatial resolution imagery used to extract more precise land use roughness coefficients could be exploited.     

基于TM影像的城市建筑用地信息提取方法研究

本文选用金华市Landsat TM影像为研究的数据源,在归一化裸露指数基础上,利用归一化植被指数提取出非植被信息,通过图像二值化、叠加分析以及掩膜处理去除了低密度植被覆盖区域的噪音信息,自动提取了金华城市建筑用地信息。研究结果表明,归一化裸露指数和归一化植被指数相结合的方法弥补了单一利用归一化裸露指数来提取城市建筑用地信息的不足,提高了提取精度,而且结果客观可信,是一种不经人为干预的、快速有效的提取城市建筑用地方法。