Use of multi‐platform,multi‐temporal remote‐sensing data for calibration of a distributed hydrological model: an application in the Arno basin,Italy

Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd.     

NORTHERN AND SOUTHERN HEMISPHERIC POLAR LOWS——A COMPARATIVE STUDY

Northern Hemispheric“polar lows”,i.e.maritime,small,but fairly intense cyclonic systems have been studied formore than two decades.Recently researchers have turned their attention towards the occurrence of polar lows and othersmall-scale cyclonic disturbances on the Southern Hemisphere.In this paper a short account of our present knowledgeof Northern Hemispheric polar lows will be given,followed by some preliminary results of an investigation of the natureand occurrence of polar lows on the Southern Hemisphere.     

基于分块分形编码的多姿态、表情人脸图像检索

利用图像分形编码中定义域块和最小均方误差一一对应的特点，提出了1种基于分形编码的多姿态、表情的人脸图像检索方法。该方法将待检索图像分割为相同大小的值域块，然后将每一值域块按给定的定义域块进行分形编码，得到最小均方误差，计算该最小均方误差与图像库中最小均方误差的欧氏距离。将待检索图像所有值域块的欧氏距离求平均，此平均欧氏距离较小的几幅图像即为检索出的图像。实验证明该方法能够准确地检索出图像库中存储的同一人的不同姿态、表情的图像。     

基于直射辐射遥感图象的地物双向反射特性研究(英文)

该文讨论了一种在ＧＩＳ支持下利用直射辐射遥感图象研究地物双向反射特性的原理和方法。其主要依据是象元地面的坡度和坡向的不同，同时改变了象元地面与太阳之间、象元地面与卫星传感器之间的相对位置，并且造成了同类象元太阳直射辐射遥感分量的差异。而正是这种差异体现了该类地物双向反射特性。在ＧＩＳ的支持下利用卫星直射辐射遥感数字图象，研究卫片象元同类地物的这种差异的规律以及它们与对应坡面上的太阳位置、卫星位置的关系，便可进行该类地物的双向反射特性研究。     

利用高分辨率卫星影像进行地震损失评价所需的城市特征识别

高分辨率卫星影像已经在一些国家的民用领域得到应用。利用高分辨率卫星影像来收集地震损失评价所需要的各类城市信息不仅高效和有较好的时间分辨率，而且它可以减少以往所必需的大量的代价高昂且费力的城市调查。基于在印度城市台拉登所做的研究，讨论了如何利用高分辨率卫星影像进行城市特征识别的一些问题，同时也简单论述了利用GIS／RS软件综合所得的数据以便用于地震损失评价的方法。     

遥感影像三维可视化研究

利用DEM高程资料实现遥感影像的三维显示可大大提高遥感监测产品的使用价值，而商用软件成本高操作繁琐普及困难。利用计算机图形学知识和Delphi编程语言，开发了一套遥感影像三维可视化软件。对在开发过程中遇到的有关DEM数据的裁剪与拼接、图像配准、地表模拟、透视变换、消隐处理、光照和阴影效果以及颜色变换等问题的解决方案和实现方法作了简要的介绍。     

GPS水汽遥感中的大气干延迟局地订正模型研究

在GPS遥感水汽过程中,大气干延迟模型的精度直接影响水汽遥感的精度。根据广东清远站1995～2001年的气象探空资料,计算了GPS水汽测量中的实际大气干延迟。在此资料基础上,利用地面气象要素建立了大气干延迟的年和分月局地订正模型。分析结果表明,年模型的精度优于目前广泛使用的普适模型;月模型与年模型相比,效果不是很明显,建立局地分月订正模型意义不大;在对高度角的敏感程度上,局地模型略大些;当高度角小于75时,大气干延迟弯曲路径与直线路径之差ΔS随天顶角增大而迅速增大。     

采用DPIV技术研究浅水平板岛屿尾流

人们对于尾流的研究大都针对绕流体在展向非常长的情形,但是在天然环境和水利工程中出现的多为水深相对很浅的流动。由于槽底和自由水面的限制,浅水域中的尾流有着与深水尾流不同的流动规律。采用数字粒子图像测速(DPIV)技术,在长30m、宽3.5m、高1m的水槽中比较系统地量测了浅水中绕平板岛屿的尾流区流场。分析了大尺度旋涡的运动规律,得到了尾流流动形式随尾流稳定性参数的变化规律。目前如此大尺度浅水岛屿尾流的定量实验研究并不多见。     

基于Google Earth的遥感影像数据资料归档系统

将Google Earth应用于遥感影像数据资料归档系统中,是一种新的遥感资料归档方法,能将归档的成果文件整合,克服了传统归档方式不能快速查看数据效果、位置、属性信息的缺点,很好地适应了信息化工作程序对图像数据库的快速响应的要求.ARCGIS补充了Google Earth薄弱的空间分析功能.系统再联合EXCLE表件,方...     

Time–space continuity of daily maps of fractional snow cover and albedo from MODIS

Using reflectance values from the seven MODIS “land” bands with 250 or 500 m resolution, along with a corresponding cloud product, we estimate the fraction of each 500 m pixel that snow covers, along with the albedo of that snow. The daily products have data gaps and errors because of cloud cover and sensor viewing geometry. Rather than make users interpolate and filter these patchy daily maps without completely understanding the retrieval algorithm and instrument properties, we use the daily time series to improve the estimate of the measured snow properties for a particular day. We use a combination of noise filtering, snow/cloud discrimination, and interpolation and smoothing to produce our best estimate of the daily snow cover and albedo. We consider two modes: one is the “predictive” mode, whereby we estimate the snow-covered area and albedo on that day using only the data up to that day; the other is the “retrospective” mode, whereby we reconstruct the history of the snow properties for a previous period.