Emerging and vector-borne diseases: Role of high spatial resolution and hyperspectral images in analyses and forecasts

 Many infectious diseases that are emerging or transmitted by arthropod vectors have a strong link to landscape features. Depending on the source of infection or ecology of the transmitting vector, micro-habitat characteristics at the spatial scale of square meters or less may be important. Recently, satellite images have been used to classify habitats in an attempt to understand associations with infectious diseases. Whether high spatial resolution and hyperspectral (HSRH) images can be useful in studies of such infectious diseases is addressed. The nature of questions that such studies address and the desired accuracy and precision of answers will determine the utility of HSRH data. Need for such data should be based on the goals of the effort. Examples of kinds of questions and applications are discussed. The research implications and public health applications may depend on available analytic tools as well as epidemiological observations. Received: 30 July 2001 / Accepted: 14 October 2001     

Description of a New Method for Retrieving the Aerosol Optical Thickness from Satellite Remotely Sensed Imagery Using the Maximum Contrast Value and Darkest Pixel Approach

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative remotely sensed imagery and quantitative data, such as aerosol optical depth which is the basic unknown parameter for any atmospheric correction method in the pre‐processing of satellite imagery. This article presents a new method for retrieving aerosol optical thickness directly from satellite remotely sensed imagery for short wavelength bands in which atmospheric scattering is the dominant contribution to the at‐satellite recorded signal. The method is based on the determination of the aerosol optical thickness through the application of the contrast tool (maximum contrast value), the radiative transfer calculations and the ‘tracking’ of the suitable darkest pixel in the scene. The proposed method that needs no a‐priori information has been applied to LANDSAT‐5 TM, LANDSAT‐7 ETM+, SPOT‐5 and IKONOS data of two different geographical areas: West London and Cyprus. The retrieved aerosol optical thickness values show high correlations with in‐situ visibility data acquired during the satellite overpass. Indeed, for the West London area a logarithmic regression was fitted for relating the determined aerosol optical thickness with the in‐situ visibility values. A high correlation coefficient (r²= 0.82; p= 0.2) was found. Plots obtained from Tanre et al. (1979, 1990) and Forster (1984 ) were reproduced and estimates for these areas were generated with the proposed method so as to compare the results. The author's results show good agreement with Forster's aerosol optical thickness vs. visibility results and a small deviation from Tanre's model estimates.     

风云静止卫星地表温度产品空值数据稳健修复

静止卫星地表温度数据是研究昼夜气候和环境变化的重要参数。但现有发布的静止卫星地表温度数据由于受到云等大气因素的影响,往往出现数值缺失现象。针对该问题,提出基于昼夜变化模型的风云静止卫星地表温度空值数据的稳健修复方法。由多项式、傅里叶函数和高斯函数构建新的昼夜变化模型,并利用LevenbergMarquardt算法进行模型参数的求解与优化,进而实现空值修复。以风云2号F星数据(FY-2F)为例,模拟不同类型的像元缺失情况进行修复,并将不同模型修复结果与真实温度值比较,同时也对真实数据进行了测试。结果表明:本文提出的修复方法能有效对温度空值数据修复,且优于传统方法。     

Damage assessment after 2001 Gujarat earthquake using Landsat-7 satellite images

In this paper, we present a method of earthquake damage detection by comparing the optical images with panchromatic bands for the Gujarat, India earthquake, which occurred on January 26, 2001. The data used in this study are optical remote sensing images taken by Landsat-7 satellite on January 8 and February 29, 2001, before and after the earthquake. We have investigated the pre and post-earthquake satellite images calculating the differences in the reflection intensity (digital number) of the two images. The estimated affected area has been subtracted on a pixel unit based on the obtained frequency distributions of the differences in the optical sensor values, which show significant changes in the reflectance due to the earthquake disaster. We have investigated the accuracy of our analysis result using a classification method for the training areas with aerial photographs taken after the earthquake. The two damage detection methods show a very similar result.     

Spatial analysis in a Markov random field framework: The case of burning oil wells in Kuwait

 This paper discusses a modeling approach for spatial-temporal prediction of environmental phenomena using classified satellite images. This research was prompted by the analysis of change and landscape redistribution of petroleum residues formed from the residue of the burning oil wells in Kuwait (1991). These surface residues have been termed “tarcrete” (El-Baz et al. 1994). The tarcrete forms a thick layer over sand and desert pavement covering a significant portion of south-central Kuwait. The purpose of this study is to develop a method that utilizes satellite images from different time steps to examine the rate-of-change of the oil residue deposits and determine where redistribution is are likely to occur. This problem exhibits general characteristics of environmental diffusion and dispersion phenomena so a theoretical framework for a general solution is sought. The use of a lagged-clique, Markov random field framework and entropy measures is deduced to be an effective solution to satisfy the criteria of determination of time-rate-of-change of the surface deposits and to forecast likely locations of redistribution of dispersed, aggraded residues. The method minimally requires image classification, the determination of time stationarity of classes and the measurement of the level of organization of the state-space information derived from the images. Analysis occurs at levels of both the individual pixels and the system to determine specific states and suites of states in space and time. Convergence of the observed landscape disorder with respect to an analytical maximum provide information on the total dispersion of the residual system. Received: 5 September 2000 / Accepted: 10 July 2001     

Evaluating patterns of temporal glacier changes in Greater Himalayan Range,Jammu & Kashmir,India

To account for the variable response of the Himalayan glaciers towards climatic warming during the recent past, an attempt has been made in the present study to evaluate the changes in glacier area and shift in glacier snout position of selected glaciers in a part of the Greater Himalayan Range (GHR), Jammu & Kashmir (J&K), India. Multi-temporal satellite images of different years viz. 1975, 1989, 1992, 2001 and 2007 were used for mapping the boundaries of glaciers. Among the three observation periods (1975–1989/1992, 1989/1992–2001 and 2001–2007), during 1989/1992–2001 the majority of the glaciers exhibited considerable decrease in area. In contrast during 2001–2007, some glaciers exhibited increase in area indicating comparatively cooler climatic conditions as compared to the previous period. With reference to snout retreat, all the glaciers had a fluctuating trend of retreat during the observation periods although the retreat rate was higher during 1989/1992–2001 in some glaciers.     

镜面投影法确定地球同步卫星精密轨道

针对地球同步卫星（GEO）轨道面变化缓慢且能知道较准确近似值的特点，提出了镜面投影法。它以轨道面作对称面（镜面），将原观测站投影生成虚拟观测站；利用原站星距构成虚拟观测值。原观测值与虚拟观测值联合用于轨道参数估计，可以大大地增强观测几何结构，改善法方程状态，提高参数估值的精度。仿真计算的结果表明，新方法的效果明显。     

基于高光谱卫星遥感数据的UPDM分析方法

提出了一种用于处理多高光谱卫星数据的UPDM分析方法。研究结果证明．该方法应用干Landsat／TM(ETM^-)、Terra／MODIS和ADEOS—II／GLI等高光谱卫星传感器时．光谱重构均方根误差小于0．029适用于研究高光谱卫星遥感数据。     

Geostatistical interpolation of SLC-off Landsat ETM+ images

The scan-line corrector (SLC) for the Enhanced Thematic Mapper Plus (ETM+) sensor, on board the Landsat 7 satellite, failed permanently in 2003. The consequence of the SLC failure (or SLC-off) is that about 20% of the pixels in an ETM+ image are not scanned. We aim to develop a geostatistical method that estimates the missing values. Our rationale is to collect three cloud-free images for a particular Landsat scene, taken within a few weeks of each other: the middle image is the target whose un-scanned locations we wish to estimate; the earlier and later images are used as secondary information. We visit each un-scanned location in the target image and, for each reflectance band in turn, predict the missing value with cokriging (resorting to kriging when there is not enough local secondary information to justify cokriging). For three Landsat scenes in different bio-regions of Queensland, Australia, we compared the performance of geostatistical interpolation with image compositing. Geostatistics was a generally superior estimator. In contrast to compositing, geostatistics was able to estimate accurately values at all un-scanned locations, and was able to quantify the variance associated with each prediction. SLC-off images interpolated with geostatistics were visually sensible, although changes in land-use from pixel to pixel affected adversely the accuracy of prediction. The primary disadvantage of geostatistics was its relatively slow computing speed. We recommend the geostatistical method over compositing, but, if speed takes priority over statistical rigour, a hybrid technique–whereby composites are corrected to the local means and variances of the bands in the target image, and any un-estimable locations are interpolated geostatistically–is an adequate compromise.     

EM算法在广播星历计算卫星位置中的应用

在利用广播星历计算卫星坐标时,为了提高效率,经常会采用多项式来进行拟合.由于多方面的原因,可能会导致拟合点数据的精度不够或者拟合点的个数不够,继续用此数据进行拟合则会大大降低拟合结果的精度和可靠性.文中使用EM（expectation maximization）算法添加有益于多项式拟合的“潜在数据”,即缺失数据的条件期望,有效提高缺失数据条件下卫星坐标的拟合精度和可靠性.     

Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley,California

Accurate monitoring of surface water location and extent is critical for the management of diverse water resource phenomena. The multi-decadal archive of Landsat satellite imagery is punctuated by missing data due to cloud cover during acquisition times, hindering the assembly of a continuous time series of inundation dynamics. This study investigated whether streamflow volume measurements could be integrated with satellite data to fill gaps in monthly surface water chronologies for the Central Valley region of California, USA, from 1984 to 2015. We aggregated measurements of maximum monthly water extent within each of the study area’s 50 8-digit hydrologic unit code (HUC) watersheds from two Landsat-derived datasets: the European Commission’s Joint Research Centre (JRC) Monthly Water History and the U.S. Geological Survey Dynamic Surface Water Extent (DSWE). We calculated Spearman rank correlation coefficients between water extent values in each HUC and streamflow discharge data. Linear regression fits of the water extent/streamflow data pairs with the highest correlations served as the basis for interpolation of missing imagery surface water values on a HUC-wise basis. Results show strong (ρ > 0.7) maximum correlations in 11 (22.4%) and 25 (51.0%) HUCs for the DSWE and JRC time series, respectively, when comparisons were restricted to imagery and gages co-located in each HUC. Strong maximum correlations occurred in 39 (79.6%; DSWE) and 42 (85.7%; JRC) HUCs when imagery was paired with discharge data from any study area gage, providing a solid basis for reconstruction of water extent values. We generated continuous time series of 30+ years in 35 HUCs, demonstrating that this technique can provide quantitative estimates of historical surface water extents and elucidate flooding or drought events over the period of data collection. Results of a non-parametric trend analysis of the long-term time series on an annual, seasonal, and monthly basis varied among HUCs, though most trends indicate an increase in surface water over the past 30 years.     

Fast spherical collocation: theory and examples

 It has long been known that a spherical harmonic analysis of gridded (and noisy) data on a sphere (with uniform error for a fixed latitude) gives rise to simple systems of equations. This idea has been generalized for the method of least-squares collocation, when using an isotropic covariance function or reproducing kernel. The data only need to be at the same altitude and of the same kind for each latitude. This permits, for example, the combination of gravity data at the surface of the Earth and data at satellite altitude, when the orbit is circular. Suppose that data are associated with the points of a grid with N values in latitude and M values in longitude. The latitudes do not need to be spaced uniformly. Also suppose that it is required to determine the spherical harmonic coefficients to a maximal degree and order K. Then the method will require that we solve K systems of equations each having a symmetric positive definite matrix of only N × N. Results of simulation studies using the method are described. Received: 18 October 2001 / Accepted: 4 October 2002 Correspondence to: F. Sansò     

Regularization of geopotential determination from satellite data by variance components

 Different types of present or future satellite data have to be combined by applying appropriate weighting for the determination of the gravity field of the Earth, for instance GPS observations for CHAMP with satellite to satellite tracking for the coming mission GRACE as well as gradiometer measurements for GOCE. In addition, the estimate of the geopotential has to be smoothed or regularized because of the inversion problem. It is proposed to solve these two tasks by Bayesian inference on variance components. The estimates of the variance components are computed by a stochastic estimator of the traces of matrices connected with the inverse of the matrix of normal equations, thus leading to a new method for determining variance components for large linear systems. The posterior density function for the variance components, weighting factors and regularization parameters are given in order to compute the confidence intervals for these quantities. Test computations with simulated gradiometer observations for GOCE and satellite to satellite tracking for GRACE show the validity of the approach. Received: 5 June 2001 / Accepted: 28 November 2001     

Modelling and Remote Sensing of Land Surface Temperature in Turkey

This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26?C45°E and 36?C42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eski?ehir, ?stanbul, ?zmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LST_ANN) and Becker and Li temperature(LST_B-L) method values have been found as 0.077?K and 0.091?K (training stations), 0.045?K and 0.003?K (testing stations), respectively.     

Estimating ground-level particulate matter concentrations using satellite-based data: a review

ABSTRACT

Particulate matter (PM) is a widely used indicator of air quality. Satellite-derived aerosol products such as aerosol optical depth (AOD) have been a useful source of data for ground-level PM monitoring. However, satellite-based approaches for PM monitoring have limitations such as impacts of cloud cover. Recently, many studies have documented advances in modeling for monitoring PM over the globe. This review examines recent papers on ground-level PM monitoring for the past 10 years focusing on modeling techniques, sensor types, and areas. Satellite-based retrievals of AOD and commonly used approaches for estimating PM concentrations are also briefly reviewed. Research trends and challenges are discussed based on the review of 130 papers. The limitations and challenges include spatiotemporal scale issues, missing values in satellite-based variables, sparse distribution of ground stations for calibration and validation, unbalanced distribution of PM concentrations, and difficulty in the operational use of satellite-based PM estimation models. The literature review suggests there is room for further investigating: 1) the spatial extension of PM monitoring to global scale; 2) the synergistic use of satellite-derived products and numerical model output to improve PM estimation accuracy, gap-filling, and operational monitoring; 3) the use of more advanced modeling techniques including data assimilations; 4) the improvement of emission data quality; and 5) short-term (hours to days) PM forecasts through combining satellite data and numerical forecast model results.     

基于RNN的空气污染时空预报模型研究

针对空气污染物时间序列中包含缺失值以及现有时间序列预报模型缺乏对时序特征状态建模的问题,该文构建了基于缺失值处理算法和RNN(循环神经网络)的时空预报框架。对空气污染物时序数据设计了3种缺失值处理算法(前向递补、均值替代和权重衰减),用缺失标签和缺失时长对缺失值建模,并在此基础上搭建含有全连接层与LSTM层的深度循环神经网络(DRNN)用于时空预报。使用深度全连接神经网络(DFNN)作为DRNN的对照,用京津冀区域的空气质量和气象数据训练模型,并比较不同模型的预测精度。通过实验,比较了3种缺失值处理方法的效果,结果表明,LSTM在空气污染时空序列预测上的表现优于传统的全连接神经网络层,证实了提出的基于深度学习的时空预报框架的有效性。     

Analysis of the bias between TOPEX and GPS vTEC determinations

The TOPEX/Poseidon satellite was jointly developed and deployed by the National Aeronautics and Space Administration (NASA), USA, and the Centre National d’Etudes Spatiales (CNES), France (for details see Chelton et al. In: Fu L-L, Cazenave A (eds) International geophysics series, vol 69, ISBN 0-12-269545-3, Academic Press, CA, pp 1–131, 2001), with the main scientific goal of sea surface height monitoring. The process that ends with the TOPEX main observable (the range between the satellite and the sea surface) involves the measurement of several parameters of the radar pulses reflected by the sea surface and the computation of several other corrections. After several calibration campaigns performed by the Calibration/Validation team of the mission, it was found that TOPEX range determinations were systematically shorter than expected and it was decided to add an empirical correction of +15 mm to the TOPEX range-computation algorithm. As a by-product, TOPEX provides vertical total electron content (vTEC) determinations which have turned out to be a very important data source for the ionospheric research community. Since TOPEX vTEC measurements became available, several comparison studies have detected a constant bias, from +2 to +5 TECu, when TOPEX is compared to other vTEC sources, e.g., Global Positioning System (GPS), Doppler Orbitography and Radio-positioning Integrated by Satellite (DORIS), (TOPEX always greater than the others). In this work, we show that miscalibration of the corrections used in the TOPEX processing algorithm can cause the shortening effect of TOPEX ranges and at the same time the constant bias on the TOPEX vTEC values. It is also shown that changes on TOPEX System Biases of less than 10 mm for the Ku-band and between 40 and 70 mm for the C-band, can make both effects disappear. The analyzed hypothesis is supported by theoretical considerations and data analysis available in the specialized literature. On behalf of the authors of the contribution ‘Analysis of the bias between TOPEX and GPS vTEC determinations’, I declare that the paper has not been, nor is in the process of being published in any other publication.     

Change detection thresholds for remotely sensed images

 The detection of change in remotely sensed images is often carried out by designating a threshold to distinguish between areas of change and areas of no change. The choice of threshold is often arbitrary however. The purpose of this paper is to offer a statistical framework for the selection of thresholds. The framework accounts for the facts that one carries out multiple tests of the null hypothesis of no change, when searching for regions of change over an image with a large number of pixels. Special attention is given to global spatial autocorrelation, which can affect the selection of appropriate threshold values. Received: 8 March 2001 / Accepted: 12 October 2001     

Evaluation of pre-CHAMP gravity models `GRIM5-S1' and `GRIM5-C1' with satellite crossover altimetry

 The new GFZ/GRGS gravity field models GRIM5-S1 and GRIM5-C1, currently used as initial models for the CHAMP mission, have been compared with other recent models (JGM 3, EGM 96) for radial orbit accuracy (by means of latitude lumped coefficients) in computations on altimetry satellite orbits. The bases for accuracy judgements are multi-year averages of crossover sea height differences from Geosat and ERS 1/2 missions. This radially sensitive data is fully independent of the data used to develop these gravity models. There is good agreement between the observed differences in all of the world's oceans and projections of the same errors from the scaled covariance matrix of their harmonic geopotential coefficients. It was found that the tentative scale factor of five for the formal standard deviations of the harmonic coefficients of the new GRIM fields is justified, i.e. the accuracy estimates, provided together with the GRIM geopotential coefficients, are realistic. Received: 20 February 2001 / Accepted: 24 October 2001     

Orthorectification of VHR optical satellite data exploiting the geometric accuracy of TerraSAR-X data

Orthorectification of satellite data is one of the most important pre-processing steps for application oriented evaluations and for image data input into Geographic Information Systems. Although high- and very high-resolution optical data can be rectified without ground control points (GCPs) using an underlying digital elevation model (DEM) to positional root mean square errors (RMSEs) between 3 m and several hundred meters (depending on the satellite), there is still need for ground control with higher precision to reach lower RMSE values for the orthoimages. The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. This is due to the fact that the SAR antenna measures distances which are mainly dependent on the terrain height and the position of the satellite. The latter can be measured with high precision, whereas the satellite attitude need not be known exactly. If the used DEM is of high accuracy, the resulting geocoded SAR data are very precise in their geolocation. This precision can be exploited to improve the orientation knowledge and thereby the geometric accuracy of the rectified optical satellite data. The challenge is to match two kinds of image data, which exhibit very different geometric and radiometric properties. Simple correlation techniques do not work and the goal is to develop a robust method which works even for urban areas, including radar shadows, layover and foreshortening effects. First the optical data have to be rectified with the available interior and exterior orientation data or using rational polynomial coefficients (RPCs). From this approximation, the technique used is the measurement of small identical areas in the optical and radar images by automatic image matching, using a newly developed adapted mutual information procedure followed by an estimation of correction terms for the exterior orientation or the RPC coefficients. The matching areas are selected randomly from a regular grid covering the whole imagery. By adjustment calculations, parameters from falsely matched areas can be eliminated and optimal improvement parameters are found. The original optical data are orthorectified again using the delivered metadata together with these corrections and the available DEM. As proof of method the orthorectified data from IKONOS and ALOS-PRISM sensors are compared with conventional ground control information from high-precision orthoimage maps of the German Cartographic Survey. The results show that this method is robust, even for urban areas. Although the resulting RMSE values are in the order of 2-6 m, the advantage is that this result can be reached even for optical sensors which do not exhibit low RMSE values without using manual GCP measurements.