Article

Restoration of the missing pixel information caused by contrails in multispectral remotely sensed imagery

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Abstract

Although removing the pixels covered by contrails and their shadows and restoring the missing information at the locations in remotely sensed imagery are important to understand contrails' effects on climate change, there are no such studies in the current literature. This study investigates the restoration of the missing information of the pixels caused by contrails in multispectral remotely sensed Landsat 5 TM imagery using a cokriging approach. Interpolation results and several validation methods show that it is practical to use the cokriging approach to restore the contrail-covered pixels in the multispectral remotely sensed imagery. Compared to ordinary kriging, the results are improved by taking advantage of both the spatial information in the original imagery and information from the secondary imagery.

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In the United States, the dramatic increase in jet fuel usage and kilometers flown has led to speculation of a similar increase in jet contrails. However, contrail occurrence depends heavily upon the meteorological conditions near cruising altitudes (i.e. the tropopause, 10–12 km altitude). This study reports a contrail mid-season contemporary climatology for the coterminous United States (2000–2002), and compares the frequencies with those previously reported for an earlier (1977–1979) period, to determine spatial and seasonal contrail frequency changes. For both climatologies, contrail occurrence is derived from the analysis of high-resolution satellite imagery. Data on US jet aircraft flight activity were obtained to assess their relationship to contrail frequency, as were NCEP-NCAR reanalysis data to determine the changes in tropopause-level atmospheric conditions. For the 2000–2002 period, contrails comprise a distinct high (low) frequency pattern in the East (West) halves of the United States. Seasonally, there is a contrail association with the latitudinal migration of the jet stream and a US-wide peak contrail frequency during winter (January). The inter-monthly variations in contrail frequency are significantly different from each other but show no association with variations in jet flight activity, indicating a greater role for meteorological conditions. Between the 1977–1979 and 2000–2002 periods, there were strong spatial and seasonal asymmetries to the contrail frequency change. These involve a cooling (warming) of the tropopause for the largest (smallest) frequency increases, which shows some association with the switch in positive and negative phases of the Arctic Oscillation. The role of upper tropospheric conditions and links to hemispheric-scale teleconnections should be considered when projecting contrail frequency changes and their future impacts on climate. Copyright
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Aviation makes a significant contribution to anthropogenic climate forcing. The impacts arise from emissions of greenhouse gases, aerosols and nitrogen oxides, and from changes in cloudiness in the upper troposphere. An important but poorly understood component of this forcing is caused by ‘contrail cirrus’—a type of cloud that consist of young line-shaped contrails and the older irregularly shaped contrails that arise from them. Here we use a global climate model that captures the whole life cycle of these man-made clouds to simulate their global coverage, as well as the changes in natural cloudiness that they induce. We show that the radiative forcing associated with contrail cirrus as a whole is about nine times larger than that from line-shaped contrails alone. We also find that contrail cirrus cause a significant decrease in natural cloudiness, which partly offsets their warming effect. Nevertheless, net radiative forcing due to contrail cirrus remains the largest single radiative-forcing component associated with aviation. Our findings regarding global radiative forcing by contrail cirrus will allow their effects to be included in studies assessing the impacts of aviation on climate and appropriate mitigation options.
Article
For the detection of contrails and cirrus clouds, new mathematical methods are developed and applied for different kinds of satellite images (AVHRR, MOS, MOMS, ATSR). As edges and skeletons are not sufficiently representative enough for the description and distinction of clouds, different stochastic properties in their combination are applied. The stochastic properties are represented mathematically by a system of coupled stochastic differential equations. Methods are developed to obtain estimation values from stochastic differential equations. The solutions obtained are given as sequential procedures using grey values of neighbouring pixels, though this is very time consuming so these procedures are approximated for usage of simpler array procedures working on the entire image. For the combination of diverse stochastic and non‐stochastic properties, different kinds of properties are represented by generalized measures. This is realized by fuzzy measures and fuzzy functions, relating to selected fuzzy measures. Whereas the fuzzy function describes rather isolated stochastic variations over small isolated regions, the fuzzy measures describe more extended regions by their stochastic properties. Both of these represented stochastic properties that are fused by a fuzzy integral. These results are used as new fuzzy measures or fuzzy functions and generate iterative procedures to select desired properties for the detection of contrails.
Chapter
The primary goal of this work is to present a geostatistical software library known as GSLIB. An important prerequisite to geostatical case studies and research is the availability of flexible and understandable software. Flexibility is achieved by providing the original FORTRAN source code. A detailed description of the theoretical background along with specific application notes allows the algorithms to be understood and used as the basis for more advanced customized programs. The three main chapters of this guidebook are based on the three major problem areas of geostatistics: quantifying spatial variability (variograms), generalized linear regression techniques (kriging), and stochastic simulation. Additional utility programs and problem sets with partial solutions are given to allow a full exploration of GSLIB and to check new software installations.
Article
The homogeneity of time series of satellite images is crucial when studying abrupt or gradual changes in vegetation cover via remote sensing data. Various sources of noise affect the information received by satellites, making it difficult to differentiate the surface signal from noise and complicates attempts to obtain homogeneous time series. We compare different procedures developed to create homogeneous time series of Landsat images, including sensor calibration, atmospheric and topographic correction, and radiometric normalization. Two seasonal time series of Landsat images were created for the middle Ebro Valley (NE Spain) covering the period 1984–2007. Different processing steps were tested and the best option selected according to quantitative statistics obtained from invariant areas, simultaneous medium-resolution images, and field measurements. The optimum procedure includes cross-calibration between Landsat sensors, atmospheric correction using complex radiative transfer models, a non-lambertian topographic correction, and a relative radiometric normalization using an automatic procedure. Finally, three case studies are presented to illustrate the role of the different radiometric correction procedures when analyzing and explaining gradual and abrupt temporal changes in vegetation cover, as well as temporal variability. We have shown that to analyze different vegetation processes with Landsat data, it is necessary to accurately ensure the homogeneity of the multitemporal datasets by means of complex radiometric correction procedures. Failure to follow such a procedure may mean that the analyzed processes are non-recognizable and that the obtained results are invalid.
Article
The Earth's surface and remotely sensed imagery contain spatial information that, if quantified, could be used to optimize many sampling procedures in remote sensing. Until recently a suitable and simple technique for the spatial characterisation of surfaces was not readily available. Now, thanks to the development of regionalized variable theory there is a near-ideal tool, the semivariogram. The semivariogram is a function that relates semivariance to sampling lag. This function can be estimated using remotely sensed data or ground data and represented as a plot that gives a picture of the spatial dependence of each point on its neighbor. This paper provides an introduction to the semivariogram and indicates how it could be employed in remote sensing research.
Article
The electromagnetic radiation (EMR) signals collected by satellites in the solar spectrum are modified by scattering and absorption by gases and aerosols while traveling through the atmosphere from the Earth's surface to the sensor. When and how to correct the atmospheric effects depend on the remote sensing and atmospheric data available, the information desired, and the analytical methods used to extract the information. In many applications involving classification and change detection, atmospheric correction is unnecessary as long as the training data and the data to be classified are in the same relative scale. In other circumstances, corrections are mandatory to put multitemporal data on the same radiometric scale in order to monitor terrestrial surfaces over time. A multitemporal dataset consisting of seven Landsat 5 Thematic Mapper (TM) images from 1988 to 1996 of the Pearl River Delta, Guangdong Province, China was used to compare seven absolute and one relative atmospheric correction algorithms with uncorrected raw data. Based on classification and change detection results, all corrections improved the data analysis. The best overall results are achieved using a new method which adds the effect of Rayleigh scattering to conventional dark object subtraction. Though this method may not lead to accurate surface reflectance, it best minimizes the difference in reflectances within a land cover class through time as measured with the Jeffries–Matusita distance. Contrary to expectations, the more complicated algorithms do not necessarily lead to improved performance of classification and change detection. Simple dark object subtraction, with or without the Rayleigh atmosphere correction, or relative atmospheric correction are recommended for classification and change detection applications.
Article
A simplified model for radiometric corrections has been used to improve nonsupervised classification of vegetation cover in a hilly area near Barcelona, Spain. A digital elevation model and standard parameters for exoatmospheric solar irradiance, atmospheric optical depth, and sensor calibration are the only inputs required. Radiometric classes obtained by cluster classification of Landsat TM images from nonradiometrically corrected images include several classes related to terrain illumination, but not to vegetation or thematic cover differences. The use of radiometric correction allows identifying all radiometric classes obtained as vegetation or thematic classes with 83.3% global accuracy. Classes obtained include Pinus halepensis, Quercus ilex, and Quercus cerrioides forests, shrublands, grasslands, urban areas with vegetation, urban areas without vegetation, and denuded areas. Radiometric correction helps in estimating surfaces and spectral features of these classes. The results are discussed considering botanical composition, date (phenology), and vegetation dynamics.
Article
It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.
Article
Report (M. Eng.)--Virginia Polytechnic Institute and State University, 1984. Vita. Abstract. Includes bibliographical references (leaves 29-30).
Article
This paper investigates the automated detection of jet contrails using data from the Advanced Very High Resolution Radiometer. A preliminary algorithm subtracts the 11.8-micron image from the 10.8-micron image, creating a difference image on which contrails are enhanced. Then a three-stage algorithm searches the difference image for the nearly-straight line segments which characterize contrails. First, the algorithm searches for elevated, linear patterns called 'ridges'. Second, it applies a Hough transform to the detected ridges to locate nearly-straight lines. Third, the algorithm determines which of the nearly-straight lines are likely to be contrails. The paper applies this technique to several test scenes.
Article
Current understanding and knowledge of the composition and structure of cirrus clouds are reviewed and documented in this paper. In addition, the radiative properties of cirrus clouds as they relate to weather and climate processes are described in detail. To place the relevance and importance of cirrus composition, structure and radiative properties into a global perspective, pertinent results derived from simulation experiments utilizing models with varying degrees of complexity are presented; these have been carried out for the investigation of the influence of cirrus clouds on the thermodynamics and dynamics of the atmosphere. In light of these reviews, suggestions are outlined for cirrus-radiation research activities aimed toward the development and improvement of weather and climate models for a physical understanding of cause and effect relationships and for prediction purposes.
Missing information in remote sensing: wavelet approach to detect and remove clouds and their shadows Master's Thesis, International Institute for Geo-information Science and Earth Observation
  • P Arellano
P. Arellano, " Missing information in remote sensing: wavelet approach to detect and remove clouds and their shadows, " Master's Thesis, International Institute for Geo-information Science and Earth Observation, Enschede (2004).
A statistical examination of sky cover changes in the contiguous United States
  • W L Seaver
  • J E Lee
W. L. Seaver and J. E. Lee, "A statistical examination of sky cover changes in the contiguous United States," J. Clim. Appl. Meteorol. 26, 88-95 (1987), http://dx.doi.org/10.1175/1520-0450(1987)026<0088:ASEOSC>2.0.CO;2.
  • E H Isaaks
  • R M Srivastava
E. H. Isaaks and R. M. Srivastava, An Introduction to Applied Geostatistics, Oxford University, New York (1989).