Optical Remote Sensing of Land Surface. Techniques and Methods

Optical Remote Sensing of Land Surface
Free download. Book file PDF easily for everyone and every device. You can download and read online Optical Remote Sensing of Land Surface. Techniques and Methods file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Optical Remote Sensing of Land Surface. Techniques and Methods book. Happy reading Optical Remote Sensing of Land Surface. Techniques and Methods Bookeveryone. Download file Free Book PDF Optical Remote Sensing of Land Surface. Techniques and Methods at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Optical Remote Sensing of Land Surface. Techniques and Methods Pocket Guide.

Contents:

  • Optical Remote Sensing of Land Surface : Techniques and Methods
  • Solar Irradiation
  • 1. Introduction
  • 1st Edition

    • New Releases. Description The considerable progress in instrumentation and in the development of methods for the processing and analysis of data places remote sensing at the center of various international programs for the surveillance and tracking of climatic and anthropogenic changes and effects on the environment.

    Optical Remote Sensing of Land Surface : Techniques and Methods

    This volume presents optical imaging and LiDAR systems: their instrumentation, physics of measurement, processing methods and data analysis. The estimation of a digital terrain model based on optical images and LiDAR data is also discussed. This book, part of a set of six volumes, has been produced by scientists who are internationally renowned in their fields.

    It is addressed to students engineers, Masters, PhD , engineers and scientists, specialists in Earth observation techniques and imaging systems. Through this pedagogical work, the authors contribute to breaking down the barriers that hinder the use of Earth observation data. In the study of the natural ecosystems of the Northeast Region of Brazil NEB where they experienced persistent drought episodes and environmental degradation recently, Barbosa et al. At a continental scale, Mayaux et al.

    Solar Irradiation

    Han et al. AVHRR imagery suffers certain limitations in calibration, geometry, orbital drift, limited spectral coverage and variations in spectral coverage especially in the early period of applications. Its utility has been restricted because its use often introduces substantial errors at various stages of processing and analysis. Nevertheless, many projects including GLCC aiming at mapping vegetation covers at continental to global scales have been carried out using AVHRR for years simply because of its low cost and easy access. IKONOS is a commercial sun-synchronous earth observation satellite launched in and was the first to collect publicly available high-resolution imagery at 1 and 4 m resolution.

    It has two imagery sensors, multispectral and panchromatic. Panchromatic sensor collects image at 1 m while the multispectral bands including blue, green, red and near infrared have a spatial resolution at 4 m. Both sensors have a swath width of 11 km and 3—5 days of revisit interval. The IKONOS observations are at a spatial scale equivalent to field measurements typically carried out in ecological and land cover research. Ideally, IKONOS can be used to map vegetation cover at a local scale or validate vegetation cover classified from other remote sensing images Goward et al.

    Similar to IKONOS, QuickBird offers highly accurate and even higher resolution imagery with panchromatic imagery at 60—70 cm resolution and multispectral imagery at 2. It is the only spacecraft able to offer sub-meter resolution imagery so far. QuickBird's global collections of images greatly facilitate applications ranging from land and asset management to ecology modeling including vegetation mapping. QuickBird images are usually used to study special topics in relatively small areas or at a local scale since it is impractical to apply QuickBird imagery for applications in large area due to its high cost and rigid technical parameters.

    Wolter et al. Coops et al. The results suggested that QuickBird imagery particularly had a valuable role to play in identifying tree crowns with red attack damages. Besides aforementioned sensors, there are many others. ASTER has been used to obtain detailed maps of land surface, reflectance and elevation in the study of habitat patterns Tuttle et al.

    Publications

    The transmitted charge coupled device and infrared multispectral scanner on aboard of Chinese—Brazilian Earth Resources Satellites, a cooperative program between China and Brazil, acquire images with spatial resolution from 20 to m Epiphanio ; Ponzoni et al. While most sensors aforementioned collect multispectral images with dozens of spectral bands, hyperspectral imagery acquired by some other sensors may have hundreds of spectral bands.

    Note that the principle for mapping vegetation cover from remote sensing images relies on the unique spectral features of different vegetation types. Thus, hyperspectral imagery contains more vegetation information and can be used for more accurate vegetation mapping.

    Vegetation extraction from remote sensing imagery is the process of extracting vegetation information by interpreting satellite images based on the interpretation elements such as the image color, texture, tone, pattern and association information, etc. Diverse methods have been developed to do this. Those methods can be broadly grouped either as supervised or as unsupervised depending on whether or not true ground data are inputted as references. General steps involved in vegetation mapping include image preprocessing and image classification.

    Image preprocessing deals with all preparatory steps necessary to improve the quality of original images, which then results in the assignment of each pixel of the scene to one of the vegetation groups defined in a vegetation classification system or a membership matrix of the vegetation groups if fuzzy classification is adopted.

    Preprocessing of satellite images prior to vegetation extraction is essential to remove noise and increase the interpretability of image data. This is particularly true when a time series of imagery is used or when an area is encompassed by many images since it is essentially important to make these images compatible spatially and spectrally.

    The ideal result of image preprocessing is that all images after image preprocessing should appear as if they were acquired from the same sensor Hall et al. Botanist and ecologist should keep in mind that while image preprocessing is the prerequisite for vegetation extraction from remote sensing images, the preprocessing procedures listed below may not be always needed because some of these preprocessing procedures may have been done by image distribution agencies.

    Thus, it is recommended to consult with the image distributor and get to know at what level the imagery is usually including level 0, 1A, 1B, 2A, 2B, 3A, 3B with image quality gradually increased before imagery purchase. For example, for most sensors, level 3A means that radiometric correction, geometric correction and orthorectification have been processed for the images.

    Image preprocessing commonly comprises a series of operations, including but not limited to bad lines replacement, radiometric correction, geometric correction, image enhancement and masking e. Bad line replacement is to determine the overall quality of the images e. The visual review is usually done at full extents while attention is focused on identifying lines or blocks of missing data in each band for further repairing.

    Image line replacement is a procedure that fills in missing lines with the line above, below or with an average of the two.

    1. Introduction

    Purchase Optical Remote Sensing of Land Surface - 1st Edition. Print Book & E- Book. Remote Sensing of Land Surface. 1st Edition. Techniques and Methods. Optical Remote Sensing of Land Surface. Techniques and Methods. Book • Edited by: Nicolas Baghdadi and Mehrez Zribi. Browse book content.

    Radiometric correction of remote sensing data normally involves the process of correcting radiometric errors or distortions of digital images to improve the fidelity of the brightness values. Factors such as seasonal phenology, ground conditions and atmospheric conditions can contribute to variability in multi-temporal spectral responses that may have little to do with the remote sensed objects themselves Song and Woodcock It is mandatory to differentiate real changes from noises through radiometric correction in cases where the spectral signals are not sufficiently strong to minimize the effects of these complicating factors.

    Several methods are available to make radioactive corrections. Some of them are based on complex mathematical models that describe the main interactions involved.

    Introduction to Remote Sensing (part2) Optical Satellites

    However, the values of certain parameters i. Other radiometric correction methods are based on the observations of reference targets e. Whatever radiometric correction methods are, they can be classified into two types: absolute and relative correction Cohen et al. The absolute radiometric correction is aimed toward extracting the absolute reflectance of scene objects at the surface of the earth, requiring the input of simultaneous atmospheric properties and sensor calibration, which are difficult to acquire in many cases Chen et al. On the other hand, the relative radiometric correction is aimed toward reducing atmospheric and other unexpected variations among multiple images by adjusting the radiometric properties of target images to match a base image Hall et al.

    Schroeder et al. Geometric correction is aimed to avoid geometric distortions from a distorted image and is achieved by establishing the relationship between the image coordinate system and the geographic coordinate system using the calibration data of the sensor, the measured data of position and altitude and the ground control points. Therefore, geometric correction usually includes the selection of a map projection system and the co-registration of satellite image data with other data that are used as the calibration reference.

    The outcome of geometric correction should obtain an error within plus or minus one pixel of its true position, which allows for accurate spatial assessments and measurements of the data generated from the satellite imagery. The first-order transformation and the nearest neighbor resampling of the uncorrected imagery are among those popularly adopted methods in geometric correction. The nearest neighbor resampling method uses the value of the closest pixel to assign to the output pixel value and thus transfers original data values without averaging them.

    Sometimes the images will be more distinguishable for interpretation if image enhancement is performed, which is aimed to emphasize and sharpen particular image features i. The traditional image enhancement include gray scale conversion, histogram conversion, color composition, color conversion between red-green-blue RGB and hue—saturation—intensity transform HSI , etc. Shyu and Leou explained the limitations of traditional image enhancement methods and proposed a genetic algorithm approach that was proved more effective than the traditional ones.

    In mapping vegetation cover using remote sensing images, especially mapping over large regions, cloud imposes a big noise for identifying vegetation and thus has to be removed or masked. Jang et al. Walton and Morgan used cloud-free space shuttle photograph to detect and remove mask unwanted cloud covers in Landsat TM scenes. Image classification, in a broad sense, is defined as the process of extracting differentiated classes or themes e. Obviously this definition includes the preprocessing of images. We here simply refer to the process following the image preprocessing as image classification.

    Techniques for extracting vegetation from preprocessed images are grouped into two types: traditional and improved methods. The traditional methods employ the classical image classification algorithms, e.

    Unsupervised approach is often used in thematic mapping including vegetation cover mapping from imagery. It is easy to apply and widely available in image processing and statistical software packages Langley et al. Both of these algorithms involve iterative procedures. In general, both of them assign an arbitrary initial cluster vector first. The second step classifies each pixel to the closest cluster. In the third step, the new cluster mean vectors are calculated based on all the pixels in one cluster.

    The second and third steps are repeated until the gap between the iteration is small enough or smaller than a preset threshold. Unsupervised classification methods are purely relying on spectrally pixel-based statistics and incorporate no priori knowledge of the characteristics of the themes being studied. The benefit of applying unsupervised classification methods is to automatically convert raw image data into useful information so long as higher classification accuracy is achieved Tso and Olsen Alternatively, rather than purely spectral, Tso and Olsen incorporated both spectral and contextual information to build a fundamental framework for unsupervised classification, Hidden Markov Models, which showed improvements in both classification accuracy and visual qualities.

    Algorithms of unsupervised classification were investigated and compared with regard to their abilities to reproduce ground data in a complex area by Duda and Canty Despite its easy application, one disadvantage of the unsupervised classification is that the classification process has to be repeated again if new data samples are added. By contrast, a supervised classification method is learning an established classification from a training dataset, which contains the predictor variables measured in each sampling unit and assigns prior classes to the sampling units Lenka and Milan The supervised classification is to assign new sampling units to the priori classes.

    1st Edition

    Thus, the addition of new data has no impact on the established standards of classification once the classifier has been set up. MLC classifier is usually regarded as a classic and most widely used supervised classification for satellite images resting on the statistical distribution pattern Sohn and Rebello ; Xu et al. However, MLC shows less satisfactory successes since the MLC assumption that the data follow Gaussian distribution may not always be held in complex areas. It is very common that the same vegetation type on ground may have different spectral features in remote sensed images.

    Also, different vegetation types may possess similar spectra, which makes very hard to obtain accurate classification results either using the traditional unsupervised classification or supervised classification.