Remote Sensing Digital Image Analysis provides a comprehensive treatment of the methods used for the processing and interpretation of remotely sensed image data. Over the past decade there have been continuing and significant developments in the algorithms used for the analysis of remote sensing imagery, even though many of the fundamentals have substantially remained the same. As with its predecessors this new edition again presents material that has retained value but also includes newer techniques, covered from the perspective of operational remote sensing.
Remote Sensing And Image Interpretation, 6th Edition.pdf ((TOP))
The book is designed as a teaching text for the senior undergraduate and postgraduate student, and as a fundamental treatment for those engaged in research using digital image analysis in remote sensing. The presentation level is for the mathematical non-specialist. Since the very great number of operational users of remote sensing come from the earth sciences communities, the text is pitched at a level commensurate with their background.
The chapters progress logically through means for the acquisition of remote sensing images, techniques by which they can be corrected, and methods for their interpretation. The prime focus is on applications of the methods, so that worked examples are included and a set of problems conclude each chapter.
John Richards has been active in remote sensing teaching and research for the past 40 years, mainly in image analysis and imaging radar. He is the author of four books, all published by Springer. He is an electrical engineering graduate from the University of New South Wales, Australia, and a life Fellow of the IEEE. He was the foundation director of the Centre for Remote Sensing at the University of New South Wales in the 1980s and has had a career that has taken him through teaching and research in remote sensing and engineering. He has also served in senior academic administration including as dean of the College of Engineering and Computer Science and vice-president of the Australian National University. He is an emeritus professor of both the Australian National University and the University of New South Wales.
Monitoring water bodies by extraction using water indexes from remotely sensed images has proven to be effective in delineating surface water against its surrounding. This study tested and assessed the Normalized Difference Water Index, Modified Normalized Difference Water Index, Automated Water Extraction Index, and near infrared (NIR) band using Landsat 8 imagery acquired on September 3, 2016. The threshold method was adapted for surface water extraction. To avoid over and under-estimation of threshold values, the optimum threshold value of each of the water indexes was obtained by implementing a geoprocessing model. Examining images of Landsat 8, NIR band has the largest difference in reflectance values between water and non-water bodies. Thus, NIR band exhibits the highest contrast between water and non-water bodies. An optimum threshold value of 0.128 for NIR band achieved an overall accuracy (OA) and kappa hat (Khat) coefficient of 99.3% and 0.986, respectively. NIR band of Landsat 8 as water index was found more satisfactory in extracting water bodies compared to the multi-band water indexes. This study shows that the optimum threshold values of each of the water indexes considered in this study were determined conveniently, where highest value of OA and Khat coefficient were obtained by creating and implementing a graphical modeler in Quantum Geographic Information System that automates from setting threshold value to accuracy assessment. This study confirms that remote sensing can extract or delineate water bodies rapidly, repeatedly and accurately.
Remote sensing is essential in several studies on surface water mapping including but not limited to water bodies extraction [13,14,15,16, 18], flood management [19, 20], and water quality [21,22,23]. Delineation of water bodies from remotely sensed imagery by extraction techniques has long been applied [13,14,15,16, 18]. The methods involved comfort with the number of bands used mainly single-band and multi-band [18, 24]. Water body extraction by multi-band water index threshold methods was introduced by McFeeters [13] from Landsat 4 Multispectral Scanner using green and near-infrared (NIR) bands, by Rogers and Kearney [14] from Landsat Thematic Mapper (TM) using red and green and shortwave infrared (SWIR) bands, by Xu [15] from Landsat 5 TM and Landsat 7 Enhanced TM using SWIR bands, and by Feyisa et al. [16] from Landsat 5 TM using green, blue, NIR, and SWIR bands. Such methods examine comprehensively the bands considered [24] in order to determine the threshold that categorizes water from non-water bodies [15]. Threshold values both in single-band and multi-band water indexes are determined based on surface reflectance between water and non-water bodies [11]. However, Xu [15] emphasized that the subjective threshold value determination could lead to under- or over-estimation of open water areas. Additionally, determination of threshold value that is producing optimum accuracy is perplexing, time-consuming, and image dependent [16, 25]. Furthermore, Feyisa et al. [16] made a comparison of optimum thresholds and found variations at different test sites.
The non-normalized AWEIsh of Feyisa et al. [16] adapted 5 out of 6 bands whereby maximizing usage of the different spectral information of Landsat 8 OLI. With this, it performs better than the normalized water indexes. However, results of this study have also indicated that NIR band of Landsat 8 OLI can be adapted more efficiently as a single-band water index compared to the multi-band water index introduced earlier by others [13,14,15,16]. The superior performance of NIR band of Landsat 8 OLI as water index can be attributed as having the narrowest bandwidth compared to bands 2, 3, 4, 6 &7 (Table 1). This feature of NIR band contributed to its largest difference in reflectance values between water and non-water bodies making it effective to discriminate non-water to water bodies as revealed in Fig. 8. Furthermore, NIR band is more suitable for elaborating water with considerable vegetation both on coastal and inland areas. The threshold value for NIR band in extracting water bodies is conveniently distinguishable since there is only minimal existence of non-water noise. Thus, a narrower NIR band as a single-band water index has the advantage of effectively discriminating water from non-water bodies. Hence, applying the previous multi-band water indexes of others [13,14,15,16] in extracting a water body using Landsat 8 OLI added some noise or that reduces contrast between water and non-water bodies. Additionally, single-band water index using NIR band of Landsat 8 OLI is simpler or less complicated, without requiring raster calculation, compared to the multi-band water indexes introduced by those investigators [13,14,15,16]. Moreover, this study shows that an optimum threshold value of the water index, where highest value of OA and Khat coefficient were obtained, is conveniently attainable by creating and implementing a geoprocessing modeler in QGIS that automates the process from setting of threshold value to accuracy assessment. This study likewise confirms that remote sensing can extract or delineate water bodies from non-water bodies rapidly, repeatedly and accurately. 2ff7e9595c
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