Spectral Mixture for Remote Sensing (eBook)
XIII, 80 Seiten
Springer International Publishing (Verlag)
978-3-030-02017-0 (ISBN)
Dr. Yosio Edemir Shimabukuro holds a degree in Forest Engineering from the Federal Rural University of Rio de Janeiro (1972), a Masters in remote sensing from the National Institute for Space Research (1977), Ph.D. in Forest Sciences/Remote Sensing from Colorado State University (1987), and was a Post-Doctoral researcher at NASA Goddard Space Flight Center (1993). He is currently a Senior Researcher in the Remote Sensing Division (DSR), Earth Observation Coordination (OBT) at the National Institute for Space Research (INPE), and professor / supervisor of the Post-Graduate Course in Remote Sensing at INPE. He has experience in Forest Resources and Forestry Engineering, with emphasis on Nature Conservation, working mainly on the following topics: Remote Sensing, Geoprocessing, Forestry Engineering and Environmental Sciences. He developed the linear spectral mixing model for remote sensing data.
Flávio Jorge Ponzoni has worked as a researcher in the Remote Sensing Division at the National Institute for Space Research since 1985. His research interests have included the spectral characterization of vegetation, and recent studies that include the effect of multi-angularity in this characterization. Recently he has been dedicated to the absolute calibration of remotely located sensors, especially those of the CBERS program. In 2009, he joined the WGCV of the CEOS committee and has been involved in international calibration and data validation missions of the IVOS sub-group. He also works as a Professor of the Post-Graduate Course in Remote Sensing of INPE's Land Observation Coordination, teaching Radiometric Transformation of Orbital Data, Spectral Behavior of Targets, and Seminars in Remote Sensing.
Dr. Yosio Edemir Shimabukuro holds a degree in Forest Engineering from the Federal Rural University of Rio de Janeiro (1972), a Masters in remote sensing from the National Institute for Space Research (1977), Ph.D. in Forest Sciences/Remote Sensing from Colorado State University (1987), and was a Post-Doctoral researcher at NASA Goddard Space Flight Center (1993). He is currently a Senior Researcher in the Remote Sensing Division (DSR), Earth Observation Coordination (OBT) at the National Institute for Space Research (INPE), and professor / supervisor of the Post-Graduate Course in Remote Sensing at INPE. He has experience in Forest Resources and Forestry Engineering, with emphasis on Nature Conservation, working mainly on the following topics: Remote Sensing, Geoprocessing, Forestry Engineering and Environmental Sciences. He developed the linear spectral mixing model for remote sensing data. Flávio Jorge Ponzoni has worked as a researcher in the Remote Sensing Division at the National Institute for Space Research since 1985. His research interests have included the spectral characterization of vegetation, and recent studies that include the effect of multi-angularity in this characterization. Recently he has been dedicated to the absolute calibration of remotely located sensors, especially those of the CBERS program. In 2009, he joined the WGCV of the CEOS committee and has been involved in international calibration and data validation missions of the IVOS sub-group. He also works as a Professor of the Post-Graduate Course in Remote Sensing of INPE's Land Observation Coordination, teaching Radiometric Transformation of Orbital Data, Spectral Behavior of Targets, and Seminars in Remote Sensing.
Chapter1: Basic concepts.- Chapter2: The origin of digital numbers (DN).- Chapter3: Orbital sensors.- Chapter4: Linear spectral mixing model.- Chapter5: Fraction images.- Chapter6: Applications of fraction images.- Chapter7: Final considerations.
Erscheint lt. Verlag | 10.11.2018 |
---|---|
Reihe/Serie | Springer Remote Sensing/Photogrammetry |
Zusatzinfo | XIII, 80 p. 38 illus., 27 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Themenwelt | Mathematik / Informatik ► Informatik ► Grafik / Design |
Naturwissenschaften ► Geowissenschaften ► Geografie / Kartografie | |
Naturwissenschaften ► Physik / Astronomie | |
Schlagworte | Digital numbers (DN) • electro-optical sensors • Elements of interpretation • Fraction images • Land Cover Mapping • Landsat MSS • Linear spectral mixing model • MODIS • Photogrammetry • Pixel • Remote Sensing • Remote Sensing/Photogrammetry • Satellites |
ISBN-10 | 3-030-02017-7 / 3030020177 |
ISBN-13 | 978-3-030-02017-0 / 9783030020170 |
Haben Sie eine Frage zum Produkt? |
![PDF](/img/icon_pdf_big.jpg)
Größe: 5,8 MB
DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasserzeichen und ist damit für Sie personalisiert. Bei einer missbräuchlichen Weitergabe des eBooks an Dritte ist eine Rückverfolgung an die Quelle möglich.
Dateiformat: PDF (Portable Document Format)
Mit einem festen Seitenlayout eignet sich die PDF besonders für Fachbücher mit Spalten, Tabellen und Abbildungen. Eine PDF kann auf fast allen Geräten angezeigt werden, ist aber für kleine Displays (Smartphone, eReader) nur eingeschränkt geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
aus dem Bereich