Advertisement

© 2019

Spectral Mixture for Remote Sensing

Linear Model and Applications

Book

Part of the Springer Remote Sensing/Photogrammetry book series (SPRINGERREMO)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Yosio Edemir Shimabukuro, Flávio Jorge Ponzoni
    Pages 1-8
  3. Yosio Edemir Shimabukuro, Flávio Jorge Ponzoni
    Pages 9-16
  4. Yosio Edemir Shimabukuro, Flávio Jorge Ponzoni
    Pages 17-22
  5. Yosio Edemir Shimabukuro, Flávio Jorge Ponzoni
    Pages 23-41
  6. Yosio Edemir Shimabukuro, Flávio Jorge Ponzoni
    Pages 43-50
  7. Yosio Edemir Shimabukuro, Flávio Jorge Ponzoni
    Pages 51-67
  8. Yosio Edemir Shimabukuro, Flávio Jorge Ponzoni
    Pages 69-69
  9. Back Matter
    Pages 71-80

About this book

Introduction

This book explains in a didactic way the basic concepts of spectral mixing, digital numbers and orbital sensors, and then presents the linear modeling technique of spectral mixing and the generation of fractional images. In addition to presenting a theoretical basis for spectral mixing, the book provides examples of practical applications such as projects for estimating and monitoring deforested areas in the Amazon region. In its seven chapters, the book offers remote sensing techniques to understand the main concepts, methods, and limitations of spectral mixing for digital image processing.

Chapter 1 addresses the basic concepts of spectral mixing, while chapters 2 and 3 discuss digital numbers and orbital sensors such as MODIS and Landsat MSS. Chapter 4 details the linear spectral mixing model, and chapter 5 explains the use of this technique to create fraction images. Chapter 6 offers remote sensing applications of fraction images in deforestation monitoring, burned-area mapping, selective logging detection, and land-use/land-cover mapping. Chapter 7 gives some concluding thoughts on spectral mixing, and considers future uses in environmental remote sensing. This book will be of interest to students, teachers, and researchers using remote sensing for Earth observation and environmental modeling.


Keywords

Photogrammetry Linear spectral mixing model Electro-optical sensors Remote sensing Digital numbers (DN) MODIS Satellites Pixel Landsat MSS Fraction images Elements of interpretation Land cover mapping

Authors and affiliations

  1. 1.Remote Sensing DivisionNational Institute for Space ResearchSão José dos CamposBrazil
  2. 2.Remote Sensing DivisionNational Institute for Space ResearchSão José dos CamposBrazil

About the authors

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.

Bibliographic information

Industry Sectors
Aerospace
Oil, Gas & Geosciences