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Historical Land Use/Land Cover Classification Using Remote Sensing

A Case Study of the Euphrates River Basin in Syria

  • Wafi Al-Fares

Part of the SpringerBriefs in Geography book series (BRIEFSGEOGRAPHY)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Wafi Al-Fares
    Pages 1-9
  3. Wafi Al-Fares
    Pages 39-48
  4. Wafi Al-Fares
    Pages 49-61
  5. Wafi Al-Fares
    Pages 63-160
  6. Wafi Al-Fares
    Pages 161-196

About this book

Introduction

Although the development of remote sensing techniques focuses greatly on construction of new sensors with higher spatial and spectral resolution, it is advisable to also use data of older sensors (especially, the LANDSAT-mission) when the historical mapping of land use/land cover and monitoring of their dynamics are needed. Using data from LANDSAT missions as well  as from Terra (ASTER) Sensors, the authors shows in his book maps of historical land cover changes with a focus on agricultural irrigation projects.


The kernel of this study was whether, how and to what extent applying the various remotely sensed data that were used here, would be an effective approach to classify the historical and current land use/land cover, to monitor the dynamics of land use/land cover during the last four decades, to map the development of the irrigation areas, and to classify the major strategic winter- and summer-irrigated agricultural crops in the study area of the Euphrates River Basin.

Keywords

Digital Remotely Sensed Data Processing Euphrates-River-Basin LANDSAT Land Use / Land Cover Remote Sensing Syria Terra (ASTER) Sensors

Authors and affiliations

  • Wafi Al-Fares
    • 1
  1. 1.Institute of Geography, Remote SensingUniversity of JenaJenaGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-00624-6
  • Copyright Information The Author(s) 2013
  • Publisher Name Springer, Heidelberg
  • eBook Packages Earth and Environmental Science
  • Print ISBN 978-3-319-00623-9
  • Online ISBN 978-3-319-00624-6
  • Series Print ISSN 2211-4165
  • Series Online ISSN 2211-4173
  • Buy this book on publisher's site
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