Percepcion remota

Environmental Modelling with GIs and Remote Sensing

Edited by Andrew Skidmore

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Taylor & Francis

LONDONAND NEWYORK

Copyright 2002 Andrew Skidmore

F'irst published 2002 by Taylor & Francis 1 1 New Fctter Lanc, London EC4P 4EE Simultaneously published In the USA and Canada by Routledgc 29 West 35th Street, NewYork, NY 10001 Reprinted 2003 (twlcc) Taylor & Fruncis is un imprintofthe Taylor & Francis Group

0 2002 Andrew Skidmore
This book has becn produced from camera ready copy supplicd by thc editor

All rights rescrvcd. No part of this book may bc rcprintcd or reproduccd or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storagc or rctrievalsystcm, without permission in writing fiom the publishers. Every effort has been made to ensure that thc advice and information in this book is true and accurate at the time of going to press. However, neither the publishcr nor thc authors can accept any lcgal rcsponsibility or liability for any errors or omissions that may be made. In thc case of drug administration, any medical procedure or the useoftechnical equipment mentioned within this book, you arc strongly adviscd to consult the manufacturer's guidelines. British Library Catalogzllng in Publication Llatu A catalogue record for this book is availablc from the British Library Librury of Congress Cataloguing in Pnblicution Datu A catalogue record has been requcsted

Copyright 2002 Andrew Skidmore

Contents

Preface List ofJiguresList of tables and boxes 1. Introduction 1.11 The challenge 1.2 Motivation to write this book 1.3 What is environmental modelling and how can GIs and remote sensing help in environmental modelling 1.4 Contents of the book 1.5 Rcfcrcnces 2. Taxonomy of environmental models in the spatial sciences 2.1 Introduction 2.2 Taxonomy of models 2.3 Models of logic 2.3.1 Deductive models 2.3.2 Inductivemodels 2.3.3 Discussion 2.4 Deterministic models 2.4.1 Empirical models 2.4.2 Knowledge driven models 2.4.3 Process driven models 2.5 Stochastic models 2.6 Conclusion 2.7 References 3. New environmental remote sensing systems 3.1 Introduction 3.2 High spatial resolution sensors 3.2.1 Historical overview 3.2.2 Overview sensors 3.2.3 IRS-1C and IRS-1D

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Copyright 2002 AndrewSkidmore

vi Contents 3.2.4 KVR-1000 3.2.5 OrbView-3 3.2.6 Ikonos 3.2.7 QuickBird 3.2.8 Eros 3.2.9 Applications and perspectives High spectral resolution satellites 3.3.1 Historical overview 3.3.2 Overview hyperspectral imaging sensors 3.3.3 Applications and perspectives High temporal resolution satellites 3.4.1 Low spatial resolution satellite system with high revisiting time 3.4.2 Mediumspatial resolution satellite systems with high revisiting time Radar 3.5.1 Historical overview 3.5.2 Overview of sensors 3.5.3 Applications and perspectives Other systems 3.6.1 Altimetry 3.6.2 Scatterometers/Spectrometers 3.6.3 Lidar Internet sources 3.7.1 High spatial resolution satellite systems 3.7.2 High spectral resolution satellite systems 3.7.3 High temporal resolution satellite systems 3.7.4RADAR satellite systems 3.7.5 General sources of information References

3.3

3.4

3.5

3.6

3.7

3.8

4. Geographic data for environmental modelling and assessment 4.1 Introduction 4.2 Land-atmosphere interaction modelling 4.3 Ecosystems process modelling 4.4 Hydrologic modelling 4.5 Dynamic biosphere modelling 4.6 Data access 4.7 Global databases 4.7.1 Multiple-theme global databases4.7.2 Hcritagc global land cover databases 4.7.3 Global land cover from satellite data 4.7.4 Topographic data 4.7.5 Soils data 4.7.6 Global population

Copyright 2002 Andrew Skidmore

Contents vii

4.8

4.7.7 Satellite data Sub-global scale databases 4.8.1 Regional land cover mapping 4.8.2 Topographic databases 4.8.3 Administrative and census data 4.8.4 Data clearinghouses 4.9 The role...