a holistic case-study approach to applying satellite

A Holistic Case-Study Approach to Applying Satellite Remote Sensing to Disaster Management


Edited by

Kazuya Kaku

A Holistic Case-Study Approach to Applying Satellite Remote Sensing to Disaster Management Edited by Kazuya Kaku This book first published 2021 Cambridge Scholars Publishing Lady Stephenson Library, Newcastle upon Tyne, NE6 2PA, UK British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Copyright © 2021 by Kazuya Kaku and contributors All rights for this book reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the copyright owner. ISBN (10): 1-5275-7148-3 ISBN (13): 978-1-5275-7148-8

To the memory of Horst and

to my wife and

Marilyn

TABLE OF CONTENTS List of Figures ............................................................................................ xi List of Tables ........................................................................................... xvii Foreword ................................................................................................ xviii Acknowledgements .................................................................................. xix Part I: Introduction

Chapter One ................................................................................................. 2 Overview of Satellite Remote Sensing for Disaster Management Kazuya Kaku

1.1 Basic principles of satellite remote sensing 1.2 Optical sensors 1.3 Synthetic aperture radar (SAR) 1.4 Infrared sensors 1.5 Microwave radiometers 1.6 Earth observation satellite

Chapter Two .............................................................................................. 54 Methodology: A Holistic Case-Study Approach to Applying Satellite Remote Sensing to Disaster Management Kazuya Kaku

2.1 A holistic case-study approach 2.2 A holistic case-study approach in Sentinel Asia Appendix 2.A: Terminology on disaster management

Table of Contents

viii

Part II: Case Studies

Chapter Three ............................................................................................ 64 Sentinel Asia: A Case Study in the Asia-Pacific Region Kazuya Kaku

3.1 Introduction 3.2 Characteristics of Sentinel Asia 3.3 SA’s framework 3.4 Implementation approach 3.5 Activities: Disaster prevention/mitigation/ preparedness phase 3.6 Activities: Disaster response phase 3.7 Activities: Disaster recovery phase 3.8 Data and information sharing/dissemination 3.9 Conclusion: Progression level of case studies

Chapter Four .............................................................................................. 99 Applying Satellite Remote Sensing to Disaster Management Cynthia S. J. Liu, Ming-Chih Cheng and Shih-Chieh Chou

4.1 Abstract 4.2 FORMOSAT-2 & FORMOSAT-5’s contributions to Sentinel

Asia 4.3 VAP of changing status monitoring according to the disaster

management cycle 4.4 Taiwan contributions to JPT3 Core Infrastructure 4.5 Additional NARLabs capacities for DRR related technologies 4.6 Conclusions

Chapter Five ............................................................................................ 120 Sentinel Asia Success Story: A Case Study in the Philippines Kazuya Kaku

5.1 Objective of Sentinel Asia success story 5.2 Framework of SA success story in the Philippines 5.3 Hazard mapping 5.4 Volcano and land subsidence monitoring using DInSAR 5.5 Landslide early warning 5.6 Conclusion


ix

Chapter Six .............................................................................................. 134 Wildfire Management using Satellite Remote Sensing: A Case Study in Sentinel Asia Kazuya Kaku and Masami Fukuda

6.1 Significance of wildfire management 6.2 Wildfire management activities 6.3 Active wildfire detection using satellites 6.4 Wildfire management support using satellite remote sensing by

Sentinel Asia 6.5 Wildfire early detection and control in Indonesia 6.6 For the future: Beyond Sentinel Asia Appendix 6.A: Overview of AHP/ANP Appendix 6.B: Calculation of ANP

Chapter Seven ......................................................................................... 163 Application of SAR Technology for Earthquakes Masahiko Nagai and Nopphawan Tamkuan

7.1 Use of SAR for Earthquakes 7.2 Case Study 1: Kumamoto Earthquake 7.3 Case Study 2: Palu Earthquake 7.4 Conclusion

Chapter Eight........................................................................................... 175 Disaster Response Support using Satellite Remote Sensing: A Case Study by JAXA in Japan, 2018 Kazuya Kaku

8.1 JAXA’s approach 8.2 Examples of JAXA’s response support 8.3 Conclusion

Chapter Nine ........................................................................................... 189 Space-based Response to the 2011 Great East Japan Earthquake: A Case Study by JAXA Kazuya Kaku

9.1 Overview of the 2011 Great East Japan Earthquake 9.2 JAXA’s response support to the 2011 Great East Japan

Earthquake 9.3 Results 9.4 Opinion survey after disaster response support

Table of Contents

x

9.5 Lessons learned and discussion for the future 9.6 Conclusion

Part III: Holistic Studies based on Case Studies Chapter Ten ............................................................................................. 244 Disaster Response Support using Satellite Remote Sensing: A Holistic Study based on User Requirements, JAXA’s Experiences, and Others Kazuya Kaku

10.1 Introduction 10.2 Operations 10.3 Framework 10.4 Requirements for applying satellite remote sensing to disaster

response support 10.5 Holistic study on global trends in applying satellite remote

sensing to disaster response support Chapter Eleven ........................................................................................ 278 Requirements for Applying Satellite Remote Sensing to Disaster Management: A Holistic Approach Kazuya Kaku

11.1 Overview 11.2 Requirements for applying satellite remote sensing to disaster

management 11.3 Conclusion

Epilogue .................................................................................................. 288 Contributors ............................................................................................. 291 Index ........................................................................................................ 293

LIST OF FIGURES

. Note: Figures in italics (1-12, 1-14 to -16, 1-18 to -22, 1-24, 4-11 and -12, 6-2,

7-3 and -4.) can also be found in the color centerfold. 1-1 Satellite remote sensing system with five components: sources of radiation,

interaction with the atmosphere, interaction with the Earth’s surface, space segment, and ground segment ............................................................................ 4

1-2 Overview of passive satellite remote sensing with respect to atmospheric windows and segregation of solar radiation and terrestrial radiation ................ 5

1-3 Terrestrial radiances from 200 to 500 K ............................................................ 6 1-4 A classification of optical Earth observation satellites, with respect to

spatial resolution and swath width, which supported the response to the Great East Japan Earthquake in 2011 .............................................................. 10

1-5 Spectral signatures of soil, vegetation, and water, with spectral bands of the OLI aboard Landsat-8/LDCM .................................................................... 11

1-6 Schematic view of SAR observation ............................................................... 12 1-7 Schematic illustration of SAR observation and imaging for a point target ......14 1-8 Schematic illustration of SAR observation timeline ....................................... 15 1-9 Properties of the electric field of an electromagnetic wave ............................. 16 1-10a Scattering characteristics of microwaves with respect to the Earth’s surface. Basic type of scattering ...................................................................... 19 1-10b Scattering characteristics of microwaves with respect to the Earth’s surface (continued). In terms of wavelength ...................................................... 20 1-11 Example of the scattering characteristics of L-band SAR. (Flooding in

New Brunswick, Canada, 2008) ...................................................................... 21 1-12 Optical and SAR images of a large-scale flood due to a dike burst in

Nepal, August 2008 .......................................................................................... 23 1-13 Schematic illustration of SAR slant-range distortion .................................... 24 1-14 Principle of pre- and post-disaster SAR color composite images ................. 27 1-15 Color composite image using ALOS/PALSAR of a flood in Vietnam on

November 5, 2008 and pre-disaster image observed on August 5, 2008 ......... 29 1-16 Images of areas A-D in Fig. 1-15 before and after the disaster, as well as

the color composite images ..............................................................................30 1-17 Site survey for heavy rain in Vietnam, October 2008.................................... 31

List of Figures

xii

1-18 Four areas of the field survey in Hanoi. (Heavy rain in Vietnam, October

2008) ................................................................................................................ 33 1-19 Interpretation of interferogram (DInSAR) ..................................................... 36 1-20 ALOS/PALSAR DInSAR analysis of the 2008 Iwate Miyagi inland

earthquake and its interpretation .................................................................... 38 1-21 Coherence obtained in the interferometric analysis of the eruption of Mt.

Agung in Bali Island, Indonesia, November 2017, using ALOS-2 (PALSAR-2) data .............................................................................................................. 39

1-22 Color composite image using ALOS-2 data with different polarized waves for a landslide in Sri Lanka on May 24, 2017 ................................................. 41

1-23 A typical way to detect active wildfires using satellites ................................ 43 1-24. Fire and water: wildfires in Indonesia and typhoon Ketsana in Vietnam

on September 28, 2009 .................................................................................... 44 1-25 Wildfire in Queensland, Australia, November 2018, with the highest alert

level (catastrophic) ever recorded in Australia ................................................ 45 1-26 JAXA global satellite mapping of precipitation (GSMaP) ............................ 47 1-27 Sun-synchronous orbit ................................................................................... 49 1-28 Sub-recurrence with a recurrence interval of n days ..................................... 50 1-29 Observation time of Earth observation satellites ........................................... 51 2-1 Objective of Sentinel Asia ................................................................................................................. 55 2-2 A holistic case-study approach in Sentinel Asia and JAXA ............................ 58 2-3 Disaster management cycle and examples of applicable items for satellite

remote sensing ................................................................................................. 61 3-1 SA’s approaches ............................................................................................... 67 3-2 SA’s framework under the APRSAF in terms of coordination hierarchy ........ 68 3-3 Framework of SA ............................................................................................ 69 3-4 SA constellation of Data Provider Node (DPN) .............................................. 70 3-5 Concept of SA Step 3....................................................................................... 73 3-6 SA activities for prevention/mitigation/preparedness phase ............................ 74 3-7 SA system operation training ........................................................................... 75 3-8 Framework of SA success story in the Philippines, including end-users ........ 76 3-9 SA success story in the Philippines ................................................................. 77 3-10 Wildfires in the world and wildfire management cycle ................................. 79 3-11 Illustration of a scheme of wildfire monitoring in Sentinel Asia .................. 81 3-12 Concept of SA flood monitoring ................................................................... 83 3-13 Precipitation in September 2007, 2008, and 2009 in Kalimantan,

Indonesia, by GFAS ......................................................................................... 84 3-14 SA mini-project to make disaster response support by emergency

observation useful for end-users in each country ............................................ 87


xiii

3-15 Emergency observation flow of SA ............................................................... 89 3-16 SA Step-2 system (Web-GIS) for data/information sharing/dissemination

using WINDS, operated during 2010–2017 by JAXA ......................................... 91 4-1 FORMOSAT satellite support EO on a yearly basis .............................................. 102 4-2 FORMOSAT image support sentinel Asia EO according to the type of

disaster ........................................................................................................... 103 4-3 Location of FORMOSAT support Sentinel Asia EO ..................................... 104 4-4 FORMOSAT-2 time series image in Sendai-Shiogama Port of Japan ........... 105 4-5 Value Added Product of forest fire in Korea made by GIC- AIT .................. 106 4-6 Mission planning of the Turkey-Earthquake even ......................................... 107 4-7 Value Added Product of the Turkey-Earthquake event made by JAXA ........ 107 4-8 FORMOSAT image Ortho system ................................................................. 108 4-9 Disaster management cycle ............................................................................ 110 4-10 Time series images along the Taimali River basin with the landfall impact

by the 2009 typhoon Morakot ......................................................................... 111 4-11 Value Added Product of flooding in Indonesia ............................................. 113 4-12 Flood area detection using SENTINEL-1 images ......................................... 114 4-13 Flood area detection module within Data Cube ........................................... 115 4-14 JPT3 Core Infrastructure and data flow ........................................................ 116 4-15 Statistics of Sentinel Asia Cloud during February and August 2020 ............ 117 5-1 Framework of SA success story in the Philippines, including end-users ...... 121 5-2 SA success story in the Philippines. Activities related to volcano

monitoring and response to volcanic eruption ............................................... 122 5-3 Hazard mapping for lahars at Mayon volcano and application to response .. 124 5-4 Ground subsidence near Manila using ALOS DInSAR (for 1472 days,

March 2007–February 2011) ......................................................................... 125 5-5 Landslide early warning prototype system in the framework of the SA

success story in the Philippines ..................................................................... 127 5-6 Improved accuracy of GSMaP data using GSMaP-IF under UNESCO

project in Pakistan ......................................................................................... 128 5-7 An example of accuracy enhancement of GSMaP data using GSMaP-IF ..... 129 5-8 A local calibration and training on the use of Web-based Landslide

Warning System in Rizal Province ................................................................ 130 6-1 Wildfires in the world ....................................................................................................................... 135 6-2 2019–20 Australian bushfires with unusually intense bushfires throughout

Australia ......................................................................................................... 136 6-3 Wildfire management cycle ........................................................................... 138 6-4 Hierarchical structure of ANP feedback system ............................................ 140 6-5 Inner-dependence among alternatives ........................................................... 140 6-6 Widespread fires in Myanmar and northern Thailand in Feb. 2009 .............. 142

List of Figures

xiv

6-7 A typical way to detect active wildfires using satellites ................................ 143 6-8 Atmospheric absorptions ............................................................................... 144 6-9 Terrestrial radiances for temperatures from 200 to 500 K ............................ 144 6-10 Operational goal of SA wildfire control initiative ....................................... 148 6-11 Fire detection and control system of the JST/JICA project on wildfire and

carbon management in a peat forest in Indonesia .......................................... 150 6-12 Sentinel Earth beyond Sentinel Asia ........................................................... 151 6-13 Structure of Analytic Hierarchy Process (AHP) .......................................... 153 6-14 Structure of Analytic Network Process (ANP) ............................................ 154 7-1 Across-track (left) and along-track (right) interferometry ............................. 164 7-2 Characteristic of interferometric coherence between pre-seismic

and co-seismic pairs ................................................................................... 166 7-3 Interferogram results along the Futagawa fault before and after the

Kumamoto Earthquake .................................................................................. 168 7-4 The coherence change result using before and after the Kumamoto

Earthquake for landslide areas ...................................................................... 169 7-5 Interferometric coherence of HH and HV polarizations for pre- and co-

disaster pairs .................................................................................................. 170 7-6 The interferometric coherence changes (red – high, blue – low) and ground

truth from google earth image interpretation and Copernicus ....................... 171 8-1 JAXA’s approach to disaster management support ....................................... 176 8-2 JAXA’s approach to disaster management support within Japan .................. 178 8-3 Observation images of lava dome by ALOS-2 (L-band SAR) and COSMO-

SkyMed (X-band SAR) at Kirishimayama (Shinmoedake) and change in lava dome ....................................................................................................... 180

8-4 Estimated flooded area by ALOS-2 color composite image before (April 15, 2018) and after (July 8, 2018) the disaster in Takahashi river and Odagawa basin of Kurashiki city in heavy rain, July 2018 ........................... 182

8-5 Estimated large-scale collapsed area in Kochi Prefecture by ALOS-2 color composite image for helicopter investigation in heavy rain, July 2018 ......... 183

8-6 Landslide dams indicated by the ALOS-2 image observed on September 6 with confirmation using an optical image observed by FORMOSAT-5 on September 6 in the framework of Sentinel Asia for the 2018 Hokkaido Eastern Iburi Earthquake ............................................................................... 185

9-1 Areas damaged by the tsunami along the Tohoku-Kanto Pacific coast (the 2011 Great East Japan Earthquake) ............................................................... 190

9-2 JAXA’s approach to disaster management support within Japan in collaboration with collaborative users. Framework of organizations and their functions at the time of the Great East Japan Earthquake ..................... 192

9-3 JAXA’s approach to disaster management support ....................................... 193


xv

9-4 FORMOSAT-2 image (observed on March 12) analysis by NARLabs. ........ 205 9-5 Optical images around Sendai airport observed by THEOS on March 13 .... 206 9-6 CARTOSAT-2 image observed on March 14, 2011, over Sendai area.

Sendai east road worked as a dike ................................................................. 207 9-7 SAR color composite images of Sendai coast area. (RADARSAT-2 and

TerraSAR-X) ................................................................................................... 211 9-8 A WorldView-2 image recorded on March 12, covering the Oshika

Peninsula in Miyagi Prefecture ...................................................................... 214 9-9 ALOS/AVNIR-2 pre- and post-disaster images from Sendai to Soma .......... 215 9-10 A proposed classification system of optical Earth observation satellites

with respect to spatial resolution and swath width ........................................ 235 9-11 An SOS signal identified in very high-resolution satellite image.

(WorldView-1) ............................................................................................... 233 9-12 A WorldView-2 image covering Rikuchuyamada in Iwate Prefecture ........ 237 9-13 Very high-resolution satellite images of Fukushima Dai-ichi nuclear

power plant in Fukushima Prefecture. (WorldView-2 and GeoEye-1) .......... 238 10-1 Framework of SA ........................................................................................ 246 10-2 A holistic approach based on case studies in Sentinel Asia and JAXA ...... 247 10-3 Emergency observation flow of SA ............................................................. 250 10-4 SA constellation of Data provider Node (DPN) .......................................... 251 10-5 JAXA’s approach to disaster response support within Japan ...................... 253 10-6 JAXA’s approach to disaster management support ..................................... 254 10-7 ALOS-2 rapid response ............................................................................... 258 10-8 A proposed classification system of optical Earth observation satellites

with respect to spatial resolution and swath width ...................................... 262 10-9 Observation of damaged area by predicted Nankai trough

earthquake/tsunami in Japan. Using seven SAR satellites (ALOS-2, COSMO-SkyMed, RADARSAT-2, and TerraSAR-X) and ALOS-3 .......... 263

10-10 SA Step-2 system (Web-GIS) for data/information sharing and dissemination, operated during 2009–2019 by JAXA ...................................... 265 10-11 Concept of SA mini-project ....................................................................... 267 10-12 SEM (Satellite-based Emergency Mapping) temporal trends during the

study period 2000–2014 ................................................................................ 272 10-13 Spatial distribution of SEM activations by disaster type ........................... 274 11-1 Conceptual illustration of applying satellite remote sensing to disaster

management ................................................................................................... 279 11-2 Framework of Sentinel Asia ........................................................................ 280 11-3 Applicable items for satellite remote sensing in terms of disaster management cycle ............................................................................................ 282 11-4 SA activities supported by human network ................................................. 283

List of Figures

xvi

11-5 SA Step-2 system (Web-GIS) for data/information sharing and Dissemination .................................................................................................. 285 E-1 Schematic illustration of applying satellite remote sensing to disaster

management, including users and human network ....................................................... 289

LIST OF TABLES

Note: Table in italics (1-2) can also be found in the color centerfold. 1-1 Three types of satellite remote sensing and typical sensors .............................. 8 1-2 Field survey results in four areas in Fig. 1-18. (Flood in Vietnam, October 2008) ................................................................... 34 3-1 Achievements and progression level of case studies ....................................... 93 4-1 The key parameter of FORMOSAT-2 and FORMOSAT-5 ............................ 101 4-2 The specification of FORMOSAT orthorectified image ................................ 107 6-2 Pairwise comparison of criterion with respect to scenario ............................ 155

6-3 Pairwise comparison of alternative with respect to criterion .................................. 156 6-4 Pairwise comparison of inner dependence among alternatives ..................... 158 9-1 List of satellites that supported relief efforts after the

Great East Japan Earthquake ......................................................................... 194 9-2 Observations conducted by ALOS, the International Charter,

Sentinel Asia, and others (March 12–24, 2011) ............................................. 196 9-3 Major analysis products provided by JAXA ................................................. 199 9-4 Data needed by collaborative users and how they employed it ..................... 201 9-5 Timeline of initial response by JAXA ........................................................... 217 9-6a Summary of the opinion survey results ....................................................... 219 9-6b Summary of the opinion survey results (continued) .................................... 223

FOREWORD This book provides case and holistic studies on “applying satellite remote sensing to disaster management” by various organizations, institutes, or universities in each country/region. It should be noted that “application of satellite remote sensing to disaster management” in this book refers to the employment of satellite-based disaster information/data by users working for disaster management, including rescue/relief/evacuation; not just disclosing them on the Internet.

Satellite remote sensing is one of the primary support tools for disaster management. However, it is not easy for people involved in this field, such as emergency responders, policy makers, administrative officials, researchers, and students, to actually use it. They have been actively seeking good practices and lessons learned, as a practical reference to their activities. It would be a great pleasure for me if this book could contribute to this.

From a methodological point of view, in such applied science research areas as "applying satellite remote sensing to disaster management”, where practical implications are often required for their research results, case studies are useful. It would be also a great pleasure for me if this book could contribute to the active application of a holistic case-study approach to this research area.

This book is published as part of the Cambridge Scholars Publishing Frontiers of Knowledge Series, inviting proposals to guest-edit collections of chapters: Cynthia S. J. Liu, Ming-Chih Cheng, Shih-Chieh Chou, Masahiko Nagai, Nopphawan Tamkuan, and Masami Fukuda contributed chapters. I wish to gratefully acknowledge them. I am also deeply grateful to Cambridge Scholars Publishing (CSP) for giving me the opportunity to write and edit this book, with the support of the whole team at CSP.

Kazuya Kaku

Guest Editor, CSP Visiting Researcher, ADRC

July 2021

ACKNOWLEDGEMENTS

I wish to gratefully acknowledge all the members of the Sentinel Asia (SA) Joint Project Team, the Japan Aerospace Exploration Agency (JAXA) SA team, and the JAXA domestic disaster response support team for their contribution and collaboration in promoting the project,

I am also grateful to Ms. Tomoko Suzuki for her support to create figures in this book.

Finally, I am grateful to JAXA and Asian Disaster Reduction Center (ADRC) for giving me the opportunity to write and edit this book.

PART I:

INTRODUCTION

CHAPTER ONE

OVERVIEW OF SATELLITE REMOTE SENSING FOR DISASTER MANAGEMENT

KAZUYA KAKU

This chapter gives an overview of satellite remote sensing for disaster management as the basic information on applying satellite remote sensing to disaster management.

1.1 Basic principles of satellite remote sensing

Remote sensing is a technology for remotely studying the properties of objects using electromagnetic radiation, without touching the objects directly. Satellite remote sensing covers wide-ranging areas, operates continually during all hours and in all types of weather, and is used to survey Earth’s surface and atmosphere to study global environmental problems, monitor disasters, explore resources, and so on.

A satellite remote sensing system (Curran, 1985) consists of five components, as shown in Fig. 1-1: sources of radiation (the Sun, the Earth, and an artificial radiation source), interaction with the atmosphere, interaction with the Earth’s surface, space segment (sensors and satellites), and ground segment. It should be noted that human factors (such as system operators and system users working in disaster management and response) in the ground segment as well as technical factors are important when applying satellite remote sensing to disaster management.

1.1.1 Sources of radiation

Everything that is hotter than 0 K emits electromagnetic radiation. The largest source of electromagnetic radiation is the Sun (solar radiation), and the Earth’s surface reflects and absorbs the solar radiation, as shown in Fig. 1-1. Furthermore, absorbed solar radiation raises the Earth’s temperature and is radiated back to space as thermal radiation according to its

Overview of Satellite Remote Sensing for Disaster Management

3

temperature (terrestrial radiation). This mechanism keeps absorbed solar radiation and emitted terrestrial radiation in balance macroscopically. Remote sensing measures the reflected solar radiation and emitted terrestrial radiation. In addition, remote sensing employs an artificial source of electromagnetic radiation; that is, the satellite itself emits electromagnetic radiation and receives the retuned electromagnetic radiation from the Earth’s surface. The former (which uses natural radiation) is called passive remote sensing; the latter (an artificial radiation source) is known as active remote sensing.

The wavelengths at which solar radiation and terrestrial radiation are employed can be shown to be almost completely distinct for remote sensing, as shown in Fig. 1-2 (for derivation of Fig. 1-2(a) and (b), refer to the reference (Kaku, 2019b, appendix A)). Although there is much more solar radiation than terrestrial radiation, because the Earth is very far from the Sun, the segregation of the solar radiation and the terrestrial radiation results at the top of the Earth’s atmosphere in satellite remote sensing. For wavelengths that are shorter than an intersection point at 𝜆𝜆0 (see Fig. 1-2), solar radiation is dominant, which is called the solar radiation (or “shortwave radiation”) range. For wavelengths that are longer than the intersection point, terrestrial radiation is dominant, which is called the terrestrial radiation (or “longwave radiation”) range.

Solar radiation, that reached the top of the Earth’s atmosphere (see Fig. 1-2(a)), reaches the Earth’s surface through the atmosphere and is reflected by the Earth’s surface and finally reaches sensors at the space segment through the atmosphere. In this process, solar radiation is influenced by the atmosphere that has unique spectral features of transmittance (see Fig. 1-2(c) and Section 1.1.2). Similarly, terrestrial radiation that reaches sensors at the space segment through the atmosphere is influenced by the atmosphere.

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Chapter One

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The intersection point (𝜆𝜆0), which is the boundary point between the solar and terrestrial radiation ranges, varies depending on the Earth’s surface temperature. Given that the range of the Earth’s surface temperature is 200–350 K (Kondo, 2000), the intersection point is rounded as a wavelength interval of ~3–8 µm, as can be seen from Fig. 1-3, where the dominance of solar or terrestrial radiation is not uniquely defined and special handling is required. It should be noted that only terrestrial radiation can be detected without the influence of solar radiation at night, regardless of wavelength. For example, an infrared wavelength of ~4 µm, which is suitable for monitoring volcanoes and wildfires, belongs to the mixing range (~3–8 µm), for which only night-time data are employed.

In conclusion, the solar radiation range and the terrestrial radiation range are separated at the intersection point, although the intersection point is not fixed and is at a wavelength range of ~3–8 µm.

Fig. 1-3. Terrestrial radiances from 200 to 500 K. For the range of the Earth’s surface temperature 200–350 K, the intersection point is rounded as ~3–8 µm. (Drawn with MAC/Graper v2.7)


7

The terrestrial radiation in the microwave band (1 mm–1 m), which is the band with the longest wavelength used for remote sensing, is very low in energy (e.g., see Fig 1-2, the radiant intensity at a wavelength of 1 cm is ~10-11 times the peak at 10 µm), which is difficult to observe using satellites, whereas microwaves can penetrate clouds and be used to observe the Earth’s surface, regardless of weather conditions.

1.1.2 Interaction with the atmosphere

Generally, electromagnetic radiation is absorbed or scattered by atmospheric gases such as water vapor, carbon dioxide, oxygen and ozone, methane, and nitrous oxide, while passing through the atmosphere. However, there are ranges of electromagnetic wavelengths to which the Earth’s atmosphere is relatively transparent (little or no absorption or scattering), which are called atmospheric windows.

In remote sensing, wavelengths in the atmospheric windows are used for observation of the Earth’s surface, whereas the wavelengths of specific absorption bands can be used to observe the absorbing substance. A profile of the atmospheric windows (atmospheric transmittance) is shown in Fig. 1-2(c). The windows are found in the visible band (0.4–0.7 µm); the partly infrared band (0.7–14 µm), consisting of the reflected infrared band (0.7–3 µm) and thermal (emitted) infrared band (3–14 µm); and the partly radio-wave band (1 mm–30 m). The visible, infrared, and microwave (1 mm–1 m) bands in the radio-wave band are employed for remote sensing.

1.1.3 Types of satellite remote sensing

Considering the sources of radiation (see Section 1.1.1) and atmospheric interaction (Section 1.1.2), satellite remote sensing is classified into three types with respect to wavelength regions, as shown in Fig. 1-2: visible and reflected infrared remote sensing, thermal (emitted) infrared remote sensing, and microwave remote sensing.

1.1.4 Typical sensors for disaster management

In the space segment, instruments (called sensors) aboard satellites receive reflected solar radiation and emitted terrestrial radiation and emit and receive artificial radiation according to commands from the ground segment (refer to Fig. 1-1).

Typical spaceborne sensors are listed in Table 1-1. Regarding disaster management activities so far, data from the following sensors have been

Chapter One

8

employed: (1) Optical sensors: passive sensors using solar radiation (visible and

reflected infrared) (2) Synthetic aperture radar (SAR): active sensors using artificial

radiation (microwave) (3) Infrared sensors: passive sensors using terrestrial radiation (thermal

(emitted) infrared) (4) Microwave radiometers: passive sensors using terrestrial radiation

(microwave) Table 1-1. Three types of satellite remote sensing and typical sensors.

Types of remote sensing (RS)

Visible and reflected infrared RS

Thermal infrared RS

Microwave RS

Atmospheric windows

Visible 0.4–

0.7 µm

Reflected infrared

0.7–3 µm (partly)

Thermal (emitted) infrared

3–14 µm (partly)

Microwave 1 mm–1 m

(partly)

Source of

electromagnetic radiation

Passive Solar radiation Terrestrial radiation

Terrestrial radiation

Active Artificial radiation not applicable (NA)

Artificial radiation

Typical sensors

Passive Optical sensors Infrared sensors Microwave radiometers

Active LiDAR (Light Detection and Ranging)

NA Radar (Radio Detection and Ranging) - Synthetic Aperture Radar (SAR) - Precipitation Radar (PR) - Radar Scatterometer - Radar Altimeter

1.2 Optical sensors

Optical sensors work in the wavelength region of visible and reflected infrared light, and they receive the solar radiation reflected by the Earth’s surface and clouds. Imagery from optical sensors is similar to photographs taken by digital cameras in an airplane (see Fig. 1-4(a)). An optical sensor cannot work without sunlight. In addition, an optical sensor cannot observe the Earth’s surface through clouds; in the case of meteorological satellites, optical sensors are also used to observe the clouds themselves.


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1.2.1 Spatial resolution and swath width

The spatial resolution of a sensor, which shows what part of an object a sensor can identify, is defined by the size of the surface area corresponding to an individual pixel of a sensor, where a pixel is a single detector element of a sensor. By zooming in on a satellite image displayed on a screen, a square mosaic appears (e.g., see Fig. 1-14), which shows the pixels of the image. The spatial resolution of a sensor is determined by the instantaneous field of view (IFOV) of the sensor and the distance from the object. A sensor at a low altitude has a higher spatial resolution than the same one at a higher altitude.

The belt-shaped area on the Earth’s surface imaged by a sensor as the satellite moves is referred to as the swath. The spatial resolution and swath width of a sensor are set for each satellite based on its mission requirements and data handling capacity. Generally, if the spatial resolution is higher, the swath width is obliged to be smaller, as exemplified in Fig. 1-4. Note that, in Fig. 1-4, “very high resolution” means a spatial resolution of less than or equal to 1 m; “high resolution,” greater than 1 m and less than or equal to 10 m; “very wide range,” a swath of more than 100 km; “wide-range,” greater than 50 km and less than or equal to 100 km. (In the classifications above, the best values for resolution and related swath width for each satellite are based on information found on the Internet.) A WorldView-2 image (Fig. 1-4(a)) recorded on March 12 immediately after the Great East Japan Earthquake (Kaku et al., 2015), which occurred on March 11, 2011, covering the Oshika Peninsula in Miyagi Prefecture, showing local conditions around Onagawa (copyright: DigitalGlobe, DLR, and Map produced by ZKI). A FORMOSAT-2 image (Fig. 1-4(b)) observed on March 12, shows Watari and surrounding areas. ALOS/AVNIR-2 images (Fig. 1-4(c)) show area from Sendai Airport (in the north) to Soma (in the south); the image on the right was recorded after the tsunami on March 14, 2011; the image on the left was recorded before the tsunami on February 27, 2011.

A higher spatial resolution or wider swath requires more pixels, which causes the data volume to become larger and makes data transmission and handling more difficult. From the viewpoint of disaster monitoring, immediately after an extensive catastrophe, users mostly request satellite observation to assess the general situation in damaged areas, for which wide-ranging surveillance is most suitable (see Fig. 1-4(b) and (c), in the case of the 2011 Great East Japan Earthquake). Users also request satellite observation to evaluate roads, key facilities, and other infrastructure in greater detail to confirm their availability and ensure routes for evacuation, rescue, and support, for which very-high-resolution observation is necessary (see Fig. 1-4(a), in the case of the 2011 Great East Japan Earthquake).

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