MASTER'S THESIS
Phase-0 study of a Disaster Management Satellite Constellation with a Focus on the
Indian Subcontinent
Eline Conijn 2015
Master of Science (120 credits) Space Engineering - Space Master
Master Thesis
Phase-0 study of a disaster management satellite constellation with a focus on the Indian subcontinent
Eline Conijn
Supervisors
Narayan Prasad Nagendra Dhruva Space Bangalore, India Marappa KRISHNASWAMY NIU Nagercoil, India Peter von Ballmoos Institut de Recherche en Astrophysique et Planétologie Toulouse, France
Johnny Ejemalm Lulea University of Technology Kiruna, Sweden
April 13, 2015
Opgedragen aan Oma Conijn Voor altijd in herinnering
Acknowledgment
Foremost, I would like to express my sincere gratitude to my direct advisor, Narayan Prasad Nagendra for the continuous support of my master thesis and research, for his patience, motiva- tion, enthusiasm, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor and mentor for my master thesis. Also I am for ever grateful to him for offering an opportunity of a life time to come to India and showing me around this amazing country. The many trips on the back of the motor cycle navigating the buzzing streets filled with life have made a lasting impression. And expressed in his own words, it was also often a humbling experience.
Besides my advisor, I would like to thank the rest of my thesis supervisors: Marappa Krish- naswany, Peter von Ballmoos and Johnny Ejemalm, for their encouragement, insightful com- ments, and hard questions.
My thanks are also given to the many former ISRO scientists I had the pleasure to meet and who gave me insight into the Indian space industry. Also many thanks to William B. Gail, who gave me great advice about the cloud coverage.
Special thanks are given to my dear colleagues at Dhruva Space, Sai, Divya and Ferran for their advice during the thesis and the "secret missions" to buy the "boss" a birthday present or eat a chicken hamburger.
I am also very grateful for my Indian host family, as well as all the extending family which I had the pleasure to get to know, including all the aunties and uncles, cousins, nephews and nieces, in-laws and grandparents. They made me feel welcome, made certain that I was well taken care off, made me try new cuisine, made me part of their world including the many rituals and made me laugh.
I pay tribute to all my fellow SpaceMasters, for making the two year adventure memorable, interesting and amazing. We were not just fellow students and friends, we became part of a special family.
Lastly but not least, I would like to thank my parents Gijs and Adri Conijn-Meiborg and my brother Arnoud. Even when they were worried when I was so far away in countries slightly or very different from the Netherlands they were always supportive and encouraging.
Résumé
This report explores the feasibility of a small satellite constellation used for disaster management in India. It shows that a small satellite constellation for the Indian subcontinent is not feasible based on the requirements and constraints set in this report and thus not worth to pursue in this form. Although it has been made clear that effective disaster management is a must, especially in India and that remote sensing from space is an excellent tool for this purpose, based on the spatial and temporal requirements derived from the occurrence and impact of the disasters, it would be impossible to propose a mission within the constraints set by this report. After a careful analysis of the Indian space budget, existing missions and the economical impact of the disasters, it is determined that a disaster management mission for the Indian subcontinent has a maximum mission budget of 30 million USD, a mass constraint of 500 kg and a volume constraint of 5 m3 for all the satellites in the constellation combined. The two instrument types with proven capabilities in remote sensing disasters, microwave and passive optical instruments, have each its own reasons to be unsuitable for a small satellite constellation. Active microwave instruments, more specific SAR, are proven to be useful in detecting and monitoring disasters. However the instruments require an antenna panel that would be too large to fit on a small satellite to meet the spatial requirements or the constellation requires too many satellites and thus exceeding the budget to meet the required revisiting time. The number of satellites and size of the SAR antenna panel is determined with a developed algorithm in MATLAB. Optical instruments are not suitable for a mission dedicated to disaster management due to the cloud cover visibility constraint and limitations set by the usage of indexes.
Denna rapport undersöker genomförbarheten av ett litet satellitkonstellation som används för katastrofhantering i Indien. Den visar att en liten satellitkonstellation för den indiska subkonti- nenten är inte möjligt utifrån de krav och begränsningar som anges i denna rapport och därmed inte värt att fortsätta i denna form. Även om det har gjorts klart att en effektiv hantering av katastrofer är ett måste, särskilt i Indien och att fjärranalys från rymden är ett utmärkt verktyg för detta ändamål, bygger på de rumsliga och tidskrav som härrör från förekomsten och effek- terna av de katastrofer, skulle det vara omöjligt att föreslå ett uppdrag inom de begränsningar som framgår av denna rapport. Efter en noggrann analys av den indiska rymdbudget, befintliga uppdrag och de ekonomiska effekterna av katastrofer är det bestämt att en katastrofuppdrag för den indiska subkontinenten har en maximal budget uppdrag på 30 miljoner USD, en massa hinder på 500 kg och en volymbegränsning på 5 m3 för alla satelliter i konstellationen kombineras. De två instrumenttyper med bevisad kapacitet i fjärranalys katastrofer, mikrovågsugn och passiva op- tiska instrument, har vardera sina egna skäl att vara olämpliga för en liten satellitkonstellation.
Aktiva mikrovågsinstrument, mer specifik SAR, har visat sig vara användbart för att upptäcka och övervaka katastrofer. Men instrumenten kräver en antenn panel som skulle vara för stor för att passa på en liten satellit för att uppfylla utrymmeskrav eller konstellationen kräver alltför många satelliter och därmed överskrider budgeten för att uppfylla kraven återbesök tiden. Antalet satelliter och storlek SAR antennpanelen bestäms med en utvecklad algoritm i MATLAB. Optiska instrument är inte lämpliga för ett uppdrag tillägnad katastrofhantering på grund av molntäcke synlighet tvång och begränsningar som fastställts av användningen av index.
Abbreviations
AC Alarm and Crisis
AVHRR Advanced Very High Resolution Radiometer
CACOLA Climatic Atlas of Clouds Over Land and
Ocean
DDI Daily Drought Index
DEM Digital Elevation Model
DInSAR Differential Interferometric Synthetic Aperture Radar
GSD Ground Spacing Distance
InSAR Interferometric Synthetic Aperture Radar
ISCPP Iternational Satellite Cloud Climatology
Project
INR INdian Rupies
IR Infra Red
ISRO INdian Space Research Organization
KP Knowledge and Prevention
NDVI Normalized Difference VegetationIndex
NIR Near Infra Red
LEO Low Earth Orbit
PC Post Crisis damage
SAR Synthetic Aperture Radar
SWIR Short Wave Infra Red
TIR ThermalInfra Red
UN United Nations
USD United States Dollars
VIS VISible
i
Symbols
Ae effective area of SAR panel m3
ASAR area of SAR panel m3
awa azimuth impulse response broadening
factor -
awr range impulse response broadening fac-
tor -
c speed of light 299792458 m/s
FN system noise factor for the receiver -
Ga Antenna gain -
K Design margin 1-3
k Boltzmann’s constant 1.3810−23J/K
La length of the SAR panel m
Latmos atmospheric loss factor due to the prop-
agating wave -
Lion loss factor due to ionosphere -
Lradar microwave transmission loss factor due to miscellaneous sources -
N nominal scene noise temperature 290 K
N Eσ0 Noise-equivalent sigma-zero -
Pavg average power at transceiver W (Js−1)
Pr received power W (Js−1)
Pt transmitted power W (Js−1)
R Range vector from target to antenna m
Ru Unambiguous range m
SN R Signal-to-Noise ratio -
vx satellite speed in slong track direction m/s
Wa width of the SAR panel m
η efficiency of SAR panel 0.6
λ wavelength m
ρy slant-range resolution required m
σ target radar cross section m2
ω angular frequency rads−1
Contents
1 Introduction 1
2 Disasters and their remote sensing requirements 2
2.1 Disasters in India and their impact . . . . 2
2.1.1 Disaster definition . . . . 2
2.1.2 Disaster data . . . . 2
2.1.3 Indian disaster scenario . . . . 3
2.1.4 Disasters, poverty and development . . . . 6
2.1.5 Disaster Management . . . . 7
2.2 Remote sensing of disasters . . . . 8
3 Small satellites 16 3.1 Definition . . . . 16
3.2 Cost estimation disaster monitor constellation mission for India . . . . 16
3.3 Constraints . . . . 17
3.3.1 Mass . . . . 17
3.3.2 Volume . . . . 17
3.3.3 Power . . . . 18
4 Satellite constellations 19 4.1 Possible constellations . . . . 19
4.1.1 Sun synchronous orbits . . . . 19
5 Feasibility of microwave instruments for the a small satellite constellation 22 5.1 Passive microwave sensor . . . . 22
5.1.1 Feasibility for small satellites . . . . 22
5.2 Active microwave sensor . . . . 22
5.2.1 Basic principles of SAR . . . . 23
5.2.2 Feasibility for small satellites . . . . 28
6 Feasibility of passive optical instruments for the a small satellite constellation 34 6.1 Visibility constraints caused by clouds . . . . 34
6.2 Constraints imposed by the use of indexes . . . . 35
7 Conclusions and recommendations 36
Bibliography 38
Appendix A 44
Appendix B 50
iii
List of Figures
2.1 Economical losses due disasters . . . . 3
2.2 Multihazard map of India . . . . 5
2.3 Importance of effective disaster management . . . . 8
4.1 Coverage entire Earth surface . . . . 21
4.2 Coverage India . . . . 21
List of Tables
2.1 Disaster data based on EM-DAT data base from a period of 1983 till 2013 for the Indian subcontinent. (for Research on the Epidemiology of Disasters(2014a)) . . 4 2.4 Overview of the disaster requirements maturity . . . . 15 4.1 Parameters obtained with developed MATLAB code and STK simulation . . . . 20 5.1 Real and theoretical values obtained with derived MATLAB code for various SAR-
instruments . . . . 27 5.2 Results of the required antenna area, average power and required number of satel-
lites for detecting and monitoring earthquakes . . . . 28 5.3 Results of the required antenna area, average power and required number of satel-
lites for detecting and monitoring landslides . . . . 29 5.4 Results of the required antenna area, average power and required number of satel-
lites for detecting and monitoring Flooding and Drought . . . . 30 5.5 Overview of weight, resolution, swath, antenna area of different SAR satellites and
the cost of the satellite the instrument is on board . . . . 32 6.1 Monthly average cloud amount in percentage estimated for India from CACOLO
(day and average) and ISCCP(average) . . . . 35
v
Chapter 1
Introduction
During the International Workshop in Small Satellites and Sensor Technology for Disaster Man- agement on 31st of March till the 2nd of April 2014 organized by CANEUS and Lockheed Martin, it became clear that there was a great need for more (space) systems dedicated to disaster man- agement. Often many speakers also mentioned the use of a constellation of small satellites to meet the frequent revisiting requirement for monitoring and detecting disasters. However a crit- ical study to determine if a constellation could meet all the requirements set by the disasters has been missing till now. Dhruva Space, a company based in Bangalore, India specializing in small satellites, is interested in exploring the possibility to propose an Indian space mission dedicated to disaster management to the Indian government. In order to determine if the company should invests its time in the proposal, it was determined that a phase-0 study of a disaster management satellite constellation with a focus on the Indian subcontinent was necessary. After familiar- ization with the subject, the task was refined, so that the study and this accompanying report will answer the following question: "Is a small satellite constellation covering India dedicated to disaster management a feasible option and worth to pursue?".
To answer the proposed question, chapter 2 discusses what the impact of the disasters is in India and the remote sensing requirements set by these disasters. Afterward a definition of small satellites and their constraints are given in chapter 3. This is followed by a proposal of a possible satellite constellation in chapter 4. The last two chapters give an answer to the question if microwave and optical instruments would be suitable on board of small satellites. This report will finish with a conclusion, in which the earlier mentioned question will be answered and some recommendations.
Chapter 2
Disasters and their remote sensing requirements
This chapter will deal with the background of the problem, the first part will treat the disasters in India and their impact on the Indian society. The second part of the chapter will deal with the remote sensing techniques used and needed to detect the various disasters.
2.1 Disasters in India and their impact
Before a solution for a problem can be found, the problem needs first to be assessed and under- stood. In this section the different disasters which India faces are listed as well as their impact on the India society. From this a recommendation can be made which areas of interests should be addressed to make the biggest difference for Indian society. First a definition of a disaster is given and an overview of various types of disasters.
2.1.1 Disaster definition
The Asian Disaster Risk Reduction Center defines disasters as “sudden events, which bring serious disruption to society with massive human, material and environmental losses, these losses always go beyond the capacity of the affected society to cope with its own resources.” (Center (2008)) However there are numerous definitions of a disaster, examples are given byPidgeon and O’Leary (2000), Denis (1995) and Keller and Al-Madhari (1996). The definition used seems dependent on the discipline using the particular term. Parker and Handmer (2013) have reviewed the concept of disaster and suggests that the preferred definition of disaster is: “An unusual or natural man-made event, including an event caused by failure of technological systems, which temporarily overwhelms the response capacity of human communities, groups of individuals or natural environments and which causes massive damage, economic loss, disruption, injury and/or loss of life.” This definition will be used in this report.
2.1.2 Disaster data
Data on disaster occurrence, their effect upon human society and their financial burden on coun- tries is unfortunately not easily and accurately available. There is not a single institution that has taken on the role of prime provider of verified data, nor is there an internationally standardized method for assessing damage for global use. This will give rise to inconsistencies, data gaps and ambiguity of terminology, which make comparisons and use of the different data sets difficult. As a result the evaluation of a disaster situation is confusing and poses severe obstacles for prevention planning and preparedness. Recognising the need for better quality data to support disaster pre- paredness and mitigation, the ProVention Consortium of the World Bank Disaster Management Facility, started an evaluation of the quality, accuracy and completeness of three global disaster data sets (Below and Guha-Sapir(2003)). Two of the sets, NatCat maintained by Munich Rein- surance Company and Sigma, maintained by the Swiss Reinsurance Company are not publically available. The third one, EM-DAT, maintained by the Centre for Research on the Epidemiology of Diasters, is publically available (for Research on the Epidemiology of Disasters(2014a)). In this evaluation it was concluded that all databases where maintained with scientific rigour and all furnish the world community with acceptable levels of data on disasters. The differences in the
2
databases reduces significantly with time. Although the report state improvements, especially for EM-DAT, it is considered that EM-DAT data will give an accurate overview of the disasters India is experiencing if the analysed time period is not too large. Also EM-DAT is conceived for scientific research and development community, while Sigma and NatCat are essentially designed to serve internal commercial policy and to service their client insurance company. Therefore the use of EM-DAT data will be more suitable for this research.
2.1.3 Indian disaster scenario
India has a diverse range of natural features and these unique geo-climatic conditions make the country among the most vulnerable to natural disasters in the world. Disasters happen with a large frequency in India and while the majority of the society has adapted itself to these regular occurrences, the economic and social costs are increasing every year, as can been seen in figure 2.1.
Figure 2.1: Increasing yearly economical costs for the India society (Srivastava(2011)).
During the last thirty years the country has been hit by 434 natural disasters and 638 man-made disasters according to data collected by EM-DAT. During these disasters 142,582 deaths out of the 170,742 total death count were caused by natural disasters. Natural disasters effected more than 1,400 million people, while man-made disasters affected around 600 thousand persons. The economic damage is estimated for natural disasters to be more than 50,000 million USD and for man-made disasters 700 million USD. As the number of people afflicted is many times higher than that of man-made disasters the focus of this report will be on the detection and monitoring of natural disasters. A complete overview of the disasters and their impact during the last thirty years can be found in table 2.1.
Disaster sub-group Disaster type Occurrence Deaths Injured Affected Homeless Total affected Total damage (x1000 USD)
Biological Epidemic 52 14266 0 397806 0 397806 0
Biological Insect infestation 1 0 0 0 0 0 0
Climatological Drought 7 320 0 751175000 0 751175000 2041122
Climatological Extreme temperature 39 11617 250 0 0 250 544000
Climatological Wildfire 2 6 0 0 0 0 2000
Complex Disasters Complex Disasters 1 0 0 710000 0 710000 0
Geophysical Earthquake (seismic activity) 15 49620 217827 26123179 2160700 28501706 5106900
Geophysical Mass movement dry 2 61 0 0 0 0 0
Hydrological Flood 193 42299 771 610156745 8648000 618805516 34168629
Hydrological Mass movement wet 33 2667 519 217200 3616285 3834004 54500
Meteorological Storm 89 21726 15774 48885201 2181345 51082320 9661484
Technological Industrial Accident 72 4781 103961 429553 0 533514 698900
Technological Miscellaneous accident 106 5013 6639 42090 12250 60979 0
Technological Transport Accident 460 18366 9976 507 0 10483 38000
Total 1072 170742 355717 1438137281 16618580 1455111578 52315535
Natural 434 142582 235141 1437665131 16606330 1454506602 51578635
man made 638 28160 120576 472150 12250 604976 736900
Table 2.1: Disaster data based on EM-DAT data base from a period of 1983 till 2013 for the Indian subcontinent. (for Research on the Epidemiology of Disasters(2014a))
4
As can be seen in 2.1 there are different disaster groups and types, the complete overview of the definitions for these disaster groups and types taken from the EM-DAT website can be found on for Research on the Epidemiology of Disasters(2014b).
Natural disaster vulnerability profile in India
As can be seen from the 2.1, India is highly vulnerable to floods, droughts, cyclones, earthquakes and landslides. Out of 35 states and union territories in the country, 27 of them are disaster prone. About 60 per cent of the landmass is vulnerable to seismic occurrences of moderate to very high intensity. Twelve percent of land, corresponding to 40 million hectares is prone to floods and river erosion, and the average area affected by floods annually is about 8 million hectares.
Approximately 5700 kilometres out of 7516 kilometres long coastline is prone to cyclones and tsunamis, and 68 percent of the cultivable area is susceptible to drought (Srivastava (2011)). A multi-hazard map of india can be seen in 2.2.
Figure 2.2: The different disasters zones are shown over a map of India based on the data and information compiled the Ministry of Urban Developement and Poverity Alleviation, India Srivastava(2011).
According to 2.2, there are five distinctive regions of the country concerning vulnerability to disaster, i.e. the Himalayan region, the alluvial plains, the hilly part of the peninsula, and the coastal zone, each having their own specific problems. The plain is affected by floods almost every year, while the Himalayan region is vulnerable to disasters like landslides and earthquakes. The desert part of the country is affected by droughts and famine, while the coastal zone is susceptible to cyclones, storms and tsunamis. The basic reason for this increased vulnerability is the natural geological setting of the country. For example the geo-tectonic features of the Himalayan region and the neighboring alluvial plains make the region susceptible to earthquakes, land slides and water erosion.
The western part of the country, including Rajasthan, Gujarat and some parts of Maharashtra are hit very frequently by drought. Other parts of the country are also facing drought if the Monsoon worsens.
Besides the natural factors, various human induced activities like increasing demographic pres- sure, deteriorating environmental conditions, deforestation, faulty agricultural practices and graz- ing, construction of large dams on river channels etc. accelerate and increase the frequency of the disasters in India.
Consequences of climate change
Climate change is expected to increase the frequency and intensity of the current extreme weather events and will cause new challenges of different spatial and social-economic impacts on commu- nities. The climate change is expected to have severe impacts on the hydrological cycle, water resource, droughts, floods, drinking water and other related areas. The impact would be par- ticularly disastrous for developing countries, including India and would be most taxing for poor vulnerable communities, which make up one quarter and one half of the population of most In- dian cities. Already there is clear evidence that the climate change is influencing India, extreme rainfall has substantially increased over large areas, particularly over the west coast and west central India (Srivastava(2011)).
2.1.4 Disasters, poverty and development
Research and practice support the theory that a strong correlation between disasters and poverty exists. It is well documented (Skoufias(2003), Rubonis and Bickman (1991), Noy (2009),Kahn (2005),Wisner and Luce(1993)) that the developing countries including India repeatedly subject to disasters experience stagnant or even negative rates of development over time. Although every disasters has unique consequences, an general overview of the ways in which disasters harm poor countries beyond the initial death, injury or destruction are discussed next.
• National and international development efforts are stunted, erased or even reversed.
• Sizeable portions of GDP must be diverted from development projects, social programs or dept repayment in order to manage the disaster consequences and start recovery efforts.
• Vital infrastructure is damaged or destroyed, requiring years to rebuild.
• Schools are damaged or destroyed, so that students are without adequate education for months or even years.
• Hospitals and clinics are damaged or destroyed, resulting in higher levels in vulnerability to disease of the affected population.
• Formal and informal business are destroyed, giving rising to unemployement and economic instability and strength.
• Desperation and poverty leads to a rapid upsurge in crime and insecurity.
• People are forced to leave the affected area, often to never return, thereby extracting in- stitutional knowledge, cultural and social identity and economic viability from areas that could not afford to spare such resources.
6
2.1.5 Disaster Management
The human response to the earlier mentioned disasters is coordinated by various groups dealing with disaster management. Modern disaster management is based upon four distinct components, mitigation, preparedness, response and recovery (Coppola (2006)).
• Mitigation involves the reducing or eliminating of the likelihood or the consequences of a disasters or both, so that the hazard impacts society to a lesser degree.
• Preparedness is equipping people who might be impacted by a hazard or who may be able to help those impacted with the tools to increase their chance of survival and to minimize their financial and other losses.
• Response involves taking action to reduce or eliminate the impact of disasters that have happened or are happening, to minimize further suffering, financial loss, or a combination of both.
• Recovery has as goal to return victims’ lives back to a normal state after the impact of the hazards consequences. This phase normally begins after the immediate response has ended.
In reference toTobias et al. (2000) disaster managements is composed out of three components, Knowledge and Prevention (KP), Alarm and Crisis (AC), and Post-Crisis damage assessment (PC). Although different names, these components (Mitigation and Preparedness are taken to- gether) are very similar as the definition given byCoppola (2006).
Importance of disaster management and remote sensing
According to the Overseas Development Institute and the United Nations(UN) the economic losses from disasters have topped one trillion USD dollars worldwide since 2000, growing at a faster rate than GDP per capita in OECD countries in the same period. Despite these escalating losses only five percent of the humanitarian fiance is spent on reducing the risk of disasters.
Without a major increase in investment to reduce current and future risks, spending on relief and reconstructing is likely to be come unsustainable (Mitchell and Wilkinson(2012)). Secondly it is proven that effective disaster management reduces the losses a society is suffering as well as enabling a faster recovering as is shown in figure 2.3. An import tool to help disaster policy makers make better decisions before, during and after the occurrence of a disaster is remote sensing from space applications. Satellites are able to view remote areas as well as to screen large areas at the same time at regular time intervals. Furthermore inTobias et al. (2000) it is stated that the maturity of the applications, technology and users and service are developed enough to be able to be a viable tool for disaster management.
Figure 2.3: The graph shows the difference in recovering between effective disaster management versus ineffective disaster management.(Coppola(2006))
2.2 Remote sensing of disasters
In this chapter an overview will be given of the different requirements to be able to monitor and detect the different disasters For each disaster the parameters that needs to be measured, what type of sensor is necessary to measure the parameters, the required sensitivity of the sensor and the required revisiting time are listed. The disasters that are discussed are earthquakes, landslides, flooding, wildfires and drought. As storm and other weather related events are already extensively monitored by the various weather satellites, storm and extreme temperature are excluded from the list. The complete overview of the derived requirements for each phase (KP, AC, PC) can be found in table 2.4.
8
Disaster Earthquake
Sensor technique Displacement de- termination with GPS
InSAR/DInSAR Gravitational
field measure- ment
Optical TIR
Short description GPS geodetic network
in the risk areas multitemporal radar observations;
ratio or difference between multi- temporal images and then apply- ing supervised or unsupervised clas- sification/phase difference of two or more SAR images are used to calcu- lated the differences in range
Measuring the gravity potential signature of an earthquake;
manual interpreta- tion of topograph- ical changes from optical imagery both in colour and panchromatic
sensors in the infrared spectrum monitoring Earth’s thermal field
Disaster parameter measured
ground deformation small-scale features in the defor- mation field associated with earth- quakes; ground changes and infras- tructure damages
gravity signature mainly associated with the vertical displacement
topographical
changes change in the Earth’s surface temperature and near-surface atmosphere layers
"Disaster Phase" KP, AC KP, PC AC, PC PC KP
Required sensitivity <1cm vertical height
difference AC<4-5cm; KP<1mm (horizontal deformation) vertical deformation:
close at rupture=1m, rest
<0.2-1mm of the
geoid <2 °C
required ground reso- lution
not relevant KP, PC<5m, 1m ideal ? KP, PC<5m, 1m
ideal <10 km
Required revisiting time
permanent coverage KP:12 hours, PC<2 days, <3 hours
ideal AC <1 hour KP:12 hours,
PC<2 days, <3 hours ideal
<5 days
optimal band/fre- quency
not relevant L-band preferred, C- and X-band also possible to generate DInSAR;
L-band: lower resolution, but greater range of surface cover types;
C-band: high resolution,only pro-
- Visible mid-IR (8-8.5 µm)
9
limitations economic constraints limit the widespread global deployment of these networks;
discrete samples
InSAR: variability of backscatter of different region, lack of quantitative estimations and dependence on in- cidence angle; both methods: only possible to measure ground defor- mation of a moderate earthquake (>M5); displacements close to fault, too large, interferometric phase can- not be reconstructed due to ambigu- ity in phase unwrapping. Dependent on spatial baseline and DEM accu- racy
Only very large earthquakes (>M8) could be measured.
(GRACE). The usefulness of tech- nique is highly debated
Can be subjective, time-consuming for widespread events, and nonrepeatable.
Only possible to be used during cloud- less day
only suitable for large earthquake M>4.5,
"normal" model neces- sary. The usefulness of the technique is highly debated
Instrument, Applica- tion, Processing Matu- rity
A A B and C A B and D
Sensor examples GPS satellites ERS 1/2, Cosmo-SkyMed GOCE, GRACE IKONOS, Quick-
bird MODIS (Terra, Aqua)
reference Tralli et al.(2005) Zhou et al. (2010), Sansosti et al.
(2014), Stramondo et al. (2011), Wood and all(2002)
Mikhailov et al.
(2014),Akhoondzadeh et al.(2011),De Vi- ron et al.(2008)
Tronin (2006), Wood and all (2002)
Ouzounov and Freund (2004),Tronin(2006)
10
Disaster Landslide Flooding
Sensor technique SAR Optical Optical SAR/InSAR
Short description two method manually/visible interpretation and multitem- poral image analysis with im- ages before and after event
3 methods: 1.) manually/vis- ible interpretation, 2.) multi- temporal image analysis with images before and after event 3.) change in DEM,
Imagery in the visible and NIR, can be used to cre- ate the Normalized Difference Vegetation Index, stereo data can be used to derive DEM’s
Distinct backscatter between land and water, a threshold technique is applied to segre- gate flooded regions in Active Microwave images. InSAR is used to compare backscatter of regions over different times.
Stereo imagery can be used to derive DEM’s
Disaster parameter measured
no distinct backscatter signa- ture, indirect measurement of changes in the landscape
no distinct backscatter signa- ture, indirect measurement of changes in the landscape
NIR: absence of light due to strong absorption of wa- ter; VIS: used to differentiate between various other object absorbing light
Backscatter of water
"Disaster Phase" (KP), AC, PC KP, AC, PC KP, AC, PC (KP), AC, PC
Required sensitivity preferred incidence angle 40 degrees to 59 degrees, stable orbit, variations not exceed- ing +/- 1km
vertical: 0.5m DEM (vertical): 1-3m, 0.10-
0.15m ideal DEM (vertical): 1-3m, 0.10- 0.15 m ideal
required ground reso- lution
KP<3m, 0.5m ideal;
AC/PC<10m; <3m ideal KP<3m, 0.5m ideal;
AC/PC<10m; <3m ideal KP: land use: 30m, 4-5m ideal, infrastructure: 5m,
<1m ideal; AC<30m, <5m ideal; PC: damage assess- ment: 2-5m, 0.3m ideal, land use: 30m, 4-5m ideal
AC<30m, <5m ideal; PC damage assessment: 2-5m, 0.3m ideal
Required revisiting time
KP/PC: 1 day-2 days; AC<1
hour KP/PC: 1 day-2 days; AC<1
hour KP: 1-3 yrs, 6 months ideal,
vegetation: 3 months, 1 month ideal; AC<1 day, <3
AC<1 day, <3hours ideal ; PC: 2-3 days/<1 day
11
optimal band/fre- quency
L-band: lower resolution, but greater range of surface cover types; C-band: high resolu- tion, t only provide reliable interferometers for coherent, non-vegetated surfaces.
panchromatic , multi- and hyper-spectral sensors in the visible, near and short wave infrared
Near infrared (0.8-1.1 µm) in combination with another Near/Mid Infrared (2.08-2.35 µm), for NDVI and DEM:
PAN/MSI
L-band (C-band possible, but not preferred)
limitations Can be subjective, time- consuming for widespread events, and non repeatable.
Slopes gives an interferomet- ric analysis
Can be subjective, time- consuming for widespread events, and non repeatable.
Clouds limits view
Cloud limits severely the view, during monsoon 60- 80% cloud coverage
Determining the threshold is not straight forward, many factors, making determining the threshold for each situa- tion different.
Instrument, Applica- tion, Processing Matu- rity
A A A A
Sensor examples ERS IKONOS, Quickbird ASTER, Quickbird, MODIS RADARSAT 1,2
reference Wood and all(2002),Singhroy and Molch (2004),Hervás et al. (2003); Nagarajan et al. (1998) Pairman et al.
(1997),Metternicht et al.
(2005),Massonnet et al.
(1993),Colesanti and Wa- sowski(2006)
Wood and all (2002) ,Cheng et al. (2004),Zhou et al. (2002), Joyce et al.
(2009),Nichol and Wong (2005),Oštir et al. (2003);
Singhroy and Molch (2004);
Casson et al. (2005); Tsut- sui et al. (2007),HUANG and CHEN (1991); Cheng et al. (2004); Zhou et al.
(2002),Hervás et al. (2003);
Nagarajan et al.(1998)
Wood and all (2002), Smith (1997),Wang et al.
(2002),Wiesnet et al. (1974);
ISLAM and Sado (2000);
Sheng et al. (2001); Jain et al. (2006),Barton and Bathols (1989),Hastings and Emery(1992),Kogan(1997)
Wood and all
(2002),Townsend (2001);
Brivio et al. (2002),Martinis et al. (2009),Nico et al.
(2000),Sanyal and Lu (2004)
12
Disaster (Wild)Fire Drought
Sensor technique Optical TIR SAR Optical Thermal In-
frared
Passive mi-
crowave/SAR Short description Imagery in the
visible and NIR, 3 methods: 1.) manually/visible interpretation, 2.) multitemporal image analysis with images before and after event 3.) change in DEM
sensors in the in- frared spectrum monitoring tem- perature elevation caused by (wild) fires
Backscatter inten- sity changes for both changes in water in vegetation and burnt areas.
Active microwave signal can be used to measure this backscatter
Vegetation indices derived from differ- ent channels in the optical spectrum can be used to de- tect changes in leaf chlorophyll, mois- ture content and thermal conditions.
Disaster parameter measured
KP: fuel mapping:
vegetation struc- ture, biomass; AC:
fire, smoke, PC:
burnt areas based on multi-temporal
KP: Risk ar- eas: vegetation stress; AC: ele- vated temperatures associated with fire
KP: fuel mapping:
vegetation struc- ture, biomass; risk areas; PC; charac- teristic backscatter burnt areas
indirect mea- surement of leaf chlorophyll, mois- ture content and thermal conditions.
indirect mea- surement of soil moisture by mea- suring land surface temperature
soil moisture
"Disaster Phase" KP, AC, PC KP, AC, PC KP, PC AC AC AC
Required sensitivity AC: not more than
5% false alerts AC: detection range at least 700 K, not more than 5% false alerts
<2 degrees,
<50W m2 accuracy better than 5%v/v, ideal 3% v/v, incidence angle preferred between 35 and 50 degrees
required ground reso- lution
KP: 5-30m;
AC<250m (de-
tection), 1
m(mapping);
KP: 5-30m; AC
<250m (detection), 1m(mapping);
PC<30m, <5 m
KP: 5-30 m; PC
<30m, <5m ideal,
<5m
<1km, <100m
ideal <100 m <50km
13
Required revisiting time
KP:<16 days, ideal: 1-2 days;
AC: <30 mins, ideal: <15 mins (mapping), < 5 mins (detection);
PC: 1 day
KP:<16 days, ideal: 1-2 days;
AC: <30 mins, ideal: <15 mins;
PC: 1 day
KP:<16 days, ideal: 1-2 days;
PC: 1 day
<7 days, ideal <3
days <7 days <7 days
optimal band/fre- quency
KP: multispec- tral(VIS) including NIR(0.9 µm), SWIR(1.6 µm)
KP: multispec- tral(VIS) including NIR(0.9 µm), SWIR(1.6 µm);
AC: SWIR (1.6 µm) MIR (3 to 4 µm), desirable:
HIR (11 µm)
L-band: lower resolution, but greater range of surface cover types;
C-band: high reso- lution, only provide reliable interfero- grams for coherent, non-vegetated surfaces.
multi-spectral with at least bands: 0.4- 0.7 µm and 0.7-1.1 µm
8-14 µm L-band
limitations Visibility severely limited by cloud cover and smoke
Visibility limited by cloud cover, however penetra- tion if light smoke and haze possible
index based, cloud cover limits unob- structed view
index based
Instrument, Applica- tion, Processing Matu- rity
A A A A B D
Sensor examples MODIS, LAND-
SAT, SPOT,
Ikonos
AVHRR, MODIS ERS, JERS AVHRR Landsat 5-7 SMOS
14
reference Wood and all (2002),Joyce et al. (2009),Roy and Landmann (2005),Trigg et al.
(2005)
Wood and all (2002),Dozier (1981),Giglio and Kendall(2001)
Wood and all (2002),Giglio
and Kendall
(2001),Cou-
turier et al.
(2001),Bourgeau- Chavez et al.
(2002); Siegert et al. (2001);
Tanase et al.(2010)
Kogan (1997),Kni- pling (1970);Wool- ley (1971); Jacque- moud and Baret (1990)
Anderson and Kus- tas (2008); Ander- son et al. (2011);
Gillies and Carlson (1995)
Entekhabi et al.
(2010)Walker and Houser (2004)
Table 2.4: Overview of the disaster requirements maturity
A: Clearly demonstrated to work using standard image processing systems and is openly available in the literature; B:Shown to work with experimental image data sets or over limited areas with very small pixels or over global scales with large pixels; C: If extent is bigger than several pixels; D: Not
15
Chapter 3
Small satellites
In this chapter a definition of a small satellite, as well as the constraints of a small satellite will be derived.
3.1 Definition
Unfortunately there is quite some confusion about the definition of small satellites as is described inSandau (2010). The IAA (International Academy of Astronautics) makes the suggestion that all small satellites below 1000 kg could be classified as small satellites (Sweeting (1996)). The problem with the previous definitions, is that all definitions are solely based on the weight of the satellites. Although there is a strong connection between the weight and the costs in general, due to miniaturization of the instruments and equipment, light weighted satellites can cost as much as heavier satellites. An example of this is the TecSAR mission, a spaceborne radar mini satellite technology demonstration mission of Israel’s Minister of Defense (Kramer (2014a)). It is one of the smallest satellites with a SAR on board and weighting only 300 kg, therefore classifying it as a small satellite according to the conventional definitions. However the cost estimation is approximately 200 million USD (Def (2014)), which is higher than satellites with that weight would normally cost (Kramer(2014a)). Furthermore most missions are driven by cost, therefore it proposed to define small satellites in terms of cost.
3.2 Cost estimation disaster monitor constellation mission for India
To determine a reasonable estimation of the maximum cost of a small satellite disaster constel- lation for India, a closer look is taken into the Indian budget of space, the cost of an existing disaster monitoring constellation, the DCM3 and the cost of the disasters.
According to the outcome budget of the year 2013-2014 prepared by the department of space of the government of India (DOS (2014)) the total budget was estimated for that year on INR 67,920.4 million (approximately 835 million euros and 1.12 milliard USD). In the same document the cost estimated for SARAL, a satellite with Argos and Altika on board with an approximate weight of 400 kg, was 737.5 million INR (approximately 9.1 million euros and 12 million USD).
This is not including the costs for the two payloads, which are carried by CNES, France and the launch. Its expected life time will five years (Kramer (2014b)). A mission dedicated to the continuous observation of the Indian subcontinent for quick monitoring of disasters, natural calamities and episodic events, GISAT will cost INR 3,920 million (48.4 million euros, 64.47 million USD), excluding the launch costs and operation costs. GISAT is a geosynchronous satellite capable of imaging in visible and thermal band with 50 meters resolution (DOS (2014)). Lastly ResourceSAT-2A, providing continuity of data in the area of natural resources management, costs 2,000 million INR (24.7 million euros, 32.9 million USD). It should be noted that this mission has a strong heritage of previous missions lowering the costs. The weight of the satellite is approximately 1200 kg and its mission duration is five years (Kramer (2014c)).
An existing small satellite constellation dedicated to disaster monitoring has been developed by Surrey Satellite Technology Ltd. The first constellation was simply called Disaster Monitor Constellation, consisting out of 5 satellites, each weighting around 100 kg (da Silva Curiel et al.
16
(2002)). The cost are claimed to be less than 50 million USD for the entire network (Ward et al. (1999)). The newest constellation developed for this purpose is the DMC3 constellation, consisting out of three SSTL-300S1 satellite platforms, weighting 350 kg each. It carries a high optical instrument with a 0.75m-1m GSD in PAN and 4m in VIS and NIR. The cost of the development, launch and insure these platforms are estimated at 110 million pounds (137 million euros, 184.8 million USD). The spacecraft design life is 7 years (Kramer (2014d)).
Lastly the damage caused by the different disasters is taken into account. It is assumed that the satellite mission should not exceed the damage cost of the natural disasters. Although it should be noted that the natural disasters also cause many casualties, which can never be expressed in currency. Based the last column of table 2.1 it is concluded that a mission detecting solely wild fires in India is most likely not cost effective. As most natural disaster can not be prevented, it is assumed that it is unlikely that a future mission would be able to reduce the cost with for example 50 percent and for the remaining disasters a reduction of 10 percent is taken as limit to be economical feasible. The cost of a mission for the detection and monitoring of landslides can not exceed 0.5-1 million USD for a two year mission and 1-3 million USD for a five year mission to be cost efficient. The cost for a space mission for the detection and monitoring of drought, landslides and earthquakes could be exceeding 10 million USD for a two year mission and 30 million dollars for a five year mission to be cost effective. However based on the Indian budget, the existing constellations and the idea to develop a low cost small satellite constellation it has been decided that the mission should not cost more than 30 million USD for a five year mission.
3.3 Constraints
This section will give an overview of the some major constraints imposed by the cost limita- tion.
3.3.1 Mass
Launching cost for small satellites are normally estimated to be around 20 percent of the total budget (Sarsfield(1998)) or even as high as 25 percent-70 percent (Koelle and Janovsky(2007)).
Although the specific transportation costs, the cost per kg payload depends strongly on payload size and launch frequency, it is normally estimated between 4,000-15,000 USD per kilogram for a LEO orbit (Fut (2002)). However after speaking with several former ISRO scientists and comparing prices atSpa(2014) the launch costs for small satellites (<300 kg) are higher per kg.
An estimation was given that the cost per kg on board of the Indian launcher PLSV is around 15,000 dollars, meaning that the total combined weight of the satellites in the constellation could be around 500 kg assuming that the cost of the launch is 25 percent of the budget.
3.3.2 Volume
Small satellites are often secondary or even tertiary payload, meaning that priorities are given to the primary payload of a launch vehicle, resulting in volume constraints. There does not appear to be an universal consensus for the the maximum volume for small satellites, but based onSpa (2014) and own estimations the total maximum volume of the entire constellation together is around 5m3.
3.3.3 Power
Again similar as for the volume constraint, there does not appear to be an universal consensus for the the maximum available (average) power. However inda Silva Curiel (2003) the available average power for a 500 kg satellite is estimated as 400 W , for a 250 kilogram satellite around 150 W and for a 100 kg satellite, between 50 and 70 W .
18
