Proceedings of
The 22nd CEReS International Symposium

29 October 2014
Sekip Room, University Club
Universitas Gadjah Mada
Jl. Pancasila No.2, Bulaksumur, Yogyakarta
Indonesia

Organizers:
Center for Environmental Remote Sensing, Chiba University, JapanThe 6th Indonesia Japan Joint Scientific Symposium (IJJSS 2014)
Chiba University Sister Universities(UI, IPB, ITB, Unpad, UGM, Undip, ITS, Unud, Unhas, BMKG)The 22nd CEReS International Symposium

The 22nd CEReS International Symposium was held at University of Gadjah Mada (UGM), Yogyakarta,
Indonesia on 29 October 2014 13:00-18:15. This symposium was realized by collaboration between staffs
and students of Center for Environmental Remote Sensing (CEReS), Chiba University, Japan and our Sister
Universities and Research Institutions in Indonesia to promote the science and technology exchange inremote sensing fields. This symposium is co-organized with The 6th Indonesia Japan Joint ScientificSymposium (IJJSS 2014, 28-30 October 2014) and sponsored by Center for Environmental Remote Sensing,
Chiba University, Japan.
Venue
Sekip Room, University Club – Universitas Gadjah Mada
Jl. Pancasila No.2, Bulaksumur, Yogyakarta
Telp: 0274-563461, Fax: 0274-563461
Organizers
Center for Environmental Remote Sensing, Chiba University, JapanThe 6th Indonesia Japan Joint Scientific Symposium (IJJSS 2014)
Chiba University Sister Universities (UI,IPB,ITB,Unpad,UGM,Undip,ITS,Unud,Unhas,BMKG)
Contact Person
Prof. Josaphat Tetuko Sri Sumantyo, Ph.D
Center for Environmental Remote Sensing, Chiba University
1-33 Yayoi, Inage, Chiba 263-8522 Japan
Tel. +81 (0)43 290 3840 / Fax +81 (0)43 290 3857
Email jtetukoss@faculty.chiba-u.jp
Website http://www2.cr.chiba-u.jp/jmrsl/

Cover

Monitoring of land deformation of Jakarta city,
Indonesia using ASTER and ALOS PALSAR PSInSAR by JMRSL (http://www2.cr.chiba-u.jp/jmrsl/)

Acknowledgement

ASTER data byThe National Institute of Advanced
Industrial Science and Technology (AIST) and ALOSPALSAR data by Japan Aerospace Exploration Agency(JAXA) PI ProjectProgram / Content
Page
13:00 – 13:15

Opening Speech & CEReS Introduction, Director CEReS Prof Ryutaro
TateishiModerator : Prof. Atsushi Higuchi
13:15 – 13:30
Indonesian Weather, Climate and Tsunami Early Warning System:
Future and Challenge
Andi Eka Sakya, Head of Indonesian Agency for Meteorological, Climatological andGeophysics (BMKG)
13:30 – 13:45
Separation of Contributions from Atmospheric Scattering and
Surface Reflectance in Optical Satellite Imagery
Hiroaki Kuze, Naohiro Manago, and Yoshikazu Iikura, CEReS Chiba
University
13:45 – 14:00
Application of Unmanned Aerial Vehicle (UAV) for Shoreline
Analysis
Muh Aris Marfai *1, Fredi Satya C. Rosaji *2, Ahmad Cahyadi *1,*2,
Muhammad Rifqi Ghozali I. *1*1 University of Gadjah Mada, 2 CV. Mitra Geotama
Optimizing Indonesian Atmospheric Database (BISMA) And
14:00 – 14:15

Implementations

Laras Tursilowati, Muzirwan, Mahmud, Edy Maryadi, Halimurrahman, and AfifBudiyono, National Institute of Aeronautics and Space (LAPAN)
14:15 – 14:30 BreakModerator : Prof Junun Sartohadi
14:30 – 14:45
Development of Synthetic Aperture Radar onboard UAV andMicrosatellites for Environmental Observation
Josaphat Tetuko Sri Sumantyo and Nobuyoshi Imura, CEReS Chiba
University
14:45 – 15:00
A Compact and Robust Telemetry Systems Construction for theEnvironmental Observations
Elyas Palantei, University of Hasanuddin (Unhas)
15:00 – 15:15
Geospatial Data Sharing/Overlay System – CEReS Gaia – byInternational Cluster Linkage
Ryutaro Tateishi, CEReS Chiba University
15:15 – 15:30
CEReS Archived Satellites Related Datasets and These Applications
Higuchi, A.*1, H. Takenaka*2, H. Hirose*1, M.K. Yamamoto*3, S.
Kotsuki*4, H. Irie*1, K. Tanaka*5, *1, M. Hayasaki*6*1 Chiba University, *2 The University of Tokyo, *3 Kyoto University, *4 RIKEN,*5 Kyoto University, *6 University of Tsukuba
15:30 – 15:45
The Dynamic Resources Management in the Active Volcanoes
Junun Sartohadi, University of Gadjah Mada (UGM)
15:45 – 16:00 BreakModerator : Dr Ilham Alimuddin
16:00 – 16:15
Application of Hyperspectral Camera for Aerosol Characterization
Naohiro Manago, Hayato Saito, Yohei Takara, Makoto Suzuki, andHiroaki Kuze, CEReS Chiba University
16:15 – 16:30
The Calculation of Natural Ventilation Rate in a Tropical Singlespan Greenhouse with Fog Cooling System
Muhammad Hasan, Handarto, and Sudaryanto, University of Padjadjaran(Unpad)16:30 – 16:45

16:45 – 17:00

Measurement of Sky Radiance using a CMOS Camera for the
Retrieval of Aerosol Optical Properties
Hiroaki Iwanade, Hayato Saito, Ilham Alimuddin, Naohiro Manago,
Hiroaki Kuze, CEReS Chiba University
Coastline changes monitoring using satellite images of Makassar
Coastal Areas
Ilham Alimuddin 1,2, R. Langkoke 2, B. Rochmanto 2, Josaphat Tetuko
Sri Sumantyo 1, and Hiroaki Kuze 1
1 Chiba University, 2 Hasanuddin University

17:00 – 17:15

Closing Remarks, Vice Director CEReS Prof Hiroaki Kuze

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UHVHDUFKSURMHFWVCEReS is waiting for you
Josaphat Tetuko Sri Sumantyo
Research fields are microwave remote sensing and its applications, especially development on synthetic
aperture radar (SAR) devices, unmanned aerial vehicle (UAV) and microsatellites.
Students can study synthetic aperture radar (SAR) image processing and its application, Synthetic
Aperture Radar (SAR) devices, unmanned aerial vehicle (UAV) and microsatellites.
http://www2.cr.chiba‐u.jp/jmrsl/

Remote Sensing

UAV experiment with a newly developed SAR

Yoshiaki Honda
Main research subject is vegetation remote sensing involved in the GCOM‐C1 Research .
Students can study vegetation remote sensing and ground validation.
Koji Kajiwara
Main research subject is vegetation biomass estimation for global/continental scale using earth
observation satellite data based on ground observation of vegetation structure and spectral information.
Students can study measurement methods for vegetation spectrum, forest structure observation using
LIDAR system, computer simulation of BRDF, and huge volume satellite data processing.

Ground validation by helicopter for vegetation remote sensing

Hiroaki Kuze
Main research subject is to develop optical methods for precise understanding of atmospheric processes
and surface reflection phenomena, with coordinated application of satellite and ground‐based
observations.
Students can study satellite and ground‐based atmospheric remote sensing.
http://www.cr.chiba‐u.jp/~kuze‐lab/index.html
Ryutaro Tateishi
Main research subject is global land cover mapping and monitoring.
Students can study information extraction of terrestrial environment by remote sensing.
http://www.cr.chiba‐u.jp/~tateishi‐lab/

Satellite andground‐based
atmosphericremotesensing
Measurementof aerosols
and pollution
gases

Global land cover mapping by MODIS 2008

Various approaches in atmospheric remote sensing

RGB composite map to diagnose land‐atmosphere coupling.
Annual mean rainfall (blue), annual mean NDVI (green), andfrequency of surface temperature stress (red).

Atsushi Higuchi
Main research interest is to understand land‐atmosphere interaction process in the Earth climate systemusing long‐term several satellite observations.
Students can study regional climate or meteorological phenomena by long‐term satellite observation
datasets.
http://www.cr.chiba‐u.jp/~higulab/top_wiki/

Global Environmental Science

Naoko Saitoh
Research field is atmospheric science utilizing satellite remote sensing.
Students can study the basis of atmospheric gas retrieval and satellite data analysis.
http://www.cr.chiba‐u.jp/~saitohlab/
Upper atmospheric CO2 concentrations
observed by GOSAT

Hitoshi Irie
Research field is atmospheric environmental science including atmospheric chemistry and physics.
Students can study the advanced utilization of remote sensing to understand where, when, and how
much atmospheric environment is changing on a global scale.
http://www.cr.chiba‐u.jp/~irielab/index_e.html

Synergistic use of ground- and space-based remotesensing for advanced atmospheric environment research.

Akihiko Kondoh
Research fields are geography and hydrology, in brief, human geosciences.
Students can study geographic analyses for the restoration of the sound relationship between human andnature.
http://dbx.cr.chiba‐u.jp/

Contribution to Society

Chiharu Hongo
Main research subjects are environmental sciences and food production.
Students can study environmental conscious food production through analysis and diagnosis of
agricultural ecosystem by remote sensing.

Map of nitrogen fertilization amount to be recommended
for potato cultivation

Center for Environmental Remote Sensing (CEReS) Chiba University, Japan, is seeking well motivated students. Please contact a secretary of the Director <yaoatsu@office.chiba‐u.jp>

BMKG

INDONESIAN WEATHER, CLIMATEAND TSUNAMI EARLY WARNINGSYSTEM:

FUTURE AND CHALLENGE
Andi E. Sakya
Director General
Presented at the 6th Indonesia Japan Joint ScientificSymposium (IJJSS 2014), Jogyakarta, 28 – 30 October 2014

CONTENTSBMKGCONTENTSINTRODUCTION

1. INTRODUCTIONPRESENT STATUS

2. PRESENT STATUS

FUTURE DEVELOPMENT

3. FUTURE DEVELOPMENTCONCLUSION

4. CONCLUSIONINTRODUCTIONBMKG

INDONESIACONTENTS–

Archipelagic country ~ 17,504
islands (10,000 small islands), rightat the equatorial line;–

4 M-km2 width of ocean and 2 Mkm2 land, 6,000 km distance fromeast to west, and 80,000 km ofcoastal length;–

Flanked by 2 ocean (India andPacific) and 2 continents (Australia
and Asia);

INTRODUCTIONPRESENT STATUS

FUTURE DEVELOPMENTCONCLUSION–

Lays above three plates moving on different speed of creeping Î
prone to Earthquake and Tsunami;–

Exposed by 3 types of rain, 2 extreeme weather on the east andwest, more than 220 seasonal variation zone.

INTRODUCTIONBMKGCONTENTSWEATHER AND CLIMATE FACTORSAgs Sep

ASIA

4

Okt Nov

INTRODUCTION

1PRESENT STATUS

1

EL NINO / LA NINA

2

3

DIPOLE MODE POSITIVE/DIPOLE MODE NEGATIVNEGATIVE
VE
SURFACE TEMPERATURE

4EASTERLY/ WESTERLY WIND

3

2

DIRECTION OF SEASONAL WINDAUG – SEPEASTERLY

FUTURE DEVELOPMENTAgs Sep

ASIA

OCT– NOVWESTERLY

Okt Nov

4
1

CONCLUSION

32

INTRODUCTIONBMKGGEOLOGICAL FACTORCONTENTSINTRODUCTIONPRESENT STATUS

90 mm/year

FUTURE DEVELOPMENTCONCLUSIONINTRODUCTIONBMKG

POTENTIAL TSUNAMI AFFECTED AREA

CONTENTSINTRODUCTIONPRESENT STATUS

FUTURE DEVELOPMENTCONCLUSION– Since 1629 until 2013 had occurred 110 significant tsunamis in Indonesia;– 100 times by tectonic earthquake, 9 times by volcanoes and 1 time by landslide;– Northern of West and middle Java tsunami caused by Krakatau explosion 1883

INTRODUCTIONBMKG

WHY DISASTER OFTEN CAUSED VICTIMS ?

CONTENTSINTRODUCTIONPRESENT STATUS

FUTURE DEVELOPMENT

1. Mechanisms from hazards todisaster are not fully understood;
2. Natural supporting capacity isgetting more and more vulnerable;

3. People are powerless.
CONCLUSION

4. Early Warning System (EWS) is not
functioning well or not exist;

PRESENT STATUS
BMKG

INA-TEWSCONTENTSINTRODUCTIONPRESENT STATUS

Teleconference

InaTEWS Inauguration

FUTURE DEVELOPMENTCONCLUSION− The 1st phase of InaTEWS isdeveloped from 2005 – 2008
after Aceh Tsunami, andlaunched in Nov 11, 2008;− 10 RCs, 163 BB SS, 281 − It involved more than 16
national institutions and 5
Intsm, 56 DVB; 28 Sirines,
international donor countries
and DSS.

PRESENT STATUS
BMKG

INA-TEWSCONTENTSINTRODUCTIONPRESENT STATUS

FUTURE DEVELOPMENTCONCLUSIONPRESENT STATUSBMKGCONTENTSINA-TEWS
2005 – 20122012 – NOW

NTSP

RTSP

INTRODUCTIONPRESENT STATUS

FUTURE DEVELOPMENTCONCLUSIONPRESENT STATUSBMKGDSS EMBEDDEDPRESENT STATUSBMKGCONTENTSINTRODUCTIONPRESENT STATUS

FUTURE DEVELOPMENTCONCLUSIONINA MEWS– 2011;– Focused on Extreme
Weather Î Heavy Rain,; Potential Flood,; Wave Height; and Potential Forest Fire;
 Etc

PRESENT STATUSBMKGINA CEWS– 2013;– Focused on Extreme
Climate Î Seasonal Onset;
 Atmospheric Dyanamic; Water Content;
 Drought
 Etc

CONTENTSINTRODUCTIONPRESENT STATUS

FUTURE DEVELOPMENTCONCLUSIONPRESENT STATUSBMKGDISSEMINATIONCONTENTS

Twitter
INTRODUCTIONPRESENT STATUS

FUTURE DEVELOPMENTCONCLUSION

GooglePublicAlertFUTURE DEVELOPMENTBMKG

MANDATECONTENTS

1. COMPETENCE
LAW 31/09
on Met, Clim
& Geo

INTRODUCTION

FUTURE DEVELOPMENTCONCLUSIONRESPOND TIMEPRESENT STATUSLong

Quick, Timely,
Accurate, Wide
Coverage andComprehensible

Climate – Air
Quality

Meteorology–––

Human Resource
Management
Standard

2 EQUIPMENT:––––

Sensor;Density;
Maintenance;
Up-dating

1. Ina-TEWS (2008);

Earthquake

2. Ina-MEWS (2011);
Short

Predictable

Unpredictable

3. Ina-CEWS (2013);

NATURAL PHENOMENON

FUTURE DEVELOPMENTBMKG

LESSON LEARNTCONTENTSINTRODUCTIONPRESENT STATUS

FUTURE DEVELOPMENTCONCLUSION

1. Displaced > ~ 20,000 people, affected about 4,000 households, and 435
people reported dead, with over 100 more missing;
2. Early Warning System wroked well as it disseminated the warning within 4
minute 46 second;
3. The epicenter is so close to the islands that a warning would probably havebeen too late in any case, as the tsunami only took about five to ten minutes
to reach Pagai;
4. Post comprehensive evaluation Î the system works well, the downstream
part is as not fast as the upstream development.

FUTURE DEVELOPMENTBMKG

CHALLENGES

1. To produce an early warning
information quickly, timely, accurately,
broadly and understandably;
2. To encourage people to act safely andproperly upon receiving the warning
information;
3. To reduce till zero casualities andeconomic loss at the event of disaster.

FUTURE DEVELOPMENTBMKG

FUTURE DIRECTIONCONTENTSINTRODUCTION

ACT SAFE
AND
PROPERPRESENT STATUS

FUTURE DEVELOPMENTCONCLUSION

Early Warning System
Establishment:
1. Continuous Learning, Effort,
Assessment and Report (CLEAR);
2. Involving
multi:
Elements,
Stakehorlders, Sectors, Aspects,
and Years (ESSAY);

Threshold
Level of
Vulnerability• Awareness;• Exposure andLevel of Risk.

Multi Scale
Approach vsPrediction• Upstream anddownstream Ù
Structural andCultural Approach

Decision BasedvsDissemination
Coverage• Train;• Practise;• Exercise

Early Warning
SystemCONCLUSIONBMKGCONTENTSINTRODUCTIONPRESENT STATUS

CONCLUDING REMARKS
1. Indonesia, as an archipelagic country and lays right on the tropicalline, is highly vulnerable to hydro-meteorological as well as
geological disasters ;2. The development and establishment of Multi-hazards Early
Warning System has proven to perform well;
3. Lesson learnt shows that continuous effort to build thedownstream part is ultimate important;

FUTURE DEVELOPMENTCONCLUSION

4. The challenge and future establishment of EWS lay on theunderstanding following elements to succeed
1)

Level of Vulnerability;

2)

Multiscale approach;3)

Dissemination coverage;

4)

Continuous effort and assessment (CLEAR ESSAY).

BMKG

THANK YOU
andi.eka.sakya@gmail.com

Separationofcontributionsfromatmospheric
scatteringand surfacereflectanceinoptical
satelliteimagery
HiroakiKuzea*,Naohiro Managoa,andYoshikazuIikurab
aCenter forEnvironmentalRemoteSensing(CEReS),

ChibaUniversity, Chiba2638522,Japan
bFaculty of ScienceandTechnology,Hirosaki University,Aomori0368561,Japan

Organizationofthispresentation
1.Introduction
2.Theory
3.Resultsanddiscussion
4.Conclusion

http://earthexplorer.usgs.gov/browse/landsat_8/2014/120/065/LC81200652014274LGN00.jpg

1.Introduction
䚔 Spectralradianceobservedwithasatellitesensoriscomposedofa
numberofcontributionsfrombothgroundreflectionandatmospheric
scattering.

3

Variationofaerosolopticalthickness(AOT)䚔 Thedifficultyinanalyzingsatelliteimagery
comesfromthevariabilityofaerosol,liquidorsolidparticlesfloatingintheatmosphere.
䚔 ThechangeinAOT(W)canbedirectly
measuredwithasunphotometerthatobserves
theintensityofsolarirradianceatseveralwavelengthbands.

I=I0exp(W)

4

2.Theory OpticalthicknessW䚔 LambertBeer’stheorystatesthatthechangeoflight
intensity(dI)isproportionaltotheproductofI anddz:
LambertBeer’slaw
constant
dI

dz

D I

I

TI 0
I 0e D z
I 0e W

I0

W = z = (nV z :opticalthickness
䠄W=1
I = 0.37 I0 䠅

I0eW䚔 ImportantaspectofAOTisthatthevalueisproportionalto
theconcentrationandcrosssectionofthetargetparticle.

5

Fine particles

㻝㻚㻜

㻜㻚㻜
㻝㻚㻜

㻌

㻜㻚㻡
㻌

㻜㻚㻡
㻜㻚㻜
㻝㻚㻜

㻌㻿㼛㼕㼘
㻌㻿㼑㼍㻌㻿㼍㼘㼠㻌㻺㼕㼠㼞㼍㼠㼑
㻌㻿㼡㼘㼒㼍㼠㼑
㻌㻻㼞㼓㼍㼚㼕㼏㼟㻌㻱㻯㻌

Coarseparticles㻌㻯㼛㼍㼞㼟㼑

㻜㻚㻡㻌㼂㼛㼘㼡㼙㼑㻌㼞㼍㼠㼕㼛㼂㼛㼘㼡㼙㼑㻌㼞㼍㼠㼕㼛㼂㼛㼘㼡㼙㼑㻌㼞㼍㼠㼕㼛LongtermaerosolanalysisatChibaUniversity

㻲㼕㼚㼑

㻜㻚㻜
㻝㻥㻥㻥 㻞㻜㻜㻜 㻞㻜㻜㻝 㻞㻜㻜㻞 㻞㻜㻜㻟 㻞㻜㻜㻠

S.Fukagawaetal.,Atmos.Environ.,40,21602168(2006)

6

Typicalspectralreflectance7

Absorptioncoefficientforwater

http://www.lsbu.ac.uk/water/vibrat.html
8

HDTVimageoftheEarthfromLunaOrbiter

SELENE,November7,2007
http://www.jaxa.jp/press/2007/11/20071113_kaguya_j.html

9SpectralIrradianceofsolarradiation

Groundvisibility:V=20km
Angstromexponent:p=1

10

CrosssectionofRayleighscattering ScatteringduetoAirMolecules(N2,O2,Ar,...)k=2S/O wavenumber
D~ molecular polarizability
At O= 550 nm,
D~1.7361u1030 [m3 ]
4SH 0

T

1.7120 @ 1064 nm
11

Polarizabilityandconstantofrefraction㻸㼛㼞㼑㼚㼠㼦㻙㻸㼛㼞㼑㼚㼦㻌㼑㼝㼡㼍㼠㼕㼛㼚

D~
4SH 0

n 1
2S n15n =1.0002770

D~ :molecularpolarizability
n15 :molecularnumberdensity
at15Υ
Constantofrefractioncanbepreciselydeterminedasafunction
ofwavelengthandtemperature(values for air molecule)n15= 2.5469 㽢1025 m-3
O= 355 nm : D~ /(4SH0) = 1.7864 㽢 10-30 m3
O= 532 nm : D~ /(4SH0) = 1.7384 㽢 10-30 m3
O= 1064 nm: D~ /(4SH0) = 1.7120 㽢 10-30 m3

LordRayleigh18421919
12

3.ResultsRadiancesimulatedforsatellitebands

䚔 Radianceduetothescatteringintheatmosphere(Lmol + Laer)
andradianceduetothesurfacereflection(Ld)aresimulated
assumingonlythesinglescatteringcontributions.

13

Effect ofmultiplescattering

䚔 SimulationwiththeMODTRANradiativetransfercodewiththefollowingparameters;atmosphericmodel=midlatitude
summer,aerosolmodel=maritime,groundvisibility=20km,
solarzenithangle=20deg,viewzenithangle=60deg,view
azimuth=sameasthesolarazimuth.
14

Relationshipbetweenthesurfacereflectance()and
aerosolopticalthickness()䚔 Typicalrelationshipbetweenandforvariousvaluesof
satelliteobservedradiance(Lobs)simulatedforMODISband4
(540 560nm).Whenissmall,thesurfacereflectancecan
readilybeestimatedfromthevalueofLobs.

15

MonthlyreflectancemapsaroundTokyobasedonMODIS䚔 ThemonthlyreflectancemapsarederivedfromMODISband4
(540 560nm)during20072009.Pertinentaerosolinformation
wasderivedfromgroundbasedspectroradiometer (EKO,MS720)
observationatChibaUniversity.

16

Separationof
surfacereflectanceand
aerosol
information
fromGMS5
meteorological
satellitedata.

䚔 Monthlycompositeapproachwastakenforderivingreflectancemap,andtheresultingsurfaceinformation(reflectancedistribution, map)wasemployedfor
estimatingthedistributionofaerosolopticalthickness(AOT, map).

17

Atmosphericcorrection
ofruggedterrain(c)isthesolarincidenceanglemeasuredfromthe
surfacenormal.

(d)Thereflectance
imageafterthe
topographic
correction(terrainslopeeffectand
illuminationeffect).

(a)OriginalimagebasedonLandsatTMdatatakenonJuly
26,1997.Theareaisinthenorthernpartofmainisland(Honshu)inJapan,withavolcanoMt.Iwaki(1625m).

18

Himawari8&9(JMA)
Advancedfeatures:䚕 500mresolution
䚕 observationevery10min䚕 RGBbands,
16bandsintotal
䚕 Tobeoperatedfrom201519

4.Conclusion
䚔 Thispaperhasdescribedthespectralappearanceofatmosphericradiancecomponentsincomparisonwiththespectralreflectance
behaviorofusuallyencounteredsurfacecoverage.䚔 Forthecaseofrelativelylimitedareacoverage(<100km),theaerosolpropertyobservedwithagroundbasedinstrumentsuchas
acompactspectroradiometer canfullybeutilizedtoimplement
preciseevaluationoftheatmosphericeffects.䚔 Forthecaseofwiderareacoverage(~1000km),asexemplifiedinthecaseofGMS5data,themonthlycompositeapproachiseffectiveforobtainingclear(lowAOT)imageswithlimitedinfluence
ofatmosphericeffects.Theresultingreflectancemap(U map)canbeexploitedforderivingtheAOTdistributionmap(Wmap)for
turbidimages.
䚔 Wehavealsodiscussedtheimplementationofatmospheric
correctionoverruggedareas,takingthedetailedtopographic
informationintoaccount.
20

Unmanned Aerial Vehicle (UAV) Technology for
Coastal Dynamic Analysis in Baron Yogyakarta

Prof. Dr.rer.nat. Muh Aris Marfai, M.Sc.
Fredi Satya Candra Rosaji, S.Si
Ahmad Cahyadi, S.Si
Mohammad Rifki G.L

Fakultas Geografi
UNIVERSITAS GADJAH MADA

Outlines
1. Introduction
2. Objective
3. Method
4. Result and discussion
5. conclusion
6. Acknowledgement

Arabian jornal of geosciences,
DOI 10.1007/s12517-013-1232-7
Received: 11 January 2013 Accepted: 11
December 2013
Published online 3 Jan 2014

Introductiony As an archipelago,

Indonesia has the longest
shoreline in the world(Supriharyono, 2009).

y Due to this condition,

Indonesia has very large
coastal areas (Christanto, 2010).
y Coastal areas become
very attractive sincethese areas have potential
resources for future
development (Tuwo, 2011 dan
Kodoatie, 2012).

Location of the study area

Topographical relief map of the Gunungsewu karst area

Introductiony Coastal dynamics is one of the important issues oncoastal zone management (Marfai, 2011)y However, spatial data with appropriate scale for coastal
monitoring is not always available
y Technology on remote sensing becomes very crucial tosolve the problem of data scarcity.

Introduction
UAV – (Unmanned Aerial Vehicle) is
promising to help solve the problem
Cheap
This mapping technology is relatively cheap
Output on 2D and 3D in detailed scale
reduces the cost significantly
Easy
Operable and understandable
Fast
For small location, data can be obtainedeasier for processing.

Objective
y To analize and to map shoreline change in Baron using

UAV technology

MethodyData aquisition of Small Format Aerial Photograph (SFAP)- Pre-aquisition data:

Aerial photo preparation (flight plan), plotting GCP
Acquiring data:- Installing gcp and measuring gcp (ground control point) using gps geodetic.
- Photo taking using UAV-

Pre-mark installment and
gcp measurement using gps
geodetic (a)Mapping of the distributionof gcp (b)MethodyData Acquisition of Small Format Aerial Photograph(SFAP)

Installing camera to thevehicle bixler (a),take off process of vehicle
bixler with hand launcher(b),pilot has his duty during
take-off and landing (c),Vehicle during the manuver(d),Co-pilot pays attention to
the vehicle moving on the
ground control station/gcs (e)MethodyDigitation of shoreline from multi-temporal images
GoogleEarth™ year 2009, 2010 and 2013
SFAP year 2014

GoogleEarth™ 2009 (left), 2010 (middle) and2013 (right)

Digitation processMethody

Shoreline change analysis using DSAS (Digital Shoreline
Analysis System)
DSAS taken from http://www.csc.noaa.gov/digitalcoast/tools/dsas
Statistically analysis to monitor the change of shoreline based on the changing
coordinate from time to time
The software (tool) can be integrated into ArcGIS software

Result and discussionySmall Format Aerial Photograph (SFAP)
Acquisition date :10 Mei 2014 , 10.55 WIB
Duration:± 74 minute including take off,
landingRun: 2 runFlying Height:
200 – 300 above ground level
Covered Areas: ± 125Hectares

Result and discussionyAtmospheric condition during acquisition is clear with a bit windy,
therefore UAV is crabbing (Figure a.)yMore detail photo higher resolution than high resolution images. The pixel
size is about <10 cmy

For example, from the picture, the boat from fisherman has around ±1.5m
lenght, on the high resolution images would be around 2 – 3 pixels (Figureb)ySince the photo has higher resolution, it is easier to recognize anddifferentiate between water and land.

Result and discussion

Result and discussiony

Shoreline delineation results

Result and discussiony

DSAS Analysis

Shoreline in Baron 2009, 2010, 2013 dan 2014 (left), transect frombaseline 1 (right above) and transect from baseline 2 (right bottom)

Result and discussiony

DSAS Analysis Results
Net Shoreline

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DSAS Analysis

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Result and discussiony

Shoreline Changes Analysis
Shoreline 2014 (May)May is considered as the beginning of east monsoon with moderate
wave and current
It is also considered as the end period of sedimentation of thematerial from bribin underground river
In year 2014 the mouth of the river is changing to the east
direction.
Shoreline 2013 (September)
In 2013 the shoreline is considered as the most closed shoreline tothe land.
During this time (2013) shoreline in the same line with theoutlet/mouth of the bribin underground river.
End of September is considered as the last east monsoon withstronger wave and current causing erosion

Result and discussiony

Shoreline Changes Analysis
Shoreline 2009 & 2010
Shoreline in 2009 and 2010 is almost the same.
The mouth of the Bribin underground river is still perpendicular tothe shoreline (not goes to the east direction)
October 2009 is considered as west monsoon shere wave andcurrent are stronger then March 2010 (east monsoon)

Conclussiony

Shoreline Changes Analysis
Dynamic of the shoreline is influenced by monsoon, wave, current, river
debit
Coastal erosion is dominant during the east monsoon and acresion occurs
during the west monsoon in general.
Acresion also occurs due to sedimentation process of the sedimen fromthe bribin underground river where end up on the Baron beach. During
the east monsoon sediment from the river is less.
Technology of UAV is reliable to provide necessary data for monitoring ofcoastal dynamic.

Acknowledgementy This research is supported by grand research of theFac. of Geography UGM in 2014.
y Thanks to Sri Annas K dan Ahmad Haidir as pilot, andcrew from the devision of UAV CV. Mitra Geotama.

Literature
y
y

y
y

y
y

y

y
y
y

Christanto, J. 2010. Pengantar Pengelolaan Berkelanjutan Sumberdaya Wilayah Pesisir
dan Pulau-Pulau Kecil. Yogyakarta: Deepublish.
Digital Shoreline Analysis System.Digital Coast Office for Coastal Management,
NOAA.http://coast.noaa.gov/digitalcoast/tools/dsas?redirect=301ocm, diakses tanggal 30 Sept2014.
Kodoatie, R.J. 2012. Tata Ruang Air Tanah. Yogyakarta: Penerbit ANDI.
Marfai MA. 2012.Preliminary assessment of coastal erosion and local community adaptation in
Sayung coastal area, Central Java – Indonesia.International Journal of Quaestiones
Geographicae,Vol. 31(3).Hal: 47–55.
Marfai MA.2011. The hazard of coastal Erosion in Central Java Indonesia: an Overview.
GEOGRAFIA, Malaysia Journal of Society and Space,Vol. 7(3). Hal: 1 – 9.
Miyamoto, M.;Yoshino, K.; Nagano,T.; Ishida,T. danSato,Y. 2004. Note Use of Balloon Aerial
Photography for Classification ofKushiro Wetland Vegetation, Northeastern Japan.
WETLANDS,Vol. 24(3).Hal: 701–710.
Ro, K.; Oh,J-S.dan Dong,L. 2007. Lessons Learned: Application of Small UAV for Urban
Highway Traffic Monitoring.45th AIAA Aerospace Sciences Meeting and Exhibit.8 - 11
January 2007, Reno, Nevada.
Supriharyono. 2009. Konservasi Ekosistem Sumberdaya Hayati di Wilayah Pesisir dan
Laut Tropis. Yogyakarta: Pustaka Pelajar.
Tuwo, A. 2011. Pengelolaan Ekowisata Pesisir dan Laut: Pendekatan Ekologi, SosialEkonomi, Kelembagaan, dan Sarana Wilayah. Surabaya: Brilian Internasional.
van Blyenburgh, P. 1999.UAVs: an Overview, In: Air & Space Europe,Vol. I (5/6). Hal: 43-47.

ōŢųŢŴġŕŰŦųŴŪŭŰŸŢŵŪĭġłŧŪŧġŃŶťŪźŰůŰĭġņťźġŎŢųźŢťŪĭġ
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ĳĳůť ńņœŦŔġŊůŵŦųůŢŵŪŰůŢŭġŔźŮűŰŴŪŶŮ
ĳĺġŐŤŵŰţŦųġĳıĲĵ
ŖňŎġȸ ŚŰŨźŢŬŢųŵŢĭġŊůťŰůŦŴŪŢ

Online Technical Guidance of
BASIS DATA ATMOSFER INDONESIA (BISMA)[INDONESIAN ATMOSPHERIC DATABASE]
http://bisma.sains.lapan.go.id/
BISMA, June 2014

BISMA, Nov 2014THANK YOU

'HYHORSPHQWRI6\QWKHWLF$SHUWXUH5DGDU
RQERDUG8$9DQG0LFURVDWHOOLWHVIRU
(QYLURQPHQWDO2EVHUYDWLRQProf.JosaphatTetukoSriSumantyo(ChibaUniversity,Japan)*,Prof.JaeHyunKim(AjouUniversity,Korea), Prof.KimTuHwan(AjouUniversity, Korea)*JosaphatMicrowaveRemoteSensingLaboratory(JMRSL),CenterforEnvironmentalRemoteSensing,ChibaUniversity
133,Yayoicho,Inageku,Chibashi2638522JapanTelp.+81(0)432903840Fax+81(0)432903857
Emailjtetukoss@faculty.chibau.jpWebsitehttp://www2.cr.chibau.jp/jmrsl/

Contents: BackgroundandObjectives
 RoadmapofChibaUniversityMicrosatellite&UAVMissions DevelopmentofUAV,Sensors,andApplications
 Summary-RVDSKDW/DERUDWRU\/DUJH6FDOH([SHULPHQWDO8QPDQQHG$HULDO9HKLFOH -;Background&Objectives






Commonspacebornesyntheticapertureradar(SAR)ismultimissions,bulky,
expensive,longmanufacturingperiodetc,thereforeweneeddevelopsmall
spaceborneSARsensorÆ lowcost,specificmission,shortmanufacturing
microsatellites&UAV
ChibaUniversitycollaborateswithAjouUniversityandKARItodevelop Lband
SARonboardmicrosatellitesforEarthsurfaceobservation.
WealsodevelopSARonboardUnmannedAerialVehicle(UAVSAR)forgroundtest (alsorescue,coastallinemonitoringetc)
Promotingmicrowaveremotesensingtechnologyforresearchandeducationon
spaceborne(microsatellite)andUAVinAsiaPacificregionÆ Exchangeprogram,
DoubleDegreeProgram,TWINCLEprogram,SS&SVProgrametc.
-;DW)XMLNDZD$LUSRUWRQ$XJXVW

&36$56HQVRU

AftersuccessfulFirstFlightofJosaphatLaboratoryUnmannedAerialVehicle(JX1)at
FujikawaAirporton7June2012+\SHUVSHFWUDOFDPHUD

RoadmapofChibaUniversityMicrosatellitesMission
IntegratedEarthEnvironmentalDiagnosisResearch
Program– ObservationofContinentalLand
DeformationusingMicrosatellitesConstellation

䕕 ProjectPeriod:FY2013– FY2016
䕕 PrincipalInvestigator:Prof.JosaphatTetukoSriSumantyo
䕕 Funding:JapanMinistryofEducationandTechnology(MEXT)

Projectabstract
Expectedimpact
ScientificImpact
CenterforEnvironmental
1䠅GNSSROonboardmicrosatellite䠄䠄GAIAI䠅䠖
RemoteSensing(CEReS)ofChibaObservationofgloballanddeformationandchangeoftotal(ChibaUniv+ LAPAN)
University isdevelopingGNSSRO
electroncontentsLandDeformationMonitoringObservationofatmospherictemperature,watervapor,sea
GAIAIIsensoronboardmicrosatellite
surfaceheight,gravityetc
Observationofearthquakeprecursorandthemechanismin
(GAIAI:50kgclass)toobserve
globalarea
Galileotherelationshipofionospheric
2䠅CPSARonboardmicrosatellite䠄GAIAII䠅䠖
phenomenonandlandObservationusingcircularpolarizationanditsstudyfornew
applications
deformation(Wideareaandlow QZS
Compass Localobservationoflanddeformation
GlobalLand
resolution).
Deformation
CEReS collaborateswith
IndonesianAerospaceAgency
(LAPAN)todevelopcircularly
polarizedsyntheticapertureradar
(CPSAR)onboardmicrosatellite
(GAIAII:100kgclass)toobservelanddeformation(localandhighresolution).

GPS

CommunityImpact

Reductionofdisasterimpactbymicrosatelliteconstellation
Widthspread collectedsatellitedataforinternationalcommunity
Reductionofdisasterimpactandrealizationofsafeandreliablecommunity

ImprovementImpact
IonosphericMonitoring
GAIAI䠄ChibaUniv䠅Promotingadvancedresearchandeducationonremotesensing
fieldGatheringacademicandresearchinstitutionstocollaborateonhightechnologyonmicrosatellite,unmannedaerialvehicleand
microwavesensorsforremotesensing

RoadmapofChibaUniversityMicrosatelliteMissions㻮㼍㼟㼕㼏㻌㻾㼑㼟㼑㼍㼞㼏㼔
20132017 LAPAN
Chibasat(360Millions
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MillionsYen)㻭㼐㼢㼍㼚㼏㼑㼐㻌㻾㼑㼟㼑㼍㼞㼏㼔

ChibaUniversity LAPAN
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II)LBandSR;Polar,
100KgChibaUniversityCOE
StartupProgram,MEXT
SpecialProjectetc.

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ChibaUniversity
Charter:
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Higher

GAIA-II
ChibaUniversity–AjouUniversity&
KARI,Korea
Polar, 100Kg

ChibaUniversity–AjouUniversity&
KARI,Korea(ChibaUniversity+LAPAN)
Polar, 100Kg

LandDeformationMonitoring
GAIAII

ChibaUniversity LAPAN
Equatorial, 100Kg

ChibaUniversity– MMU,Malaysia
Equatorial, 100Kg

JX1

Galileo

GAIA-IILand
deformation
etc

QZS

JX2

ChibaUniv.,KyotoUniv.,JAXA,
Taiwan NCU,Polar,50Kg
GPSROonboardMicrosatellite

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GAIA-I
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JosaphatLaboratoryGroundExperimental
SyntheticApertureRadaronboard
UnmannedAerialVehicle(JXSeries)

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20102015㻯㻛㼄㻌㻮㼍㼚㼐㻌㻿㻭㻾

20162020㼙㼙㼃 㻮㼍㼚㼐㻌㻿㻭㻾20212025㻹㼡㼘㼠㼕㼎㼍㼚㼐㻌㻿㻭㻾㻌㻹㼕㼟㼟㼕㼛㼚20262030

20312035

RoadmapofChibaUniversityUnmannedAerialVehicle(UAV)
ResearchandHumanResourcesDevelopmentonFixedWing
LargeScaleUnmannedAerialVehicle(UAV)

Large UAV JX-1
Long range & wide coverage
Payload:25kg
MultimediaUniversity,Malaysia(MMU)
IndonesianAerospaceAgency(LAPAN)etc

JX1

Large UAV JX-2
Long range & wide coverage

JX1
Payload:25kg
MultimediaUniversity,Malaysia(MMU)
IndonesianAerospaceAgency(LAPAN)etc

JX2

DevelopmentofJosaphat Laboratory
GroundExperimentalLargeScale
UnmannedAerialVehicle(UAV)JXSeries

Mini UAV JX-3
Medium range & coverage䝬䝹䝏䝞䞁䝗䝭䝑䝅䝵䞁
Payload:10kg
MultimediaUniversity,Malaysia(MMU)
IndonesianAerospaceAgency(LAPAN)etc

Small UAV JX-4
Short range & small coverage
Payload:5kg
MultimediaUniversity,Malaysia(MMU)
IndonesianAerospaceAgency(LAPAN)etc

DevelopmentofLbandUAVSAR

Low
Resolution

LBandSAR

20102015

MediumResolution

High
Resolution

C/XBandSAR

20162020

mmW BandSAR

20212025

Multi
Resolution

MultibandSARMission20262030

20312035

TargetofCPSARonboardMicrosatellite(GAIAII)&UAVMission

Basicexperiment

Items

Details

Scatteringmechanism
ofcircularlypolarized
microwave

Scatteringmechanismfromvegetations,
cryosphere,soilandrocks,desertetc

Interferometry• Linearvs CircularPolarization
Interferometry SAR• DEMextractionbyCPwave

Axialratioimage(ARI)

Vegetation,geologic,cryosphere etcmappingbyusingARI

Landcover mapping• Forest– nonforestareaclassification• Treeheightestimation• Paddyfieldextraction• Wetlandextraction• Mangroveareamapping• Snow– icebergdetection

Applications
Disastermonitoring

Earthquake,volcanoeruption,flood,
forestfireetc

Cryospheremonitoring

Iceberg,glacier,artic routeetc

OceanmonitoringOilspill,oceanwaveetc

LL

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48.0

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16.0

Wing(2unitx@10kg)

20.0

Ladder(2 unitx@7kg)

14.0

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7.5

Gasoline(20liters)

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25.0

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10 km
0.75 m x 0.4 m x 4 panels
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Josaphat Tetuko Sri Sumantyo, Chapter
12. Circularly Polarized Synthetic
Aperture Radar onboard Unmanned
Aerial Vehicle (CPSAR UAV), Kenzo
Nonami et al. edn., Autonomous Control
SystemsandVehicles,Springer,
December 2012.

JosaphatLaboratoryExperimentalUnmannedAerialVehicle(JX2)

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ChibaUniversityApplicationsofSARImages
29July1997SPOTHRV
29July1997JERS1SAR

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No.4,pp.879 884,February2003
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JERS 1SAR1996111819970809“LongtermcontinuouslyDInSARtechniquefor
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ProcessingincludingBasicSARImageprocessing,InSAR,DInSAR,PSInSAR etc.

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J.T.SriSumantyo,RyutaroTateishi,andN.Takeuchi,“EstimationofburntcoalseamthicknessincentralBorneousingaJERS1SARimage,”InternationalJournalofRemoteSensing,Vol.24,
No.4,pp.879 884,February2003
J.T.SriSumantyo,MasanobuShimada,PierrePhillipeMathieu,andHasanuddinZainalAbidin,“LongtermcontinuouslyDInSARtechniquefor
volumechangeestimationofsubsidence,“IEEE
Trans.GeoscienceandRemoteSensing, Vol.50,
No.1,pp.259– 270,January2012(SICE award)

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ALOSPALSAR
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5.9+5.9cmALOSPALSAR2008011720081204

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0
5.9 0 +5.9cm+5.9cmMalayPeninsulaLandslideMonitoringusing
SmallUAV:CameroonHighland

CameroonHighlandgroundcampaignwithMalaysianGovernmentJGR,
UniversitiSainsMalaysia,andJICAon20September2014

MalayPeninsulaLandslideMonitoringusingSmallUAV:
CameroonHighland– PointCloud

MalayPeninsulaLandslideMonitoringusingSmallUAV:
CameroonHighland– DensePointCloudMalayPeninsulaLandslideMonitoringusingSmallUAV:
CameroonHighland– DensePointsCloud

MalayPeninsulaLandslideMonitoringusingSmallUAV:
CameroonHighland– DenseCloudClassesSummary` ChibaUniversity,Ajou UniversityandKARIcollaboratetodevelopSARonboardmicrosatellitesandUAVforEarth
monitoring` Applicationdevelopment:
National Security,disastermonitoring,coastguard,
logisticdelivery,remotesensingmissionsetc

29August2013,Fujikawa Airfield,Japan

StaffofChibaUniversity,Ajou UniversityandKARI

Thankyouforyourattention!
Continuously Evolving

JX1

JX2

JSPSJENESISprogramforAsiaPasific SARResearch&Education&COEProgramforSAR
UAV&MicrosatellitesforEarthDiagnosisProgram

ContactPerson:Prof. JosaphatTetukoSriSumantyo,Ph.D
JosaphatMicrowaveRemoteSensingLaboratory(JMRSL)
Center forEnvironmentalRemoteSensing,ChibaUniversity
133,Yayoicho,Inageku,Chibashi 2638522Japan
Telp.+81(0)432903840Fax+81(0)432903857
Emailjtetukoss@faculty.chibau.jp
Websitehttp://www2.cr.chibau.jp/jmrsl/

Fujikawa Airfield,29August2013UTC06:09:54.521

Telecommunication, Radio and Microwave Laboratory
Department of Electrical Engineering andCenter of Technology (COT), Faculty of Engineering
HASANUDDIN UNIVERSITY, Makassar Indonesia

A COMPACT AND ROBUST TELEMETRYSYSTEMS CONSTRUCTION FORENVIRONMENTAL OBSERVATIONS

Presented by ELYAS PALANTEIThe 22nd CEReS International Symposium and The 6th Indonesia Japan
Joint Scientific Symposium (IJJSS)
University of Gadjah Mada (UGM), Yogyakarta, Indonesia
28-30 OCTOBER 2014

UNHAS

Telecommunication, Radio and Microwave
Laboratory (TMRL), Hasanuddin University,
Makassar, Sulawesi Selatan, Indonesia

Outline of Talk
1. Introduction
2. Various Developments of Telemetry
Systems
3. Constraint Factors and Future Works
4. Conclusions
5. References

1. Introduction (1)UNHAS

Why and how is “Telemetry System” ?
Î Telemetry is the highly automated communications process by whichmeasurements are made and other data collected at remote or inaccessible
points and transmitted to receiving equipment for monitoring
http://medical-dictionary.thefreedictionary.com/telemetry [29 October
2014]Îtelemetry /te·lem·e·try/ (tĕ-lem´ĕ-tre) the making of measurements at
a distance from the subject, the measurable evidence of phenomena
under investigation ...
Î http://www.wisegeek.org/what-is-a-telemetry-unit.htm [29 October 2014]
A telemetry unit is a unit in a hospital where patients are under continuous
electronic monitoring. Telemetry, the practice of sending electronic signals
from one place to another, is a tremendously useful tool in hospitals, as itallows hospital personnel to monitor heart rate, heart rhythm, breathing, andother things both by the patient's bed and at a remote location like a nursing
station.

1. Introduction (1)UNHAS

Why and how is “Telemetry System” ?
http://www.britannica.com/EBchecked/topic/585928/telemetry [29October 2014]
Î Telemetry, highly automated communications process by whichmeasurements are made and other data collected at remote or inaccessible
points and transmitted to receiving equipment for monitoring, display, andrecording.
Wire vs Wireless

http://www.merriam-webster.com/dictionary/telemetry [29 October
2014]
telemetry /te·lem·e·try/ (tĕ-lem´ĕ-tre) the process of using special
equipment to take measurements of something (such as pressure, speed, or
temperature) and send them by radio to another place
Full Definition of TELEMETRY
1 : the science or process of telemetering data
2 : data transmitted by telemetry
3 : BIOTELEMETRY

1. Introduction (1)UNHAS

Why and how is “Telemetry System” ?
Currently, the telemetry systems are widely applied in the broad wirelesstechnology applications to perform various different measurement tasks froma remote location.
Some of the serious tasks are including to mapping the potential natural
resources (e.g. minerals and biological) using the remote sensing
techniques; for sub-marine observations; for the real-time monitoring of theenvironment conditions (e.g. temperature, relative atmospheric humidity, air
quality, the power and direction of the wind flows and weather forecasting);
and for predicting and mitigating the potential disaster impacts of various
natural phenomena such as the extreme weather changes and others.
Telecommunication, Radio and Microwave Laboratory, Department ofElectrical Engineering and Center of Technology (COT), Faculty ofEngineering, HASANUDDIN UNIVERSITY, Makassar Indonesia … We
have developed two classes of telemetry systems:
short distance range telemetry VS long distance range telemetry
 Telemetry Î Military and Civil Applications
 Determine the reliability of various applications

UNHAS

2. Various Developments of Telemetry Systems
 First Design: 433 MHz Telemetry
System for Submarine Environmental
Monitoring and Observation

UNHAS

Local Data Collector: anantenna system, atemperature sensor
LM35, a microcontroller
ATMega 8535 andTransceiver chip YS1020UA
Central Data Monitoring:an antenna system,
Transceiver chip YS1020UA , Laptop set installedwith the designed Delphi
7 software Î dataplotting/displaying,recording and analysing

2. Various Developments of Telemetry Systems

UNHAS

start
Port Initialization

ADC Configuration for Reading
Temperature Sensor

Plot Collected Data
Voltages into
Temperature Data

LCD Display
No
Forward Data to Serial Port tobe Display via Laptop Monitor

Yes
End

2. Various Developments of Telemetry Systems

UNHAS

2. Various Developments of Telemetry Systems

UNHAS

 Second Design: 875-925 MHz/1800 MHz
GSM Application for steering a mobile
object movements Î DTMF

2. Various Developments of Telemetry Systems
 875-925 MHz/1800 MHz
GSM Application forsteering a mobile object
movements
Steerable mobile object:

Underwater remote controller:

No

Water Depth( cm )

MobileResponse( Receiver )Condition of
LEDs( Receiver )

1

5

OK

Light

2

10

OK

Light

3

15

OK

Light

4

20

OK

Light

5

25

OK

Light

6

30

OK

Light

7

35

OK

Light

8

40

OK

Light

9

>40

OK

Light

2. Various Developments of Telemetry Systems
Platform

XBeeZBRF data rate

XBeePRO ZB

UNHAS

UNHAS

Programmable
XBeePRO ZB

250 Kbps

Indoor/Urba
n range

40 m

90 m

Outdoor/RF
Line of Sight
Range

120 m

3200 m/1500 m

Transmit
Power

1.25
mW(+1
dBm)/2mW(+3dBm)boost
mode

63 mW (+18 dBm)/Int’l
10 mW (+10 dBm)

ReceiverSensitivity(1% PER)-96 dBmin boost
mode-102 dBm

Data transfer
Receivingunit

 Third Design: 2400-2500
MHzTelemetry System deployedon the constructed small satellite2. Various Developments of Telemetry Systems

UNHAS

Arduino Mega2560 integrated
with Xbee-Pro Chip Tranceiver

BMP085
Digitalpressure
sensor 2400-2500MHz
Telemetry System
deployed on theconstructed small satellite

2. Various Developments of Telemetry Systems

UNHAS

Ground station based PC/Laptop

Environmental Sensors Module Error percentage (%)
Temperature (3.27%)
Free space pressure (0.69%),
Height/Altitude (1.26 %)2. Various Developments of Telemetry Systems

UNHAS Fourth Design: A Compact
FM Telemetry System

2. Various Developments of Telemetry Systems
Platform

Specifications

Frequency
Operation Range

88 MHz- 175 MHz(88-108 MHz)RF Power Amplifier
using Transistor
2SC1946A

30 Watt

Antenna TypeV dipole

Antenna Gain

10 dB

Coaxial Cable

RG58 (50 Ohm)

No. Transistor Pout
Types
(Watt)

UNHAS
Note

1

BLF244MOSFET

15

Good load stability

2

BLF245MOSFET30

C Class RF
Amplifier up to
200 MHz3

MRF31545

42SC2782
Toshiba80

NPN Silicon
Transistor

5

BLF177MOSFET

150

Low distortion,
Easy power control

6

BLF278MOSFET

300

Good load stability,
Easy power control

7

BLF574MOSFET

600

RF Amplifier up to
225 MHz, 50 V, IDQ= 1000 A

3. Constraint Factors and Future WorksÎLimited RF Power for the transmission and UNHAS
reception tasks (Essential issue to cover a longrange data transmission Î to boost the telemetry
performance) Î Environmental Monitoring and
Observations
Î Lack of the appropriate design of the electrical
power supply system supporting the constructed
telemetry systems e.g. for mini satellite
prototypes (high altitude communication system,
automobile under water objects (sub marine
observation and monitoring etc… Í RES
Î Lack of the appropriate electronic components
available in the local market Î difficult Î compact
and powerful telemetry systems

3. Constraint Factors and Future Works
UNHASÎLimited high quality electronic instruments for
performing various measurement activities (e.g.
for measuring the developed under watercommunication stuffs Î requires more R&D
collaborations Î Universities, Industries andBusiness sectors
Î The frequency operation selections Í must be
applied and choose more appropriatelyÎ Require more attention on the circuit design andfabrication to obtain more robust telemetry
systems and proportional physical size

UNHAS

Telecommunication, Radio and Microwave
Laboratory (TMRL), Hasanuddin University,
Makassar, Sulawesi Selatan, Indonesia

Conclusions

4. Conclusions

Î Further R&D activities should address someconstraint factors on advanced developing steps
Î the more robust, compact, and applicative
telemetry systems (Essential elements for various
applications Î civil and military applications) Î
Environmental Monitoring and Observations
Î More R&D collaborations are very welcome toinitiate and to strengthen the MoU between Chiba
University and UNHAS especially, and improvethe mutual cooperation in academic and researchamongst Indonesia Universities, in general
Telecommunication, Radio and Microwave Laboratory, UNHASVI. References

Telecommunication,
Radio and Microwave
Laboratory (TMRL)[1] Elyas Palantei, Syafruddin Syarif, Bayu Topalaguna and Zakiy Ubaid, ”FourElements Array of Lungs Shape Patch Antenna for Nanosatellite Telemetry,”
Proceedings of IEEE Antennas and Propagation Society and 2013 USNC/URSINational Radio Science Meeting, Orlando, Florida, USA, 7-14 July 2013.
[2] Elyas Palantei, Fahry Shamad, Marita Taif and Zulfajri Basri Hasanuddin,“Pico-Satellite 2.4 GHz ISM Band: Preliminary Design for Telemetry
Application,” Proceedings of The 3rd MICEEI (Makassar International
Conference on Electrical Engineering and Informatics), Makassar Golden Hotel(MGH), Losari Beach, Makassar, Indonesia, 28 November-1 December 2012.
[3] Merna Baharuddin, Elyas Palantei and Martina Pineng, “An Experimental
Evaluations of Nanosatellite Transmitter and Receiver Systems for Telemetry
Application,” submitted to The 4th MICEEI (Makassar International Conference
on Electrical Engineering and Informatics), Imperial Aryaduta Hotel andConvention, Losari Beach, Makassar, Indonesia, 26-30 November 2014.

Telecommunication, Radio and Microwave Laboratory, UNHAS

VI. References

Telecommunication,
Radio and Microwave
Laboratory (TMRL)[4] Andani Achmad, Elyas Palantei, Ashadi Amir, and Hasrianto, “Dual Tone Multi
Frequency (DTMF) Applications for Robot Control Based Microcontroller andPerformance Testing in Underwater Environment,” Proceedings of The 3rd
MICEEI (Makassar International Conference on Electrical Engineering andInformatics), Makassar Golden Hotel (MGH), Losari Beach, Makassar,
Indonesia, 28 November-1 December 2012.
[5] Anggita Lestari, Erna Alimuddin, Elyas Palantei and Novy NRA Mokobombang,“Antenna System Construction Suitable for Underwater Communication,”
Proceedings of The 3rd MICEEI (Makassar International Conference on ElectricalEngineering and Informatics), Makassar Golden Hotel (MGH), Losari Beach,
Makassar, Indonesia, 28 November-1 December 2012.

Telecommunication,
Radio and Microwave
Laboratory
Wassalamu ‘Alaikum Warrahmatullahi Wabarakatuh
Thank you so much

Terima kasih
GraciasKo pun ma krab

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InitialData䞉㻸㼍㼚㼐㼟㼍㼠 㼐㼍㼠㼍䠖 㻸㼍㼚㼐㼟㼍㼠㻌㻳㼑㼛㻯㼛㼢㼑㼞䞉㻹㻻㻰㻵㻿 㼐㼍㼠㼍䠖 㼓㼘㼛㼎㼍㼘㻘㻌㻞㻜㻜㻟㻌㼍㼚㼐㻌㻞㻜㻜㻤䞉㻰㻹㻿㻼 㻻㻸㻿㼐㼍㼠㼍䠖㼚㼕㼓㼔㼠㼠㼕㼙㼑㻌㼘㼕㼓㼔㼠㼟䞉㻰㻱㻹䠖㼓㼘㼛㼎㼍㼘㻌㻭㻿㼀㻱㻾㻌㻳㻰㻱㻹䞉㼜㼛㼜㼡㼘㼍㼠㼕㼛㼚 㼐㼍㼠㼍䠖㻸㼍㼚㼐㻌㻿㼏㼍㼚㻔㻞㻜㻜㻤㻕䞉㼠㼔㼑㻌㻴㼍㼞㼙㼛㼚㼕㼦㼑㼐㻌㼃㼛㼞㼘㼐㻌㻿㼛㼕㼘㻌㻰㼍㼠㼍㼎㼍㼟㼑㻌㼎㼥㻌㻲㻭㻻㻘㻌㼑㼠㼏㻚䞉㼏㼛㼚㼟㼠㼞㼡㼏㼠㼑㼐㻌㼕㼙㼜㼑㼞㼢㼕㼛㼡㼟 㼟㼡㼞㼒㼍㼏㼑㼟 㼎㼥㻌㻺㻳㻰㻯䞉㼓㼘㼛㼎㼍㼘㻌㼘㼍㼚㼐㻌㼏㼛㼢㼑㼞㻦㻌㻳㻸㻯㻺㻹㻻 㼍㼚㼐㻌㼕㼠㼟㻌㼠㼞㼍㼕㼚㼕㼚㼓㻌㼐㼍㼠㼍䞉㼓㼘㼛㼎㼍㼘㻌㼜㼑㼞㼏㼑㼚㼠㻌㼠㼞㼑㼑㻌㼏㼛㼢㼑㼞,QYLWDWLRQWR&(5H6*DLD
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2010

2012.82014

Startofdevelopment

Launchthe
systemInternational
expansion

Thankyou%NWUVGTU[UVGOInternationallinkage%'4G5 )CKC

RyutaroTateishi
tateishi@faculty.chibau.jp

2015/2/12

The 22nd CEReS Int. Sym. in IJJSS, UGM, Yogyakarta, Indonesia,
29 October 2014

CEReS archived satellites related
datasets and these applications

A. Higuchi*1#, H. Takenaka*2, H. Hirose*1, M.K. Yamamoto*3,
S. Kotsuki*4, H. Irie*1, K. Tanaka*3,*1, M. Hayasaki*5# higu@faculty.chiba-u.jp*1:CEReS, Chiba University, Japan*2: AORI, the University of Tokyo, Japan*3: Kyoto University, Japan*4: AICS, RIKEN, Japan*5: University of Tsukuba, Japan

VL formation (http://www.cr.chiba‐u.jp/~4vl/)
AORI, UTokyo
MIROC, NICAM, Climate ..
CO2, Radiation, Ocean …

WC, CReSS, Radar ..

CAOS,
Tohoku U

HyARC,
Nagoya U

Sat, Vegetation, Skynet ..

CEReS,
Chiba U
Since 2007 ‐ current

1

2015/2/12

Meteosat‐IDOC(Meteosat‐7)

HIMAWARI(MTSAT2)

GOES‐WEST GOES‐EAST(GOES‐13) (GOES‐13)

FY2‐D

Meteosat(MSG2, now 3)

More than a ten year (at least) six GEO data archiving
Grided data product generation & sensor calibration algorithm
Open data (JMA GEOs, GOES‐W, ‐E, FY‐C, ‐D) via anonymous ftp
servers, MTSAT2, FY, GOES‐W, ‐E are quasi‐realtime processing &data released.
ALL channels VIS (0.63 μm), IR1&2 (10.8, 12.0 μm) WV (6.75 μm), released

GEO name

Gridedresolution

Zonal Range

Gridedversion

Ver 2(improvever.)
44.6°–164.6°(-C, - Ver 2
VIS: 0.01°
FY2-C, -D, (-E)E)
(improve
IR: 0.04°
26°- 146°(-D) ver.)Ver. 2
METEOSAT(improve
0.04°-2.5°–117.5°
-IDOC
ver.)
MTSAT-1R -2

VIS: 0.01°
IR: 0.04°

80°–200°(-1R)
85°- 205° (-2)

StatusAll past records ○Quasi-realtime ○All past records △Quasi-realtime ○Internal data use
only(EUMETSAT policy)
Internal data use
Ver. 2
METEOSAT,(improve
only (EUMETSAT
0.04°-60°–60°
MSG
ver.)policy)
Ver.2
Since
1986
Grided format (level1b) common specifications All past
225°–345°
GOES-W, -E(improve
0.04°
records FD: ○, NH,
165°–285°
ver.)
SH: ○; Q-realtime ○60°S–60°N，basically hourly (depend on GEO operation schedules)
1 channel 1 simple binary file (DN), vis.tar.bz2, irs.tar.bz2
Calibration tables released by each agencies also included.
For 2 byte raw data (more than 8 bits), big endian byte order stored.
anonymous ftp servers (access limit for Meteosat dataset)2

2015/2/12

Current GEOs Archive & Process Status*1: 3hourly data only
Asia 1(JMA
GEO)

GMS1*1
198103‐
198406

GMS2*1
198112‐198409GMS3*1198409‐198912

GMS4
198912‐
199506GMS5
199506‐200305GOES9200305‐200507

Asia 2(CMA
GEO)(Meteo‐IDOC)

MFG4
198912‐199402MFG5199402‐
199707MFG6199610‐200212

America(GOES‐W)

MFG7
199806‐200607

GOES‐08
199409‐200303(GOES‐E)

Pacific

200506‐201007MTSAT2
201006‐Now

FY‐2C
200605‐
200809FY‐2D
200809‐Now

Meteosat(MFG)5
199804‐200702

Asia 3

EU‐Africa(EUMET)

MTSAT1R

GOES‐07 GOES‐09‐199509 199507‐
199807MSG1
200401‐
200612MSG2200609‐Now

GOES‐12200304‐201004

GOES‐10199807‐200606

MFG7
200607‐Now
MSG3
201212‐Now
GOES‐13
201004‐Now

GOES‐11 GOES‐15
200606‐ 201112‐
201112Now

http://www.cr.chiba‐u.jp/~4vl/
Quasi‐realtime images

FY2‐D

MTSAT‐2 GOES‐W

GOES‐E

VIS

IR1

IR2

IR3
(WV)

Since 2009 Sep., MTSAT, IR4
FY2, and GOES can get byquasi-realtime by collaboration
With Weathernews Inc.

3

2015/2/12

Development of vicarious calibration technique
We have a joint research with the Meteorological Satellite Center/Japan Meteorological
Agency (MSC/JMA) to develop vicarious calibration and global synthesis techniques forgeostationary satellites. Our activities are development of visible channel calibration
technique and development of global data set of radiation budget products.
Vicarious calibration of
Validation of Aerosol and cloudThree components
リンク されたイメージを表示できません。ファイルが移動または削除されたか、名前が変更された可能性があります。 リンクに正しいファイル名と場所が指定されていることを確認してください。

Calibrated

Calibrated

Pre-launchPre-launch

Cloud optical
Aerosol optical
thicknessthickness
Comparison of calibration coefficient between ISCCP and New method
New method

ISCCP

ISCCP

New method

EXAM system by Dr. Takenaka, AORI, UT•Neural Network (NN) retrieve “real-time” radiation product
http://atmos.cr.chiba-u.ac.jp/takenaka/en/ Takenaka et al. (2011): JGR

4

2015/2/12

Global composite prototype product（DW SW，sfc，2002/Sep/02‐03, 3 hourly)

2002/Sep/02‐03 Day‐time Avagage

Meteosat7

Meteosat5

GMS5

GOES-W

GOES-E© by Dr. Takenaka, AORI, UT

SKYNET• An observation network dedicated foraerosol-cloud-radiation interaction researches(e.g., Takamura et al., 2004; Nakajima et al., 2007).
• Initiated under the WCRP/GAME project andexpanded focusing on East Asia as GLI
validation activity.
• A growing network linking more than 60 sites(as of 2013) all over the world.
http://atmos.cr.chiba-u.ac.jp/

5

2015/2/12

Validation with sky‐camera

Precipitation Potential Map(PPM) revealed by GEOs
MSG2

MET7

MTSAT‐1R

GOES‐11

GOES‐122007 July’s Possibility of Rain Occurrence (%)• Estimates high‐time res. (hourly) PPM by geostationary
meteorological satellites (GEOs)– Merged by MTSAT, Meteosat‐IDOC, MSG, GOES‐W, GOES‐E• Objective: Improve rainfall estimation quality of GSMaP without
Microwave signatures6

2015/2/12

PPM estimation algorithm• Truth: TRMM/PR observations as a flying rain gauge

IR1‐WV Tbb Diff. （K）

Look Up Table (LUT) for PPM
PPM by TRMM PR (％)• To generate Look Up
Table (LUT): Same target
observation by
TRMM/PR and GEOS
• Use parameters:
IR1 Tbb & IR1 – WV
• Why use IR1‐WV:
Low diff in IR1‐WV→Provability of
precipitation (POD) ishigh(cf. Ohsawa et al., 2001)

GEO IR1 Tbb（K）

Test: Using Radar-AMEDAS for
validation in Japan
Threat Score• Refine precipitation area by PPM in
GSMaP: validate by Radar‐AMEDAS[Even though with microwave
signature]‐ Improve over‐estimation on
ocean[Without microwave observation]
 Both ocean & land can improvethreat score (0.3  0.38)
Red：Before re‐define
Green：After re‐define7

2015/2/12

Water & Light: Primary force for hydrological model
2014/2/28

Water：GSMaP Product (10km)

We can drivehydrological model by
Light ：EXAM Product (5km)satellite revealed forcing
data as input!
http://sharaku.eorc.jaxa.jp/GSMaP/

Ushio et al. (2009, JMSJ)

GPM/DPR & GPM/GMI
Hou et al. (2014, BAMS)

気象庁

2002/Sep/02‐03 Daytime Av.

2014/10/07
HIMAWARI 8 launched!

Takenaka et al. (2011, JGR)

Numerical Experiment Design

Resolution

Ex. 1

Ex. 2

Ex. 3

Ex. 4

01 km

01 km

01 km

10 km

Model

SiBUC‐SIMRIW

Rainfall

Radar reanalysis (Radar‐AMEDAS)

Shortwave
Radiation

Inferring by
GPV/MSM

Longwave
Radiation(cf. Kondo andMiura 1985)

Temp, Hum,
Pres., WS

GPV/MSM

JRA55(MSM_Lsurf)

JRA55(anal_surf125)

EXAM(Takenaka et al. 2011)
JRA55(MSM_Lsurf)
JRA55(anal_surf125)

8

2015/2/12

Effect of EXAM（Shortwave Radiation forcing）

SMF: Seto‐‐‐‐‐: Exp.1 GPV/MSM, ‐‐‐‐‐: Exp.2 JRA55, ――: Exp.3 JRA55+EXAM

Effect of EXAM (with in-situ JapanFlux observations)Represented bySites / Function

FHK

SMF

TKC

TKY

Experiment 1
SWn

LWn

Experiment 2

SHF

LHF

0.413

0.790

39.4

37.9

0.483

0.233

0.528

36.6

42.3

R

SWn

−
0.720

LWn

Experiment 3

SHF

LHF

0.478

0.779

32.3

27.1

0.837

0.434

0.653

31.7

37.2

SWn

−

RMSE
R

correlation coefficient [R] &Root Mean Squire Error [RMSE]

RMSE

52.9

28.6

R

0.740

0.386

RMSE

58.0

35.0

R

0.676

0.396

0.486

RMSE

65.0

40.6

30.5

0.899

LWn

SHF

LHF

0.689

0.767

−

32.9

21.6

0.833

0.724

46.2

23.9

0.762

0.856

0.726

0.532

36.9

46.5

30.6

27.2

−

0.953

28.0

24.5

0.879

0.566

0.699

29.4

34.7

22.8

19.5

0.922

0.783

33.9

21.0

0.828

0.945

0.781

0.663

0.893

25.8

28.4

28.4

23.2

22.7

−

−

SWn: Net‐SW Radiation, LWn: Net‐LW Radiation, SHF: Sensible Heat Flux, LHF: Latent Heat Flux

Best

Worst

‐‐‐‐‐: Exp.1 GPV/MSM, ‐‐‐‐‐: Exp.2 JRA55, ――: Exp.3 JRA55+EXAM

9

2015/2/12

Fine Res. Land Surface Reanalysis
SiBUC‐SIMRIW + RIVER (Kotsuki et al., 2013)

Agriculture・Surface T.
・Crop Growth・Rice Yield
・Soil Moisture・Irrigation
Requestetc…

Atmosphere

Hydrology

・SW/LW Rad
・SHF/LHF
・Surface Albedo
etc…

・Runoff・WaterResources
・River runoff
・Snow water・Water storage
etc…

Latent heat flux

Rice Yield

Water
Resources

HIMAWARI‐8 successfully launched in
07 October 2014
Approx.
100TB /yr!

CEReS will contribute as archive center10

2015/2/12

Summary• CEReS has GEO datasets (MTSAT, GOES‐E, ‐W,
Meteosat‐IDOC, MSG) more than a ten year:– Except Meteosat, MSG, all of data can access via
anonymous ftp servers (mtsat, fy, goes; free of charge)• Short‐wave radiation product (EXAM) processes
in quasi‐real‐time & start to produce re‐analysis
product– Available at ftp://amaterass.cr.chiba‐u.ac.jp/• Skynet, radiation observation network in Asia
now renewal the portal (coming soon).
• Several application projects & products are on
the way (PPM, Food Security package program),these will be release

ftp servers& website• VL‐CEReS: http://www.cr.chiba‐u.jp/~4vl/– CEReS DB : http://www.cr.chiba‐u.jp/ ~database‐jp/– MTSAT: ftp://mtsat.cr.chiba‐u.ac.jp/– GMS5&GOES9: ftp://geoinfo.cr.chiba‐u.jp/pub/satellites/geostationary/– FY2‐C, ‐D: ftp://fy.cr.chiba‐u.ac.jp/– GOES‐E, ‐W: ftp://goes.cr.chiba‐u.ac.jp/ (now renewal)– Meteosat: internal only (but can access via collaborative
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'+.!2-,*,""#%0"2'-, -2& ',1'2# ,"-$$1'"#

ApplicationofHyperspectralCamera
forAerosolCharacterization
N.Manago,H.Saito,Y.Takara,
M.Suzuki,H.Kuze

The22ndCEReSInternationalSymposium@UniversityofGadjah Mada

 HyperspectralCameraisakind
TargetIncidentLightof“colorcamera”,whichcan
distinguishnotonly3colorsbuthundredsofcolors.
Lens&
DiffractionGrating
 AnoblongobjectisspreadintheXdirectiononaCMOS
sensor.
 Differentwavelengthsare
spreadintheotherdirection.
CMOS Sensor
 Verticalscanisneededtoseea
prolongobject.
XDirection

Wavelength

PrincipleofHyperspectralCamera

SkylightObservation
 Skylightisscattered/absorbedbymolecules
andaerosols.
 FunctionsofScatteringAngle/Wavelength dependonAerosolsize,absorbanceHyperspectralImageoftheSky

O =550nm16

 X/Ypixelnumberэ SeparationAngle
 Wpixelnumberэ Wavelength Signalcountэ Radiance350Ң 1100nmHyperspectralCameraforOurStudy Manufacturer:± EBAJAPANCo.,Ltd.

 Optics± TransmissionDiffractionGrating± Lesssensitivetopolarization

 Portable± Builtinscanningsystem± NodeviceotherthanaPCis
needed Customizable

MajorSpecificationsofNH2HScamera
SensorType

CMOS

Max.ImageSize752㽢 480

Field ofView

16| 㽢 10|

Sampling/Resol.

0.02| /0.07|

WavelengthSize

480

WavelengthRange

350 – 1100nm

Sampling/Resol.

1.6nm /6nm

ColorDepth

10bitBodySize

76㽢62㽢193mm3Weight850g± NH2wascustomizedforChibaU.

HowtoRetrieveAerosolCharacteristics
1. ObtainSpectralRadianceDistributionofSkylight(SRDS)¾ Calibration¾ Wavelength,Radiance,ViewingAngle,etc.

2. CalculateSRDSusingRadiativeTransferSimulation¾ MODTRAN


¾
¾
¾


AtmosphericModel
AerosolModel
ComplexRefractiveIndex
SizeDistribution
VerticalProfile
InstrumentModel

3. ModifyaerosolmodelparameterstoreproducetheobservedSRDS

AerosolModel(ComplexRefractiveIndex)

 3ComponentAerosolModel± MixingRatioof3components(watersoluble,oceanic,andsoot) 1or2ComponentAerosolModel± Real/Imaginary partsofcomplexrefractiveindex

AerosolModel(SizeDistribution)ModeRadius

Width LognormalSizeDistribution± ModeRadius/Width± Moderadiusandwidthcannotbeoptimizedindependently
э WidthparameterisfixedAerosolModel(VerticalProfile) MODTRAN’sbuiltinmodelsareused.
± Absolutevalueofextinctioncoefficientadjustedsothatintegratedvaluebecomestheoptical
depth.

RadiativeTransferSimulation
 MODTRAN(ver.4)¾ ExtinctionCoeff.
¾ AbsorptionCoeff.
¾ PhaseFunction¾ VerticalProfile ExtinctionCoeff.,
AbsorptionCoff.,and
Phasefunctioncanbe
calculatedfromtheaerosolmodelwiththeMietheory.

SunlightSkylight

Calibration(Wavelength/Radiance) Wavelengthcalibration

 Radiancecalibration

 14emissionliness areusedtoobtain5th orderpolynomial
 Fittingerror:0.29nm

 Peaksensitivity@550nm Sensitivityrange350– 1100
nm± DischargeLampHg,Ar,Cd,Kr)andNd:YAG Laser± Skylight
± IntegratingSphere

Calibration(TemperatureDependence)
40

890

884

Temperature
Wavelength30

1H
Temperature
Signal2H0

 Wavelength Radiance± XenonLamp± HalogenLamp

 Wavelengthshift: Correctionfactor:¾ 1nm– 1nm(<30&)¾ 2nm(<40&) Fittingerror:0.1nm± 5%(500nm– 900nm)± 30%(otherwavelengths)

 Fittingerror:
¾ 0.2%(<40&)

10ObservationatChibaUniversity

TestObservation(May,24,2013)

O =550nmTestObservationResult Skylightismeasuredatthezenithonly.
 Aureoleisusedfortheangulardependenceat550nmonly.
 Thetwocomponentaerosolmodelcanreproducetheangulardependence
ofaureolewell,whiletheonecomponentaerosolmodelcannotreproducethesteepslopenearthesun.

 Thankyouverymuchforyourattention!

Calibration(SensorUniformity)
555nm621nm WavelengthUniformity:± FluorescentLamp+diffuser

 Nonuniformity(RMS):
0.4nm RadianceUniformity:± Twilightflat Sensornonuniformity:1.4%
 Opticsnonuniformity:1.2%(includingthelightsource)

Calibration(ViewingAngle)

 Relationshipbetweenpixelnumberandangular
coordinatesareestimatedfromaimageofcross
sectionpaper.
 Xdirection:0.022deg/pixelpixelsize Ydirection:0.020deg/pixelstepsizeofscanner Imagedistortionissmall

Calibration(SpatialResolution)

 PointSpreadFunction± Xdirection:0.057degree± Ydirection:0.053degree± Radialdirection:0.07degree

Calibration(PolarizationDependence) Polarizationdependenceislessthan2%

The Calculation of Natural Ventilation Ratein a Tropical Single-span Greenhousewith Fog Cooling System
Presented by:
Muhammad Hasan, Handarto, Sudaryanto
Department of Agricultural Engineering
Faculty of Agricultural Industrial Technology
Padjadjaran University, Indonesia

Outlinea. Introduction
b. Objectives
c. Materials and Methods
d. Resultse. Conclusion

a. Introduction
1) Naturally ventilated greenhouses are influenced
by wind characteristics: wind speed and winddirection;2) Ventilation rate will be an important factor for
effectiveness of fog cooling system use at thosegreenhouses;
3) The optimum ventilation should be considered tocreate the optimum ranges of air temperatureand relative humidity in naturally ventilated
greenhouses equipped with fog cooling system.

4) Role of evaporative cooling system (Pad-and Fan
System; Misting System; and Fog Cooling System)in creating the optimum air temperature andrelative humidity ranges for plant production
inside the greenhouse.
e.g. for tomato Æ T=21-28 oC; RH=80%

b. Objectives
1) to calculate ventilation rate (energy balanceand vapor mass balance-based) on naturally
ventilated greenhouse;2) to analyze changes of air temperature andrelative humidity inside the tropical singlespan greenhouse equipped by fog coolingsystem.

c. Materials and Method
1) Experimental Greenhousei.

Naturally ventilated single-span greenhouse.
Roof Type: Monitor
length = 8 m; width = 8 m; height of ridge = 6 m.

ii. The side-ventilator opening was set at three levels:

1) 2-level close

2) 1-level close

3) fully open

iii. The changes of air temperature and relative humidity
were observed under three levels of solar radiation
ranges:1) <400 W.m-22) 400-800 W.m-2
3) >800 W.m-2

2) Fog Cooling System
Ppump = 1.0 MPa

NozzleQpump = 0.8 kg.min-1
hnozzle = 3.0 m from the floor

Operation Pattern:• Intermittently• 1 min ON ; 4 min OFFRP3) Measurement Instruments
Parameters

Instrument

Outside:air temperature; relative
humidity, solar radiation;wind speed; wind direction

HOBO U-30
Mini Weather Station

Inside:Air temperature; relative
humidity

Environtmeter Lutron
EM-9000

4) Calculation of Ventilation Rate

(Si – (Tdi – Tdo).N.w
V=(Ii – Io)

(Mihara, 1983)

where:
V = ventilation rate per unit floor area on a mass basis [kg m-2 s-1]
Si = net radiation inside the greenhouse [W m-2]
Tdi = dry bulb temperature inside the greenhouse [ 0C]
Tdo = dry bulb temperature outside the greenhouse [ 0C]
N = thermal transmission rate of glazing per degree difference intemperature [W m-2 0C-1]
w = area ratio of glazing surface to floor surface [decimal ratio, >1]
Ii = enthalpy inside the greenhouse [J kg -1 DA]
Io = enthalpy outside the greenhouse [J kg-1 DA]

d. Results

2-level close

Side Ventilator Opening Level
1-level close
fully open

Fig 1. Changes of Air Temperature, Relative Humidity, and Ventilation Rateunder Solar Radiation <400 W.m-2 Condition2-level close

Side Ventilator Opening Level
1-level close
fully open

Fig 2. Changes of Air Temperature, Relative Humidity, and Ventilation Rateunder Solar Radiation 400-800 W.m-2 Condition

Side Ventilator Opening Level
2-level close

1-level close

fully open

Fig 3. Changes of Air Temperature, Relative Humidity, and Ventilation Rateunder Solar Radiation >800 W.m-2 Condition

ON
OFF

Air Temperature [0C]

Solar radiation [W.m-2]

OFF

InsideInsideInsideOutsideOutsideOutsideRelative Humidity [%]Ventilation Rate [kg.m-2.s-1]

ON

OFF

ON

OutsideOutsideOutsideInsideInsideInside

ON

OFF

ON

OFF

Time

Time

ON

OFF

Time

Fig 4. Changes of Air Temperature, Relative Humidity, and Ventilation Rate under
Various Solar Radiation Condition when Fog Cooling System is Operated

ON

ON

OFF

ON

OFF

Wind Speed [m.s-1]

Wind Direction

Ventilation Rate
[kg.m-2.s-1]

OFF

Time

Time

Time

Fig 5. Changes of Wind Speed, Wind Direction and Ventilation Rate under
Various Solar Radiation Condition

Tab 1. Resume of Air Temperature and Relative Humidity Profiles under Various
Solar Radiation Condition

Side-Ventilator
Opening Level

2-level Close

1-level Close

Fully Opene. Conclusion
1) When fog cooling system is operated or not, at all
the side-ventilator opening levels, energy balancebased ventilation rate increased with increase ofsolar radiation;2) At all solar radiation ranges, vapor mass balancebased ventilation rate decreased with increase ofside-ventilator opening level.

Measurementofskyradianceusinga
CMOScamerafortheretrievalofaerosol
opticalproperties
HiroakiIwanadea*,Hayato Saitoa,Ilham Alimuddina,b,
Naohiro Managoa,HiroakiKuzea
aCenter forEnvironmentalRemoteSensing(CEReS),

ChibaUniversity, Chiba2638522,Japan
bFaculty ofGeography,Hasanuddin University,

Makassar90245,Indonesia

Organizationofthispresentation
1.Introduction
2. Instrumentation
CMOScamera
Spectroradiometer
Sunphotometer

3. Resultsanddiscussion
ConversionfromDNtoradiance
Eliminationofblueshift
Flatfieldanddistortioncorrections

4.Summaryandfuturework
Downwardshortwaveflux(SKYNET)2

1.Introduction
䚔 Aerosolimpactstheenergybalanceoftheatmosphericsystem
throughthereflectionandabsorptionofincidentsolarradiation.䚔 Forthemeasurementofopticalpropertiesofaerosolparticles,
groundbasedinstrumentssuchasasunphotometeroraskyradiometerareemployed.
䚔 Aerosolparameterssuchastheaerosolopticalthickness(AOT),size
distribution,andcomplexrefractiveindexcanberetrievedfromthe
measurementsoftheskyradianceanddirectsolarirradiance.
䚔Asunphotometer isaninstrumentthatmeasurestheintensityofsolarirradiancetoobtainAOTand/orcloudopticalthickness,whileaskyradiometerisusedtoretrieveaerosolopticalparametersby
measuringtheratiobetweenthescatteredanddirectintensities.
3(cont’d)䚔 Theproblemsoftheseinstrumentsare
thattheyareexpensiveandbulky,mainly
becauseofthesuntracker.
䚔 Thepresentpaperdescribesanattemptinwhichanimagingspectrometeris
developedonthebasisofacommercially
availableCMOScamera,withopticalfilters
andarotatingstage.䚔 Wehopethatthisapproachmakesit
possibletoretrieveaerosoloptical
characteristicswithouttheneedfor
precisetrackingofthesolarposition,
whichcanreadilybedeterminedinan
image.
4

2.Instrument
CMOScamera(IDS,UI3480CPM)
䚔black&white䚔2560(H) 1920(V)
䚔Radiancedepth12bits

䚔Thecameraandfilter
wheelareinstalledona
rotationstage,whichenablesthe360degree
observationaroundtheverticalaxis.䚔Transmissionofnarrowbandpassfilters(400,500,670,and870nm)

5Spectroradiometer(EKO, MS720)usedfor
thecalibrationoftheCMOScamera
SpectralIrradiance(mW/m2/sr/nm)

6.00E+00

SpectralMS720 ᐃ್
Irradianceofskylight
SpectralIrradiance

5.00E+00
4.00E+00
3.00E+00
2.00E+00
1.00E+00
0.00E+00
0Specification
Wavelength

350 - 1050 nm

Sampling Interval

3.3 nm

Optical Resolution

10 nm

Total Field of View

20㼻(with adopter)

200

400
600
Wavelength(nm)

800

1000

䚔Theinstrumentwascalibrated
usingtheLangleyplotmethodatthe
topofMaunaKeamountainin
Hawaii.䚔Also,anintegratingsphere(JAXA)
hasbeenusedtocheckthestability
ofcalibration.
6

ConversionofDigitalNumber(DN)toRadiance
CMOSCamera

N

NormalizedDN

MS720Spectral
Irradiance
Conversion
EquationN1 § F ·¨¨ ¸¸t © F0 ¹2³ I Ȣ T (Ȣ) d O³ T (Ȣ) d O

I

I

kN

N䠖 Normalized DN
N1: DN (0~255)
t : Exposure Time
F: F-number
F0䠖 Maximum F Number
I(): Spectral Radiance
T()䠖Filter Transmission
k: Conversion factor foreach filter

䚔 SimultaneousobservationofcloudlessskyismadewiththeCMOS
cameraandMS720spectroradiometer.
䚔Inthisway,wecandeterminetheconversionfactork,which
convertstheDNvaluesfromthecameratospectralradianceforthewavelengthrangeofeachfilter.
7

Calibrationprocedure

07:19onSeptember20,2013

Measurementofzenithsky
䚔 TheDNvaluesareaveragedforthe

pixelsinsidetheviewingangleofless
than20degrees.Theresultingvalue
canbedirectlycomparedwiththe
spectralradiancederivedfromtheMS720instrument.
8

3.Resultsanddiscussion
Temporalvariationandcorrelationplotforeachwavelength
400nmk400=0.255㼼0.008

500 nmk500=0.082㼼0.004670nmk670=0.099㼼0.006

9Comparisonwithsunphotometer measurementofdirectsolarradiation

䚔 TherightpanelshowstheimageoftheSun,takenwitha
combinationofabandpassandaneutraldensity(ND)filter.䚔 TheagreementbetweentheCMOScameraandthesunphotometer wasquitereasonable,asshownintheleftpanel.
10Removaloftheblueshiftofnarrowbandpassfilters
䚔 Thefiltertransmissionshifts
towardtheshorterwavelength
(blueshift)whentheincidentlight
istilted.䚔 Inordertoeliminatethiseffect,atelecentricopticalsystemhas
beenconstructedusingapinholeandnonsphericallensessothattheverticalincidenceisensured.

Shiftoftransmissionwavelengthfor
tiltedincidence(500nm)

Blueshiftproblemhasbeeneliminatedby
introducingtelecentricopticalsystem.

11

Radiancemeasurementoverthewholehemisphere askyradiometerbasedonaCMOScameraimager
RGBcomposition
Angular
coverageof26
deg1280pixels
0.02deg/pixel

Angularcoverageof20deg,
960pixels

䚔 Theinstrumentwasputonarotatingtable.Theelevation
directionwascoveredwith3differentcameradirections(25degstepinterval).Thescanningtimedurationwasabout20min.
12

Flatfieldandimagedistortioncorrections
Flatfieldcorrectionbasedonahomogeneousillumination.

Imagedistortioncorrectionusingatestchart.

13

Hemisphericradiancedistribution(September29,2014)
Compositeimagebeforesmoothing
400nm

500nm

670nm

400nm Beforeflatfieldcorrection

670nm saturatedpixels

670nm Compositionof6images
Flower
like
shape
14

Hemisphericradiancedistribution(September29,2014)
Resampling overagridof0.1deg

䚔 Thecompositeimageforeachofthethreebands(400,500and670nm)hasbeenresampled
overagridof0.1deg.
䚔Inthisway, theoverlappingregionsofneighboringimagescanbesmoothlyaveraged
 thetotalimagesizecanbereducedsubstantially,
and
 thepixelinformation
canbereadilyusedfor
radiativeanalysis.
15

Hemisphericradiancedistribution(September29,2014)
 Compositeimageaftersmoothing䚔Thecombinationofmeasurementswithand
withouttheNDfilter
willbeusefulfortheradianceobservation
includingtheaureole(justaroundtheSun).
䚔Heretheaureole
imagetakenwitharod
likeshadowobjectissuperposedoneachofthecameraimages.
䚔 Thebuildingimages
areoverlaidwithoutsmoothing.
16

Radiativetransfer
calculation
usingtheskyrad.pack

https://directory.eoportal.org/web/eoportal/satellitemissions/e/earthcare

䚔 Therearevariousprocessesintheatmospherewhichdeterminetheradiation
environmentontheEarthsurface.
䚔 Theopticalpropertiesofaerosolparticlearecloselyrelatedwiththeradiation
budget,themostimportantfactorinthediscussionofclimatechange.
䚔 TheCMOScameraapproachcanprovideaninexpensivewayforthe
implementationoftheradianceanalysisinmanyplacesoftheworld.
17

4.Summaryandfuture work
䚔Thepresentobservationshaveindicatedthata
commerciallyavailableCMOScameracanbeappliedtotheimageobservationofdirectandscatteredsolarradiation.
(1)Quantitativecomparisonhasbeenmadewithbotha
compactspectroradiometerandasunphotometer.
(2)Flatfieldcorrectionanddistortioncorrectionhavesuccessfullybeenapplied.
(3)Theblueshiftproblemassociatedwiththeoblique
incidenceonnarrowbandpassfiltershasalsobeen
eliminatedowingtothetelecentricopticalconfiguration.

䚔 Furthercheckonthetemperaturedependenceofthecalibrationconstantmaybenecessary.Afterthat,theimages
canbesubjectedtothecomparisonwiththe radiativetransfer
calculationsbasedonSkyrad.pack.

18

Acknowledgement
Theauthorsgratefullyacknowledgethefinancial
supportfromtheJSTCRESTprogram“Creationand
integrationoftheoreticalaswellastechnicalbasisfor
thedevelopmentofdispersiveyetcooperativeenergymanagementsystem”.

http://helios.aori.utokyo.ac.jp/teedda/fs/en/about.html
19

The 22nd CEReS International Symposium

Coastline changes monitoring using satellite
images of Makassar Coastal Areas

Ilham Alimuddin 1,2, R. Langkoke2,B. Rochmanto2,J.S.T Sumantyo1,Hiroaki Kuze1
1Centre for Environmental Remote Sensing (CEReS), Chiba University, Japan
2Department of Geology, Faculty of Engineering, Hasanuddin University, Indonesia

Yogyakarta, 29-30 October 2014

FloodsOutlines

www.themegallery.com

1

Background2

Research Objectives

3Site, Data and Methodology

4

Data Processing & Analysis

5

Result and Discussion

6

Conclusion and Further Works

Company Logo

Chaussard et al. 2013,
Remote Sensing of Environment, 128

Background¾ Urban centres have historically been connected tocoastal or riverside areas people tend to dwell where
they find easy access to living supportive resources.
¾ Many big cities in the world along with their big ports
have developed and grown with the increase of their
population and settlement.
¾ The city will expand and change along with its coastal
areas which are usually recognized as a line.
¾ For the purpose of creating a better condition in the
future, monitoring and recording the spatial changes
along this coastal line is important for understanding
what is existing now and also what has been existing in
the past.

Background
According to Pardo-Pascual et.al (2012), coastline canchange for two distinct reasons;
1) short-term variations of sea level that depend on
astronomical and meteorological factors and2) 2) alterations in the shape and volume of
sediments along the coastline.
These latter changes in morphology are much lesspredictable because they are a response of the shore
system to the ocean wave conditions. Suchmorphology changes can also be caused by theaccumulation or erosion for a longer term.

Research Objectives
1. To study the changes over time and examine anychanges in geomorphological aspects by extracting
thematic information from visible optical satellite
images (Landsat and Ikonos) and microwave.
2. To show the capability of DInSAR processing of
showing surface displacement on the case Makassar
City Subsidence
3. To validate the changes using satellite data andground checking
7

Study Site
INDONESIA

Makassar- 406yrs , Jakarta 486- 10th population- Biggest, east Ind- 175,77 km² area

Geology and Topography
Geology Map

ASTER, GDEM

Population Statistics
Year

1971

1980

1990

2000

2008

2009

2010

Population 434.766

708.46570
7
0944.372

1.130.384
0.3
0.

1.253.656
25
2
5

1.272.349
1

1.338.663

1990

1993

2000

2002

1996

1999

2004

2007

10

Data Used
Optical Image Data :1. Aster, September 7, 2006
2. Ikonos series 2000-2013
3. SPOT 4, 5 Dec 2003
4. Landsat images ranges from 1990-2013
SAR Data
JERS -1 SAR (L Band, 23.6 cm wavelength) data acquired on
19930318,19940111,19950928,199609141,19970422, 19970901
and two scenes from 1998 data, 19980111 and 19980819,
All JERS-1 data were taken on the descending modes
GPS Measurement campaign, Makassar September 2009, andupdated in 2012, 2014

Data Type and Acquisition Date
Type of data Date
Landsat 5TM

19901216
19940829
19940202
19950606
19951212
20030707
20030721
20030808
20040707
20040925
20060919
20071004
20080819
20080830
20090502
20090721
20090801
20091025
20100214
20100724

Type of data

DateType of data

Date

Landsat 7
ETM

19990920
20000720
20001109
20010520
20010707
20020726
20020912
20020928
20030526
20040901
20070521
20100310
20100411
20110703

IKONOS (PanSharpened
Multispectral)

20001014
20010812
20040701
20050827
20060718
20070424
20090430
20100713
20121124
20131019
20140810

Landsat 8
OLI

20130202
20140601
20140716

SPOT,
5 Dec,
2003

Landsat & SAR Intensity
A. Landsat TM_FCC_432 /19940829 B. Landsat ETM_FCC_432 / 19990920

JERS-1 19941011. JERS-1 19980819

14

JERS-1 SAR Data acquisition and the pairs
Pairr (RSPP 78
78/309/ 09)
19930318/19941011

Week
k Differ
82

Basee (m)

Bp
p (m)

Bh
h (m)

1250.271044.60

687.01

19941011/19950928

504648.04

3321.52-3251.43

19950928/19960914

50

1052.95

871.98590.2019960914/19970422

26

1646.18

1277.33

1038.4219970422/19970901

371708.451126.50-1284.45

19970901/19980111

193376.06

2357.112416.99

19980111/19980819

30

1338.97

1178.81-635.01

Intensity SAR Images

DInSAR Images
93/94

95/96

94/95

-5.9 cm

96/97

97/98

0

5.9 cm98/98

Antenna Direction
Satellite
Direction

17

Image Analysis
Coherence image of 1995/1996

DInSAR of 1995/1996 images

The deformation image after theunwrapping process

Antenna Direction
Satellite
Direction

Deep

Shallow

Pair (RSP 78/309)

Week Differ

Base (m)Bp (m)Bh (m)

19930318/19941011

82

1250.271044.60

687.01

19941011/19950928

504648.04

3321.52-3251.43

19950928/19960914

50

1052.95

871.98590.2019960914/19970422

26

1646.18

1277.33

1038.4219970422/19970901

371708.451126.50-1284.45

19970901/19980111

193376.06

2357.112416.99

19980111/19980819

30

1338.97

1178.81-635.0118

Accepted for publication in Journal of Civil Engineeringand Architecture. JCEA

Focus Area
P1

P1

P2

P4
P3

P2

P3

P4

19

Landsat Time series
A

B

CLand use Map of Makassar City.
A Based on Landsat 1999

B. Based on Landsat 2009

A

B

10132000

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05152001

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09282004

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07172006

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04012007

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05292009

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07122010

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05132012www.themegallery.com

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06032012

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10202012www.themegallery.com

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03032013

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12161990 Landsat

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Abrasion and Development

Kerusakan vegetasi akibat pengurugan lahan untuk jalan
di Pantai Tanjung Bunga. (Langkoke, 2010)

Ground Pictures, 2014

Field Survey
P1

P2

P3

P4

P5

P6

5° 8'58.56"S ,119°23'58.27"E

Light House

Subsidence Indication within 10 years (2001
P7 2010), 2.5 m or 25 cm/year

P8

GPS on SPOT image

Beach Profile

Conclusion











DInSAR method is used to estimate subsidence
phenomena which has been derived and applied in thisstudy
Synthetic aperture radar (SAR) was able to provide
excellent data in tropical region.
We have applied JERS-1 SAR although not all pairs cangive good coherence due to the baseline and atmospheric
aspects
Subsidence map shows the subsidence in every point ofobservation.
Shorelines changes 25 m in 10 years or rate of 25 cm/year
The incidences in some areas show evidence of from
5-15 cms of subsidence shown by field observation
39

Terimaa kasihk
Thank yo
you
ou

䛹䛖䜒
䜒 䛒䜚䛜䛸䛖䛖 䛤䛦䛔䜎䛧䛯Center for Environmental Remote Sensing, Chiba University
1-33 Yayoi, Inage, Chiba 263-8522 Japan
Tel. +81 (0)43 290 3832 / Fax +81 (0)43 290 3857
Email bee3832@office.chiba-u.jp

Website http://www.cr.chiba-u.jp