Trainings and capacity buildings of space

Transcription

1 Trainings and capacity buildings of space technology, GIS and SAR products development for disaster management for DAN Dr. Masahiko NAGAI, Prof. Ryosuke Shibasaki Center for Spatial Information Science, The University of Tokyo, Japan The First Steering Committee(FSC) (FSC)Meeting of the Sentinel lai Asia Step October 2015

2 Satellite Observation Disasters Data Utilization Information Provision Space Agencies Data Request Disaster Management Agencies DPN DAN End User

3 Satellite Observation Capacity Building for DAN Advanced analysis InSAR, etc. Modeling Training for Disasters instructor Capacity Building for End User How to use SAS Basic RS and GIS process Image interpretation Map making Data Utilization Information Provision Space Agencies Data Request Disaster Management Agencies DPN DAN End User

4 Strategy of Capacity Building Disaster Management Agency University Network University of Tokyo Keio University Tokyo University of Marine Science and Technology AIT University of Philippines Indonesia University Chulalongkorn University National University of Laos etc. 4

5 Strategy of Capacity Building Disaster Management Agency University Network University of Tokyo Cloud based System for Archive, Integration and Analysis Keio University Tokyo University of Marine Science and Technology AIT University of Philippines Indonesia University Chulalongkorn University Satellite data National University of Laos etc. 5

6 Geo spatial and Space Technology consortium for Innovative Social Services 6

7 University CORS Network University of The Philippines, Manila Chulalongkorn University, Bangkok University of Indonesia, Jakarta

8 Education Materials for Geospatial Analytics Geospatial analytical tools Servers for geospatial analytics and visualization International network of regional information archives Collaborative projects Urban/regional economic data Topographic/geological data Geospatial data services with open source GIS Development project data YOU Transportation network data Satellite images 8

9 e Learning System of G SPASE System configuration contents e-learning e-learning (material) (course) 9

10 Summary SAR of methodology Interferometry of InSAR processing Preprocess SAR processing Registration RAW data Sensor Parameter Processing Parameter Interferogram Generation Phase Unwrapping Geocoding Range Doppler processing Range Compression Azimuth Compression Single look complex (SLC) Multi look image 10

11 Summary SAR of methodology Interferometry of InSAR Summary of methodology of InSAR processing processing Preprocess Registration Interferogram Generation Co registration of SLC Data: co registration is the process of fitting one SAR image accurately upon another SAR image. Co registration of complex images involves two steps. 1) the location of each pixel in the slave image is changed with respect to the master image. 2) the amplitudes and phases of the sensors are recalculated by interpolation. Phase Unwrapping Geocoding GMTSAR is used for this research using cross correlation algorithm (xcorr) for registration. xcorr uses window size of 64 pixels and has never failed to provide accurate co registration even in cases where the interferometric coherence is close to zero. 11

12 Summary SAR of methodology Interferometry of InSAR processing Preprocess Concept of repeat orbit interferometry Registration = Bsin(θ α) ( ) Interferogram Generation θ α = Bcos(θ α) Phase Unwrapping ρ range from reference track to reflector B total baseline distance between reference and repeat track θ look angle α angle between the baseline vector and the tangent plane Geocoding The phase difference φ to point on the ground is related to the range difference ;Therefore 12

13 Summary SAR of methodology Interferometry of InSAR processing Preprocess Correction of earth curvature and topographic phase Registration Interferogram Generation Interpolate the topography Phase Unwrapping topography (range, azimuth) Geocoding For other information, it can be computed from SAR data b(a) radius of the spacecraft orbit for the reference image B(a) the length of the baseline α(a) the orientation of the baseline 13

14 Summary SAR of methodology Interferometry of InSAR processing Preprocess Phase due to earth curvature and topography Law of cosine Registration It can be rewritten as Interferogram Generation It can be rewritten as δρ = B sin (θ α) Phase Unwrapping Geocoding Phase to range = 14 Reference: GMTSAR (2011)

15 SAR Interferometry Preprocess Registration Interferogram Generation Phase unwrapping: The interferometric phase shows many discontinuities, which originate the classical fringe pattern. The interferometric phase is almost linearly proportional to the topographic height. The difficulty is that the interferometricphase t i isonly known to module 360 degrees. If thephaseh variation exceeds 360 degrees, it wraps around to 0 degrees. It is necessary to unwrap the phase in the interferogram by using several algorithms for converting the interferometric phase to topographic phase. Phase Unwrapping Geocoding 15

16 SAR Interferometry Preprocess The final step is to geocode all the products by transforming from the range/azimuth coordinate system of the master image to longitude and latitude. Registration Interferogram Generation Geocode Amplitude image Phase Unwrapping Geocoding Interferogram image 16

17 Satellite image provision Satellite data Map products Concepts To conduct satellite data analysis for disaster emergency situation. To conduct more advanced data analysis This is academic activity, resarch and education. This is voluntary based activity. DPN DAN Professors To train and educate students for data analysis. To apply mew technologies Professors are JPT member. Academic member University of Tokyo Students AIT RS/GIS and Non RS/GIS students work together. CRIPS/NUS Sichuan University, China RS/GIS students analyze satellite data. University Chinese University of Hong Kong Non RS/GIS students collect local information. Yamaguchi University, Japan Network for Data This activity ii is part of academic activity, ii research, Chubu University, Japan Analysis National Central University lectures, etc. + etc. GESTISS Expected effects To secure data analysis resources in case of huge Activity in University Professors JPT member disaster. To collect local disaster information. To build up Asian academic network. To build up capacity for students. Student RS/GIS Non RS/GIS Students will be very imporant member in the team students students future. 17

18 To secure data analysis resources in case of huge disaster. It is very difficult to analyze all data by only disaster management agencies during emergency operation. University Resource is very important. To producemore applicable maps. To use academic network to conduct advanced data analysis. To have good Sustainability. To develop satellite data analysis network in Asia. To collaborate with academia for capacity building. To train young researcher for the future Sentinel Asia. 18