Spatial and temporal data mining of remote sensing data

Transcription

1 Spatial and temporal data mining of remote sensing data Applied to erosion process discovery and analysis Rémi Andreoli Bluecham SAS Nazha Selmaoui-Folcher UNC Jonathan Maura Bluecham SAS Mougel Pierre-Nicolas UNC Claire Tinel CNES Delphine Fontanaz CNES Pléiades Days 2014, April 1-3, 2014, Toulouse - France

2 Spatial and temporal data mining of remote sensing data INTRODUCTION

3 Remote sensing data mining Space at the heart of your decisions The ANR-11-Cosinus FOSTER Project To discover, identify and monitor earth processes Applied to erosion In New Caledonia: The Great South Area In Europ: Super Sauze landslide(fr) Computer scientists, geologistsand engineerin decisionsupport systems PPME (University of New Caledonia New Caledonia) LIRIS (Lyon 1 University France) icube(strasbourg University France) LISTIC (Annecy-Chambéry University) Bluecham SAS (New Caledonia)

4 FOSTERS AIMS FOSTER s aims: Exploit remotesensingdata for erosionprocessesidentification and discovery Through 2 types of Spatio-temporal data mining Pixel based Object based Integrated in a collaborative process Heterogeneous data mining Enhance models results Results Integration to decision-making systems(qëhnelö Platform)

5 Spatial and temporal data mining of remote sensing data DATABASE OVER THE GREAT SOUTH OF NEW CALEDONIA

6 FromHR to VHR remotesensingdata 35 Remotesensingdata over the Great South of New Caledonia 23 Landsat7 ETM+ data 15 m to 30 m resolution 1999 to SPOT 4 & 5 data CNES ISIS project 20 m to 2.5 m resolution 1999 to GeoEye-1 coverage GeoEye Int Nov and June cm resolution

7 ORFEO Program : N 46 FOSTER / Erosion 2 main areas : -Yaté Coastalarea (560 km²) - Populated places - Mining areas - Worldclass industrial site -MerletReef(80 km²) - Natural Reserve - UNESCO World Heritage

8 651 km² < 1% cloud cover Space at the heart of your decisions Pléiades data over the main land Pleiadesdata of the 13th of July 2012 Tri-stereoscopic Pan+XS data over the main land Pleiades data of the 28th of September 2013 Pan+XS data over the Main land Pleiades data of the 14th of December 2013 Pan+XS data over the Main land Pleiades data of the 28th of December 2013 Pan+XS data over the Main land Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit 29 mars 2014 Bluecham Pléiades data acquired the 28/09/2013 CNES 2013, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit Pléiades data acquired the 14/12/2013 CNES 2013, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit Pléiades data acquired the 28/12/2013 CNES 2013, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit

9 Erosion landforms as seen by Pléiades VHR data Bare soils Lavaka Gullies Sediments plumes Sediments fan Sediments deposits Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit 29 mars 2014 Bluecham

10 Impact of minig activities as seen by Pléiades VHR data Bare soils Increasing of linear erosion (gullies) Mud flow and landslides Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit VALE New Caledonia Mining site 29 mars 2014 Bluecham

11 Spatial and temporal data mining of remote sensing data METHODS AND FIRST RESULTS

12 From data to models Data preprocessing Orthorectification Radiometric calibration Radiometric indexes as inputs for data mining processes NDVI Redness Brightness Blue, Green, Red and NIR bands individually

13 Multi-strip DSM generation Compared with Arnaud Durand, Rémi Andreoli, Claire Tinel, Hervé Yésou, 2013 ; Multi-strip DSM generation with Pleiades-HR data over a coastal and mountainous mining landscape, 33 rd EARSeL Symposium, 3-6 June 2013, Matera, italy Derived from Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit

14 Spatio-temporal patterns Region based approaches Weighted path in directed attributed graphs Condensed attributed tree Cohesive Co-Evolution Patterns Pixel based approach SFG Patterns historical soils degadation Appliedon time series (more than2 images) Julea A., Méger N., Rigotti C., Trouvé E., Jolivet R., Bolon P., Efficient Spatiotemporal Mining of Satellite Image Time Series for Agricultural Monitoring. In journal: Transactions on Machine Learning and Data Mining, Volume 5, Number 1, pp 23-44, July 2012, ISSN Méger N., Rigotti C., Gueguen L., Lodge F., Pothier C., Andréoli R., Datcu M., Normalized Mutual Information-based Ranking of Spatio-temporal Maps, In Proc. of the 8th Conf. on Image Information Mining: Knowledge Discovery from Earth Observation Data (ESA-EUSC 2012), German Aerospace Centre (DLR), Oberpfaffenhofen, Germany, October 2012, 4 pages, CD-ROM. Derived and contains Landsat 7 ETM+ data acquired between 1999 and 2010 USGS mars 2014 Bluecham

15 Erosion forms identification and classification Needof VHR remotesensingdata (< 2 m resolution) Automatic discovery of Lavaka Image analysis(index and Sobelfiltering) combinedwithdata mining Edge detection + Clustering Preliminary results are very promising Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit

16 Inputs in existing models Hybrid Erosion model(bluecham SAS with UNC and Landcare Research- NZ) RUSLE modified for New Caledonia Including physical paramters Soils type and landforms ( t/ha/year) Vegetation cover(in %) DEM parameters(slopes, morphometric index, streams) All these parameters can be derived from Pléiades tristereo multispectral data 29 mars 2014 From Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit Bluecham

17 Spatial and temporal data mining of remote sensing data RESULTS

18 Data Space at the heart of your decisions Integration of processes and results for decision-makers Inside the Qëhnelö core Web processing Web mapping Results Inside dedicated datastores FOSTER project Universities datastores Analysis tools Active Learning (icube/ Strasbourg University) Contains Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit

19 Case story: heavyrainsof 03rd of July 2013 Heavyrainsbetweenthe 2 nd and the 3 rd of July 2013 Exceptionnal rains over Yaté 714 mm in 24 hoursin Yaté Event return period estimated to 40 years. Main impact : sedimentsplumes withinthe bays Coral bleeching Dead fishes Wherethe sedimentsare comingfrom? Sediment production and transport estimates DSM from Pléiades tristereo data (13/07/2012) Landuse Pléiades tristereo data (13/07/2012) Bluecham erosion model Presentation Spatio-temporal data mining for erosion monitoring

20 Space at the heart of your decisions Case story: heavyrainsof 03rd of July 2013

21 Space at the heart of your decisions Case story: heavyrainsof 03rd of July 2013

22 July 2013 heavy rains Decision-makers Support to negociation between industries, Province and the town council Mining activities impact Rehabilitation areas Revegetation program Information to population Contains Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit

23 Spatial and temporal data mining of remote sensing data CONCLUSION

24 Conclusion Pléiades VHR tristereo data Incredible inputs for existing erosion models Verypromisingresultsfor gulliesand lavakaidentification and mapping Direct users appropriation Integration of data and processes into a decision-making system Qëhnelö Platform Data as well as remote sensing time series processing Further works Data miningof VHR remotesensingdata exploitingthe 2013 Pléiades acquisition Explore the possibilites of tristereo DSM/DEM to characterize gullies and lavaka

25 ThankYou! MERCI Bluecham SAS 101 Promenade Roger Laroque BPA Nouméa CEDEX Nouvelle-Calédonie