Mangroves monitoring using VHR Pléiades data
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1 Mangroves monitoring using VHR Pléiades data Under mining constraints Rémi Andreoli Bluecham SAS Cyril Marchand IRD Audrey Léopold UNC/IRD Claire Tinel CNES Delphine Fontanaz CNES Pléiades Days 2014, April 1-3, 2014, Toulouse - France
2 Mangroves monitoring using VHR Pléiades data INTRODUCTION
3 Mangroves: an ecosystem between sea and land Specific intertidal ecosystem of tropical areas Selective conditions : salinity, topography, submersion time, soils composition Under exogenous pressures : nutrients incomes, sediments incomes, sea level rise A keyecosystem Food producing for local population (fishes, crabs, shells) Production and transformation of the organic matters Buffer zone betweenseaand land: erosionreduction, cyclone and tsunmai impact reduction Mangroves economic value : Between 200k and 900k USD/ha/year (sources : conservatoire du littoral, IFRECOR, French state) Restoration cost 225k USD/ha
4 Mangroves in New Caledonia ha 55% Rhizophora spp. 14% Avicennia marina ha of baresoils Space at the heart of your decisions Mangroves in New Caledonia A biodiversity hotspot 24 species Including6 speciesof Rhizophora 1 endemic species
5 Space at the heart of your decisions Mangrove monitoring using remote sensing data 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
6 Mangrove monitoring using remote sensing data Mangroves monitoring under mining constraints( ) CNRT «Nickel et son Environnement» Spatio-temporalmangroves monitoring usinghr DEIMOS-1 data Elecnor Deimos ORFEO program: Mangroves monitoring using VHR Pléiades data CNES
7 Mangroves monitoring using VHR Pléiades data PLEIADES DATABASE
8 N 46 Mangroves monitoring 1 main areas : -Yaté Coastalarea (560 km²) - Populated places - Mining areas - Worldclass industrial site -KwéBinyiand Nu Neae mangroves -200 ha of mangroves -80% of mangroves trees -between5 to 10 species -Sources : ZonEco
9 651 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 Pléiades data acquired the 28/09/2013 CNES 2013, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit Bluecham 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
10 Mangroves monitoring using VHR Pléiades data METHODOLOGY
11 Processing chain Preprocessing Remote sensing dedicated index Radiometric indexes Texture index Mangroves classification Functional vegetation type Global to zonal indicators Individual tree or group of trees identification Fine scale monitoring Local indicators
12 An essential step for mangrove monitoring Fine scale orthorectification (+/- 50 cm RMSE) Data preprocessing 1 avicennia Radiometric calibration Parameters provided by CNES
13 Radiometric indexes Normalized Vegetation Index (Bannari, 1995) Redness Index (Pouget et al., 1990) Textural indexes Haralick indexes (1974) Indexes Variance Mean Enthropy Standard deviation Second moment Correlation Contraste Homogeneity Directivity Uniformity Gradient and Sobel local max. Modified(Bluecham SAS)
14 Mangroves classification based on functional biodiversity Species community Red mangroves trees: Rhizophoraceae Grey mangroves trees: Avicennia Mixed vegetation Baresoils Spatio-temporal variations Sediment incomes Nutrient incomes Sealevelrise
15 Mangrove classification methodology Validation process Field survey (GPS) THR Remote sensing data Data preprocessing Preprocessing Image interpretation DEM Validation Reference map Classification THR data Calibrated and orthorectified Validation Mangroves Textural indexes Mangroves area Radiometric indexes Classification Validation Mangroves vegetation types Baresoils/ Avicennia / Rhizophoraceae copyright Bluecham 2011
16 Methodology validation using THR remote sensing data Bare soils Avicennias Rhizophora bush Rhizophora trees Good allocation 97,16 % 91,51 % 93, 68 % 94,57% Under estimate 5,74 % 7,93 % 5,93 % 9,08 % Over estimate 2,84 % 8,49 % 6,32 % 5,43 % Results using GeoEye-1 (Nov. 2010) data over the Vavouto mangrove. Digitalglobe 2010, Processing Bluecham SAS mars 2014
17 Methodology adaptation to Pléaides VHR data Radiometry of multispectral product Vegetation type differentiation within the RGB space Resolution over small object Avicennia marina Add elevation information Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit
18 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
19 Mangrove classification methodology updated for Pléiades tristereo data Validation process Field survey (GPS) THR Remote sensing data Data preprocessing Preprocessing Image interpretation Pléiades DSM Validation Reference map Classification THR data Calibrated and orthorectified Validation Mangroves Textural indexes Mangroves area Radiometric indexes Classification Validation Mangroves vegetation types Baresoils/ Avicennia / Rhizophoraceae
20 Mangroves monitoring using VHR Pléiades data RESULTS
21 Mangroves of KwéBinyi& Nu Neae: Results Derived and contains Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit
22 Mangroves tree repartition 81% of Rhizophoraceae Space at the heart of your decisions Mangroves of KwéBinyi& Nu Neae: Results 18% of Rhizophora trees 63% of Rhizophora bush 12% of mixed vegetation Derived and contains Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit Only4% of Avicennia and 3% of baresoils 12% 4% 3% 18% Rhizophorace trees Rhizophorace bush Mixed vegetation Avicennia Bare soils 63%
23 Use of Pléiades tristereo DSM Tree heights Abnormaltreeaccordingto vegetation types Soil topography Space at the heart of your decisions Work in progress 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 Small trees Mean tree heights Tall trees Derived and contains Pléiades data acquired the 13/07/2012 CNES 2012, Distribution Astrium Services / Sot Image S.A., France, tous droits réservés. Usage commercial interdit
24 Mangroves monitoring using VHR Pléiades data CONCLUSION
25 Conclusion VHR Remotesensingusedfor functionalclassification of mangroves Operational, robust and scientificaly validated Methodology proposed to the French Conservatoire du Littoral Methodology must be adapt to Pléiades data Radiometry Resolution Provide very good results Valuable input from tristereo data Avicennia identification Tree heights determination(in progress) Soil topography
26 ThankYou! MERCI Bluecham SAS 101 Promenade Roger Laroque BPA Nouméa CEDEX Nouvelle-Calédonie
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