Tiger project #2 : Biomass evaluation of tropical dry and wet forests. Climate change impacts

Transcription

1 Tiger II Workshop Hartebeeshoek, South Africa, December 2011 Tiger project #2 : Biomass evaluation of tropical dry and wet forests. Climate change impacts Laboratory Institution Partners Project investigator name : Remote Sensing and Environmental Geophysics Laboratory : Institute and Geophysical Observatory of Antananarivo (IOGA) Univ. of Antananarivo, Madagascar : DBEV, Fac Science; MNHN/CNRS France; University of Marne La Vallée France; ONG GoodPlanet; WWF; TU Delft : Prof. Solofo RAKOTONDRAOMPIANA 1

2 Tiger II Workshop Hartebeeshoek, South Africa, December 2011 Towards Forest Biomass Presented by Solofoarisoa RAKOTONIAINA (IOGA, Univ. Antananarivo) 16/01/2012 Towards Forest Biomass 2

3 Forest Biomass Outline: Forest definition: Area (forest cover), Canopy and Height Area + Canopy coverage + Height -> biomass Parameters determination : - Optical and Radar images -> Area - LAI, field work by foresters -> Canopy - LiDAR -> Height 3

4 Project summary (1/3) Context: Forests in Madagascar: tropical wet and dry forests Forests and biomass degradation -> climate change Use of remotely-sensed data Methodologies: Image classification and forest land cover mapping with different algorithms (ML, SVM, k-nn, ICM, ) and with two approaches (per pixel, object-based) Change detection of forest land cover Biomass evaluation Satellite images used : Landsat ETM+ (1993,2005), SPOT 5 (2008) to be used: ALOS PALSAR, LiDAR, RADARSAT-2 (?), acquisition dates: 1993, 2005, 2008, sites of study: Zahamena and Anosy 4

5 Sites of study Madagascar Zahamena / tropical wet forests Anosy (and Androy) / tropical dry forests 5

6 Project summary (2/3) 6 master thesis done «Land cover mapping and change detection analysis : case of the protected area of Zahamena (Madagascar) using Landsat ETM+ (1993, 2005)», Tahiana RATSIMBAZAFY, June «Image classification with decision trees algorithm», Rija RAKOTOARIMANANA, June «Classifiers combination using the Dempster Shafer rule», Sitraka RANOELIARIVAO, June «Very high resolution remotely sensed images for the evaluation of carbone in semi-arid tropical area. Applications in a REDD+ project in the Androy and Anosy regions, Madagascar», Lova RAKOTOVAO and Tahiana RAJOSARIMALALA, Aug-Sept «Using SVM algorithm in remotely-sensed data classification», Maeva Dhoimiri ANWAR, July «Using Fuzzy C-means algorithm in image classification», Pascal RAKOTOMANDRINDRA, Oct

7 Project summary (3/3) 3 master thesis in final state, 2 doctoral thesis in progress «Classification of a SPOT-5 Very High Resolution image : case of the Protected Area of Zahamena», Fety Abel RAKOTOMALALA, dec 2011 «Relationship between HRV-SPOT 5 satellite imagery data and ecological data (LAI,..)», Sedraniaina RANAIVOARIMANANA, dec 2011 «Biomass studies», Falitiana ANDRIAMALALA, dec 2011 «Biomass estimation from Lidar data of ICESat satellite», Maeva ANWAR «Classifiers combination and change detection», Sitraka RANOELIARIVAO 7

8 Zahamena site lat. Sud long Est 8

9 Description of Zahamena site A part of multidisciplinary research in biodiversity in the protected area of Zahamena, region of Alaotra Mangoro, East part of Madagascar, 150 km North-East away from Antananarivo. Alaotra-Mangoro region: first region of rice culture area in Madagascar Area constitued essentially by tropical dense wet forest Area of the site study : ~8000 km² 9

10 Land cover mapping of Zahamena site obtained with the Maximum Likelihood classifier (Tahiana Ratsimbazafy, Master thesis, 2009) Landsat image Landsat image

11 Other principal results (1/2) Change detection analysis in Zahamena site between 1993 and % of total forest degradation: 19% : dense humide forests -> degraded forests 12% : dense humide forests -> crop fields 4% : dense humide forests -> other types of cover (grassland, savannah, ) 76 % of bare soil regeneration : 9% : bare soil -> crop fields 67% : bare soil -> other types of cover (savannah, ) 11

12 Other principal results (2/2) For both 2 sites: Land cover maps / forests delimitation Improvement of classification accuracy in general with SVM and DT algorithms compared to ML method Improvement of classification accuracy using object-based approach compared to pixel-based approach 12

13 Zahamena Land Cover mapping using a Very High Resolution image (SPOT-5) (Fety Rakotomalala, Master thesis, dec 2011) Methodologies : Image fusion (XS multipsectral image + PAN image) : Gram-Schmidt Spectral Sharpening method (OTB- Monteverdi software) Object-based classification with ML, k-nn, SVM 13

14 Zahamena Land Cover mapping Main results (obtained from an extraction area of 432 x 1164 pixels) ML, Landsat 30m ML-OP, SPOT-5 2.5m ML-OO, SPOT-5 2.5m Classes LANDSAT (30 m) SPOT (10 m) SPOT (2,5 m)-op SPOT (2,5 m)-oo FDHSMA 99,45 63,92 68,22 81,47 FDHSMA-FS - 83,80 85,23 95,33 FDHSBA 97, Savanes herbeuses 84,04 97,19 97,21 98,50 Sols nus Pseudosteppes Recrus après feux 47,17 90,70 88,48 96,12 Mosaïques de cultures 91,3 99, FDHSBA dégradées 59, Peuplement d'eucalyptus 93,85 96, Formations marécageuses 73,91 91,40 92,69 97,37 Rizières 92, FDHSMA dégradées 60 97,30 94,76 97,83 Prairie 81, Eau turbide Eau claire

15 Correlation Images-LAI (1/5) (Sedraniaina Ranaivomanana, Master thesis dec 2011) LAI Field works Implantation des 5 parcelles et la ligne du transect (1254 m) des points de mesure de LAI 15

16 LAI measures Correlation Images-LAI (2/5) 16

17 Correlation Images-LAI (3/5) LAI 6,199exp(0,014 * PVI) R²=0,327 ; MCE=0,335 17

18 Correlation Images-LAI (4/5) CNDVI XS XS3 XS 2 XS 4 XS4 3 XS 2 XS4 XS4 (1 min ); max min ( 7) XS RSR XS 2 XS 4 XS 4 3 XS4 XS4 (1 min ); max min (8) RATIO XS3 2 ;(1) XS LAI= * RATIO * CNDVI * RSR R 2 = 0.398; MCE=

19 Correlation Images-LAI (5/5) LAI maps Land cover map obtained by an object-based k-nn classification LAI map of the site study obtained from exponentiel regression model with PVI LAI map of the site study obtained from multiple linear regression model with 3 variables (RATIO, CNDVI, RSR) 19

20 Future works Use of Optical multitemporal data for surveying and monitoring (modelisation evolution and change detection) Use of Radar data (ALOS PALSAR, Radarsat-2): Biomass evaluation For complementary studies:» Improving land cover maps, forests delimitation and area» Solving the problem of clouds coverage in Optical images processing Use of LiDAR data to estimate trees heights Dry forest biomass evaluation 20

21 Data already acquired (1/2) For Zahamena site: Landsat ETM+ (ONE) : april 1993, april 2000 (heavy cloudy), march 2005 SPOT 5 (CNES-ISIS) : sept ALOS PALSAR L-band (ESA, univ. Marne-La-Vallée France) : nov 2006 Coordinates : S; E For Anosy site : SPOT 5 : march-april 2009 (WWF, GoodPlanet) Coordinates : S, E 21

22 Data already acquired (2/2) LiDAR data acquired over all Madagascar Island: - Satellite: ICESat (Ice, Cloud,and land Elevation Satellite) - Instrument: GLAS (Geoscience Laser Altimeter System) - Acquisition dates: between 2003 and More details: Goal: trees heights estimation 22

23 Data ordered via EOLI and downloaded 50 ALOS data (according to JAXA and ESA agreements): to be downloaded Both optical and radar images Sensors: PRISM, AVNIR-2, PALSAR Acquisition dates: 2006, 2008 and 2010 SPOT data: (ESA, 15 images to be downloaded, 1 downloaded) Acquisition dates: 2006, 2008 and 2010 Radarsat-2 data: dependance on SOAR Africa evolution (Canadian Space Agency) 23

24 Thanks a lot for your attention 24