Assessing aboveground tropical forest biomass from optical very high resolution images

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1 Assessing aboveground tropical forest biomass from optical very high resolution images Pierre COUTERON IRD AMAP Lab, Montpellier, France N. Barbier, J-F Bastin, V. Droissart, R. Pélissier, P. Ploton, C. Proisy, B. Sonké, G. Viennois FORESEE workshop 10/10/2014, Nancy 1

France N. Barbier, J-F Bastin, V. Droissart, R. Pélissier, P.

2 Highlights Quantifying canopy texture from VHSR optical data Correlating texture with some stand variables as to predict biomass (inversion) Building 3D stand models and simulate images to test the inversion process Towards regional studies: across several forest types and heterogeneous acquisition conditions of images 2

models and simulate images to test the inversion process Towards regional

3 Tree/stands measurements In the field From space dbh - Direct measurement of dbh Height and crown: costly and of limited accuracy Canopy + tree allometries = Meeting point - Direct data on height or crowns of canopy trees Limited information on boles and understorey 3 3

Canopy + tree allometries = Meeting point - Direct data on height

4 What makes AGB gradients in forests? Variation in: Canopy closeness/openness Stature of canopy trees Most of AGB in: Canopy trees tree boles Need of proxies observable from space: Height => lidar : GLAS ( ), airborne small footprint (still costly) Crown dimensions? Potential of VHR optical imagery 4

AGB in: Canopy trees tree boles Need of proxies observable from space:

5 RCA: GeoEye pansharpened G NIR B 5

6 Very high spatial resolution optical data Potentiel of VHR (pixel < 1 2 m) : Satellites Quickbird, Ikonos, GeoEye, Worldview, Pléiades, Spot6/7, Increase in affordability, agility, build up of archives (Google Earth, ) Our aim: towards operational methods for stand type and biomass mapping Three steps: Objective/automatic measuring of canopy texture Correlate with field measurements of stand parameters Benchmark the inversion process by simulating images from modelled (and known) 3D forest structures 6

methods for stand type and biomass mapping Three steps: Objective/automatic measuring of canopy texture Correlate with field

7 Quantifying canopy image texture Senescent Mature Adult Young Pionnier «radial» spectrum Dominant frequencies 2D Fourier spectra French Guiana, mangroves Frequencies (cycles/km) Proisy et al. RSE (2007) 7

frequencies 2D Fourier spectra French Guiana,

8 8

9 Textural ordination: principal axes of variation 100 m FOTO analysis on digitized photographs in French Guiana Consistent with visual interpretation Couteron et al. J Appl Ecol (2005) 9

10 Heterogeneous canopy Medium crowns Logging concession L ARGER CROWNS / GAPS S MALLER CROWNS Canopy texture analysis: the FOTO method From N. Barbier, unpubl. Km

11 /10/ Broad-leaved forests, North-Eastern France

000000 1097200.000000 1097500.000000 1097800.000000 1098100.000000 1098400.000000 1096900.

12 /10/

13 Relationships with forest parameters (AGB) No saturation! No saturation Biomass prediction using Ikonos imagery in mangrove stands; French Guiana Proisy et al. RSE (2007) 13

14 AGB prediction in the evergreen forests, Western Ghats of Karnataka (India) Panchro Ikonos and Google Earth images (1 m) RMSE = 55 t DM ha -1 (< 15%) Good results were also obtained with India s Cartosat 1 (2 m) G Rajashekar (unpl.) AGB assessed from field plots, local allometry (t DM ha -1 ) Références: Ploton et al. (2012) Ecol. Appl. 14

also obtained with India s Cartosat 1 (2 m) G Rajashekar (unpl.

15 AGB Mapping from Ikonos/Google Earth images Western Ghats of Karnataka, India Ploton et al. (2012) Ecol. Appl. 15

16 Why does it work? Allometric relationships Dbh - AGB : main way of assessing AGB Dbh - Height : high variability Dbh crown : is probably more stable can a dbh 1.27 a can dbh 1.36 India (WG) Antin et al Panama (BCI) Muller-Landeau et al. Ecol. Lett. (2006) Crown sizes inform about dbhs of canopy trees and AGB 16

high variability Dbh crown : is probably more stable can a dbh 1.27 a can dbh 1.

17 Simulating canopy images Measures 3D stand models Canopy images D1 D2 D3 D4 Radiative transfer (DART) D5 D6 D7 D8 3D mockup (Stretch) 17

18 Radiative transfer DART, well established physical model: Ray tracing (turbid/solid media) in 3D scene The only model to produce scene images Gastellu Etchegorry. Meteorol Atm Phys (2008) 18

19 TERRESTRIAL LIDAR FOR THE STUDY OF LARGE TREE ARCHITECTURE (CAMEROUN) P. Ploton, umpublished 50 m Triplochiton scleroxylon RAUH S MODEL Terminalia superba AUBRÉVILLE S MODEL Piptadeniastrum africanum TROLL S MODEL 19

20 Examples from succession stages in Amazonian mangroves Proisy et al., 2012, Intech 20

21 Test of model inversion from simulated images The potential of inversion is established for: Mean crown width Quadratic mean dbh (predictor of biomass) PCA 2 <D crown > mean DBH [cm] R²= PCA1 Texture index (PCA1) of simulated forests PCA 1 Simulated images automatically ordinated along texture gradients Barbier et al. Ann. For. Sci.,

22 10/10/2014 Effect of image acquisition conditions (scene geometry) vz sv sz year Meters 360 Santarem, Brazil 22

23 Combining images in forward configuration for diachronic and/or regional studies Sun zenith = Sun zenith = GeoEye Ikonos Quickbird Sun zenith = Santarem, Brazil (diachronic) Central Africa Ikonos vs GeoEye (+10 years interval) No bias in the main FOTO index (PCA1) for matching areas between images N. Barbier, unpubl. 23

24 Mitigating instrumental bias 24

25 Biomass texture calibration by forest types RDC several forest types, Quickbird + GeoEye Bastin et al. (2014), Ecological Applications Predicted biomass by 2D-FFT (t/ha) Without clustering With clustering Observed biomass (t/ha) Observed biomass (t/ha) R² = RMSE = 80.6 (~30%) R² = 0.79 (after cross val) RSME = 42.4 (~15%) 25

26 Biomass calibration All plots, R²= RMSE= Images Predicted biomass Work in progress (more plots needed) Samples in 3 countries Including the main forest types: Mixed evergreen Evergreen monodominant Mixed evergreen/ deciduous Open canopy with Marantaceae Biomass tdm /ha N. Barbier, unpubl. 26

27 Conclusion Potential of VHSR optical images Canopy aspect information is relevant to predict stand structure variables (including AGB) via texture metrics Effects of varying acquisition parameters can be controlled and even mitigated (to someextent avoid«hot spots») Upscaling from stand to landscape Field > airborne (lidar) > VHSR > High SR > Moderate SR Affordability thanks to the nested sampling Prospects/ ongoing Better understanding of noise and bias across scales via 3D stand/radiative transfer modelling Calibrating and cross validating for diverse African forests, but more data (plots + images) are needed 27

28 A matter of team and field work Thank you! Merci! 28

29 References Bastin J-F, Barbier N, Adams B, Shapiro A, Couteron P, Bogaert J, De Cannière C 2014 Aboveground biomass mapping of African forest mosaics using canopy texture analysis from contrasted acquisitions: towards a regional approach. Ecological Applications,. Couteron, P., Barbier, N., Proisy, C., Pélissier, R., Vincent, G., Linking remote-sensing information to tropical forest structure: the crucial role of modelling. Earthzine, : 1-4. / Barbier, N., Couteron, P., Gastellu-Etchegorry, J. P., Proisy, C., Linking canopy images to forest structural parameters: potential of a modeling framework. Annals of Forest Science, 69 (2): Ploton, P., Pélissier, R., Proisy, C., Flavenot, T., Barbier, N., Rai, S. N., Couteron, P., Assessing above-ground tropical forest biomass using Google Earth canopy images. Ecological Applications, 22 (3): Barbier, N., Proisy, C., Vega, C., Sabatier, D., Couteron, P., Bidirectional texture function of metric resolution optical images of tropical forest: an approach using LiDAR hillshade simulations. Remote Sensing of Environment, 115 (1): Barbier, N., Couteron, P., Proisy, C., Malhi, Y., The variation of apparent crown size and canopy heterogeneity across lowland Amazonian forests. Global ecology and biogeography, 19 (1): Proisy, C., Couteron, P., Fromard, F., Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination (FOTO) of IKONOS images. Remote Sensing of Environment, 109 (3): Couteron, P., Pélissier, R., Nicolini, E., Paget, D., Predicting tropical forest stand structure parameters from Fourier transform of very high resolution canopy images. Journal of Applied Ecology, (42):

Assessing aboveground tropical forest biomass using Google Earth canopy images

Ecological Applications, 22(3), 2012, pp. 993 1003 Ó 2012 by the Ecological Society of America Assessing aboveground tropical forest biomass using Google Earth canopy images PIERRE PLOTON, 1,2 RAPHAËL