Is ground-based phenology of deciduous tree species consistent with the temporal pattern observed from Sentinel-2 time series?

Transcription

1 Is ground-based phenology of deciduous tree species consistent with the temporal pattern observed from Sentinel-2 time series? N. Karasiak¹, D. Sheeren¹, J-F Dejoux², J-B Féret³, J. Willm¹, C. Monteil¹. ¹DYNAFOR, Université de Toulouse, INRA, Castanet-Tolosan, France ²CESBIO, Université de Toulouse, CNES, CNRS, IRD, INRA, Toulouse, France. ³TETIS, IRSTEA, Montpellier, France.

2 NDVI of Oak tree What is phenology and why it may be useful? Winter Spring Summer Fall - Annual cycle of the vegetation - Different behaviours according to tree species - Enhance tree species recognition - Follow climate evolution Winter 2

3 Annual phenological cycle Branches bare Budburst Leaf coloring and falling Budset 3

4 Annual phenological cycle Branches bare Budburst Leaf coloring and falling Budset 4

5 Annual phenological cycle Branches bare Budburst Leaf coloring and falling Budset 5

6 Annual phenological cycle Branches bare Budburst Leaf coloring and falling Budset 6

7 Research questions 1- Is Sentinel-2 time series able to identify tree species? 2- What is the consistency between the phenology observed in-situ and the one observed from S2 time series? 7

8 Study site - Extent of 25x25km (Subset of Sentinel-2) - Most of the site is composed of crops and small private forests - Temperate mixed forest, less than 10% (54km²) of the landcover FRANCE T31TCJ Sentinel-2 Tile 20km 8

9 Data / Images Sentinel-2 Time Series, T31TCJ tile, Level 2A¹: - 37 images, from 26 january 2017 to 27 december Temporal resampling every 5 days Clouds >80% or NA 60 to 80% 40 to 60% 20 to 40% <20% ¹Hagolle et al

10 Data / Tree species survey Total : 1682 pixels 1 sample = 1 pixel of Sentinel-2 Species Broadleaf Conifers Samples Forest stands Silver birch (Betula pendula) 64 3 Oak (Quercus robur/pubescens/petraea) Red Oak (Quercus rubra) European ash (Fraxinus excelsior) Aspen (Populus tremula) Black Locust (Robinia pseudoacacia) 88 6 Willow (Salix alba) 52 6 Eucalyptus(Eucalyptus spp.) Corsican Pine (Pinus nigra subsp. Laricio) Maritime Pine (Pinus pinaster) Black Pine (Pinus nigra) 52 2 Silver fir (Abies alba) 53 3 Douglas fir (Pseudotsuga menziesii) 72 8 Cypress (Cupressus)

11 Data / Phenological survey Since septembre 2017 : 10-days revisit of 14 plots (2 plots per species) - Phenological stage (using standard BBCH-scale¹) - Canopy Cover computed with fisheye² - Application GLAMA for Android Fisheye for smartphone devices - Chlorophyll measure with SPAD - Existence of the understory vegetation ¹ Badeau et al Plot ² Tichy L, Collins B km 11

12 BBCH 09 : bud burst Stage 0 Stage number Awakening Stage 1 Completion percentage (x10) of the stage Leaves coming out 00 : sleep BBCH 05 : bud swell 12 : 20% BBCH17 : 70% BBCH BBCH Branches bare Budburst Stage 9 Stage 3 Leaves coloring/falling 92 : 20% BBCH 97 : 70% Leaves growing 32 : 20% full size BBCH 37 : 70% BBCH BBCH Leaf coloring and falling Budset 12

13 november april may Quercus palustris Quercus pubescens 13

14 november april may Quercus palustris Quercus pubescens 14

15 november BBCH = 15 april may Quercus palustris BBCH = 18 Stage 1 Leaves coming out Quercus 15 : 50% pubescens 18 : 80% 15

16 Methodology / 1 Classification - Preprocessing : Gap-filling on detected clouds using linear interpolation - Supervised classification : - SVM with RBF Kernel (hyperparameters fixed by cross-validation) - Cross-validation : Spatial Leave-One-Out (SLOO)¹ to limit spatial autocorrelation ¹ Le Rest et al

17 Methodology / Sampling references for classification Spatial Leave-One-Out¹ sampling. ¹ Le Rest et al

18 Methodology / Sampling references for classification Spatial Leave-One-Out¹ sampling. Validation pixel ¹ Le Rest et al

19 Methodology / Sampling references for classification Spatial Leave-One-Out¹ sampling. Validation pixel 320m ¹ Le Rest et al Using Moran s Index to compute autocorrelation distance Median value from all bands 19

20 Methodology / Sampling references for classification Spatial Leave-One-Out¹ sampling. Validation pixel 320m Spatially correlated pixels ¹ Le Rest et al Using Moran s Index to compute autocorrelation distance Median value from all bands 20

21 Methodology / Sampling references for classification Spatial Leave-One-Out¹ sampling. Validation pixel Spatially correlated pixels ¹ Le Rest et al Using Moran s Index to compute autocorrelation distance Median value from all bands 21

22 Methodology / Sampling references for classification Spatial Leave-One-Out¹ sampling. Validation pixel Training pixels ¹ Le Rest et al

23 Methodology / Sampling references for classification Spatial Leave-One-Out¹ sampling. Validation pixel Training pixels Repeated as many times as the number of samples in the least populated class. Here 42 times (i.e. number of samples of cypress) ¹ Le Rest et al

24 Methodology / 2 Phenology - Smoothing with Double logistic1,2 using NDVI ( in R band) - Seasonal amplitude for Start Of Season : 10%3 - Seasonal amplitude for End Of Season : 90% - - Objective is to find the time where all leaves fell/colored. Comparison with ground survey ¹ Yang and Zhang (2012) ² Tan et al. (2010) ³ Jönsson and Eklundh (2002) 24

25 Results 25

26 Results 1 Tree species classification Phenology from ground survey (14 plots) 2 Phenology computed from NDVI (930 references, 7 species) 26

27 1 Species classification 77% kappa True label Results / using Spatial Leave-One-Out (SLOO) 27 Predicted label

28 1 Species classification 77% kappa True label Results / using Spatial Leave-One-Out (SLOO) 28 Predicted label

29 Tree species map with Spatial Leave-One-Out 1km 29

30 Tree species map with Spatial Leave-One-Out 1km 30

31 Tree species map with Spatial Leave-One-Out Silver birch Douglas fir Black pine Silver fir 150m IGN - ORTHO-SAT SPOT 6/

32 Tree species map with Spatial Leave-One-Out Silver birch Douglas fir Black pine Silver fir 150m IGN - ORTHO-SAT SPOT 6/

33 feb 15 april 20 Fern is growing at the same time as the canopy may 2

34 feb 15 11% april 20 55% Fern is growing at the same time as the canopy may 2 58%

35 NDVI NDVI 2 Phenology / Silver birch (plot n 1) CACO / Leaves fall / Leaves out (%) Results / 35

36 Results / 2 Phenology / Silver birch (plot n 1) All leaves are out Beginning of leaf falling or colouring 90% of leaves still on tree NDVI CACO / Leaves fall / Leaves out (%) NDVI Still 20% leaves on tree No more leaves Budburst Start Of Season 36

37 Results / 2 Phenology / Silver birch (plot n 1) All leaves are out Beginning of leaf falling or colouring 90% of leaves still on tree NDVI CACO / Leaves fall / Leaves out (%) NDVI CACO Canopoy Cover (%) Still 20% leaves on tree No more leaves Budburst Start Of Season 37

38 2 NDVI NDVI Phenology / Black locust (plot n 2) CACO / Leaves fall / Leaves out (%) Results / 38

39 Results / 2 Phenology / Black locust (plot n 2) All leaves are out CACO / Leaves fall / Leaves out (%) Beginning of leaf falling or colouring 90% of leaves still on tree NDVI NDVI Still 20% leaves on tree No more leaves Budburst Start Of Season 39

40 Results / 2 Phenology / Black locust (plot n 2) All leaves are out CACO / Leaves fall / Leaves out (%) Beginning of leaf falling or colouring 90% of leaves still on tree NDVI NDVI CACO Still 20% leaves on tree No more leaves Budburst Start Of Season 40

41 2 Phenology / Black locust All leaves are out CACO / Leaves fall / Leaves out (%) Results / Budburst Start Of Season 41

42 Results / 2 Phenology from SITS (n = 980) 20 days 42

43 Results / 2 Phenology from SITS (n = 980) ground truth 20 days 43

44 nov 7 nov 17 nov 27 Red oak plantation

45 nov 7 nov 17 nov 27 Red oak plantation

46 Start Of Season 02/05 03/02 03/27 End Of Season Aspen plantation 9/18 11/07 12/27 46

47 Conclusion and beyond 1 Classification, high potential but : - Importance of spatial autocorrelation (77% kappa Vs 97%) Mixed effects with the understory vegetation (Helman 2018) Unexpected high accuracy for several conifer species (F1>90%) Inconsistency with previous results (S2 versus Formosat-2, Sheeren et al. 2016) 2 Phenology : - High influence of understory vegetation (changing the phenological cycle) Consistent with ground survey, except Red oak Need to finish year 2018 to have a full vegetation cycle and SOS validation. 47

48 Presented by Nicolas Karasiak Thanks for listening 48

49 97% kappa using 50% random sampling True label Results / Species classification 49 Predicted label

50 50

51 NDVI (+0.05 in red) mean ± std Oak Silver birch 51

52 NDVI (+0.05 in red) mean ± std Black locust Oak European ash 52

53 NDVI (+0.05 in red) mean ± min/max value Black locust Oak European ash 53