1. Jin Wu 1, Neill Prohaska 1, Kenia T. Wiedemann 1,2, Loren P. Albert 1, Alfredo R. Huete 3*, and Scott R. Saleska 1* Observing canopy demography of a tropical moist forest with spectral unmixing of hyperspectral images: a model of HyspIRI science application Email Contact: jinwu@email.arizona.edu or saleska@email.arizona.edujinwu@email.arizona.edusaleska@email.arizona.edu (1: Department of Ecology and Evolutionary Biology, University of Arizona; 2: School of Engineering and Applied Sciences, Harvard University; 3: Plant Functional Biology & Climate Change Cluster, University of Technology Sydney) *: Corresponding Authors

2. Outline of the Talk 1: Motivation—canopy demography matters ecological processes of forest systems 2: Methodology—remote sensing perspective of demography 4: Canopy Demography Modeling—case study from a tower mounted hyperspectral imaging system 5: Inference to HyspIRI Study 3: Study Site and Materials 6: Summary and Future Work

3. Motivation O2CO2, H2O Photosynthesis is one of the most important features that define environment-vegetation interaction

4. Motivation (cont..) O2CO2, H2O Both environments and phenology control photosynthesis seasonality; Phenology includes the seasonality of both leaf amount (measured by LAI) and canopy average leaf physiology Soil Moisture Nutrients Environments Leaf Area Index (LAI) Canopy Average Leaf Physiology* Phenology *Canopy leaf average physiology is an Euler Product of canopy demographic composition and physiology of each demographic component

5. Motivation (cont..) O2CO2, H2O Soil Moisture Nutrients Environments Leaf Area Index (LAI) Canopy average leaf physiology* Phenology *Canopy leaf average physiology is an Euler Product of canopy demographic composition and the physiology of each demographic component In a word, leaf amount seasonality and canopy demography seasonality are two important perspectives of plant phenology Both environments and phenology control photosynthesis seasonality; Phenology includes the seasonality of both leaf amount (measured by LAI) and canopy average leaf physiology

6. Motivation (cont..) Deciduous Forests & Evergreen Tropical Forests Deciduous Broadleaf Forest, Takayama, Japan. (Muraoka et al. 2010) 100 150200250 300 Day of Year 100 150200250 300 Day of Year Vcmax 25 (umol/m2/s) 0 20 40 60 80 LAI (m2/m2) 0 2 4 6 (a) (b)

7. Motivation (cont..) Deciduous Forests & Evergreen Tropical Forests Deciduous Broadleaf Forest, Takayama, Japan. (Muraoka et al. 2010) 100 150200250 300 Day of Year 100 150200250 300 Day of Year Vcmax 25 (umol/m2/s) 0 20 40 60 80 LAI (m2/m2) 0 2 4 6 (a) (b) Albert & Wu. 1-m Branch Sample from Uxi Lista, 08/24/2012. Albert et al. In Preparation Leaf Age Tropical Moist Evergreen Forest, Tapajos, Para, Brazil (c) (d)

8. Motivation (cont..) Deciduous Forests & Evergreen Tropical Forests Deciduous Broadleaf Forest, Takayama, Japan. (Muraoka et al. 2010) 100 150200250 300 Day of Year 100 150200250 300 Day of Year Vcmax 25 (umol/m2/s) 0 20 40 60 80 LAI (m2/m2) 0 2 4 6 (a) (b) Albert & Wu. 1-m Branch Sample from Uxi Lista, 08/24/2012. Albert et al. In Preparation Leaf Age Tropical Moist Evergreen Forest, Tapajos, Para, Brazil (c) (d) 80 0 2 4 (b) Albert & Wu. 1-m Branch Sample from Uxi Lista, 08/24/2012. Leaf amount seasonality and demography seasonality are both important to tropical photosynthesis processes

9. Model perspective of evergreen tropical photosynthesis seasonality ED: Ecosystem Demographic Model Restrepo-Coupe et al. (In Review) IBIS: the Integrated Biosphere Simulator CLM: the NCAR Community Land Model JULES: the joint UK Land Environment Simulator Motivation (cont..) Dry Season GPP (gC m -2 d -1 ) Tapajos National Forest, KM67 site, Para, Brazil Observation Month of Year

10. Model perspective of evergreen tropical photosynthesis seasonality Restrepo-Coupe et al. (In Review) Motivation (cont..) Dry Season GPP (gC m -2 d -1 ) Observation Month of Year ED JULES CLMIBIS Soil moisture “Stress” ( 1=no stress) LAI Photosyntehetic Capacity (umol/m2/s) Month of Year Observation

11. Model perspective of evergreen tropical photosynthesis seasonality Restrepo-Coupe et al. (In Review) Motivation (cont..) Dry Season GPP (gC m -2 d -1 ) Observation Month of Year ED JULES CLMIBIS Soil moisture “Stress” ( 1=no stress) LAI Photosyntehetic Capacity (umol/m2/s) Month of Year Observation ED JULES CLMIBIS LAI (m2/m2) Summary of Motivation (1)Both leaf amount seasonality and demography seasonality are two important perspectives of plant phenology (2) Leaf amount seasonality and demography seasonality are both I important for tropical forest photosynthesis processes; (3) There is still of great need for the studies to incorporate both leaf amount seasonality and demography seasonality for future photosynthesis modeling studies in tropical evergreen forest systems.

12. Methodology: Leaf demography Field methods (accurate, time/cost consuming, spatial limit) (1) leaf level—tagging leaves and revisiting; (2) canopy level—regular harvesting branch sample and conducting demographic survey Remote sensing methods Seasonal RGB Images of a deciduous forest (Bartlett Forest, USA) in 2008 Richardson. 2010. Dublin Land Product Validation Subgroup. How about evergreen tropical forests?

13. Study Site: TNF-KM67, Para, Brazil Study Site: Tapajos National Primary Forest, KM67 site (TNF-KM67), Para, Brazil 57.8 m eddy sample 64 m SOC camera Eddy Covariance Hyperspectral Vegetation Imaging System (HVIS) Data available from 01/2002 to 01/2006, and 08/2008-12/2012 Equipped by Surface Optics Corporation (SOC) 710 camera Tropical evergreen moist forest with ~5 month dry season

14. Materials: Hyperspectral Vegetation Imaging System B. RGB Image A. SOC Camera C. SOC Composited Image Key parameters: * Spectral resolution: 4nm, 128 bands from 385nm to 1050 nm * Field of view: 36×27 degree * Temporal resolution: image/10 minutes, from 6 am to 6 pm * Data available: Jul-Dec, 2012 * Image dimension: 520*696 Other parameters: * The camera system was programed to adjust their view angles from nadir view to very oblique view (75 o ) every 10 minutes, and repeated every hour

15. * 6 focal trees+14 understory trees were selected; * samples were collected in the early, middle, and late of dry season (Mid-Aug to Early-Dec) Materials: 2012 Fall Phenology Campaign

16. Materials: Leaf Demography Survey Bud scarsYoung LeavesOld Leaves Collected one meter branches multiple times throughout the dry season. Branches from sun and shade microenvironments within the trees. Counted young, mature and old leaves on sampled branches.

17. Canopy Demography Modeling Step1 : Acquire canopy hyperspectral reflectance from SOC camera images DOY 315 265 240 221 Tree #9, sun spot, average reflectance for 10*10 pixels; the images selected were under low cloud cover condition; all images selected for this analysis is in between 11 am-12pm local time

18. Canopy Demography Modeling Step2 : Spectral Unmixing Method Vegetation Percentage: f V Soil Percentage: f S One Image Pixel R composited = f V *R V + f S *R S Composited Reflectance Pure Vegetation Reflectance Pure Soil Reflectance Previous Studies (e.g. Asner and Lobell, 2000, RSE)

19. Canopy Demography Modeling Step2 : Spectral Unmixing Method Mature Leaves Percentage: f M Old Leaves Percentage: f O One Image Pixel R camera = f M *R M + f O *R O Camera observed Reflectance Pure Mature Reflectance Pure Old Reflectance This Study

20. 221 Old Recently Mature (a) (b) (a) Time series of 1-m branch composition; (b) the trimmed SOC spectra 240 265 315 Canopy Demography Modeling Step3 : Identify pure old leaf reflectance and mature leaf reflectance

21. 221 Old Recently Mature (a) (b) (a) Time series of 1-m branch composition; (b) the trimmed SOC spectra Doy 221, pure old leaf 240 265 315 Canopy Demography Modeling Step3 : Identify pure old leaf reflectance and mature leaf reflectance

22. 221 Old Recently Mature (a) (b) (a) Time series of 1-m branch composition; (b) the trimmed SOC spectra Doy 221, pure old leaf Doy 315, pure mature leaf 240 265 315 Canopy Demography Modeling Step3 : Identify pure old leaf reflectance and mature leaf reflectance

23. Mature Leaves Percentage: f M Old Leaves Percentage: f O One Image Pixel R camera = f M *R M + f O *R O Camera observed Reflectance Pure Mature Reflectance Pure Old Reflectance Old Young & Mature (a) (b) 221 240 265 315 Canopy Demography Modeling Step 4 : conduct linear spectral unmixing

24. Spectral Prediction Observation Canopy Demography Modeling Step5 : predict the seasonality of canopy demography R2=0.8656 P-value=0.0102

25. Inference to HyspIRI Study 315 265 240 221 188 221 269 315 Satellite Hyperion (Landscape) Camera (Tree Crown)

26. Inference to HyspIRI Study 315 265 240 221 188 221 269 315 Satellite Hyperion (Landscape) Camera (Tree Crown) Landscape mature leaf Landscape old leaf

27. Summary 1. Canopy demography seasonality is an equally important component of plant phenology as leaf amount seasonality 2. Canopy demography seasonality might be extremely important for understanding evergreen tropical forest photosynthesis processes 3. Spectral unmxing approach was attempted for the first time here for extracting canopy demographic composition based on canopy spectral reflectance acquired by a tower mounted imaging system

28. Summary 4. Canopy spectral predicted demography seasonality showed a good agreement with ground surveyed demography (R 2 =0.87, P-Value=0.01), suggesting a feasible way for monitoring canopy demography by using advanced space techniques in future. However, the robustness/applicability of this study still demands more quantitative analysis and similar studies. 5. Time-series of canopy spectra (via tower mounted camera system) showed similar trend as that from Hyperion. This suggests the former can be a model system for the study of Hyperion, while the later can expand the spatial scale of the former measurements.

29. Future Work 1. Technical Attempt Mature Leaves Percentage: f M Old Leaves Percentage: f O One Image Pixel R camera = f M *R M + f O *R O Camera observed Reflectance Pure Mature Reflectance Pure Old Reflectance (b) 221 240 265 315 (1) Give more weight on the spectral reflectance at visible light region (2) Downscale spectral resolution to be as comparable as multi-spectral sensor (like MODIS)

30. Future Work 2. Hypothesis Test 221 240 265 315 (a) Recently Mature Old (b) (H1) (H2) The change of spectra in dry season is driven by the change of (H1) canopy demography and/or (H2) leaf amount, and/or other process (e.g. epiphyll)

31. Future Work 2. Hypothesis Test 221 240 265 315 (a) Recently Mature Old (b) (H1) (H2) It will be great to incorporate some processes based model (e.g. Prospect+SAIL) to quantitatively assess these two hypotheses

32. Acknowledgments Sensor Calibration and Discussion William van Leeuwen (UofA) Nikolaus Anderson (UofA) Stuart Biggar (UofA) David J. Moore (UofA) Joost van Haren (UofA) Field Work & Help Cecilia Chavana-Bryant (Oxford U) Mick Eltringham (UK) Kleber Silva Campos (UFOPA) Marielle Smith (UofA) Ty Taylor (UofA) Scott Stark (MSU) Brad Christofferson (Uof Edinburgh) Rodrigo da Silva (UFOPA) Raimundo Cosme Oliveiera (UFOPA) Steve Bissell (US) Funding Amazon-PIRE NASA Terrestrial Ecology Program Photo Credit: Neill Prohaska

33. Tree #/Name Nome CientíficoFamília Tr# 9 Erisma uncinatum Warm. Vochysiaceae Tr# 11 Ocotea sp.Lauraceae Tr# 118 Tachigali eriopetala (Ducke) L.G.Silva & H.C.Lima Leguminosae -Caesalp. Tr# 500 Manilkara huberi (Ducke) A.Chev. Sapotaceae Tr# 504 Coussarea paniculata (Vahl) Standl. Rubiaceae Sap Flow Tree Chamaecrista scleroxylon (Ducke) H.S.Irwin & Barneby Leguminosae -Caesalp. * 6 focal trees+14 understory trees were selected; * samples were collected in the early, middle, and late of dry season (Mid-Aug to Early-Dec) Materials: 2012 Fall Phenology Campaign

34. Leaf Spectra & Canopy Spectra 221 240 265 315 Young Young/Mature Mature Old Old & Epiphyll Leaf level hyperspectra (via a handheld ASD) Canopy level hyperspectra (via tower mounted camera) become older

35. R2=0.8656 P-value=0.0102 Spectral Prediction Observation Canopy Demography Modeling Model Prediction

36. Methodology: Reflectance Calculation #Tr9 #Tr11 #Tr118 #Tr13 #Tr14 Premise from equation (1) to equation (2) is DN linearly scales with radiation for each wavelength ( was tested in the lab!!) : leaf reflectance at wavelength : reference reflectance at wavelength : leaf surface radiation at wavelength : camera based digital number at wavelength Calibrated by an known surface reflectance Spectralon Under clear sky condition Diurnal pattern for characterizing BRDF of reference plates

37. Methodology (Example of ) SOC Camera Reference Plates Equipment controlling Camera View Angle Characterizing diurnal BRDF of Reference Plates at UA campus, clear sky condition

38. NDVI-Leaf Fresh Mass Tr9 Tr11Tr13Tr118 Tr14 Each point represents one time sample from 1-m sun branch; all the branches selected here for each tree have very similar branch diameter across the time.

39. Fresh mass (g) @ 1m branch Tr9 Fresh mass (g) @ 1m branch Tr11 NDVI-Leaf Fresh Mass

40. Spectra-Age Model Temporal changes in spectral reflectance (Roberts et al., 1998 ) Example species: Aldina (species name), Fabaceae (family name)

41. Spectra-Age Model Temporal changes in spectral reflectance (Roberts et al., 1998 ) Example species: Aldina (species name), Fabaceae (family name)

42. Spectra-Age Model Temporal changes in spectral reflectance (Roberts et al., 1998 ) Spectra-Age Model by Doughty and Goulden (2008)