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 3: Case study from a tower mounted hyperspectral vegetation imaging system in a tropical moist forest system 4: Inference to HyspIRI study

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 plant photosynthesis; 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 Both environments and phenology control plant photosynthesis; 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 the physiology of each demographic component In sum, leaf amount seasonality and canopy demography seasonality are two important perspectives of plant phenology

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 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 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 (m2/m2) Photosyntehetic Capacity (umol/m2/s) Month of Year

11. Model perspective of 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 (m2/m2) Photosyntehetic Capacity (umol/m2/s) Month of Year (1)Leaf demography is an important component of leaf phenology; (2) Leaf demography is especially important for tropical forest photosynthesis processes; (3) currently, there is still lacking study of tropical demography using space techniques

12. Methodology: Leaf demography Field methods (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. Case Study: 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

14. Case Study: 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: July-December, 2012 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. 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) Case Study: 2012 Fall Phenology Campaign (Review)

16. Case Study: 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

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

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

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

26. Summary

27. Future Work

28. 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 NANA Terrestrial Ecology Program Photo Credit: Neill Prohaska