Using Airborne Lidar for the Assessment of Canopy Structure Influences on CO 2 Fluxes

Transcription

1 Using Airborne Lidar for the Assessment of Canopy Structure Influences on CO 2 Fluxes L. Chasmer 1, A. Barr, A. Black, C. Hopkinson, N. Kljun, H. McCaughey 1, and P. Treitz 1 1 Department of Geography, Queen s University, Kingston ON lechasme@yahoo.ca Presented at SilviLaser 2007, Espoo Finland

2 Rationale CO 2, H 2 O, and energy fluxes vary spatially and temporally Results in variable CO 2 and H 2 O flux dynamics. Vegetation structure plays a role, but to what degree? Are these more important than meteorological driving mechanisms?

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4 Study Site Mature jack pine site (OJP) Fluxnet-Canada & BERMS Eddy Covariance Study period: 3 ~2-week periods during 2002 growing season. Lidar data collected of watershed in August 2005.

5 Methodology 1. Residual analysis: 2. Footprint analysis: 3. Airborne lidar data analysis: Increases in structural indicators (height, foliage, and canopy depth) ~ increased biomass. GOAL: To quantify canopy structural indicator influences on CO22 flux variability.

6 Footprint/Lidar Extraction 1. Footprint contours extracted. 2. Parameterisation based on: EC height; planetary boundary layer height; roughness length; σ vertical wind velocity; and surface friction velocity. 3. Extraction of contour lines 4. Footprint comparisons between canopy height, canopy depth, foliage density, and elevation. 5. Comparisons between 30-minute average GEP, NEP, and Re.

7 Meteorological Tower Wind Direction

8 Meteorological Tower Wind Direction

9 Results: Removal of Met. Influences Comparison between above canopy incoming PAR and NEP (example): GEP (%) NEP (%) Re (%) PAR (µmol m -2 s -1 ) Air Temperature ( o C) Relative Humidity (%) Soil moisture (m 3 m -3 ) Soil Temperature ( o C) VPD

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11 Results: Structural Indicator Influences on CO 2 Fluxes for All Days (coef. of determination) Month (flux type) June (NEP) July (NEP) August (NEP) June (GEP) Average Lidar Canopy Height Influences on CO 2 (%) Average Lidar Canopy Depth Influences on CO 2 (%) Average Lidar Foliage Fractional Cover Influences on CO 2 (%) Total number of days influenced by structure 4 of 6 6 of 9 7 of 7 1 of 6 July (GEP) of 9 August (GEP) of 7 June (Re) of 6 July (Re) of 9 August (Re) of 7

12 Results: Structural Indicator Influences on CO 2 Fluxes for All Days (coef. of determination) Month (flux type) June (NEP) July (NEP) August (NEP) June (GEP) July (GEP) August (GEP) Largest Influence on CO 2 Fluxes! Average Lidar Canopy Height Influences on CO 2 (%) July Month where 5 fluxes are most affected 14 by canopy structure 9 Average Lidar Canopy Depth Influences on CO 2 (%) June Month where uptake is -7 least affected by canopy structure Average Lidar Foliage Fractional Cover Influences on CO 2 (%) Canopy structure 3 inversely related to ecosystem 7 respiration 16 Total number of days influenced by structure Many days affected! 4 of 6 6 of 9 7 of 7 1 of 6 7 of 9 7 of 7 June (Re) July (Re) August (Re) June Month -18 where respiration is most affected -9 by canopy structure of 6 4 of 9 4 of 7

13 Wind Direction Majority of CO 2 fluxes originate from this area (for entire year) Ave ht = 15.7 m Ave foliage cover = 0.27 Ave depth = 9 m Few CO 2 fluxes originate from this area Ave ht = 14.3 m Ave foliage = 0.28 Ave depth = 8.44 m Respiration

14 Conclusions 1.Structure indicators = 7% and 20% of total CO 2 flux variability (~ 80% of days). 2.Structure indicators account for up to 46% of flux variability. 3.Structure is less important than meteorology on most days. 4. Locations with increased foliage and vegetation height are ~ increased CO 2 uptake. 5. Increased elevation and decreased biomass ~ increased Re in July and August. 6. Eddy covariance at OJP is likely overestimating NEP.

15 Conclusions Ecosystem sensitivity to structural and topographic heterogeneity may tip the balance between carbon source and sink in heterogeneous northern environments.

16 Acknowledgements Fluxnet-Canada Research Network and the Boreal Ecosystem Research and Monitoring Sites (for eddy covariance flux and meteorological data). Applied Geomatics Research Group (for lidar data collection and processing, as well as resources). Field support from: Chris Beasy, Bruce Davison, and Jordan Erker. Also thanks to the late Werner Bauer (site manager) and Jessika Toyra (NWRI). Funding has been provided by: CFCAS, NSERC, and BIOCAP Canada Foundation. Laura has been generously supported by graduate student scholarships from NSERC and OGSST.