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Remote Sens. 2015, 7(12), 17272-17290;

Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity

Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada
Department of Geography, University College London, 26 Bedford Way, London WC1H 0AP, UK
Faculty of Land and Food Systems, University of British Columbia, 2357 Main Mall, Vancouver, BC V6T 1Z4, Canada
Author to whom correspondence should be addressed.
Academic Editors: Qi Chen, Huaiqing Zhang, Dengsheng Lu, Ronald E. McRorberts, Erkki Tomppo, Guangxing Wang, Randolph H. Wynne and Prasad S. Thenkabail
Received: 22 September 2015 / Revised: 1 December 2015 / Accepted: 10 December 2015 / Published: 18 December 2015
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
View Full-Text   |   Download PDF [4666 KB, uploaded 18 December 2015]   |  


Eddy-covariance carbon dioxide flux measurement is an established method to estimate primary productivity at the forest stand level (typically 10 ha). To validate eddy-covariance estimates, researchers rely on extensive time-series analysis and an assessment of flux contributions made by various ecosystem components at spatial scales much finer than the eddy-covariance footprint. Scaling these contributions to the stand level requires a consideration of the heterogeneity in the canopy radiation field. This paper presents a stochastic ray tracing approach to predict the probabilities of light absorption from over a thousand hemispherical directions by thousands of individual scene elements. Once a look-up table of absorption probabilities is computed, dynamic illumination conditions can be simulated in a computationally realistic time, from which stand-level gross primary productivity can be obtained by integrating photosynthetic assimilation over the scene. We demonstrate the method by inverting a leaf-level photosynthesis model with eddy-covariance and meteorological data. Optimized leaf photosynthesis parameters and canopy structure were able to explain 75% of variation in eddy-covariance gross primary productivity estimates, and commonly used parameters, including photosynthetic capacity and quantum yield, fell within reported ranges. Remaining challenges are discussed including the need to address the distribution of radiation within shoots and needles. View Full-Text
Keywords: laser scanning; canopy structure; gross primary productivity; eddy covariance; data fusion laser scanning; canopy structure; gross primary productivity; eddy covariance; data fusion

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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van Leeuwen, M.; Coops, N.C.; Black, T.A. Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity. Remote Sens. 2015, 7, 17272-17290.

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