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

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 eco...

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Bibliographic Details
Main Authors:	Martin van Leeuwen, Nicholas C. Coops, T. Andrew Black
Format:	Article
Language:	English
Published:	MDPI AG 2015-12-01
Series:	Remote Sensing
Subjects:	laser scanning canopy structure gross primary productivity eddy covariance data fusion
Online Access:	http://www.mdpi.com/2072-4292/7/12/15875

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spelling	doaj-86db780fdf9d4bd7be63596fb8aac0772020-11-24T20:58:43ZengMDPI AGRemote Sensing2072-42922015-12-01712172721729010.3390/rs71215875rs71215875Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary ProductivityMartin van Leeuwen0Nicholas C. Coops1T. Andrew Black2Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, CanadaForest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, CanadaFaculty of Land and Food Systems, University of British Columbia, 2357 Main Mall, Vancouver, BC V6T 1Z4, CanadaEddy-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.http://www.mdpi.com/2072-4292/7/12/15875laser scanningcanopy structuregross primary productivityeddy covariancedata fusion
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Martin van Leeuwen Nicholas C. Coops T. Andrew Black
spellingShingle	Martin van Leeuwen Nicholas C. Coops T. Andrew Black Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity Remote Sensing laser scanning canopy structure gross primary productivity eddy covariance data fusion
author_facet	Martin van Leeuwen Nicholas C. Coops T. Andrew Black
author_sort	Martin van Leeuwen
title	Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
title_short	Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
title_full	Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
title_fullStr	Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
title_full_unstemmed	Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
title_sort	using stochastic ray tracing to simulate a dense time series of gross primary productivity
publisher	MDPI AG
series	Remote Sensing
issn	2072-4292
publishDate	2015-12-01
description	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.
topic	laser scanning canopy structure gross primary productivity eddy covariance data fusion
url	http://www.mdpi.com/2072-4292/7/12/15875
work_keys_str_mv	AT martinvanleeuwen usingstochasticraytracingtosimulateadensetimeseriesofgrossprimaryproductivity AT nicholasccoops usingstochasticraytracingtosimulateadensetimeseriesofgrossprimaryproductivity AT tandrewblack usingstochasticraytracingtosimulateadensetimeseriesofgrossprimaryproductivity
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Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity

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