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Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index

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Abstract
In situ leaf area index (LAI) measurements are essential to validate widely-used large-area or global LAI products derived, indirectly, from satellite observations. Here, we compare three common and emerging ground-based sensors for rapid LAI characterisation of large areas, namely digital hemispherical photography (DHP), two versions of a widely-used commercial LAI sensor (LiCOR LAI-2000 and 2200), and terrestrial laser scanning (TLS). The comparison is conducted during leaf-on and leaf-off conditions at an unprecedented sample size in a deciduous woodland canopy. The deviation between estimates of these three ground-based instruments yields differences greater than the 5% threshold goal set by the World Meteorological Organization. The variance at sample level is reduced when aggregated to plot scale (1 ha) or site scale (6 ha). TLS shows the lowest relative standard deviation in both leaf-on (11.78%) and leaf-off (13.02%) conditions. Whereas the relative standard deviation of effective plant area index (ePAI) derived from DHP relates closely to us in leaf-on conditions, it is as large as 28.14-29.74% for effective wood area index (eWAI) values in leaf-off conditions depending on the thresholding technique that was used. ePAI values of TLS and LAI-2x00 agree best in leaf-on conditions with a concordance correlation coefficient (CCC) of 0.796. In leaf-off conditions, eWAI values derived from DHP with Ridler and Calvard thresholding agrees best with TLS. Sample size analysis using Monte Carlo bootstrapping shows that TLS requires the fewest samples to achieve a precision better than 5% for the mean +/- standard deviation. We therefore support earlier studies that suggest that TLS measurements are preferential to measurements from instruments that are dependent on specific illumination conditions. A key issue with validation of indirect estimates of LAI is that the true values are not known. Since we cannot know the true values of LAI, we cannot quantify the accuracy of the measurements. Our radiative transfer simulations show that ePAI estimates are, on average, 27% higher than eLAI estimates. Linear regression indicated a linear relationship between eLAI and ePAI-eWAI (R-2 = 0.87), with an intercept of 0.552 and suggests that caution is required when using LAI estimates.
Keywords
CAVElab, :Sensor comparison, Leaf area index, Terrestrial LiDAR, Hemispherical photography, LAI-2200, Validation, DIGITAL HEMISPHERICAL PHOTOGRAPHY, GAP FRACTION, TERRESTRIAL LIDAR, STRUCTURAL PARAMETERS, SPRING PHENOLOGY, FOREST, LASER, SYSTEM, ECOSYSTEMS, RETRIEVAL

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Chicago
Calders, Kim, Niall Origo, Mathias Disney, Joanne Nightingale, William Woodgate, John Armston, and Philip Lewis. 2018. “Variability and Bias in Active and Passive Ground-based Measurements of Effective Plant, Wood and Leaf Area Index.” Agricultural and Forest Meteorology 252: 231–240.
APA
Calders, Kim, Origo, N., Disney, M., Nightingale, J., Woodgate, W., Armston, J., & Lewis, P. (2018). Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index. AGRICULTURAL AND FOREST METEOROLOGY, 252, 231–240.
Vancouver
1.
Calders K, Origo N, Disney M, Nightingale J, Woodgate W, Armston J, et al. Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index. AGRICULTURAL AND FOREST METEOROLOGY. 2018;252:231–40.
MLA
Calders, Kim, Niall Origo, Mathias Disney, et al. “Variability and Bias in Active and Passive Ground-based Measurements of Effective Plant, Wood and Leaf Area Index.” AGRICULTURAL AND FOREST METEOROLOGY 252 (2018): 231–240. Print.
@article{8550998,
  abstract     = {In situ leaf area index (LAI) measurements are essential to validate widely-used large-area or global LAI products derived, indirectly, from satellite observations. Here, we compare three common and emerging ground-based sensors for rapid LAI characterisation of large areas, namely digital hemispherical photography (DHP), two versions of a widely-used commercial LAI sensor (LiCOR LAI-2000 and 2200), and terrestrial laser scanning (TLS). The comparison is conducted during leaf-on and leaf-off conditions at an unprecedented sample size in a deciduous woodland canopy. The deviation between estimates of these three ground-based instruments yields differences greater than the 5\% threshold goal set by the World Meteorological Organization. The variance at sample level is reduced when aggregated to plot scale (1 ha) or site scale (6 ha). TLS shows the lowest relative standard deviation in both leaf-on (11.78\%) and leaf-off (13.02\%) conditions. Whereas the relative standard deviation of effective plant area index (ePAI) derived from DHP relates closely to us in leaf-on conditions, it is as large as 28.14-29.74\% for effective wood area index (eWAI) values in leaf-off conditions depending on the thresholding technique that was used. ePAI values of TLS and LAI-2x00 agree best in leaf-on conditions with a concordance correlation coefficient (CCC) of 0.796. In leaf-off conditions, eWAI values derived from DHP with Ridler and Calvard thresholding agrees best with TLS. Sample size analysis using Monte Carlo bootstrapping shows that TLS requires the fewest samples to achieve a precision better than 5\% for the mean +/- standard deviation. We therefore support earlier studies that suggest that TLS measurements are preferential to measurements from instruments that are dependent on specific illumination conditions. A key issue with validation of indirect estimates of LAI is that the true values are not known. Since we cannot know the true values of LAI, we cannot quantify the accuracy of the measurements. Our radiative transfer simulations show that ePAI estimates are, on average, 27\% higher than eLAI estimates. Linear regression indicated a linear relationship between eLAI and ePAI-eWAI (R-2 = 0.87), with an intercept of 0.552 and suggests that caution is required when using LAI estimates.},
  author       = {Calders, Kim and Origo, Niall and Disney, Mathias and Nightingale, Joanne and Woodgate, William and Armston, John and Lewis, Philip},
  issn         = {0168-1923},
  journal      = {AGRICULTURAL AND FOREST METEOROLOGY},
  language     = {eng},
  pages        = {231--240},
  title        = {Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index},
  url          = {http://dx.doi.org/10.1016/j.agrformet.2018.01.029},
  volume       = {252},
  year         = {2018},
}

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