Estimation of Gap Fraction and Foliage Clumping in Forest Canopies

Kuusk, Andres; Písek, Jan; Lang, Mait; Märdla, Silja

doi:10.3390/rs10071153

Cited by 26 publications

(9 citation statements)

References 26 publications

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“…Additional problems might also appear when a laser beam hits a canopy object, because the strength of the reflected signal depends on several factors that includes the reflection coefficient, surface roughness, the incidence angle and the fraction of the pulse cross section covered by the target [30]. As a result, it seems the edge of the canopy objects will lack of laser returns and assumed as gaps in the angular grid.…”

Section: Angular Resolution and Gfmentioning

confidence: 99%

“…TLS can estimate GF on discrete periods of time [18,28,30], and also employed to continuously monitor the canopy structure dynamics [31,32]. Consequently, TLS provides a non-destructive alternative to measure GF that is independent from natural light conditions [24].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, TLS provides a non-destructive alternative to measure GF that is independent from natural light conditions [24]. In this context, GF is a key forest structure feature to determine the radiate transfer through the canopy [30]. Moreover, the distribution and directions of gaps is a valuable information to improve the understanding of light regimes under the canopy [33], the recruitment and establishment of trees [34], and the estimation of the canopy clumping that corrects the effective LAI estimations obtained from indirect methods [18,35,36].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Canopy Gap Fraction from Terrestrial Laser Scanner Using an Angular Grid to Take Advantage of the Full Data Spatial Resolution

et al. 2020

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This paper develops an algorithm to estimate vegetation canopy gap fraction (GF), taking advantage of the full Terrestrial Laser Scanner (TLS) resolution. After calculating the TLS angular resolution, the algorithm identifies the missing laser hits (gaps) within an angular grid in the azimuthal and zenithal directions. The algorithm was first tested on angular data simulations with random (R), cluster (C) and random and cluster together (RC) gap pattern distributions. Noise introduced in the simulations as a percentage of the resolution accounted for the effect of TLS angular uncertainty. The algorithm performs accurately if angular noise is <6% of the angular resolution. To assess the impact of the change in projection, this study compared these GF estimates from angular grid simulations to their transformation into simulated hemispherical images (SHI). SHI with C patterns perform accurately, but R and RC patterns underestimate GF, especially for GF values below 0.6. The SHI performance to estimate GF was always far below the algorithm developed here with the angular grid simulations. When applied to actual TLS data acquired over individual Quercus ilex L. trees, the algorithm rendered a GF between 0.26 and 0.40. TLS had an angular noise <6%. Converting the angular grid into simulated HI (TLS-SHI) provided a better agreement with actual HI acquired in the same location as the TLS data, since they are in the same projection. The TLS-SHI underestimated GF by an average of 4% compared to HI. HI and TLS-SHI presented 14% and 17% lower values than the GF calculated from the angular grids, respectively. Nevertheless, the results from the simulations indicate that the algorithm based on the angular grid should be closer to the actual GF of the tree canopy.

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Section: Angular Resolution and Gfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Canopy Gap Fraction from Terrestrial Laser Scanner Using an Angular Grid to Take Advantage of the Full Data Spatial Resolution

et al. 2020

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“…Gap fraction t(θ) can be calculated as a ratio from the below-and above-canopy radiation measurements (Welles & Norman, 1991) using plant canopy analysers. Kuusk et al (2018) showed that substantial discrepancies exist between estimates of t(θ) when using different optical methods. Cescatti (2007) published the LinearRatio method and showed how to use two digital hemispherical cameras as a pair of plant canopy structure analyser devices.…”

Section: Introductionmentioning

confidence: 99%

Tracking the long-term structure changes of a mature deciduous broadleaf forest stand using digital hemispherical photography

Lang

Písek

2019

Forestry Studies

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Hemispherical photography provides permanent records of forest canopy structure. We analysed digital hemispherical images taken during the period of 2007–2018 in a mature silver birch stand located in Järvselja, Estonia. The stand was thinned in 2004. Understory trees were removed in the spring of 2018. Images were processed using the LinearRatioSC method. Effective plant area index Leff during the leafless phenophase increased as a result of tree growth from 0.92 to 1.24 and understory cutting was not detectable. During the full foliage condition Leff increased from 3.6 in 2008 to 5.8 in 2017. After removal of understory trees from the stand Leff decreased, and repeated measurements in the summer of 2018 estimated the plant area index range 4.5 < Leff < 4.8. The results are in agreement with the expected changes following forest growth and demonstrate that LinearRatioSC is a suitable method for the estimation and long-term monitoring of forest canopy properties from digital hemispherical images.

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“…Kobayashi et al [2], in turn, introduce the first in situ continuous measurements of canopy scale (overstory + understory) and understory spectral reflectance and vegetation index in an open boreal forest in interior Alaska. Kuusk et al [3] offer the most comprehensive comparison of different instruments and methods for estimating the gap fraction of (hemi-)boreal canopies to date. Oveland et al [4] compare both the accuracy and efficiency of three different ground-based laser scanning methods (terrestrial laser scanner, handheld laser scanner and a backpack laser scanner) for tree stem detection in southeastern Norway.…”

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confidence: 99%