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Open AccessArticle

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

1
Instituto de Bosques y Sociedad, Facultad de Ciencias Forestales Y Recursos Naturales, Universidad Austral de Chile, Campus Isla Teja, Valdivia 5090000, Chile
2
Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab), Spanish National Research Council (CSIC), 28037 Madrid, Spain
3
Center for Spatial Technologies and Remote Sensing (CSTARS), University of California Davis, 139 Veihmeyer Hall, One Shields Avenue, Davis, CA 95616, USA
4
Environmental Remote Sensing Research Group, Department of Geology, Geography and the Environment, Universidad de Alcalá. Calle Colegios 2, 28801 Alcalá de Henares, Spain
5
Complutum Tecnologías de la Información Geográfica S.L. (COMPLUTIG), 2, 28801 Alcalá de Henares, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1596; https://doi.org/10.3390/rs12101596
Received: 2 April 2020 / Revised: 14 May 2020 / Accepted: 14 May 2020 / Published: 17 May 2020
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing)
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. View Full-Text
Keywords: terrestrial laser scanner; gap fraction; leaf area index; angular grid; hemispherical images terrestrial laser scanner; gap fraction; leaf area index; angular grid; hemispherical images
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MDPI and ACS Style

Gajardo, J.; Riaño, D.; García, M.; Salas, J.; Martín, M.P. Estimation of Canopy Gap Fraction from Terrestrial Laser Scanner Using an Angular Grid to Take Advantage of the Full Data Spatial Resolution. Remote Sens. 2020, 12, 1596.

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