Home > New Publication: Simone Vaccari Publishes Paper on Bias in LiDAR-Based Canopy Gap Fraction Estimates

New Publication: Simone Vaccari Publishes Paper on Bias in LiDAR-Based Canopy Gap Fraction Estimates

Simone Vaccari of LTS has published a paper on bias in lidar-based canopy gap fraction estimates in Remote Sensing Letters.

Simone’s work using lidar in the context of forest biomass measurement is highly relevant to LTS’ REDD+ services.

The paper can be downloaded/purchased using the link below.

Vaccari, S., van Leeuwen, M., Calders, K., Coops, N. C., and Herold, M., 2013. Bias in lidar-based canopy gap fraction estimates, Remote Sensing Letters 4(4), pp. 391-399

Abstract: Leaf area index and canopy gap fraction (GF) provide important information to forest managers regarding the ecological functioning and productivity of forest resources. Traditional measurements such as those obtained from hemispherical photography (HP) measure solar irradiation, penetrating the forest canopy, but do not provide information regarding the three-dimensional canopy structure. Terrestrial laser scanning (TLS) is an active sensor technology able to describe structural forest attributes by measuring interceptions of emitted laser pulses with the canopy and is able to record the spatial distribution of the foliage in three dimensions. However, due to the beam area of the laser, interceptions are detected more frequently than using conventional HP, and GF is typically underestimated. This study investigates the effects of laser beam area on the retrieval of GF by using morphological image processing to describe estimation bias as a function of canopy perimeters. The results show that, using canopy perimeter, improvements in correlation between HP and TLS can be achieved with an increase in the coefficient of determination R 2 up to 28% (from an original R 2 of 0.66 to an adjusted R 2 of 0.85).

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Service areas:
redd
FLEGT
climate change
m&E
water
ecosystems