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Forests 2016, 7(12), 307; doi:10.3390/f7120307

Forest Inventory Attribute Prediction Using Lightweight Aerial Scanner Data in a Selected Type of Multilayered Deciduous Forest

1
National Forest Centre-Forest Research Institute Zvolen, T. G. Masaryka 22, Zvolen 96092, Slovakia
2
Department of Biosciences and Territory, University of Molise, C.da Fonte Lappone, Pesche 86090, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Chris Hopkinson, Laura Chasmer and Craig Mahoney
Received: 7 September 2016 / Revised: 30 November 2016 / Accepted: 1 December 2016 / Published: 7 December 2016
(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources)
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Abstract

Airborne laser scanning is a promising technique for efficient and accurate, remote-based forest inventory, due to its capacity for direct measurement of the three-dimensional structure of vegetation. The main objective of this study was to test the usability and accuracy of an individual tree detection approach, using reFLex software, in the evaluation of forest variables. The accuracy assessment was conducted in a selected type of multilayered deciduous forest in southern Italy. Airborne laser scanning data were taken with a YellowScan Mapper scanner at an average height of 150 m. Point density reached 30 echoes per m2, but most points belonged to the first echo. The ground reference data contained the measured positions and dimensions of 445 trees. Individual tree-detection rates were 66% for dominant, 48% for codominant, 18% for intermediate, and 5% for suppressed trees. Relative root mean square error for tree height, diameter, and volume reached 8.2%, 21.8%, and 45.7%, respectively. All remote-based tree variables were strongly correlated with the ground data (R2 = 0.71–0.79). At the stand-level, the results show that differences ranged between 4% and 17% for stand height and 22% and 40% for stand diameter. The total growing stock differed by −43% from the ground reference data, and the ratios were 64% for dominant, 58% for codominant, 36% for intermediate, and 16% for suppressed trees. View Full-Text
Keywords: forest inventory; LiDAR; individual tree detection approach forest inventory; LiDAR; individual tree detection approach
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MDPI and ACS Style

Sačkov, I.; Santopuoli, G.; Bucha, T.; Lasserre, B.; Marchetti, M. Forest Inventory Attribute Prediction Using Lightweight Aerial Scanner Data in a Selected Type of Multilayered Deciduous Forest. Forests 2016, 7, 307.

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