Application of a single-tree identification algorithm to LiDAR data for the simulation of stem volume current annual increment

Lorenzo Bottai; Lorenzo Arcidiaco; Marta Chiesi; Fabio Maselli

doi:10.1117/1.JRS.7.073699

24 September 2013 Application of a single-tree identification algorithm to LiDAR data for the simulation of stem volume current annual increment

Lorenzo Bottai, Lorenzo Arcidiaco, Marta Chiesi, Fabio Maselli

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 7, Issue 1, 073699 (September 2013). https://doi.org/10.1117/1.JRS.7.073699

Abstract

A single-tree identification method has been applied to light detection and ranging (LiDAR) data acquired over a protected coastal area in Tuscany (San Rossore Regional Park, Central Italy). The method, which is based on the computation of the convergence index from the LiDAR tree-height image, is capable of identifying individual pine trees in densely populated stands. The main features of each pine tree (height and crown size) are also estimated, which allows the final prediction of stem volume. The accuracy of the stem volume estimates is first assessed through a comparison with the ground measurements of a recent forest inventory of the park [San Rossore Forest Inventory (SRFI)]. This test indicates that stem volume is predicted with moderate accuracy at stand level (r around 0.65). The stem volume estimates are then used to drive a modeling strategy which, on the basis of remotely sensed and ancillary data, is capable of predicting stem volume current annual increment (CAI). A final accuracy assessment indicates that the use of LiDAR stem volumes in place of the SRFI measurements only slightly deteriorates the quality of the obtained stand CAI estimates.

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Lorenzo Bottai, Lorenzo Arcidiaco, Marta Chiesi, and Fabio Maselli "Application of a single-tree identification algorithm to LiDAR data for the simulation of stem volume current annual increment," Journal of Applied Remote Sensing 7(1), 073699 (24 September 2013). https://doi.org/10.1117/1.JRS.7.073699

Published: 24 September 2013

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