Estimating average tree crown size using high-resolution airborne data

Olga Brovkina; Iscander Latypov; Emil Cienciala

doi:10.1117/1.JRS.9.096053

13 May 2015 Estimating average tree crown size using high-resolution airborne data

Olga Brovkina, Iscander Latypov, Emil Cienciala

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 096053 (May 2015). https://doi.org/10.1117/1.JRS.9.096053

Abstract

Tree crown size is a key parameter of tree structure that has a variety of uses, including assessment of stand density, tree growth, and amount of timber volume assessment. Remote sensing techniques provide a potentially low-cost alternative to field-based assessments, but require the development of algorithms to easily and accurately extract the required information. This study presents a method for average crown diameter estimation on a plot level based on high-resolution airborne data. The method consists of the combination of a window binarization procedure and a granulometric algorithm. This approach avoids the complicated crown delineation procedure that is currently used to estimate crown size. The method was applied to a spruce mountain forest and was verified on 23 reference plots. The method achieved best results of R²=76% [RMSE=0.37 m (11.2% of the observed mean)] and R²=79% [RMSE=0.49 m (16.7% of the observed mean)]. The study investigates the dependence of the algorithm results on the sun altitude of each image, and determines the optimal combination of spectral bands from hyperspectral airborne images for the application of the method.

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Olga Brovkina, Iscander Latypov, and Emil Cienciala "Estimating average tree crown size using high-resolution airborne data," Journal of Applied Remote Sensing 9(1), 096053 (13 May 2015). https://doi.org/10.1117/1.JRS.9.096053

Published: 13 May 2015

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