Improved method for estimating tree crown diameter using high-resolution airborne data

Olga Brovkina; Iscander  S. Latypov; Emil Cienciala; Tomas Fabianek

doi:10.1117/1.JRS.10.026006

12 April 2016 Improved method for estimating tree crown diameter using high-resolution airborne data

Olga Brovkina, Iscander S. Latypov, Emil Cienciala, Tomas Fabianek

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 2, 026006 (April 2016). https://doi.org/10.1117/1.JRS.10.026006

Abstract

Automatic mapping of tree crown size (radius, diameter, or width) from remote sensing can provide a major benefit for practical and scientific purposes, but requires the development of accurate methods. This study presents an improved method for average tree crown diameter estimation at a forest plot level from high-resolution airborne data. The improved method consists of the combination of a window binarization procedure and a granulometric algorithm, and avoids the complicated crown delineation procedure that is currently used to estimate crown size. The systematic error in average crown diameter estimates is corrected with the improved method. The improved method is tested with coniferous, beech, and mixed-species forest plots based on airborne images of various spatial resolutions. The absolute (quantitative) accuracy of the improved crown diameter estimates is comparable or higher for both monospecies plots and mixed-species plots than the current methods. The ability of the improved method to produce good estimates for average crown diameters for monoculture and mixed species, to use remote sensing data of various spatial resolution and to operate in automatic mode promisingly suggests its applicability to a wide range of forest systems.

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Olga Brovkina, Iscander S. Latypov, Emil Cienciala, and Tomas Fabianek "Improved method for estimating tree crown diameter using high-resolution airborne data," Journal of Applied Remote Sensing 10(2), 026006 (12 April 2016). https://doi.org/10.1117/1.JRS.10.026006

Published: 12 April 2016

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KEYWORDS

Spatial resolution

Error analysis

Critical dimension metrology

LIDAR

Remote sensing

Near infrared

Airborne remote sensing

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