Modified gap fraction model of individual trees for estimating leaf area using terrestrial laser scanner

Donghui Xie; Yan Wang; Ronghai Hu; Yiming Chen; Guangjian Yan; Wuming Zhang; Peijuan Wang

doi:10.1117/1.JRS.11.035012

24 August 2017 Modified gap fraction model of individual trees for estimating leaf area using terrestrial laser scanner

Donghui Xie, Yan Wang, Ronghai Hu, Yiming Chen, Guangjian Yan, Wuming Zhang, Peijuan Wang

Author Affiliations +

J. of Applied Remote Sensing, 11(3), 035012 (2017). https://doi.org/10.1117/1.JRS.11.035012

Abstract

Terrestrial laser scanners (TLS) have demonstrated great potential in estimating structural attributes of forest canopy, such as leaf area index (LAI). However, the inversion accuracy of LAI is highly dependent on the measurement configuration of TLS and spatial characteristics of the scanned tree. Therefore, a modified gap fraction model integrating the path length distribution is developed to improve the accuracy of retrieved single-tree leaf area (LA) by considering the shape of a single-tree crown. The sensitivity of TLS measurement configurations on the accuracy of the retrieved LA is also discussed by using the modified gap fraction model based on several groups of simulated and field-measured point clouds. We conclude that (1) the modified gap fraction model has the potential to retrieve LA of an individual tree and (2) scanning distance has the enhanced impact on the accuracy of the retrieved LA than scanning step. A small scanning step for broadleaf trees reduces the scanning time, the storage volume, and postprocessing work in the condition of ensuring the accuracy of the retrieved LA. This work can benefit the design of an optimal survey configuration for the field campaign.

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Donghui Xie, Donghui Xie, Yan Wang, Yan Wang, Ronghai Hu, Ronghai Hu, Yiming Chen, Yiming Chen, Guangjian Yan, Guangjian Yan, Wuming Zhang, Wuming Zhang, Peijuan Wang, Peijuan Wang, "Modified gap fraction model of individual trees for estimating leaf area using terrestrial laser scanner," Journal of Applied Remote Sensing 11(3), 035012 (24 August 2017). https://doi.org/10.1117/1.JRS.11.035012 . Submission: Received: 18 January 2017; Accepted: 1 August 2017

Received: 18 January 2017; Accepted: 1 August 2017; Published: 24 August 2017

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