Continuous Leaf Area Index (LAI) Observation in Forests: Validation, Application, and Improvement of LAI-NOS

Zhentao Gao; Yunping Chen; Zhengjian Zhang; Tianxin Duan; Juncheng Chen; Ainong Li

doi:10.3390/f15050868

Forests (May 2024)

Continuous Leaf Area Index (LAI) Observation in Forests: Validation, Application, and Improvement of LAI-NOS

Zhentao Gao,
Yunping Chen,
Zhengjian Zhang,
Tianxin Duan,
Juncheng Chen,
Ainong Li

Affiliations

Zhentao Gao: School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West High-Tech Zone, Chengdu 611731, China
Yunping Chen: School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West High-Tech Zone, Chengdu 611731, China
Zhengjian Zhang: Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, No. 9, Section 4, South Renmin Road, Chengdu 610041, China
Tianxin Duan: School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West High-Tech Zone, Chengdu 611731, China
Juncheng Chen: School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West High-Tech Zone, Chengdu 611731, China
Ainong Li: Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, No. 9, Section 4, South Renmin Road, Chengdu 610041, China

DOI: https://doi.org/10.3390/f15050868
Journal volume & issue: Vol. 15, no. 5
p. 868

Abstract

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The leaf area index (LAI) is one of the core parameters reflecting the growth status of vegetation. The continuous long-term observation of the LAI is key when assessing the dynamic changes in the energy exchange of ecosystems and the vegetation’s response indicators to climate change. The errors brought about by non-standard operations in manual LAI measurements hinder the further research utilization of this parameter. The long-term automatic LAI observation network is helpful in reducing errors from manual measurements. To further test the applicability of automatic LAI observation instruments in forest environments, this study carried out comparative validation research of the LAI-NOS (LAI automatic network observation system) at the Wanglang Mountain Ecological Remote Sensing Comprehensive Observation Station, China, comparing it with the results measured by the LAI-2200 Plant Canopy Analyzer (LI-COR, Lincoln, NE, USA), the LAI-probe handheld instrument, and a fisheye lens digital camera (DHP method). Instead of using the original “smoothest window” method, a new method, the “sunrise–sunset” method, is used to extract daily LAI-NOS LAI, and the corresponding confidence level is used to filter the data. The results of the data analysis indicate the following: LAI-NOS has a high data stability. The automatically acquired daily data between two consecutive days has a small deviation and significant correlations. Single-angle/multi-angle LAI measurement results of the LAI-NOS have good correlations with the LAI-2200 (R2 = 0.512/R2 = 0.652), the LAI-probe (R2 = 0.692/R2 = 0.619), and the DHP method (R2 = 0.501/R2 = 0.394). The daily LAI obtained from the improved method, when compared to the original method, both show the same vegetation growth trend. However, the improved method has a smaller dispersion. This study confirms the stability and accuracy of automatic observation instruments in mountainous forests, demonstrating the distinct advantages of automatic measurement instruments in the long-term ground observation of LAIs.

Published in Forests

ISSN: 1999-4907 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science: Botany: Plant ecology
Website: http://www.mdpi.com/journal/forests/

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