Comparison of LiDAR-based Models for True Leaf Area Index and Effective Leaf Area Index Estimation in Young Beech Forests

The leaf area index (LAI) is one of the most common leaf area and canopy structure quantifiers. Direct LAI measurement and determination of canopy characteristics in larger areas is unrealistic due to the large number of measurements required to create the distribution model. This study compares the regression models for the ALS-based calculation of LAI, where the effective leaf area index (eLAI) determined by optical methods and the LAI determined by the direct destructive method and developed by allometric equations were used as response variables. LiDAR metrics and the laser penetration index (LPI) were used as predictor variables. The regression models of LPI and eLAI dependency and the LiDAR metrics and eLAI dependency showed coefficients of determination (R2) of 0.75 and 0.92, respectively; the advantage of using LiDAR metrics for more accurate modelling is demonstrated. The model for true LAI estimation reached a R2 of 0.88.

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Improving accuracy of optical methods in estimating leaf area index through empirical regression models in multiple forest types

Trees ◽

10.1007/s00468-016-1437-y ◽

2016 ◽

Vol 30 (6) ◽

pp. 2101-2115 ◽

Cited By ~ 1

Author(s):

Zhili Liu ◽

Guangze Jin

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Regression Models ◽

Optical Methods ◽

Forest Types ◽

Area Index ◽

Improving Accuracy

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On improving the accuracy of digital hemispherical photography measurements of seasonal leaf area index variation in deciduous broadleaf forests

Canadian Journal of Forest Research ◽

10.1139/cjfr-2014-0351 ◽

2015 ◽

Vol 45 (6) ◽

pp. 721-731 ◽

Cited By ~ 8

Author(s):

Zhili Liu ◽

Xingchang Wang ◽

Jing M. Chen ◽

Chuankuan Wang ◽

Guangze Jin

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Seasonal Changes ◽

Optical Methods ◽

Hemispherical Photography ◽

Area Index ◽

Clumping Index ◽

Area Variation ◽

Major Factors

Optical methods have been widely used to estimate seasonal changes of the leaf area index (LAI) in forest stands because they are convenient and effective; however, their accuracy in deciduous broadleaf forests has rarely been evaluated. We estimate the seasonal changes in the LAI by combining periodic observations of leaf area variation with litter collection (LAIdir) in deciduous broadleaf forests and use these estimates to evaluate the accuracy of optical LAI measurements made using digital hemispherical photography (DHP). We also propose a method to correct DHP-derived LAI (LAIDHP) values for seasonal changes in major factors that influence the determination of LAI, including the woody to total area ratio (α), the element clumping index (ΩE, using three different methods), and the photographic exposure setting (E). Before these corrections were made, LAIDHP underestimates LAIdir by 14%–55% from 21 May to 1 October but overestimates it by 78% on 12 May and by 226% on 11 October. Although pronounced differences are observed between LAIdir and LAIDHP, they are significantly correlated (R2 = 0.85, RMSE = 0.32, P < 0.001). After considering seasonal changes in α, ΩE, and E, the accuracy of LAIDHP improves markedly, with a mean difference between the corrected LAIDHP and LAIdir of <17% in all periods. The results suggest that the proposed scheme for correcting LAIDHP is useful and effective for estimating seasonal LAI variation in deciduous broadleaf forests.

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An Effective Leaf Area Index Estimation Method for Wheat from UAV-Based Point Cloud Data

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8899075 ◽

2019 ◽

Author(s):

Yang Song ◽

Jinfei Wang ◽

Bo Shan

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Point Cloud ◽

Estimation Method ◽

Point Cloud Data ◽

Area Index ◽

Cloud Data ◽

Index Estimation ◽

Effective Leaf Area Index

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Usage of Lidar Data for Leaf Area Index Estimation

GeoScience Engineering ◽

10.2478/gse-2014-0013 ◽

2014 ◽

Vol 60 (3) ◽

pp. 10-18 ◽

Cited By ~ 4

Author(s):

Jan Sabol ◽

Zdeněk Patočka ◽

Tomáš Mikita

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Laser Scanning ◽

Remote Sensing Data ◽

Optical Methods ◽

Light Transmittance ◽

Indirect Measurements ◽

Area Index ◽

Index Estimation

Abstract Leaf area index (LAI) can be measured either directly, using destructive methods, or indirectly using optical methods that are based on the tight relationship between LAI and canopy light transmittance. Third, innovative approach for LAI measuring is usage of remote sensing data, especially airborne laser scanning (ALS) data shows itself as a advisable source for purposes of LAI modelling in large areas. Until now there has been very little research to compare LAI estimated by the two different approaches. Indirect measurements of LAI using hemispherical photography are based on the transmission of solar radiation through the vegetation. It can thus be assumed that the same is true for the penetration of LiDAR laser beams through the vegetation canopy. In this study we use ALS based LiDAR penetration index (LPI) and ground based measurement of LAI obtained from hemispherical photographs as a reference in-situ method. Several regression models describing the corellation LAI and LPI were developed with various coefficients of determination ranging up to 0,81. All models were validated and based on the tests performed, no errors were drawn that would affect their credibility.

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