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

Trees ◽  
2016 ◽  
Vol 30 (6) ◽  
pp. 2101-2115 ◽  
Author(s):  
Zhili Liu ◽  
Guangze Jin
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Regression Models ◽  
Optical Methods ◽  
Forest Types ◽  
Area Index ◽  
Improving Accuracy

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

2020 ◽  
Vol 68 (3) ◽  
pp. 559-566
Author(s):  
Zdeněk Patočka ◽  
Kateřina Novosadová ◽  
Pavel Haninec ◽  
Radek Pokorný ◽  
Tomáš Mikita ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Regression Models ◽  
Canopy Structure ◽  
Optical Methods ◽  
Distribution Model ◽  
Area Index ◽  
Index Estimation ◽  
Effective Leaf Area Index

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.

scholarly journals LaiPen LP 100 – a new device for estimating forest ecosystem leaf area index compared to the etalon: A methodologic case study

2018 ◽  
Vol 64 (No. 11) ◽  
pp. 455-468
Author(s):  
Jakub Černý ◽  
Jan Krejza ◽  
Radek Pokorný ◽  
Pavel Bednář
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Methods ◽  
Ecological Studies ◽  
Area Index ◽  
New Device ◽  
Area Efficiency ◽  
Wide Range ◽  
World Standard

Fast and precise leaf area index (LAI) estimation of a forest stand is frequently needed for a wide range of ecological studies. In the presented study, we compared side-by-side two instruments for performing LAI estimation (i.e. LaiPen LP 100 as a “newly developed device” and LAI-2200 PCA as the “world standard”), both based on indirect optical methods for performing LAI estimation in pure Norway spruce (Picea abies (Linnaeus) H. Karsten) stands under different thinning treatments. LAI values estimated by LaiPen LP 100 were approximate 5.8% lower compared to those measured by LAI-2200 PCA when averaging all collected data regardless of the thinning type. Nevertheless, when we considered the differences among LAI values at each measurement point within a regular grid, LaiPen LP 100 overestimated LAI values compared to those from LAI-2200 PCA on average by 1.4%. Therefore, both instruments are comparable. Similar LAI values between thinning from above (A) and thinning from below (B) approaches were indirectly detected by both instruments. The highest values of canopy production index and leaf area efficiency were observed within the stand thinned from above (plot A).

scholarly journals Spatial and seasonal variations of leaf area index (LAI) in subtropical secondary forests related to floristic composition and stand characters

2016 ◽  
Author(s):  
Wenjuan Zhu ◽  
Wenhua Xiang ◽  
Qiong Pan ◽  
Yelin Zeng ◽  
Shuai Ouyang ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Variations ◽  
Basal Area ◽  
Floristic Composition ◽  
Controlling Factors ◽  
Forest Types ◽  
Area Index ◽  
Subtropical Forests

Abstract. Leaf area index (LAI) is an important parameter related to carbon, water and energy exchange between canopy and atmosphere, and is widely applied in the process models to simulate production and hydrological cycle in forest ecosystems. However, fine-scale spatial heterogeneity of LAI and its controlling factors have not been fully understood in Chinese subtropical forests. We used hemispherical photography to measure LAI values in three subtropical forests (i.e. Pinus massoniana – Lithocarpus glaber coniferous and evergreen broadleaved mixed forests, Choerospondias axillaris deciduous broadleaved forests, and L. glaber – Cyclobalanopsis glauca evergreen broadleaved forests) during period from April, 2014 to January, 2015. Spatial heterogeneity of LAI and its controlling factors were analysed by using geostatistics method the generalised additive models (GAMs), respectively. Our results showed that LAI values differed greatly in the three forests and their seasonal variations were consistent with plant phenology. LAI values exhibited strong spatial autocorrelation for three forests measured in January and for the L. glaber – C. glauca forest in April, July and October. Obvious patch distribution pattern of LAI values occurred in three forests during the non-growing period and this pattern gradually dwindled in the growing season. Stand basal area, crown coverage, crown width, proportion of deciduous species on basal area basis and forest types affected the spatial variations in LAI values in January, while species richness, crown coverage, stem number and forest types affected the spatial variations in LAI values in July. Floristic composition, spatial heterogeneity and seasonal variations should be considered for sampling strategy in indirect LAI measurement and application of LAI to simulate functional processes in subtropical forests.

scholarly journals Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice

2020 ◽  
Vol 13 (1) ◽  
pp. 84
Author(s):  
Tomoaki Yamaguchi ◽  
Yukie Tanaka ◽  
Yuto Imachi ◽  
Megumi Yamashita ◽  
Keisuke Katsura
Keyword(s):  
Deep Learning ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Regression Models ◽  
Estimation Model ◽  
Area Index ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Multispectral Camera

Leaf area index (LAI) is a vital parameter for predicting rice yield. Unmanned aerial vehicle (UAV) surveillance with an RGB camera has been shown to have potential as a low-cost and efficient tool for monitoring crop growth. Simultaneously, deep learning (DL) algorithms have attracted attention as a promising tool for the task of image recognition. The principal aim of this research was to evaluate the feasibility of combining DL and RGB images obtained by a UAV for rice LAI estimation. In the present study, an LAI estimation model developed by DL with RGB images was compared to three other practical methods: a plant canopy analyzer (PCA); regression models based on color indices (CIs) obtained from an RGB camera; and vegetation indices (VIs) obtained from a multispectral camera. The results showed that the estimation accuracy of the model developed by DL with RGB images (R2 = 0.963 and RMSE = 0.334) was higher than those of the PCA (R2 = 0.934 and RMSE = 0.555) and the regression models based on CIs (R2 = 0.802-0.947 and RMSE = 0.401–1.13), and comparable to that of the regression models based on VIs (R2 = 0.917–0.976 and RMSE = 0.332–0.644). Therefore, our results demonstrated that the estimation model using DL with an RGB camera on a UAV could be an alternative to the methods using PCA and a multispectral camera for rice LAI estimation.

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

2015 ◽  
Vol 45 (6) ◽  
pp. 721-731 ◽  
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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