Digital Cover Photography for Estimating Leaf Area Index (LAI) in Apple Trees Using a Variable Light Extinction Coefficient

Grain yield was greatly increased during the genetic improvement of japonica inbred rice since the 1980s in Jiangsu, east China; thus, an improved plant morphology should be expected, considering that plant morphology is a decisive factor determining grain yield. Twelve representative japonica inbred rice released from 1983 to 2013 were grown in the same fields in 2019 and 2020. Grain yield increased (p < 0.01) at 63.3 kg ha−1 year−1 across 2 years among rice cultivars released in different periods. The genetic improvement in grain yield was associated with increased spikelets per panicle. Single panicle weight, number of primary and secondary branches, and number of grains on primary and secondary branches were all increased with a year of release. Generally, the width of top three leaves positively correlated (while angle of top three leaves and light extinction coefficient negatively) correlated (p < 0.01) with year of release. Leaf area per tiller and leaf area index at heading and maturity, specific leaf weight, leaf photosynthetic rate, and SPAD values after heading were all increased linearly with year of release. Plant height exhibited a positive (p < 0.01) trend with year of release, as well as stem weight per tiller and K and Si concentrations of stem. Spikelets per panicle, width of top three leaves, plant height, and leaf area index, and specific leaf weight after heading positively correlated (while angle of top three leaves and light extinction coefficient negatively) correlated (p < 0.01) with grain yield and single panicle weight. Our results suggested that modern japonica inbred rice exhibited expanded sink size by spikelets per panicle, higher leaf area through leaf width, optimized leaf photosynthetic capacity, lower leaf angle and light extinction coefficient, and enhanced stem strength. These improved plant morphologies facilitated yield increases of japonica inbred rice since the 1980s in east China.

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Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing

Remote Sensing ◽

10.3390/rs13163263 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3263

Author(s):

Zhijie Liu ◽

Pengju Guo ◽

Heng Liu ◽

Pan Fan ◽

Pengzong Zeng ◽

...

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Shaanxi Province ◽

Gradient Boosting ◽

Support Vector ◽

Apple Orchards ◽

Apple Trees ◽

Area Index ◽

Spatial Efficiency

The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees.

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Canopy architecture assessment of cherry trees by cover photography based on variable light extinction coefficient modelled using artificial neural networks

Acta Horticulturae ◽

10.17660/actahortic.2019.1235.24 ◽

2019 ◽

pp. 183-188

Author(s):

E.J. Tongson ◽

S. Fuentes ◽

M. Carrasco-Benavides ◽

M. Mora

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Extinction Coefficient ◽

Light Extinction ◽

Canopy Architecture ◽

Light Extinction Coefficient ◽

Artificial Neural ◽

Variable Light ◽

Cherry Trees

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Retrieving Leaf Area Index and Extinction Coefficient of Dominant Vegetation Canopy in Meijiang Watershed of China Using ETM+ Data

2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering ◽

10.1109/rsete.2012.6260715 ◽

2012 ◽

Cited By ~ 1

Author(s):

Geying Lai ◽

Lingling Zhang ◽

Ying Liu ◽

Fazhao Yi ◽

Xianggui Zeng ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Extinction Coefficient ◽

Vegetation Canopy ◽

Area Index

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Retrieving Directional Gap Fraction, Extinction Coefficient, and Effective Leaf Area Index by Incorporating Scan Angle Information From Discrete Aerial Lidar Data

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2016.2611651 ◽

2017 ◽

Vol 55 (1) ◽

pp. 577-590 ◽

Cited By ~ 18

Author(s):

Guang Zheng ◽

Lixia Ma ◽

Jan U. H. Eitel ◽

Wei He ◽

Troy S. Magney ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Extinction Coefficient ◽

Lidar Data ◽

Area Index ◽

Gap Fraction ◽

Effective Leaf Area Index

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Radiative behavior and canopy light extinction coefficient in a savanna urban area

Ciência e Natura ◽

10.5902/2179460x34031 ◽

2020 ◽

Vol 42 ◽

pp. e18

Author(s):

Levi Pires de Andrade ◽

Jonathan Willian Zangeski Novais ◽

Marta Cristina de Jesus Albuquerque Nogueira ◽

Luciana Sanches ◽

José De Souza Nogueira ◽

...

Keyword(s):

Urban Area ◽

Extinction Coefficient ◽

Incident Radiation ◽

Light Extinction ◽

Area Index ◽

Transmitted Radiation ◽

Light Extinction Coefficient ◽

Radiation Incidence ◽

Radiative Characteristics

The knowledge of the radiative characteristics of an area is essential to understanding the flows of matter and energy. The value of the Light Extinction Coefficient (K) is a parameter that describes the efficiency of the interception of light in a given canopy, being required, as input, for several SWAP (Soil-Water-Atmosphere-Plant) models, which allow the characterization of the interactive properties among soil, plant and atmosphere concerning these exchanges of matter and energy. This study aimed to obtain the light extinction coefficient (K) for a savanna fragment located in the urban area of Cuiabá. The used data correspond to one measurement each month, totaling twelve measurements in 30 points during the period from October 2014 to September 2015. The measured variables were the LAI (Leaf Area Index), the photosynthetically active incident radiation (PARinc) and the transmitted radiation (PARtrans), and the calculated ones were the zenith angle (Zh) and the extinction coefficient (K). Was observed an annual variability for the light extinction coefficient between 0.49 and 0.69. There are seasonal changes that interfere with the canopy geometry and the position of the study area in relation to the solar radiation incidence, concluding that the K variability is predominantly temporal.

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