scholarly journals Test of an extended mathematical approach to calculate maize leaf area index and leaf angle distribution

2001 ◽  
Vol 108 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Mauro A Homem Antunes ◽  
Elizabeth A Walter-Shea ◽  
Mark A Mesarch
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Leaf Angle ◽  
Angle Distribution ◽  
Mathematical Approach ◽  
Area Index ◽  
Maize Leaf ◽  
Leaf Angle Distribution

scholarly journals A comparison of different methods for assessing leaf area index in four canopy types

2019 ◽  
Vol 65 (2) ◽  
pp. 67-80 ◽  
Author(s):  
Cristina Ariza-Carricondo ◽  
Francesca Di Mauro ◽  
Maarten Op de Beeck ◽  
Marilyn Roland ◽  
Bert Gielen ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Direct Methods ◽  
Leaf Angle ◽  
Pedunculate Oak ◽  
Angle Distribution ◽  
Indirect Methods ◽  
Area Index ◽  
Short Rotation ◽  
Maize Field

Abstract The agreement of Leaf Area Index (LAI) assessments from three indirect methods, i.e. the LAI–2200 Plant Canopy Analyzer, the SS1 SunScan Canopy Analysis System and Digital Hemispherical Photography (DHP) was evaluated for four canopy types, i.e. a short rotation coppice plantation (SRC) with poplar, a Scots pine stand, a Pedunculate oak stand and a maize field. In the SRC and in the maize field, the indirect measurements were compared with direct measurements (litter fall and harvesting). In the low LAI range (0 to 2) the discrepancies of the SS1 were partly explained by the inability to properly account for clumping and the uncertainty of the ellipsoidal leaf angle distribution parameter. The higher values for SS1 in the medium (2 to 6) to high (6 to 8) ranges might be explained by gap fraction saturation for LAI–2200 and DHP above certain values. Wood area index –understood as the woody light-blocking elements from the canopy with respect to diameter growth– accounted for overestimation by all indirect methods when compared to direct methods in the SRC. The inter-comparison of the three indirect methods in the four canopy types showed a general agreement for all methods in the medium LAI range (2 to 6). LAI–2200 and DHP revealed the best agreement among the indirect methods along the entire range of LAI (0 to 8) in all canopy types. SS1 showed some discrepancies with the LAI–2200 and DHP at low (0 to 2) and high ranges of LAI (6 to 8).

scholarly journals Estimating Crown Structure Parameters of Moso Bamboo: Leaf Area and Leaf Angle Distribution

Forests ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 686 ◽  
Author(s):  
Xuhan Wu ◽  
Weiliang Fan ◽  
Huaqiang Du ◽  
Hongli Ge ◽  
Feilong Huang ◽  
...  
Keyword(s):  
Leaf Area ◽  
Tree Height ◽  
Moso Bamboo ◽  
Leaf Angle ◽  
Angle Distribution ◽  
Angle Measurements ◽  
Crown Structure ◽  
Leaf Angle Distribution ◽  
Carbon Balances ◽  
Two Parameters

Both leaf area (LA) and leaf angle distribution are the most important eco-physiological measures of tree crowns. However, there are limited published investigations on the two parameters of Moso bamboo (Phyllostachys edulis (Carrière) J. Houz., abbreviated as MB). The aim of this study was to develop allometric equations for predicting crown LA of MB by taking the diameter at breast height (DBH) and tree height (H) as predictors and to investigate the leaf angle distribution of a MB crown based on direct leaf angle measurements. Data were destructively sampled from 29 MB crowns including DBH, H, biomass and the area of sampled leaves, biomass of total crown leaves, and leaf angles. The results indicate that (1) the specific leaf area (SLA) of a MB crown decreases from the bottom to the top; (2) the vertical LA distribution of MB crowns follow a “Muffin top” shape; (3) the LA of MB crowns show large variations, from 7.42 to 74.38 m2; (4) both DBH and H are good predictors in allometry-based LA estimations for a MB crown; (5) linear, exponential, and logarithmic regressions show similar capabilities for the LA estimations; (6) leaf angle distributions from the top to the bottom of a MB crown can be considered as invariant; and (7) the leaf angle distribution of a MB crown is close to the planophile case. The results provide an important tool to estimate the LA of MB on the standing scale based on DBH or H measurements, provide useful prior knowledge for extracting leaf area indexes of MB canopies from remote sensing-based observations, and, therefore, will potentially serve as a crucial reference for calculating carbon balances and other ecological studies of MB forests.

The quality of short-wave radiation within plant canopies

1969 ◽  
Vol 47 (12) ◽  
pp. 1989-1994 ◽  
Author(s):  
T. B. Daynard
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Communities ◽  
Short Wave ◽  
Wave Radiation ◽  
Leaf Angle ◽  
Visible Radiation ◽  
Area Index ◽  
Plant Canopies

In this paper, a mathematical attempt is made to predict the effects of leaf area index, leaf angle, and leaf spectral properties on changes in the relative composition of short-wave radiant fluxes as they penetrate plant canopies. Results of this theoretical analysis indicate that a sizeable change in the quality of visible radiation will only occur if the canopy is sufficiently dense to intercept at least 98% of the incident flux one or more times. By contrast, a significant increase in the proportion of infrared radiation is predicted within plant communities, even those of a low effective leaf area index. For natural plant communities, the results would indicate a minimal change in the composition of penetrating radiation at solar noon and a maximal change at sunrise or sunset.The implications of these phenomena to plant morphogenesis and to radiation-measuring techniques are discussed.

scholarly journals Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Guangjian Yan ◽  
Hailan Jiang ◽  
Jinghui Luo ◽  
Xihan Mu ◽  
Fan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Inclination Angle ◽  
Three Dimensional ◽  
Good Choice ◽  
Optical Remote Sensing ◽  
Angle Distribution ◽  
Area Index ◽  
Leaf Inclination

Both leaf inclination angle distribution (LAD) and leaf area index (LAI) dominate optical remote sensing signals. The G-function, which is a function of LAD and remote sensing geometry, is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD. Large uncertainties are thus introduced. However, because numerous tiny leaves grow on conifers, it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval. In this study, we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval. Specifically, a Multi-Directional Imager (MDI) was developed to capture stereo images of the branches, and the needles were reconstructed. The accuracy of the inclination angles calculated from the reconstructed needles was high. Moreover, we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and three-dimensional (3D) tree models. Results show that the constant G assumption introduces large errors in LAI retrieval, which could be as large as 53% in the zenithal viewing direction used by spaceborne LiDAR. As a result, accurate LAD estimation is recommended. In the absence of such data, our results show that a viewing zenith angle between 45 and 65 degrees is a good choice, at which the errors of LAI retrieval caused by the spherical assumption will be less than 10% for coniferous canopies.

scholarly journals A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

2021 ◽  
Vol 14 (1) ◽  
pp. 98
Author(s):  
Quanjun Jiao ◽  
Qi Sun ◽  
Bing Zhang ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Leaf Angle ◽  
Transfer Model ◽  
Growth Monitoring ◽  
Random Forest Algorithm ◽  
Area Index ◽  
Important Indicator

Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for crop CCC retrieval. However, PROSAIL’s homogeneous canopy hypothesis limits the ability to use the PROSAIL-based CCC estimation across different crops with a row structure. In addition to leaf area index (LAI), average leaf angle (ALA) is the most important canopy structure factor in the PROSAIL model. Under the same LAI, adjustment of the ALA can make a PROSAIL simulation obtain the same canopy gap as the heterogeneous canopy at a specific observation angle. Therefore, parameterization of an adjusted ALA (ALAadj) is an optimal choice to make the PROSAIL model suitable for specific row-planted crops. This paper attempted to improve PROSAIL-based CCC retrieval for different crops, using a random forest algorithm, by introducing the prior knowledge of crop-specific ALAadj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALAadj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALAadj information was developed for wheat and soybean CCC retrieval. Ground-measured CCC measurements were used to validate the CCC retrieved from canopy spectra. The results showed that the ALAadj values (62 degrees for wheat; 45 degrees for soybean) that were parameterized for the PROSAIL model demonstrated good discrimination between the two crops. The proposed algorithm improved the CCC retrieval accuracy for wheat and soybean, regardless of whether continuous visible to near-infrared spectra with 50 bands (RMSE from 39.9 to 32.9 μg cm−2; R2 from 0.67 to 0.76) or discrete spectra with 13 bands (RMSE from 43.9 to 33.7 μg cm−2; R2 from 0.63 to 0.74) and nine bands (RMSE from 45.1 to 37.0 μg cm−2; R2 from 0.61 to 0.71) were used. The proposed hybrid algorithm, based on PROSAIL simulations with ALAadj, has the potential for satellite-based CCC estimation across different crop types, and it also has a good reference value for the retrieval of other crop parameters.

scholarly journals Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1942 ◽  
Author(s):  
Sosdito Mananze ◽  
Isabel Pôças ◽  
Mario Cunha
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Cost Functions ◽  
Support Vector ◽  
Area Index ◽  
Maize Leaf ◽  
Multispectral Data ◽  
Simple Ratio ◽  
Sentinel 2

Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.

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