Canopy light transmittance in a chronosequence of mixed-species deciduous forests

1994 ◽  
Vol 24 (8) ◽  
pp. 1694-1703 ◽  
Author(s):  
Martin J. Brown ◽  
Geoffrey G. Parker
Keyword(s):  
Leaf Area ◽  
Geometric Mean ◽  
Canopy Structure ◽  
Three Dimensional ◽  
Distinct Pattern ◽  
Light Transmittance ◽  
Dimensional Structure ◽  
Stand Age ◽  
Area Index ◽  
Frequency Distributions

We measured the photosynthetically active radiation transmitted through the canopies of 24 Maryland forest stands, each around midday in midsummer. We then compared the observed values of PAR transmittance with stand age and measures of canopy structure. Generally, transmittance was low, with positively skewed frequency distributions. The geometric mean transmittance followed a distinct pattern with stand age. It was lowest (about 1%) in the youngest stands, increased to about 2.5% as forests approached ages of about 50 years, and then declined with age in the oldest sites (65–340 years). Transmittance was not significantly correlated with many simple measures of forest structure, including estimated aboveground biomass and leaf area index. Better predictions of transmittance used information on the vertical arrangement of the canopy, such as leaf area density. The results are contrary to the common assumptions that forests get consistently darker through time, and that transmittance is inversely proportional to the sheer mass or leaf area of the canopy. The Beer–Lambert extinction coefficient, k, changed with stand age and structure and was especially high in very young stands. We suggest that the variation in light transmittance, and k, can be explained by successional changes in the three-dimensional structure of the canopy.

scholarly journals Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5270
Author(s):  
Yuta Ohashi ◽  
Yasuhiro Ishigami ◽  
Eiji Goto
Keyword(s):  
Leaf Area ◽  
Plant Height ◽  
Canopy Structure ◽  
Three Dimensional ◽  
Dry Weight ◽  
Growth And Yield ◽  
Sensor Technology ◽  
3D Scanner ◽  
Area Index ◽  
Surface Models

Monitoring the growth of fruit vegetables is essential for the automation of cultivation management, and harvest. The objective of this study is to demonstrate that the current sensor technology can monitor the growth and yield of fruit vegetables such as tomato, cucumber, and paprika. We estimated leaf area, leaf area index (LAI), and plant height using coordinates of polygon vertices from plant and canopy surface models constructed using a three-dimensional (3D) scanner. A significant correlation was observed between the measured and estimated leaf area, LAI, and plant height (R2 > 0.8, except for tomato LAI). The canopy structure of each fruit vegetable was predicted by integrating the estimated leaf area at each height of the canopy surface models. A linear relationship was observed between the measured total leaf area and the total dry weight of each fruit vegetable; thus, the dry weight of the plant can be predicted using the estimated leaf area. The fruit weights of tomato and paprika were estimated using the fruit solid model constructed by the fruit point cloud data extracted using the RGB value. A significant correlation was observed between the measured and estimated fruit weights (tomato: R2 = 0.739, paprika: R2 = 0.888). Therefore, it was possible to estimate the growth parameters (leaf area, plant height, canopy structure, and yield) of different fruit vegetables non-destructively using a 3D scanner.

Forest structure estimation and pattern exploration from discrete-return lidar in subalpine forests of the central Rockies

2008 ◽  
Vol 38 (8) ◽  
pp. 2081-2096 ◽  
Author(s):  
K. R. Sherrill ◽  
M. A. Lefsky ◽  
J. B. Bradford ◽  
M. G. Ryan
Keyword(s):  
Leaf Area ◽  
Forest Structure ◽  
Stand Structure ◽  
Forest Biomass ◽  
Canopy Structure ◽  
Primary Source ◽  
Subalpine Forest ◽  
Stand Age ◽  
Area Index ◽  
The North

This study evaluates the relative ability of simple light detection and ranging (lidar) indices (i.e., mean and maximum heights) and statistically derived canonical correlation analysis (CCA) variables attained from discrete-return lidar to estimate forest structure and forest biomass variables for three temperate subalpine forest sites. Both lidar and CCA explanatory variables performed well with lidar models having slightly higher explained variance and lower root mean square error. Adjusted R2 values were 0.93 and 0.93 for mean height, 0.74 and 0.73 for leaf area index, and 0.93 and 0.85 for all carbon in live biomass for the lidar and CCA explanatory regression models, respectively. The CCA results indicate that the primary source of variability in canopy structure is related to forest height, biomass, and total leaf area, and the second most important source of variability is related to the amount of midstory foliage and tree density. When stand age is graphed as a function of individual plot scores for canonicals one and two, there is a clear relationship with stand age and the development of stand structure. Lidar-derived biomass and related estimates developed in this work will be used to parameterize decision-support tools for analysis of carbon cycle impacts as part of the North American Carbon Program.

The validation of a model estimating the Leaf Area Index of grasslands in southern China

2013 ◽  
Vol 35 (3) ◽  
pp. 245 ◽  
Author(s):  
Chengming Sun ◽  
Zhengguo Sun ◽  
Tao Liu ◽  
Doudou Guo ◽  
Shaojie Mu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Southern China ◽  
Carbon Sink ◽  
Measurement Data ◽  
Light Transmittance ◽  
Area Index ◽  
Normalised Difference Vegetation Index

In order to estimate the leaf area index (LAI) over large areas in southern China, this paper analysed the relationships between normalised difference vegetation index (NDVI) and the vegetation light transmittance and the extinction coefficient based on the use of moderate resolution imaging spectroradiometer data. By using the improved Beer–Lambert Law, a model was constructed to estimate the LAI in the grassy mountains and slopes of southern China with NDVI as the independent variable. The model was validated with field measurement data from different locations and different years in the grassland mountains and slopes of southern China. The results showed that there was a good correlation between the simulated and observed LAI values, and the values of R2 achieved were high. The relative root mean squared error was between 0.109 and 0.12. This indicated that the model was reliable. The above results provided the theoretical basis for the effective management of the grassland resources in southern China and the effective estimation of grassland carbon sink.

scholarly journals Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)

2021 ◽  
Author(s):  
Félicien Meunier ◽  
Sruthi M. Krishna Moorthy ◽  
Marc Peaucelle ◽  
Kim Calders ◽  
Louise Terryn ◽  
...  
Keyword(s):  
Leaf Area ◽  
Forest Structure ◽  
Laser Scanning ◽  
Initial Conditions ◽  
Structural Parameters ◽  
Terrestrial Laser Scanning ◽  
Dimensional Structure ◽  
Forest Site ◽  
Ecosystem Structure ◽  
Area Index

Abstract. Terrestrial Biosphere Modeling (TBM) is an invaluable approach for studying plant-atmosphere interactions at multiple spatial and temporal scales, as well as the global change impacts on ecosystems. Yet, TBM projections suffer from large uncertainties that limit their usefulness. A large part of this uncertainty arises from the empirical allometric (size-tomass) relationships that are used to represent forest structure in TBMs. Forest structure actually drives a large part of TBM uncertainty as it regulates key processes such as the transfer of carbon, energy, and water between the land and atmosphere, but remains challenging to measure and reliably represent. The poor representation of forest structure in TBMs results in models that are able to reproduce observed land fluxes, but which fail to realistically represent carbon pools, forest composition, and demography. Recent advances in Terrestrial Laser Scanning (TLS) techniques offer a huge opportunity to capture the three-dimensional structure of the ecosystem and transfer this information to TBMs in order to increase their accuracy. In this study, we quantified the impacts of integrating structural observations of individual trees (namely tree height, leaf area, woody biomass, and crown area) derived from TLS into the state-of-the-art Ecosystem Demography model (ED2.2) at a temperate forest site. We assessed the relative model sensitivity to initial conditions, allometric parameters, and canopy representation by changing them in turn from default configurations to site-specific, TLS-derived values. We show that forest demography and productivity as modelled by ED2.2 are sensitive to the imposed initial state, the model structural parameters, and the way canopy is represented. In particular, we show that: 1) the imposed openness of the canopy dramatically influenced the potential vegetation, the optimal ecosystem leaf area, and the vertical distribution of light in the forest, as simulated by ED2.2; 2) TLS-derived allometric parameters increased simulated leaf area index and aboveground biomass by 57 and 75 %, respectively; 3) the choice of model structure and allometric coefficient both significantly impacted the optimal set of parameters necessary to reproduce eddy covariance flux data.

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.

Yield, leaf-area index and photosynthetic rate in some perennial ryegrass (Lolium perenne L.) selections

1972 ◽  
Vol 78 (3) ◽  
pp. 509-511 ◽  
Author(s):  
Ian Rhodes
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Lolium Perenne ◽  
Photosynthetic Rate ◽  
Perennial Ryegrass ◽  
Canopy Structure ◽  
Base Population ◽  
Area Index ◽  
Lolium Perenne L ◽  
Unit Leaf

SUMMARYYield, critical LAI and apparent photosynthetic rate per unit leaf area were measured in four families selected from L. perenne S. 321. Differences in yield were attributable to differences in canopy structure producing differing critical LAI. The most productive family, which was 33% more productive than the base population, produced the largest critical LAI but had the lowest photosynthetic rate.

Comparing nondestructive measures of forage structure and phytomass

2000 ◽  
Vol 80 (3) ◽  
pp. 565-573 ◽  
Author(s):  
B. E. Olson ◽  
R. T. Wallander ◽  
J. M. Beaver
Keyword(s):  
Radiative Transfer ◽  
Leaf Area ◽  
Canopy Structure ◽  
Plant Canopy ◽  
Native Range ◽  
Area Index ◽  
Crested Wheatgrass ◽  
Canopy Volume ◽  
Peak Standing Crop ◽  
Intercepted Radiation

Nondestructive radiative transfer and canopy volume methods were compared with the destructive hand-clipping method to determine forage structure and phytomass. On a native range site, fifteen 1-m2 circular plots were located at each of five microsites. On a crested wheatgrass site, thirty 1-m2 plots were located in grazed and in ungrazed areas. At peak standing crop, all plots were measured with a LI-COR Plant Canopy Analyzer to determine leaf area index (LAI), diffuse non-intercepted radiation (DNIR), and mean tilt angle (MTA) of leaves. Then, plants within plots were measured with a ruler to determine volume. Finally, all phytomass within plots was harvested. At the native range site, plant volume was related with LAI and DNIR on four of five microsites. Phytomass was related with LAI and DNIR on two microsites. At the crested wheatgrass site, volume and phytomass were related with LAI, DNIR, and MTA on grazed plots. Only phytomass was related with LAI and DNIR on ungrazed plots. The Plant Canopy Analyzer measures canopy structure and phytomass; it is fast, and its data are transferred directly to a computer. Measuring plant volume is inexpensive and requires minimal training. Determining phytomass by clipping is accurate and requires minimal training, but it is time-consuming and destructive. Key words: Leaf area, canopy, volume, phytomass, radiative transfer

scholarly journals Seasonal Variation in Canopy Aerodynamics and the Sensitivity of Transpiration Estimates to Wind Velocity in Broadleaved Deciduous Species

2016 ◽  
Vol 17 (12) ◽  
pp. 3029-3043 ◽  
Author(s):  
D. M. Barnard ◽  
W. L. Bauerle
Keyword(s):  
Seasonal Variation ◽  
Wind Speed ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Structure ◽  
Biophysical Model ◽  
Structure Model ◽  
Area Index ◽  
Wind Speeds ◽  
The Impact

Abstract Characterization of seasonal dynamics in wind speed attenuation within a plant canopy α is necessary for modeling leaf boundary layer conductance , canopy–atmosphere coupling Ω, and transpiration at multiple scales. The goals of this study were to characterize seasonal variation in α in four tree species with canopy wind profiles and a canopy-structure model, to quantify the impact of α on estimates of and Ω, and to determine the influence of variable wind speed on transpiration estimates from a biophysical model [Multi-Array Evaporation Stand Tree Radiation Assemblage (MAESTRA)]. Among species, α varied significantly with above-canopy wind speed and seasonal canopy development. At the mean above-canopy wind speed (1.5 m s−1), α could be predicted using a linear model with leaf area index as the input variable (coefficient of determination R2 = 0.78). However, the canopy-structure model yielded improved predictions (R2 = 0.92) by including canopy height and leaf width. By midseason, increasing canopy leaf area and α resulted in lower within-canopy wind speeds, a decrease in by 20%–50%, and a peak in Ω. Testing a discrete increase in wind speed (0.6–2.4 m s−1; seasonal mean plus/minus one standard deviation) had variable influence on transpiration estimates (from −30% to +20%), which correlated strongly with vapor pressure deficit (R2 = 0.83). Given the importance of α in accurate representation of , Ω, and transpiration, it is concluded that α needs to be given special attention in plant canopies that undergo substantial seasonal changes, especially densely foliated canopies (i.e., leaf area index >1) and in areas with lower native wind speeds (i.e., <2 m s−1).

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