The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform
Guang Zheng - One of the best experts on this subject based on the ideXlab platform.
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Leaf Orientation retrieval from terrestrial laser scanning tls data
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Guang Zheng, L M MoskalAbstract:Tree Leaf Orientation, including the distribution of the inclinational and azimuthal angles in the canopy, is an important attribute of forest canopy architecture and is critical in determining the within and below canopy solar radiation regimes. Characterizing Leaf Orientation is a key step to the retrieval of Leaf area index (LAI) based on remotely sensed data, particularly discrete point data such as that provided by light detection and ranging. In this paper, we present a new method that indirectly and nondestructively retrieves foliage elements' Orientation and distribution from point cloud data (PCD) obtained using a terrestrial laser scanning (TLS) approach. An artificial tree was used to develop the method using total least square fitting techniques to reconstruct the normal vectors from the PCD. The method was further validated on live tree crowns. An equation with a single parameter for characterizing the Leaf angular distribution of crowns was developed. The TLS-based algorithm captures 97.4% (RMSE = 1.094 degrees, p <; 0.001) variation of the Leaf inclination angle compared to manual measurements for an artificial tree. When applied to a live tree seedling and a mature tree crown, the TLS-based algorithm predicts 78.51% (RMSE = 1.225 degrees, p <; 0.001) and 57.28% (RMSE = 4.412 degrees, p <; 0.001) of the angular variability, respectively. Our results indicate that occlusion and noisy points affect the accuracy of normal vector estimation. Most importantly, this work provides a theoretical foundation for retrieving LAI from PCD obtained with a TLS.
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Leaf Orientation Retrieval From Terrestrial Laser Scanning (TLS) Data
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Guang Zheng, Monika L. MoskalAbstract:Tree Leaf Orientation, including the distribution of the inclinational and azimuthal angles in the canopy, is an important attribute of forest canopy architecture and is critical in determining the within and below canopy solar radiation regimes. Characterizing Leaf Orientation is a key step to the retrieval of Leaf area index (LAI) based on remotely sensed data, particularly discrete point data such as that provided by light detection and ranging. In this paper, we present a new method that indirectly and nondestructively retrieves foliage elements' Orientation and distribution from point cloud data (PCD) obtained using a terrestrial laser scanning (TLS) approach. An artificial tree was used to develop the method using total least square fitting techniques to reconstruct the normal vectors from the PCD. The method was further validated on live tree crowns. An equation with a single parameter for characterizing the Leaf angular distribution of crowns was developed. The TLS-based algorithm captures 97.4% (RMSE = 1.094 degrees, p
Walter F Mahaffee - One of the best experts on this subject based on the ideXlab platform.
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rapid measurement of the three dimensional distribution of Leaf Orientation and the Leaf angle probability density function using terrestrial lidar scanning
Remote Sensing of Environment, 2017Co-Authors: Brian N Bailey, Walter F MahaffeeAbstract:Abstract At the plant or stand level, Leaf Orientation is often highly anisotropic and heterogeneous, yet most analyses neglect such complexity. In many cases, this is due to the difficulty in measuring the spatial variation of the Leaf angle distribution function. There is a critical need for a technique that can rapidly measure the Leaf angle distribution function at any point in space and time. A new method was developed and tested that uses terrestrial LiDAR scanning data to rapidly measure the three-dimensional distribution of Leaf Orientation for an arbitrary volume of leaves. The method triangulates laser-Leaf intersection points recorded by the LiDAR scan, which allows for easy calculation of normal vectors. As a byproduct, the triangulation also yields continuous surfaces that reconstruct individual leaves. In order to produce a probability density function for Leaf Orientation from triangle normal vectors, it is critical that the proper weighting be applied to each triangle. Otherwise, results will heavily bias toward normal vectors pointed toward the the LiDAR scanner. The method was validated using artificially generated LiDAR data where the exact Leaf angle distributions were known, and in the field for an isolated tree and a grapevine canopy by comparing LiDAR-generated distribution functions to manual measurements. The artificial test cases demonstrated the consistency of the method, and quantitatively showed that errors in the predicted Leaf angle distribution functions decreased as scan resolution was increased or as the density of leaves was increased. The isolated tree field validation showed qualitatively similar trends between manual and LiDAR measurements of distribution functions. Manual measurements of Leaf Orientation in the vineyard were shown to have large errors due to high Leaf curvature, which illustrated the benefits of the more detailed LiDAR measurement method.
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rapid high resolution measurement of Leaf area and Leaf Orientation using terrestrial lidar scanning data
Measurement Science and Technology, 2017Co-Authors: Brian N Bailey, Walter F MahaffeeAbstract:The rapid evolution of high performance computing technology has allowed for the development of extremely detailed models of the urban and natural environment. Although models can now represent sub-meter-scale variability in environmental geometry, model users are often unable to specify the geometry of real domains at this scale given available measurements. An emerging technology in this field has been the use of terrestrial LiDAR scanning data to rapidly measure the three-dimensional geometry of trees such as the distribution of Leaf area. However, current LiDAR methods suffer from the limitation that they require detailed knowledge of Leaf Orientation in order to translate projected Leaf area into actual Leaf area. Common methods for measuring Leaf Orientation are often tedious or inaccurate, which places constraints on the LiDAR measurement technique. This work presents a new method to simultaneously measure Leaf Orientation and Leaf area within an arbitrarily defined volume using terrestrial LiDAR data. The novelty of the method lies in the direct measurement of the fraction of projected Leaf area $G$ from the LiDAR data which is required to relate projected Leaf area to total Leaf area, and in the new way in which radiation transfer theory was used to calculate Leaf area from the LiDAR data. The method was validated by comparing LiDAR-measured Leaf area to 1) `synthetic' or computer-generated LiDAR data where the exact area was known, and 2) direct measurements of Leaf area in the field using destructive sampling. Overall, agreement between the LiDAR and reference measurements was very good, showing a normalized root-mean-squared-error of about 15% for the synthetic tests, and 13% in the field.
John M Pleasants - One of the best experts on this subject based on the ideXlab platform.
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development of Leaf Orientation in the prairie compass plant silphium laciniatum l
Bulletin of the Torrey Botanical Club, 1991Co-Authors: Hanzhong Zhang, John M Pleasants, Thomas W JurikAbstract:ZHANG, H., J. M. PLEASANTS AND T. W. JURIK (Department of Botany, Iowa State University, Ames, IA 50011). Development of Leaf Orientation in the prairie compass plant, Silphium laciniatum L. Bull. Torrey Bot. Club 118: 33-42. 1991.-Leaves of Silphium laciniatum L. are nearly vertical and have compass Orientation, i.e., the surfaces face east and west. We studied the manner in which compass Orientation develops. Compass Orientation was verified by measurements of azimuths for three sets of leaves in the field. Averages of Leaf azimuths were 77, 86, and 87 degrees from north. Averages of the absolute value of the differences between Leaf azimuth and 900 (east/west facing) were 24, 32, and 15 degrees (random would be 450). Adaxial and abaxial Leaf surfaces were equally likely to face east. Newly emerged leaves had random Orientation, but within 2-3 weeks compass Orientation was achieved by twisting of the Leaf petiole. Leaves turned either clockwise or counterclockwise, whichever produced compass Orientation with the minimum amount of turning (<900). Orientation involved a growth response; the ability to orient was lost on completion of Leaf expansion. Directional sunlight cues were necessary for compass Orientation to occur; leaves provided with only overhead light exhibited random Orientation. In the absence of directional light, leaves displayed an endogenous, unidirectional turning pattern; a majority of leaves turned counterclockwise. We hypothesize that endogenous turning enables leaves to sample the light environment and that leaves use the position of the sun in the early morning to determine compass Orientation.
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ecophysiological consequences of non random Leaf Orientation in the prairie compass plant silphium laciniatum
Oecologia, 1990Co-Authors: Thomas W Jurik, Hanzhong Zhang, John M PleasantsAbstract:The prairie compass plant (Silphium laciniatum L.) has vertical leaves that are characteristically oriented in a north-south plane (i.e., the flat surfaces of the lamina face east and west). We explored the consequences of this Orientation by determining basic photosynthetic and water use characteristics in response to environmental factors and by determining total daily photosynthesis and water use of leaves held in different Orientations. Average maximum CO2 exchange rate (CER) of leaves near Ames, IA was constant at 22 micromol m−2 s−1 from May through August and then declined. CER did not exhibit a distinct lightsaturation point. CER at photon flux densities near full sunlight was constant from 22 to 35°C Leaf temperature but declined at higher temperatures. However, Leaf temperatures rarely exceed 35°C during the growing season. There was no change in the pattern of response of CER to temperature over the growing season. We constrained leaves to face east-west (EW,=natural), to face north-south (NS), or to be horizontal (HOR) on eight days in 1986–1988. EW leaves had the highest light interception, Leaf temperatures, CER, and transpiration early and late in the day, whereas HOR leaves had the highest values in the middle of the day. Integrations of CER and transpiration over the eight daytime periods showed EW and HOR leaves to have equivalent carbon gain, higher than that of NS leaves. HOR leaves had the highest daily transpiration. Daily water use efficiency (WUE, carbon gained/water lost) was always highest in EW leaves, with the HOR leaves having 16% lower WUE and NS leaves having 33% lower WUE. The natural Orientation of compass plant leaves results in equivalent or higher carbon gain and in increased WUE when compared to leaves with other possible Orientations; this is likely to have a selective advantage in a prairie environment.
Monika L. Moskal - One of the best experts on this subject based on the ideXlab platform.
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Leaf Orientation Retrieval From Terrestrial Laser Scanning (TLS) Data
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Guang Zheng, Monika L. MoskalAbstract:Tree Leaf Orientation, including the distribution of the inclinational and azimuthal angles in the canopy, is an important attribute of forest canopy architecture and is critical in determining the within and below canopy solar radiation regimes. Characterizing Leaf Orientation is a key step to the retrieval of Leaf area index (LAI) based on remotely sensed data, particularly discrete point data such as that provided by light detection and ranging. In this paper, we present a new method that indirectly and nondestructively retrieves foliage elements' Orientation and distribution from point cloud data (PCD) obtained using a terrestrial laser scanning (TLS) approach. An artificial tree was used to develop the method using total least square fitting techniques to reconstruct the normal vectors from the PCD. The method was further validated on live tree crowns. An equation with a single parameter for characterizing the Leaf angular distribution of crowns was developed. The TLS-based algorithm captures 97.4% (RMSE = 1.094 degrees, p
L M Moskal - One of the best experts on this subject based on the ideXlab platform.
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Leaf Orientation retrieval from terrestrial laser scanning tls data
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Guang Zheng, L M MoskalAbstract:Tree Leaf Orientation, including the distribution of the inclinational and azimuthal angles in the canopy, is an important attribute of forest canopy architecture and is critical in determining the within and below canopy solar radiation regimes. Characterizing Leaf Orientation is a key step to the retrieval of Leaf area index (LAI) based on remotely sensed data, particularly discrete point data such as that provided by light detection and ranging. In this paper, we present a new method that indirectly and nondestructively retrieves foliage elements' Orientation and distribution from point cloud data (PCD) obtained using a terrestrial laser scanning (TLS) approach. An artificial tree was used to develop the method using total least square fitting techniques to reconstruct the normal vectors from the PCD. The method was further validated on live tree crowns. An equation with a single parameter for characterizing the Leaf angular distribution of crowns was developed. The TLS-based algorithm captures 97.4% (RMSE = 1.094 degrees, p <; 0.001) variation of the Leaf inclination angle compared to manual measurements for an artificial tree. When applied to a live tree seedling and a mature tree crown, the TLS-based algorithm predicts 78.51% (RMSE = 1.225 degrees, p <; 0.001) and 57.28% (RMSE = 4.412 degrees, p <; 0.001) of the angular variability, respectively. Our results indicate that occlusion and noisy points affect the accuracy of normal vector estimation. Most importantly, this work provides a theoretical foundation for retrieving LAI from PCD obtained with a TLS.