Calibration and Validation of a Detailed Architectural Canopy Model Reconstruction  for the Simulation of Synthetic Hemispherical Images and Airborne LiDAR Data

Bremer, Magnus; Wichmann, Volker; Rutzinger, Martin

doi:10.3390/rs9030220

Cited by 15 publications

(13 citation statements)

References 47 publications

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“…A promising alternative to generate realistic hemispherical images from laser scans is to use terrestrial laser scanning (TLS; Bremer et al, ), but TLS provides point‐based samples and is thus not available continuously over larger areas. Moeser et al () further found that ALS‐derived hemispherical images tend to overestimate canopy closure under very dense canopies, probably because small canopy gaps are underrepresented in such conditions.…”

Section: Discussionmentioning

confidence: 99%

“…A promising solution to overcome these limitations is to approximate light regimes from synthetic hemispherical photographs derived from ALS point clouds and a regional terrain model (Bremer, Wichmann, & Rutzinger, ; Moeser et al, ; Varhola, Frazer, Teti, & Coops, ). We therefore extended a previously developed tool by Moeser et al () to generate such synthetic hemispherical images to include the following new properties: (a) topographic shading and accounting for the accurate position of canopy elements on sloping terrain and (b) distinguishing between diffuse and direct light reaching the forest understorey.…”

Section: Introductionmentioning

confidence: 99%

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Estimating below‐canopy light regimes using airborne laser scanning: An application to plant community analysis

Zellweger

Baltensweiler

Schleppi

et al. 2019

Ecology and Evolution

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Light is a key driver of forest biodiversity and functioning. Light regimes beneath tree canopies are mainly driven by the solar angle, topography, and vegetation structure, whose three‐dimensional complexity creates heterogeneous light conditions that are challenging to quantify, especially across large areas. Remotely sensed canopy structure data from airborne laser scanning (ALS) provide outstanding opportunities for advancement in this respect. We used ALS point clouds and a digital terrain model to produce hemispherical photographs from which we derived indices of nondirectional diffuse skylight and direct sunlight reaching the understory. We validated our approach by comparing the performance of these indices, as well as canopy closure (CCl) and canopy cover (CCo), for explaining the light conditions experienced by forest plant communities, as indicated by the Landolt indicator values for light (Llight) from 43 vegetation surveys along an elevational gradient. We applied variation partitioning to analyze how the independent and joint statistical effects of light, macroclimate, and soil on the spatial variation in plant species composition (i.e., turnover, Simpson dissimilarity, βSIM) depend on light approximation methodology. Diffuse light explained Llight best, followed by direct light, CCl and CCo (R2 = .31, .23, .22, and .22, respectively). The combination of diffuse and direct light improved the model performance for βSIM compared with CCl and CCo (R2 = .30, .27 and .24, respectively). The independent effect of macroclimate on βSIM dropped from an R2 of .15 to .10 when diffuse light and direct light were included. The ALS methods presented here outperform conventional approximations of below‐canopy light conditions, which can now efficiently be quantified along entire horizontal and vertical forest gradients, even in topographically complex environments such as mountains. The effect of macroclimate on forest plant communities is prone to be overestimated if local light regimes and associated microclimates are not accurately accounted for.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating below‐canopy light regimes using airborne laser scanning: An application to plant community analysis

Zellweger

Baltensweiler

Schleppi

et al. 2019

Ecology and Evolution

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“…TLS has emerged as among the most promising remote sensing technologies, providing detailed, spatially explicit, 3D information on plant structure, for operational applications in a wide range of disciplines related to the extraction of plant morphological features. These applications include, as reported in previous literature, the estimation of forest biophysical parameters such as gap fraction [68] and leaf area index [25,69]. These methods mostly aim to extract the vertical and horizontal distributions of biophysical variables or to determine an empirical correlation formula between laser scanning and in situ data sets.…”

Section: Discussionmentioning

confidence: 99%

Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data

Cao

Liu

et al. 2018

Remote Sensing

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Leaf attribute estimation is crucial for understanding photosynthesis, respiration, transpiration, and carbon and nutrient cycling in vegetation and evaluating the biological parameters of plants or forests. Terrestrial laser scanning (TLS) has the capability to provide detailed characterisations of individual trees at both the branch and leaf scales and to extract accurate structural parameters of stems and crowns. In this paper, we developed a computer graphic-based 3D point cloud segmentation approach for accurately and efficiently detecting tree leaves and their morphological features (i.e., leaf area and leaf angle distributions (leaf azimuthal angle and leaf inclination angle)) from single leaves. To this end, we adopted a sphere neighbourhood model with an adaptive radius to extract the central area points of individual leaves with different morphological structures and complex spatial distributions; meanwhile, four auxiliary criteria were defined to ensure the accuracy of the extracted central area points of individual leaf surfaces. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to cluster the central area points of leaves and to obtain the centre point corresponding to each leaf surface. We also achieved segmentation of individual leaf blades using an advanced 3D watershed algorithm based on the extracted centre point of each leaf surface and two morphology-related parameters. Finally, the leaf attributes (leaf area and leaf angle distributions) were calculated and assessed by analysing the segmented single-leaf point cloud. To validate the final results, the actual leaf area, leaf inclination and azimuthal angle data of designated leaves on the experimental trees were manually measured during field activities. In addition, a sensitivity analysis investigated the effect of the parameters in our segmentation algorithm. The results demonstrated that the segmentation accuracy of Ehretia macrophylla (94.0%) was higher than that of crape myrtle (90.6%) and Fatsia japonica (88.8%). The segmentation accuracy of Fatsia japonica was the lowest of the three experimental trees. In addition, the single-leaf area estimation accuracy for Ehretia macrophylla (95.39%) was still the highest among the three experimental trees, and the single-leaf area estimation accuracy for crape myrtle (91.92%) was lower than that for Ehretia macrophylla (95.39%) and Fatsia japonica (92.48%). Third, the method proposed in this paper provided accurate leaf inclination and azimuthal angles for the three experimental trees (Ehretia macrophylla: leaf inclination angle: R 2 = 0.908, RMSE = 6.806° and leaf azimuth angle: R 2 = 0.981, RMSE = 7.680°; crape myrtle: leaf inclination angle: R 2 = 0.901, RMSE = 8.365° and leaf azimuth angle: R 2 = 0.938, RMSE = 7.573°; Fatsia japonica: leaf inclination angle: R 2 = 0.849, RMSE = 6.158° and leaf azimuth angle: R 2 = 0.947, RMSE = 3.946°). The results indicate that the proposed method is effective and operational for providing accurate, detailed information on single leaves and vegetation structure from scanned data. This capability facilitates improvements in applications such as the estimation of leaf area, leaf angle distribution and biomass.

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“…Canopy morphology and structure are visualized on computer, and relevant parameter information such as canopy height and volume are extracted by the developed object-based image analysis (OBIA) algorithm. These data can provide theoretical basis and technical support for the intelligent control, monitoring, and management of orchards [5,6].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Morphological Measurement Method for a Fruit Tree Canopy Based on Kinect Sensor Self-Calibration

2019

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Perception of the fruit tree canopy is a vital technology for the intelligent control of a modern standardized orchard. Due to the complex three-dimensional (3D) structure of the fruit tree canopy, morphological parameters extracted from two-dimensional (2D) or single-perspective 3D images are not comprehensive enough. Three-dimensional information from different perspectives must be combined in order to perceive the canopy information efficiently and accurately in complex orchard field environment. The algorithms used for the registration and fusion of data from different perspectives and the subsequent extraction of fruit tree canopy related parameters are the keys to the problem. This study proposed a 3D morphological measurement method for a fruit tree canopy based on Kinect sensor self-calibration, including 3D point cloud generation, point cloud registration and canopy information extraction of apple tree canopy. Using 32 apple trees (Yanfu 3 variety) morphological parameters of the height (H), maximum canopy width (W) and canopy thickness (D) were calculated. The accuracy and applicability of this method for extraction of morphological parameters were statistically analyzed. The results showed that, on both sides of the fruit trees, the average relative error (ARE) values of the morphological parameters including the fruit tree height (H), maximum tree width (W) and canopy thickness (D) between the calculated values and measured values were 3.8%, 12.7% and 5.0%, respectively, under the V1 mode; the ARE values under the V2 mode were 3.3%, 9.5% and 4.9%, respectively; and the ARE values under the V1 and V2 merged mode were 2.5%, 3.6% and 3.2%, respectively. The measurement accuracy of the tree width (W) under the double visual angle mode had a significant advantage over that under the single visual angle mode. The 3D point cloud reconstruction method based on Kinect self-calibration proposed in this study has high precision and stable performance, and the auxiliary calibration objects are readily portable and easy to install. It can be applied to different experimental scenes to extract 3D information of fruit tree canopies and has important implications to achieve the intelligent control of standardized orchards.

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Calibration and Validation of a Detailed Architectural Canopy Model Reconstruction for the Simulation of Synthetic Hemispherical Images and Airborne LiDAR Data

Cited by 15 publications

References 47 publications

Estimating below‐canopy light regimes using airborne laser scanning: An application to plant community analysis

Estimating below‐canopy light regimes using airborne laser scanning: An application to plant community analysis

Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data

Three-Dimensional Morphological Measurement Method for a Fruit Tree Canopy Based on Kinect Sensor Self-Calibration

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