Growth Height Determination of Tree Walls for Precise Monitoring in Apple Fruit Production Using UAV Photogrammetry

In apple cultivation, spatial information about phenotypic characteristics of tree walls would be beneficial for precise orchard management. Unmanned aerial vehicles (UAVs) can collect 3D structural information of ground surface objects at high resolution in a cost-effective and versatile way by using photogrammetry. The aim of this study is to delineate tree wall height information in an apple orchard applying a low-altitude flight pattern specifically designed for UAVs. This flight pattern implies small distances between the camera sensor and the tree walls when the camera is positioned in an oblique view toward the trees. In this way, it is assured that the depicted tree crown wall area will be largely covered with a larger ground sampling distance than that recorded from a nadir perspective, especially regarding the lower crown sections. Overlapping oblique view images were used to estimate 3D point cloud models by applying structure-from-motion (SfM) methods to calculate tree wall heights from them. The resulting height models were compared with ground-based light detection and ranging (LiDAR) data as reference. It was shown that the tree wall profiles from the UAV point clouds were strongly correlated with the LiDAR point clouds of two years (2018: R2 = 0.83; 2019: R2 = 0.88). However, underestimation of tree wall heights was detected with mean deviations of −0.11 m and −0.18 m for 2018 and 2019, respectively. This is attributed to the weaknesses of the UAV point clouds in resolving the very fine shoots of apple trees. Therefore, the shown approach is suitable for precise orchard management, but it underestimated vertical tree wall expanses, and widened tree gaps need to be accounted for.

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Sunny Hollow Orchard: A Decision Case as a Basis for Classroom Discussion

HortTechnology ◽

10.21273/horttech.7.2.187 ◽

1997 ◽

Vol 7 (2) ◽

pp. 187-191

Author(s):

Doug S. Foulk ◽

Emily E. Hoover

Keyword(s):

Crop Production ◽

Fruit Production ◽

Apple Orchard ◽

Classroom Discussion ◽

Susceptible Cultivar ◽

Perennial Crop ◽

Management Decisions ◽

Orchard Management ◽

Production Practices

This decision case concerns the need to make management decisions in a commercial apple orchard planted largely with `Haralson,' a russet-susceptible cultivar. The growers described in this situation had to decide whether applying GA4+7 for russet suppression was appropriate for their operation, given the financial, cultural and pesticide issues that required addressing. The case is intended for use in fruit production or other intermediate to advanced undergraduate horticulture courses and assumes a knowledge of basic perennial-crop production practices. Students assume the role of a decisionmaker in the complicated issue of orchard management.

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OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽

10.3390/ma14061563 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1563

Author(s):

Ruibing Wu ◽

Ziping Yu ◽

Donghong Ding ◽

Qinghua Lu ◽

Zengxi Pan ◽

...

Keyword(s):

Additive Manufacturing ◽

Point Cloud ◽

Point Clouds ◽

Iterative Closest Point ◽

Multiple Point ◽

Icp Algorithm ◽

Registration Algorithm ◽

Cloud Models ◽

Comparison Results ◽

Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

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CLASSIFICATION OF RAILWAY ASSETS IN MOBILE MAPPING POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b1-2020-219-2020 ◽

2020 ◽

Vol XLIII-B1-2020 ◽

pp. 219-225

Author(s):

M. Corongiu ◽

A. Masiero ◽

G. Tucci

Keyword(s):

Spatial Information ◽

Point Clouds ◽

Railway Track ◽

Neural Net ◽

Mobile Mapping ◽

Geospatial Information ◽

Railway System ◽

Point Neighborhood ◽

Mobile Mapping Systems ◽

Mapping Systems

Abstract. Nowadays, mobile mapping systems are widely used to quickly collect reliable geospatial information of relatively large areas: thanks to such characteristics, the number of applications and fields exploiting their usage is continuously increasing. Among such possible applications, mobile mapping systems have been recently considered also by railway system managers to quickly produce and update a database of the geospatial features of such system, also called assets. Despite several vehicles, devices and acquisition methods can be considered for the data collection of the railway system, the predominant one is probably that based on the use of a mobile mapping system mounted on a train, which moves all along the railway tracks, enabling the 3D reproduction of the entire railway track area.Given the large amount of data collected by such mobile mapping, automatic procedures have to be used to speed up the process of extracting the spatial information of interest, i.e. assets positions and characteristics.This paper considers the problem of extracting such information for what concerns cantilever and portal masts, by exploiting a mixed approach. First, a set of candidate areas are extracted and pre-processed by considering certain of their geometric characteristics, mainly extracted by using eigenvalues of the covariance matrix of a point neighborhood. Then, a 3D modified Fisher vector-deep learning neural net is used to classify the candidates. Tests on such approach are conducted in two areas of the Italian railway system.

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VISUALIZATION OF POINT CLOUD MODELS IN MOBILE AUGMENTED REALITY USING CONTINUOUS LEVEL OF DETAIL METHOD

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliv-4-w1-2020-167-2020 ◽

2020 ◽

Vol XLIV-4/W1-2020 ◽

pp. 167-170

Author(s):

L. Zhang ◽

P. van Oosterom ◽

H. Liu

Keyword(s):

Augmented Reality ◽

Point Cloud ◽

Cloud Model ◽

Point Clouds ◽

Level Of Detail ◽

Mobile Augmented Reality ◽

Cloud Models ◽

Point Cloud Model ◽

Visual Artifacts ◽

Mobile Ar

Abstract. Point clouds have become one of the most popular sources of data in geospatial fields due to their availability and flexibility. However, because of the large amount of data and the limited resources of mobile devices, the use of point clouds in mobile Augmented Reality applications is still quite limited. Many current mobile AR applications of point clouds lack fluent interactions with users. In our paper, a cLoD (continuous level-of-detail) method is introduced to filter the number of points to be rendered considerably, together with an adaptive point size rendering strategy, thus improve the rendering performance and remove visual artifacts of mobile AR point cloud applications. Our method uses a cLoD model that has an ideal distribution over LoDs, with which can remove unnecessary points without sudden changes in density as present in the commonly used discrete level-of-detail approaches. Besides, camera position, orientation and distance from the camera to point cloud model is taken into consideration as well. With our method, good interactive visualization of point clouds can be realized in the mobile AR environment, with both nice visual quality and proper resource consumption.

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GNSS SATELLITE VISIBILITY ANALYSIS BASED ON 3D SPATIAL INFORMATION IN URBAN AREAS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b4-2020-123-2020 ◽

2020 ◽

Vol XLIII-B4-2020 ◽

pp. 123-128

Author(s):

Y.-H. Lu ◽

J.-Y. Han

Keyword(s):

Spatial Data ◽

Urban Areas ◽

Spatial Information ◽

Signal Transmission ◽

Point Clouds ◽

Satellite System ◽

Topographic Effects ◽

Analysis Technique ◽

Satellite Visibility ◽

Global Navigation Satellite

Abstract. Global Navigation Satellite System (GNSS) is a matured modern technique for spatial data acquisition. Its performance has a great correlation with GNSS receiver position. However, high-density building in urban areas causes signal obstructions and thus hinders GNSS’s serviceability. Consequently, GNSS positioning is weakened in urban areas, so deriving proper improvement resolutions is a necessity. Because topographic effects are considered the main factor that directly block signal transmission between satellites and receivers, this study integrated aerial borne LiDAR point clouds and a 2D building boundary map to provide reliable 3D spatial information to analyze topographic effects. Using such vector data not only reflected high-quality GNSS satellite visibility calculations, but also significantly reduced data amount and processing time. A signal obstruction analysis technique and optimized computational algorithm were also introduced. In conclusion, this paper proposes using superimposed column method to analyze GNSS receivers’ surrounding environments and thus improve GNSS satellite visibility predictions in an efficient and reliable manner.

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Estimating Canopy Parameters Based on the Stem Position in Apple Trees Using a 2D LiDAR

Agronomy ◽

10.3390/agronomy9110740 ◽

2019 ◽

Vol 9 (11) ◽

pp. 740 ◽

Cited By ~ 4

Author(s):

Nikos Tsoulias ◽

Dimitrios S. Paraforos ◽

Spyros Fountas ◽

Manuela Zude-Sasse

Keyword(s):

Incident Angle ◽

Structural Parameters ◽

Point Clouds ◽

Absolute Error ◽

Apple Orchard ◽

Stem Diameter ◽

Coefficient Of Determination ◽

Individual Tree ◽

Canopy Volume ◽

Stem Position

Data of canopy morphology are crucial for cultivation tasks within orchards. In this study, a 2D light detection and range (LiDAR) laser scanner system was mounted on a tractor, tested on a box with known dimensions (1.81 m × 0.6 m × 0.6 m), and applied in an apple orchard to obtain the 3D structural parameters of the trees (n = 224). The analysis of a metal box which considered the height of four sides resulted in a mean absolute error (MAE) of 8.18 mm with a bias (MBE) of 2.75 mm, representing a root mean square error (RMSE) of 1.63% due to gaps in the point cloud and increased incident angle with enhanced distance between laser aperture and the object. A methodology based on a bivariate point density histogram is proposed to estimate the stem position of each tree. The cylindrical boundary was projected around the estimated stem positions to segment each individual tree. Subsequently, height, stem diameter, and volume of the segmented tree point clouds were estimated and compared with manual measurements. The estimated stem position of each tree was defined using a real time kinematic global navigation satellite system, (RTK-GNSS) resulting in an MAE and MBE of 33.7 mm and 36.5 mm, respectively. The coefficient of determination (R2) considering manual measurements and estimated data from the segmented point clouds appeared high with, respectively, R2 and RMSE of 0.87 and 5.71% for height, 0.88 and 2.23% for stem diameter, as well as 0.77 and 4.64% for canopy volume. Since a certain error for the height and volume measured manually can be assumed, the LiDAR approach provides an alternative to manual readings with the advantage of getting tree individual data of the entire orchard.

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Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds

Remote Sensing ◽

10.3390/rs11121447 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1447 ◽

Cited By ~ 6

Author(s):

Frederic Brieger ◽

Ulrike Herzschuh ◽

Luidmila A. Pestryakova ◽

Bodo Bookhagen ◽

Evgenii S. Zakharov ◽

...

Keyword(s):

High Resolution ◽

Forest Structure ◽

Stand Structure ◽

Structural Information ◽

Carbon Sink ◽

Local Maximum ◽

Window Size ◽

Point Clouds ◽

Percentage Error ◽

Individual Tree

Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra–taiga ecotone should be adapted to the forest structure and have a radius of >15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure.

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PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation

Sensors ◽

10.3390/s20061573 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1573 ◽

Cited By ~ 1

Author(s):

Haojie Liu ◽

Kang Liao ◽

Chunyu Lin ◽

Yao Zhao ◽

Meiqin Liu

Keyword(s):

Point Cloud ◽

Spatial Information ◽

Interpolation Method ◽

Low Frequency ◽

Point Clouds ◽

Autonomous Driving ◽

Sensor Data ◽

Navigation Systems ◽

Intermediate Point ◽

Cascade Structure

LiDAR sensors can provide dependable 3D spatial information at a low frequency (around 10 Hz) and have been widely applied in the field of autonomous driving and unmanned aerial vehicle (UAV). However, the camera with a higher frequency (around 20 Hz) has to be decreased so as to match with LiDAR in a multi-sensor system. In this paper, we propose a novel Pseudo-LiDAR interpolation network (PLIN) to increase the frequency of LiDAR sensor data. PLIN can generate temporally and spatially high-quality point cloud sequences to match the high frequency of cameras. To achieve this goal, we design a coarse interpolation stage guided by consecutive sparse depth maps and motion relationship. We also propose a refined interpolation stage guided by the realistic scene. Using this coarse-to-fine cascade structure, our method can progressively perceive multi-modal information and generate accurate intermediate point clouds. To the best of our knowledge, this is the first deep framework for Pseudo-LiDAR point cloud interpolation, which shows appealing applications in navigation systems equipped with LiDAR and cameras. Experimental results demonstrate that PLIN achieves promising performance on the KITTI dataset, significantly outperforming the traditional interpolation method and the state-of-the-art video interpolation technique.

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Landslide and Rockfall failures Characterization with Object-Based 3D Analysis

10.5194/egusphere-egu2020-21880 ◽

2020 ◽

Author(s):

Efstratios Karantanellis ◽

Vassilios Marinos ◽

Emmanouel Vassilakis

Keyword(s):

Spatial Information ◽

Point Clouds ◽

3D Models ◽

Vital Role ◽

3D Analysis ◽

Rockfall Hazard ◽

Area Of Interest ◽

Object Based ◽

Great Heterogeneity ◽

Close Range

<p>Geological failures from massive rockfall failures to small landslides of few cubic meters are a major geological hazard in many parts of the world. Based on the latest developments, close-range photogrammetry and individually UAV photogrammetry and Light Detection and Ranging systems have become indispensable tools for geo-experts in order to provide ultra high-resolution 3D models of the failure site. TLS suffers from the fact that is sometimes tricky to capture the holistic area of interest from the ground, while some areas may often be obscured by vegetation or negative inclinations. The science of photogrammetry has long been used to accurately detect and characterize landslide and rockfall failures. Due to the continuously increasing spatial resolution capability of new generation sensors, traditional pixel-based approaches are not capable to cope with the level of detail resulted from those sensors. Mostly, landslides present complex and dynamic geomorphological features with great heterogeneity in their spatial, spectral and contextual properties dependent on the specific failure mechanism. In the current study, an object-based 3D approach for the automated detection of landslide and rockfall hazard is presented based on detailed topographic photogrammetric point clouds and 3D analysis. Recent trends show that close photogrammetry will play a vital role on the geological and engineering geological assessments concerning geo-failures. The results show that object-based approach is closer to human interception due to integration of contextual and semantic, spectral and spatial information rather than translating pixel&#8217;s spectral information solely. The current procedure provides a detailed objective quantification of landslide characteristics and automated semantic landslide modelling of the case site.</p>

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