Accuracy of tree geometric parameters depending on the LiDAR data density

Hadas, Edyta; Estornell, Javier

doi:10.5721/eujrs20164905

Cited by 21 publications

(21 citation statements)

References 35 publications

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“…The maximum absolute error reached 0.24 m. Surprisingly, most of the tree heights obtained from LiDAR data were slightly overestimated in comparison to field measurements. The mean difference was equal to +0.04 m. This is not a typical case (Hadas et al, 2016) and we explain this by the low quality of DTM below the trees. Moreover, trees are not rigid objects, and the highest branch swings easily on the wind.…”

Section: Tree Heightmentioning

confidence: 68%

Determining Geometric Parameters of Agricultural Trees From Laser Scanning Data Obtained With Unmanned Aerial Vehicle

Hadas

Jóźków

Walicka

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately.<br> In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200&thinsp;points/m<sup>2</sup>) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92&thinsp;% of trees in the test area. 6&thinsp;% of trees in orchard were not separated from each other and 2&thinsp;% were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09&thinsp;m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory.

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Section: Tree Heightmentioning

confidence: 68%

Determining Geometric Parameters of Agricultural Trees From Laser Scanning Data Obtained With Unmanned Aerial Vehicle

Hadas

Jóźków

Walicka

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Many examples of LiDAR applications can be examined, for instance, from the punctual morphology representation of hydrogeological hazard areas, to the urban and infrastructure modeling and planning, to the design of power and energy distribution lines, and inventory and management of forests [9][10][11][12][13]. Moreover, the combination of LiDAR information with data from other sources can greatly help in improving the features' extraction and classification for Earth Monitoring and Remote Sensing as highlighted in [20] and discussed later in this paper.…”

Section: The Lidar Systemmentioning

confidence: 99%

LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

Ullo

Zarro

Wojtowicz

et al. 2020

Sensors

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The aim of this paper is to highlight how the employment of Light Detection and Ranging (LiDAR) technique can enhance greatly the performance and reliability of many monitoring systems applied to the Earth Observation (EO) and Environmental Monitoring. A short presentation of LiDAR systems, underlying their peculiarities, is first given. References to some review papers are highlighted, as they can be regarded as useful guidelines for researchers interested in using LiDARs. Two case studies are then presented and discussed, based on the use of 2D and 3D LiDAR data. Some considerations are done on the performance achieved through the use of LiDAR data combined with data from other sources. The case studies show how the LiDAR-based systems, combined with optical Very High Resolution (VHR) data, succeed in improving the analysis and monitoring of specific areas of interest, specifically how LiDAR data help in exploring external environment and extracting building features from urban areas. Moreover the discussed Case Studies demonstrate that the use of the LiDAR data, even with a low density of points, allows the development of an automatic procedure for accurate building features extraction, through object-oriented classification techniques, therefore by underlying the importance that even simple LiDAR-based systems play in EO and Environmental Monitoring.

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“…palm, mangrove, olive (e.g. Sharfi et al, 2011;Malek et al, 2014;Hung et al, 2012;Hadaś and Estornell, 2016). So far, there has been little attention focussing on the citrus trees (e.g.…”

Section: Previous Studiesmentioning

confidence: 99%

Detection of Citrus Trees From Uav DSMS

Ozdarici-Ok

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents an automated approach to detect citrus trees from digitals surface models (DSMs) as a single source. The DSMs in this study are generated from Unmanned Aerial Vehicles (UAVs), and the proposed approach first considers the symmetric nature of the citrus trees, and it computes the orientation-based radial symmetry in an efficient way. The approach also takes into account the local maxima (LM) information to verify the output of the radial symmetry. Our contributions in this study are twofold: (i) Such an integrated approach (symmetry + LM) has not been tested to detect (citrus) trees (in orchards), and (ii) the validity of such an integrated approach has not been experienced for an input, e.g. a single DSM. Experiments are performed on five test patches. The results reveal that our approach is capable of counting most of the citrus trees without manual intervention. Comparison to the stateof-the-art reveals that the proposed approach provides notable detection performance by providing the best balance between precision and recall measures.

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Accuracy of tree geometric parameters depending on the LiDAR data density

Cited by 21 publications

References 35 publications

Determining Geometric Parameters of Agricultural Trees From Laser Scanning Data Obtained With Unmanned Aerial Vehicle

Determining Geometric Parameters of Agricultural Trees From Laser Scanning Data Obtained With Unmanned Aerial Vehicle

LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

Detection of Citrus Trees From Uav DSMS

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