Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective

Wu, Jianwei; Yao, Wei; Polewski, Przemyslaw

doi:10.3390/rs10091403

Cited by 34 publications

(24 citation statements)

References 41 publications

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“…Nowadays, laser scanning is very popular in forestry including the detailed mapping and classification of tree species [ 30 , 31 ], forest inventory [ 32 ], and the estimation of above-ground biomass of large tropical trees [ 33 ]. Most of these applications require the integration of terrestrial and aerial laser scanning, also in combination with digital photogrammetry.…”

Section: Methodsmentioning

confidence: 99%

Accuracy Assessment of Semi-Automatic Measuring Techniques Applied to Displacement Control in Self-Balanced Pile Capacity Testing Appliance

Muszyński

Rybak

Kaczor

2018

Sensors

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Static load tests of foundation piles are the basic method for the designing or verification of adopted design solutions which concern the foundation of a building structure. Preparation of a typical test station using the so-called inverted beam method is very expensive and labor-intensive. The settlement values of the loaded pile are usually recorded using accurate dial gauges. These gauges are attached to a reference beam located in close proximity to the pile under test, which may cause systematic errors (difficult to detect) caused by the displacement of the adopted reference beam. The application of geodetic methods makes it possible to maintain an independent, external reference system, and to verify the readouts from dial gauges. The article presents an innovative instrumentation for a self-balanced stand for the static load test made from a closed-end, double steel pipe. Instead of typical, precise geometric leveling, the semi-automatic measuring techniques were used: motorized total station measurement and terrestrial laser scanning controlled by a computer. The processing of the acquired data made it possible to determine the vertical displacements of both parts of the examined pile and compare displacements with the results from the dial gauges. On the basis of the excess of the collected observations, it was possible to assess the accuracy, which confirmed the usefulness of measuring techniques under study.

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Section: Methodsmentioning

confidence: 99%

Accuracy Assessment of Semi-Automatic Measuring Techniques Applied to Displacement Control in Self-Balanced Pile Capacity Testing Appliance

Muszyński

Rybak

Kaczor

2018

Sensors

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“…Finally, the mean shift algorithm on the LiDAR point cloud did not perform as strongly as the other approaches for two main reasons; low point density and the influence of forest composition. First, the mean shift algorithm benefits from high point density (20 and 60 pulses per m2, Tusa et al 2020) providing abundant information on the spatial distribution of the neighbors of each point (Wu, Yao & Polewski, 2018). A low point density does not provide enough definition among clusters, which leads to undersegmentation, as was seen in the results.…”

Section: Delineationmentioning

confidence: 97%

Data science competition for cross-site delineation and classification of individual trees from airborne remote sensing data

Graves

Marconi

Stewart

et al. 2021

Preprint

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Delineating and classifying individual trees in remote sensing data is challenging. Many tree crown delineation methods have difficulty in closed-canopy forests and do not leverage multiple datasets. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for delineation of individual crowns and classification to determine species identity. This competition included data from multiple sites to assess the methods' ability to generalize learning across multiple sites simultaneously, and transfer learning to novel sites where the methods were not trained. Six teams, representing 4 countries and 9 individual participants, submitted predictions. Methods from a previous competition were also applied and used as the baseline to understand whether the methods are changing and improving over time. The best delineation method was based on an instance segmentation pipeline, closely followed by a Faster R-CNN pipeline, both of which outperformed the baseline method. However, the baseline (based on a growing region algorithm) still performed well as did the Faster R-CNN. All delineation methods generalized well and transferred to novel forests effectively. The best species classification method was based on a two-stage fully connected neural network, which significantly outperformed the baseline (a random forest and Gradient boosting ensemble). The classification methods generalized well, with all teams training their models using multiple sites simultaneously, but the predictions from these trained models generally failed to transfer effectively to a novel site. Classification performance was strongly influenced by the number of field-based species IDs available for training the models, with most methods predicting common species well at the training sites. Classification errors (i.e., species misidentification) were most common between similar species in the same genus and different species that occur in the same habitat. The best methods handled class imbalance well and learned unique spectral features even with limited data. Most methods performed better than baseline in detecting new (untrained) species, especially in the site with no training data. Our experience further shows that data science competitions are useful for comparing different methods through the use of a standardized dataset and set of evaluation criteria, which highlights promising approaches and common challenges, and therefore advances the ecological and remote sensing field as a whole.

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“…As a result, UAV laser scanning can now compete with Terrestrial Laser Scanning (TLS) [37][38][39][40][41][42] and ground-based Mobile Laser Scanning (MLS) (including smartphone [43,44], handheld [45][46][47], backpack [48][49][50], and vehicle-based [48,[51][52][53][54][55] laser scanning methods) in retrieving the DBH, while being less time-and labor-consuming. In that respect, the study of [56] was the first to directly estimate the DBH from UAV-mounted LiDAR, followed by [57][58][59][60][61].…”

Section: Challenges and Research Objectivesmentioning

confidence: 99%

Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning

2021

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Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose.

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Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective

Cited by 34 publications

References 41 publications

Accuracy Assessment of Semi-Automatic Measuring Techniques Applied to Displacement Control in Self-Balanced Pile Capacity Testing Appliance

Accuracy Assessment of Semi-Automatic Measuring Techniques Applied to Displacement Control in Self-Balanced Pile Capacity Testing Appliance

Data science competition for cross-site delineation and classification of individual trees from airborne remote sensing data

Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning

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