Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

Yao, Wei; Krull, Jan; Krzystek, Peter; Heurich, Marco

doi:10.3390/f5061122

Cited by 43 publications

(36 citation statements)

References 25 publications

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“…Overviews were provided by Vauhkonen et al (2011b), Kaartinen et al (2012), Koch et al (2014), and Eysn et al (2015). Typically, the smoothed canopy height models (CHM) or laser point clouds are used for local maxima detection and expansion (Koch et al 2006, Zhang et al Sačkov I et al -iForest 10: 459-467 2015, watershed-based delineation (Yao et al 2014) and point-cloud clustering (Pirotti 2010, Gupta et al 2010. Hybrid techniques that combine the ALS data with different kinds of geo-data and a variety of a priori information are also used (Heinzel et al 2010, Lähivaara et al 2014.…”

Section: Introductionmentioning

confidence: 99%

Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Sačkov¹,

Hlásny²,

Bucha³

et al. 2017

iForest

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Airborne laser scanning (ALS) has recently gained increasing attention in forestry, as ALS data may facilitate the efficient assessment of forest inventory attributes and ecological indicators related to forest stand structure. This paper presents a novel workflow for individual tree detection and tree crown delineation using ALS data. The developed point-based approach included several tree allometry rules on permissible tree heights and crown dimensions to increase the likelihood of detecting the actual tree profiles. The accuracy of the method was assessed in a heterogeneous forest with a complex stand structure in Slovakia (Central Europe). ALS measurements were taken using a RIEGL Q680i scanner at 700 m of height with a point density of 20 echoes per m 2 . The ground reference data included the measured positions and dimensions of 1332 trees in nine plots distributed across the region. We found that the number of individual trees detected by the algorithm using ALS data was systematically underestimated by 34 ± 15% relative to the reference data. The delineated crown coverage was underestimated by 2 ± 6% as well, but the latter difference was not statistically significant (p>0.05).

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Section: Introductionmentioning

confidence: 99%

Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Sačkov¹,

Hlásny²,

Bucha³

et al. 2017

iForest

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“…However, the fieldwork required for establishing large plots is expensive, while changing LiDAR flight parameters (reduced flight speed, lower flight altitude, multiple passes) to achieve the required point densities becomes a limiting factor over large areas due to time and/or costs constraints. Overall, the point densities needed for individual tree detection and measurements are in the range of 10 points m -2 , with higher point densities only marginally improving the performance of 3D tree detection (Yao et al, 2014).…”

Section: Ground Reference Datamentioning

confidence: 99%

Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [<i>Picea abies</i> (L.) Karst.] Test Site in Romania

Apostol

Lorenţ

Petrila

et al. 2016

Not Bot Horti Agrobo

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The objective of this study was to analyze the efficiency of individual tree identification and stand volume estimation from LiDAR data. The study was located in Norway spruce [Picea abies (L.) Karst.] stands in southwestern Romania and linked airborne laser scanning (ALS) with terrestrial measurements through empirical modelling. The proposed method uses the Canopy Maxima algorithm for individual tree detection together with biometric field measurements and individual trees positioning. Field data was collected using Field-Map real-time GIS-laser equipment, a high-accuracy GNSS receiver and a Vertex IV ultrasound inclinometer. ALS data were collected using a Riegl LMS-Q560 instrument and processed using LP360 and Fusion software to extract digital terrain, surface and canopy height models. For the estimation of tree heights, number of trees and tree crown widths from the ALS data, the Canopy Maxima algorithm was used together with local regression equations relating field-measured tree heights and crown widths at each plot. When compared to LiDAR detected trees, about 40-61% of the field-measured trees were correctly identified. Such trees represented, in general, predominant, dominant and co-dominant trees from the upper canopy. However, it should be noted that the volume of the correctly identified trees represented 60-78% of the total plot volume. The estimation of stand volume using the LiDAR data was achieved by empirical modelling, taking into account the individual tree heights (as identified from the ALS data) and the corresponding ground reference stem volume. The root mean square error (RMSE) between the individual tree heights measured in the field and the corresponding heights identified in the ALS data was 1.7-2.2 meters. Comparing the ground reference estimated stem volume (at trees level) with the corresponding ALS estimated tree stem volume, an RMSE of 0.5-0.7 m 3 was achieved. The RMSE was slightly lower when comparing the ground reference stem volume at plot level with the ALS-estimated one, taking into account both the identified and unidentified trees in the LiDAR data (0.4-0.6 m 3 ).

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“…1 ∑ | − | Wang et al, 2016;Yao et al, 2014) to evaluate the performance of object detection algorithms. These were calculated using the manually extracted segments as reference.…”

Section: = 100mentioning

confidence: 99%

Wheat Ear Detection in Plots by Segmenting Mobile Laser Scanner Data

Velumani¹,

Elberink²,

Yang³

et al. 2017

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

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ABSTRACT:The use of Light Detection and Ranging (LiDAR) to study agricultural crop traits is becoming popular. Wheat plant traits such as crop height, biomass fractions and plant population are of interest to agronomists and biologists for the assessment of a genotype's performance in the environment. Among these performance indicators, plant population in the field is still widely estimated through manual counting which is a tedious and labour intensive task. The goal of this study is to explore the suitability of LiDAR observations to automate the counting process by the individual detection of wheat ears in the agricultural field. However, this is a challenging task owing to the random cropping pattern and noisy returns present in the point cloud. The goal is achieved by first segmenting the 3D point cloud followed by the classification of segments into ears and non-ears. In this study, two segmentation techniques: a) voxelbased segmentation and b) mean shift segmentation were adapted to suit the segmentation of plant point clouds. An ear classification strategy was developed to distinguish the ear segments from leaves and stems. Finally, the ears extracted by the automatic methods were compared with reference ear segments prepared by manual segmentation. Both the methods had an average detection rate of 85%, aggregated over different flowering stages. The voxel-based approach performed well for late flowering stages (wheat crops aged 210 days or more) with a mean percentage accuracy of 94% and takes less than 20 seconds to process 50,000 points with an average point density of 16 points/cm 2 . Meanwhile, the mean shift approach showed comparatively better counting accuracy of 95% for early flowering stage (crops aged below 225 days) and takes approximately 4 minutes to process 50,000 points.

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Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

Cited by 43 publications

References 25 publications

Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [<i>Picea abies</i> (L.) Karst.] Test Site in Romania

Wheat Ear Detection in Plots by Segmenting Mobile Laser Scanner Data

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