Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates

Ene, Liviu Theodor; Næsset, Erik; Gobakken, Terje

doi:10.1080/01431161.2012.657363

Cited by 102 publications

(66 citation statements)

References 38 publications

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“…Solberg et al (2006), investigating a heterogeneous spruce forest, used LiDAR data to correctly identify 93% of dominant trees, 63% of co-dominant trees, 38% of the subdominant trees, 19% of suppressed trees and 9% of dead trees. Ene et al (2012) detected approximately 46-50% of the total number of trees in a structurally heterogeneous boreal forest dominated by Norway spruce and Scots pine. Edson and Wing (2011) used Fusion software to identify trees in structurally different stands, not by employing an automatic local maxima algorithm but instead by (Solberg et al, 2006;Ene et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Ene et al (2012) detected approximately 46-50% of the total number of trees in a structurally heterogeneous boreal forest dominated by Norway spruce and Scots pine. Edson and Wing (2011) used Fusion software to identify trees in structurally different stands, not by employing an automatic local maxima algorithm but instead by (Solberg et al, 2006;Ene et al, 2012). In our study, we used a resolution of 0.5 m, which is consistent with those used in similar studies (Hyyppä et al, 2001;Lindberg and Hollaus, 2012).…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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 segmentation was carried out in a rasterized representation of the ALS data, but the crown segments used in the subsequent analysis were constructed as a convex hull in the horizontal plane of the ALS echoes within each raster segment (depicted in Figure 4). The reader is referred to Ene et al [6] for further details about the segmentation algorithm. Figure 4.…”

Section: Single-tree Segmentationmentioning

confidence: 99%

“…The ALS data were in the present study delineated into single-tree segments using a fully automated segmentation algorithm [6]. A marker-based watershed algorithm was used to delineate single-tree segments from a canopy height model.…”

Section: Single-tree Segmentationmentioning

confidence: 99%

“…An increasing number of forest inventories are based on data collected with airborne laser scanning (ALS) [1]. While commercial and operational ALS-based forest inventories most frequently are conducted according to the so-called area-based approach, as described by Naesset [2], methods targeting single trees have also been proposed [3][4][5][6]. The latter methods usually require ALS data with higher resolution, but intend to give information on a single-tree level, contrary to the area-based information provided by the former.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data

Hauglin

Gobakken

Astrup³

et al. 2014

Forests

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Several methods to conduct single-tree inventories using airborne laser scanning (ALS) have been proposed, and even terrestrial laser scanning (TLS) has recently emerged as a possible tool for the collection of forest inventory data. In the present study, a novel methodological framework for a combined use of ALS and TLS in an inventory was tested and compared to a method without the use of TLS. Single-tree Norway spruce crown biomass was predicted using an ALS-model with training data obtained by TLS. ALS and TLS data were collected for sets of sample trees, including 68 trees with both ALS and TLS data. In total, 29 destructively sampled trees were used to fit a TLS crown biomass model, which then was used to predict crown biomass in a separate set of 68 trees. This dataset was subsequently used to fit an ALS crown biomass model. When validating the model, using a separate dataset with accurately measured crown biomass obtained through destructive sampling, the mean error was 32% of the observed mean biomass. Corresponding crown biomass predictions derived with ALS-predicted diameters and the use of conventional and existing allometric models resulted in a mean error of 35%. Thus, in the present study, a slight improvement, in terms of prediction accuracy, was found when using training data with ground reference values obtained by TLS. OPEN ACCESSForests 2014, 5 385

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Data Fusion for Evaluation of Woodland Parameters

Favorskaya

Jain

2017

Intelligent Systems Reference Library

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Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates

Cited by 102 publications

References 38 publications

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

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

Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data

Data Fusion for Evaluation of Woodland Parameters

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