Stem Volume and Above-Ground Biomass Estimation of Individual Pine Trees From LiDAR Data: Contribution of Full-Waveform Signals

Allouis, T.; Durrieu, S.; Vega, Cédric; Couteron, Pierre

doi:10.1109/jstars.2012.2211863

Cited by 58 publications

(58 citation statements)

References 37 publications

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“…Published studies focused on tree-level biomass estimation by exploring ALS data (especially full-waveform data) are few. Allouis et al (2013) [36] assessed the capability of full-waveform data to improve the estimation of individual tree-based biomass in a black pine forest in southern France.…”

Section: Introductionmentioning

confidence: 99%

Aboveground Biomass Estimation of Individual Trees in a Coastal Planted Forest Using Full-Waveform Airborne Laser Scanning Data

Cao

Gao

et al. 2016

Remote Sensing

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Abstract:The accurate estimation of individual tree level aboveground biomass (AGB) is critical for understanding the carbon cycle, detecting potential biofuels and managing forest ecosystems. In this study, we assessed the capability of the metrics of point clouds, extracted from the full-waveform Airborne Laser Scanning (ALS) data, and of composite waveforms, calculated based on a voxel-based approach, for estimating tree level AGB individually and in combination, over a planted forest in the coastal region of east China. To do so, we investigated the importance of point cloud and waveform metrics for estimating tree-level AGB by all subsets models and relative weight indices. We also assessed the capability of the point cloud and waveform metrics based models and combo model (including the combination of both point cloud and waveform metrics) for tree-level AGB estimation and evaluated the accuracies of these models. The results demonstrated that most of the waveform metrics have relatively low correlation coefficients (<0.60) with other metrics. The combo models (Adjusted R 2 = 0.78-0.89), including both point cloud and waveform metrics, have a relatively higher performance than the models fitted by point cloud metrics-only (Adjusted R 2 = 0.74-0.86) and waveform metrics-only (Adjusted R 2 = 0.72-0.84), with the mostly selected metrics of the 95th percentile height (H 95 ), mean of height of median energy (HOME µ ) and mean of the height/median ratio (HTMR µ ). Based on the relative weights (i.e., the percentage of contribution for R 2 ) of the mostly selected metrics for all subsets, the metric of 95th percentile height (H 95 ) has the highest relative importance for AGB estimation (19.23%), followed by 75th percentile height (H 75 ) (18.02%) and coefficient of variation of heights (H cv ) (15.18%) in the point cloud metrics based models. For the waveform metrics based models, the metric of mean of height of median energy (HOME µ ) has the highest relative importance for AGB estimation (17.86%), followed by mean of the height/median ratio (HTMR µ ) (16.23%) and standard deviation of height of median energy (HOME σ ) (14.78%). This study demonstrated benefits of using full-waveform ALS data for estimating biomass at tree level, for sustainable forest management and mitigating climate change by planted forest, as China has the largest area of planted forest in the world, and these forests contribute to a large amount of carbon sequestration in terrestrial ecosystems.

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Section: Introductionmentioning

confidence: 99%

Aboveground Biomass Estimation of Individual Trees in a Coastal Planted Forest Using Full-Waveform Airborne Laser Scanning Data

Cao

Gao

et al. 2016

Remote Sensing

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“…Subsequently, crown length (CL), crown width (CW) and crown volume (CV) are calculated as well as the ratios of CL and CW and of TH and CW. The CL is defined as the height difference between the CB height and TH (Allouis et al, 2013;Holmgren and Persson, 2004). The CW is derived by the convex hull of the tree segment and its maximum diameter (Yu et al, 2014).…”

Section: Feature Derivationmentioning

confidence: 99%

“…The crown bottom is determined by comparing the extent of point distribution (area) per height interval (slice) and detecting the first high increase/decrease in area (Allouis et al, 2013). A distinction is made between trees having a constant decrease with increasing tree height (e.g.…”

Section: Crown Base Detectionmentioning

confidence: 99%

“…in forestry (Hyyppä et al, 2012;Naesset, 2007), plantation (Fieber et al, 2013;Görgens et al, 2015) and urban areas (Höfle et al, 2012;Richter et al, 2013;Yan et al, 2015). The estimation of tree characteristics like the structure and canopy profile (Latifi et al, 2015;Leiterer et al, 2012;Lindberg et al, 2012), the leaf area index (Alonzo et al, 2015;Fieber et al, 2014), the volume and above ground biomass (Allouis et al, 2013;Cao et al, 2014;Pirotti et al, 2014)as well as the classification of tree species (Ghosh et al, 2014;GuangCai et al, 2012;Heinzel and Koch, 2011;Hollaus et al, 2009;Holmgren and Persson, 2004;Hovi et al, 2016) have been applied, especially in forested areas. In contrast to closed forest stands, urban trees are characterized by large species diversity within small areas and a complex (typically anthropogenic) shape.…”

Section: Introductionmentioning

confidence: 99%

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Urban Tree Classification Using Full-Waveform Airborne Laser Scanning

Koma¹,

Koenig²,

Höfle³

2016

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

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ABSTRACT:Vegetation mapping in urban environments plays an important role in biological research and urban management. Airborne laser scanning provides detailed 3D geodata, which allows to classify single trees into different taxa. Until now, research dealing with tree classification focused on forest environments. This study investigates the object-based classification of urban trees at taxonomic family level, using full-waveform airborne laser scanning data captured in the city centre of Vienna (Austria). The data set is characterised by a variety of taxa, including deciduous trees (beeches, mallows, plane trees and soapberries) and the coniferous pine species. A workflow for tree object classification is presented using geometric and radiometric features. The derived features are related to point density, crown shape and radiometric characteristics. For the derivation of crown features, a prior detection of the crown base is performed. The effects of interfering objects (e.g. fences and cars which are typical in urban areas) on the feature characteristics and the subsequent classification accuracy are investigated. The applicability of the features is evaluated by Random Forest classification and exploratory analysis. The most reliable classification is achieved by using the combination of geometric and radiometric features, resulting in 87.5% overall accuracy. By using radiometric features only, a reliable classification with accuracy of 86.3% can be achieved. The influence of interfering objects on feature characteristics is identified, in particular for the radiometric features. The results indicate the potential of using radiometric features in urban tree classification and show its limitations due to anthropogenic influences at the same time.

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“…Airborne light detection and ranging (LiDAR) can partially penetrate the canopy and reveal the vertical structure of the canopy by using small-footprint laser data, such as discrete returns or digitalized waveforms [9]. There are two typical processing approaches to extract the CHP with small-footprint LiDAR data: (1) derivation of gap probability and the corresponding incremental distribution at optional areas for LiDAR point clouds with high point densities [10][11][12][13]; and (2) the methodology used in spaceborne large-footprint full waveform LiDAR [14,15], but small-footprint LiDAR waveforms first need to be aligned according to the elevation and summed to generate an accumulative large-footprint waveform [16][17][18]. According to the character of full waveforms and their relationships with CVS, Harding et al [15] provided the procedures of transforming waveform data of the Scanning LiDAR Imager of Canopies by Echo Recovery (SLICER) into the CHP and validated the results with the ground-based measurements.…”

Section: Introductionmentioning

confidence: 99%

The Comparison of Canopy Height Profiles Extracted from Ku-band Profile Radar Waveforms and LiDAR Data

et al. 2018

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An airborne Ku-band frequency-modulated continuous waveform (FM-CW) profiling radar, Tomoradar, records the backscatter signal from the canopy surface and the underlying ground in the southern boreal forest zone of Finland. The recorded waveforms are transformed into canopy height profiles (CHP) with a similar methodology utilized in large-footprint light detection and ranging (LiDAR). The point cloud data simultaneously collected by a Velodyne ® VLP-16 LiDAR on-board the same platform represent the frequency of discrete returns, which are also applied to the extraction of the CHP by calculating the gap probability and incremental distribution. To thoroughly explore the relationships of the CHP derived from Tomoradar waveforms and LiDAR data we utilized the effective waveforms of one-stripe field measurements and comparison them with four indicators, including the correlation coefficient, the root-mean-square error (RMSE) of the difference, and the coefficient of determination and the RMSE of residuals of linear regression. By setting the Tomoradar footprint as 20 degrees to contain over 95% of the transmitting energy of the main lobe, the results show that 88.17% of the CHPs derived from Tomoradar waveforms correlated well with those from the LiDAR data; 98% of the RMSEs of the difference ranged between 0.002 and 0.01; 79.89% of the coefficients of determination were larger than 0.5; and 98.89% of the RMSEs of the residuals ranged from 0.001 to 0.01. Based on the investigations, we discovered that the locations of the greatest CHP derived from the Tomoradar were obviously deeper than those from the LiDAR, which indicated that the Tomoradar microwave signal had a stronger penetration capability than the LiDAR signal. Meanwhile, there are smaller differences (the average RMSEs of differences is only 0.0042 when the total canopy closure is less than 0.5) and better linear regression results in an area with a relatively open canopy than with a denser canopy.

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Stem Volume and Above-Ground Biomass Estimation of Individual Pine Trees From LiDAR Data: Contribution of Full-Waveform Signals

Cited by 58 publications

References 37 publications

Aboveground Biomass Estimation of Individual Trees in a Coastal Planted Forest Using Full-Waveform Airborne Laser Scanning Data

Aboveground Biomass Estimation of Individual Trees in a Coastal Planted Forest Using Full-Waveform Airborne Laser Scanning Data

Urban Tree Classification Using Full-Waveform Airborne Laser Scanning

The Comparison of Canopy Height Profiles Extracted from Ku-band Profile Radar Waveforms and LiDAR Data

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