Canopy Gap Detection and Analysis with Airborne Laser Scanning

St-Onge, Benoît; Vepakomma, Udayalakshmi; Sénécal, Jean-François; Kneeshaw, Daniel; Doyon, Frédérik

doi:10.1007/978-94-017-8663-8_21

Cited by 14 publications

(13 citation statements)

References 41 publications

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“…Canopy gaps are small openings of stands where there are smaller or no trees [30]. They are often found in mature stands, and reduce the overall stand volume.…”

Section: Discussionmentioning

confidence: 99%

Modeling the Effect of the Spatial Pattern of Airborne Lidar Returns on the Prediction and the Uncertainty of Timber Merchantable Volume

et al. 2017

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Lidar data are regularly used to characterize forest structures. In this study, we determine the effects of three lidar attributes (density, spacing, scanning angle) on the accuracy and the uncertainty of timber merchantable volume estimates of balsam fir stands (Abies balsamea (L.) Mill.) in eastern Canada. We used lidar point clouds to compute predictor variables of the merchantable volume in a nonlinear model. The best model included the mean height of first returns, the proportion of first returns below 2 m and the canopy surface roughness index. Our analysis shows a high correlation between lidar and field data of 119 plots (pseudo-R 2 = 0.91), however, residuals were heteroscedastic. More precise parameter estimates were obtained by adding to the model a variance function of variables describing the mean height of returns and the skewness of the area distribution of triangulated lidar returns. The residual standard deviation was better estimated (3.7 m 3 ha −1 multiplied by the variance function versus 28.0 m 3 ha −1 ). We found no effect of density on the predictions (p-value = 0.74). This suggests that the height and the spatial pattern of returns, rather than the density, should be considered to better assess the uncertainty of merchantable volume estimates.

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“…Canopy gaps are small openings of stands where there are smaller or no trees [30]. They are often found in mature stands, and reduce the overall stand volume.…”

Section: Discussionmentioning

confidence: 99%

Modeling the Effect of the Spatial Pattern of Airborne Lidar Returns on the Prediction and the Uncertainty of Timber Merchantable Volume

et al. 2017

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“…St-Onge et al [49] have reviewed LiDAR based techniques that may be applied to automatically detect and characterise canopy gaps. The accuracy and resolution output of existing techniques is adequate for our purposes, considering [50] used an automated object-based technique to delineate canopy gaps with an accuracy of 96%, whereas [51] used multi-temporal LiDAR to identify newly opened gaps less than 1 m Figure 7.…”

Section: Improving the Ncut Algorithmmentioning

confidence: 99%

Using Tree Detection Algorithms to Predict Stand Sapwood Area, Basal Area and Stocking Density in Eucalyptus regnans Forest

et al. 2015

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Managers of forested water supply catchments require efficient and accurate methods to quantify changes in forest water use due to changes in forest structure and density after disturbance. Using Light Detection and Ranging (LiDAR) data with as few as 0.9 pulses m −2 , we applied a local maximum filtering (LMF) method and normalised cut (NCut) algorithm to predict stocking density (SDen) of a 69-year-old Eucalyptus regnans forest comprising 251 plots with resolution of the order of 0.04 ha. Using the NCut method we predicted basal area (BAHa) per hectare and sapwood area (SAHa) per hectare, a well-established proxy for transpiration. Sapwood area was also indirectly estimated with allometric relationships dependent on LiDAR derived SDen and BAHa using a computationally efficient procedure. The individual tree detection (ITD) rates for the LMF and NCut methods respectively had 72% and 68% of stems correctly identified, 25% and 20% of stems missed, and 2% and 12% of stems over-segmented. The significantly higher computational requirement of the NCut algorithm makes the LMF method more suitable for predicting SDen across large forested areas. Using NCut derived ITD segments, observed versus predicted stand BAHa had R 2 ranging from 0.70 to 0.98 across six catchments, whereas a generalised parsimonious model applied to all sites used the portion

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“…A canopy gap is defined as a small opening within a continuous and relatively mature canopy, where trees are absent (i.e., non-forest gaps) or much smaller than their immediate neighbors (i.e., forest gaps) [1]. Canopy gaps are usually formed from natural disturbances, such as individual tree mortality events caused by insect, disease [2], or silvicultural thinning or harvesting activities [3].…”

Section: Introductionmentioning

confidence: 99%

“…Canopy gaps are usually formed from natural disturbances, such as individual tree mortality events caused by insect, disease [2], or silvicultural thinning or harvesting activities [3]. Canopy gaps play an important role in forest regeneration, turnover, and overall dynamics of forest ecosystems [1]. In northern hardwood forests, for example, the size of gaps plays a critical role in controlling the regeneration of tree species that are not tolerant of deep shade [4].…”

Section: Introductionmentioning

confidence: 99%

“…These studies demonstrate that broad-band multispectral images produced promising results for canopy gap identification. However, the saturation of visible-near-infrared [1] signals makes it a challenge to discriminate between tree canopies and forest gaps [12]. Narrow-band hyperspectral images could solve this problem but their prohibitive acquisition cost limits the exploitation of their full potential [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

et al. 2015

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Abstract:Delineating canopy gaps and quantifying gap characteristics (e.g., size, shape, and dynamics) are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. Both high spatial resolution optical and light detection and ranging (LiDAR) remote sensing data have been used to identify canopy gaps through object-based image analysis, but few studies have quantified the pros and cons of integrating optical and LiDAR for image segmentation and classification. In this study, we investigate whether the synergistic use of optical and LiDAR data improves segmentation quality and classification accuracy. The segmentation results indicate that the LiDAR-based segmentation best delineates canopy gaps, compared to segmentation with optical data alone, and even the integration of optical and LiDAR data. In contrast, the synergistic use of two datasets provides higher classification accuracy than the independent use of optical or LiDAR (overall accuracy of 80.28%˘6.16% vs. 68.54%˘9.03% and 64.51%˘11.32%, separately). High correlations between segmentation quality and object-based classification accuracy indicate that classification accuracy is largely dependent on segmentation quality in the selected experimental area. The outcome of this study provides valuable insights of the usefulness of data integration into segmentation and classification not only for canopy gap identification but also for many other object-based applications.

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Canopy Gap Detection and Analysis with Airborne Laser Scanning

Cited by 14 publications

References 41 publications

Modeling the Effect of the Spatial Pattern of Airborne Lidar Returns on the Prediction and the Uncertainty of Timber Merchantable Volume

Modeling the Effect of the Spatial Pattern of Airborne Lidar Returns on the Prediction and the Uncertainty of Timber Merchantable Volume

Using Tree Detection Algorithms to Predict Stand Sapwood Area, Basal Area and Stocking Density in Eucalyptus regnans Forest

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

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