Individual snag detection using neighborhood attribute filtered airborne lidar data

Wing, Brian M.; Ritchie, Martin W.; Boston, Kevin; Cohen, Warren B.; Olsen, Michael J.

doi:10.1016/j.rse.2015.03.013

Cited by 60 publications

(55 citation statements)

References 39 publications

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“…Figure 3 shows that plots with different mortality ratios have different lidar intensity distributions. The importance of lidar intensity for the identification of dead trees has often been studied in forests where dead trees are abundant, such as in high mortality sites [9][10][11][12]38]. This study confirms that the lidar intensity can discriminate well interspersed dead trees in low mortality sites (mortality ratio <10.5%) where, for example, only natural mortality is observed.…”

Section: Discussionsupporting

confidence: 78%

“…Lidar intensity can, therefore, be used as a classifier of the status (live or dead) of trees. Kim et al [10] and Wing et al [12] found similar results on mixed conifer forests. Figure 3 shows that plots with different mortality ratios have different lidar intensity distributions.…”

Section: Discussionmentioning

confidence: 56%

“…Spruce budworm (Choristoneura fumiferana) disturbance and the hemlock looper (Lambdina fiscellaria) major outbreak in 2012 have caused cumulative defoliation and increased the mortality in stands [19,20]. Many of these studies have been carried out on sites where insect, disease or fire disturbances have caused significant damage to the forests [9][10][11][12]. However, we did not find any study demonstrating the ability of lidar sensors to detect dead trees for sites with low mortality where, for example, only natural mortality is observed.…”

Section: Study Areamentioning

confidence: 99%

“…Lidar intensity, defined as the quantification of the strength of the pulse backscattering received from the object, has been proven useful in feature extraction [6], species identification [7], land-cover classification [8], forest attribute modelling [9,10], snag detection [11], and point cloud filtering [12]. For example, Bright et al [9] used lidar intensity to estimate dead basal areas in beetle-affected pine forests.…”

Section: Introductionmentioning

confidence: 99%

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Lidar and Multispectral Imagery Classifications of Balsam Fir Tree Status for Accurate Predictions of Merchantable Volume

Yoga

Bégin

St-Onge

et al. 2017

Forests

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Recent increases in forest diseases have produced significant mortality in boreal forests. These disturbances influence merchantable volume predictions as they affect the distribution of live and dead trees. In this study, we assessed the use of lidar, alone or combined with multispectral imagery, to classify trees and predict the merchantable volumes of 61 balsam fir plots in a boreal forest in eastern Canada. We delineated single trees on a canopy height model. The number of detected trees represented 92% of field trees. Using lidar intensity and image pixel metrics, trees were classified as live or dead with an overall accuracy of 89% and a kappa coefficient of 0.78. Plots were classified according to their class of mortality (low/high) using a 10.5% threshold. Lidar returns associated with dead trees were clipped. Before clipping, the root mean square errors were of 22.7 m 3 ha −1 in the low mortality plots and of 39 m 3 ha −1 in the high mortality plots. After clipping, they decreased to 20.9 m 3 ha −1 and 32.3 m 3 ha −1 respectively. Our study suggests that lidar and multispectral imagery can be used to accurately filter dead balsam fir trees and decrease the merchantable volume prediction error by 17.2% in high mortality plots and by 7.9% in low mortality plots.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 56%

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lidar and Multispectral Imagery Classifications of Balsam Fir Tree Status for Accurate Predictions of Merchantable Volume

Yoga

Bégin

St-Onge

et al. 2017

Forests

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“…At present, many scholars has carry on the research of laser point cloud filtering and classification method, according to the different mode of scanning, filtering algorithm are divided for airborne laser scanning filtering (ALS) algorithm, mobile laser scanning(MLS) filtering algorithm and terrestrial laser scanning (TLS) filtering algorithm.The filtering algorithm of ALS contains multiresolution hierarchical classification (MHC) algorithm (Chuanfa Chen et al, 2013),Progressive TIN densification (PTD) algorithm (Jixian Zhang and Xiangguo Lin, 2013),intensity and density statistics algorithm( Brian M. Wing et al, 2015),Multi-directional Ground Filtering (MGF) algorithm (Xuelian Meng et al, 2009), Parameter-free ground filtering algorithm(Domen Mongus and Borut Žalik, 2012) and so on; The filtering(classification) algorithm of MLS contains regular voxelization of the space(C. Cabo et al, 2014), combine intensity textures and local geometry (Luca Penasa et al, 2014), shape-based segmentation method(Bisheng Yang and Zhen Dong, 2013) and so on; The filtering(classification) algorithm of TLS contains analysis of the 3D geometric texture methods(Ahlem Othmani et al, 2013;Yi Lin et al, 2016), super-voxels and multi-scale conditional random fields (Ee Hui Lim et al, 2009) and so on. In general, different filtering algorithms are proposed for different sources of laser scanning and obtained good results.…”

Section: Introductionmentioning

confidence: 99%

Tunnel Point Cloud Filtering Method Based on Elliptic Cylindrical Model

Zhu¹,

Jiaa²,

Luo³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The large number of bolts and screws that attached to the subway shield ring plates, along with the great amount of accessories of metal stents and electrical equipments mounted on the tunnel walls, make the laser point cloud data include lots of non-tunnel section points (hereinafter referred to as non-points), therefore affecting the accuracy for modeling and deformation monitoring. This paper proposed a filtering method for the point cloud based on the elliptic cylindrical model. The original laser point cloud data was firstly projected onto a horizontal plane, and a searching algorithm was given to extract the edging points of both sides, which were used further to fit the tunnel central axis. Along the axis the point cloud was segmented regionally, and then fitted as smooth elliptic cylindrical surface by means of iteration. This processing enabled the automatic filtering of those inner wall non-points. Experiments of two groups showed coincident results, that the elliptic cylindrical model based method could effectively filter out the non-points, and meet the accuracy requirements for subway deformation monitoring. The method provides a new mode for the periodic monitoring of tunnel sections all-around deformation in subways routine operation and maintenance.

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Historical harvests reduce neighboring old‐growth basal area across a forest landscape

Bell

Spies

Pabst

2017

Ecological Applications

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While advances in remote sensing have made stand, landscape, and regional assessments of the direct impacts of disturbance on forests quite common, the edge influence of timber harvesting on the structure of neighboring unharvested forests has not been examined extensively. In this study, we examine the impact of historical timber harvests on basal area patterns of neighboring old-growth forests to assess the magnitude and scale of harvest edge influence in a forest landscape of western Oregon, USA. We used lidar data and forest plot measurements to construct 30-m resolution live tree basal area maps in lower and middle elevation mature and old-growth forests. We assessed how edge influence on total, upper canopy, and lower canopy basal area varied across this forest landscape as a function of harvest characteristics (i.e., harvest size and age) and topographic conditions in the unharvested area. Upper canopy, lower canopy, and total basal area increased with distance from harvest edge and elevation. Forests within 75 m of harvest edges (20% of unharvested forests) had 4% to 6% less live tree basal area compared with forest interiors. An interaction between distance from harvest edge and elevation indicated that elevation altered edge influence in this landscape. We observed a positive edge influence at low elevations (<800 m) and a negative edge influence at moderate to high elevations (>800 m). Surprisingly, we found no or weak effects of harvest age (13-60 yr) and harvest area (0.2-110 ha) on surrounding unharvested forest basal area, implying that edge influence was relatively insensitive to the scale of disturbance and multi-decadal recovery processes. Our study indicates that the edge influence of past clearcutting on the structure of neighboring uncut old-growth forests is widespread and persistent. These indirect and diffuse legacies of historical timber harvests complicate forest management decision-making in old-growth forest landscapes by broadening the traditional view of stand boundaries. Furthermore, the consequences of forest harvesting may reach across ownership boundaries, highlighting complex governance issues surrounding landscape management of old-growth forests.

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Individual snag detection using neighborhood attribute filtered airborne lidar data

Cited by 60 publications

References 39 publications

Lidar and Multispectral Imagery Classifications of Balsam Fir Tree Status for Accurate Predictions of Merchantable Volume

Lidar and Multispectral Imagery Classifications of Balsam Fir Tree Status for Accurate Predictions of Merchantable Volume

Tunnel Point Cloud Filtering Method Based on Elliptic Cylindrical Model

Historical harvests reduce neighboring old‐growth basal area across a forest landscape

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