A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data

Hamraz, Hamid; Contreras, Marco A.; Zhang, Jun

doi:10.1016/j.jag.2016.07.006

Cited by 75 publications

(75 citation statements)

References 35 publications

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“…We then randomly selected a first return point within each cell and kept all returns associated with the LiDAR pulse generating that first return 49,56 . For segmenting trees within the decimated point cloud, we stratified the point cloud to its canopy layers and used the surface-based method presented by Hamraz et al 26 to segment trees within each layer. We evaluated the tree segmentation accuracy in terms of recall (measure of tree detection rate), precision (measure of correctness of the detected trees), and F-score (combined measure) (see Methods).…”

Section: Required Point Density For a Reasonable Segmentation Of Treesmentioning

confidence: 99%

“…Due to penetration capability, the LiDAR data contains vertical vegetation structure within which understory trees can also be segmented 17,18 . Although understory trees provide limited financial value and a minor proportion of total above ground biomass, they influence canopy succession and stand development, form a heterogeneous and dynamic habitat for numerous wildlife species, hence are an essential component of ecosystem functioning [19][20][21][22] .Several tree segmentation methods for LiDAR point clouds are by design unable to detect understory trees because they only consider top of vegetation or surface points [23][24][25][26][27][28][29] . More recent methods process the entire LiDAR point clouds to utilize all vertical structure information representing different vegetation layers.…”

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confidence: 99%

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Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds

Hamraz

Contreras

Zhang

2017

Sci Rep

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Airborne laser scanning (LiDAR) point clouds over large forested areas can be processed to segment individual trees and subsequently extract tree-level information. Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers. Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and influencing stand development. Here we model the occlusion effect in terms of point density. We estimate the fractions of points representing different canopy layers (one overstory and multiple understory) and also pinpoint the required density for reasonable tree segmentation (where accuracy plateaus). We show that at a density of ~170 pt/m² understory trees can likely be segmented as accurately as overstory trees. Given the advancements of LiDAR sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources. The methodology can also be adopted for other similar remote sensing or advanced imaging applications such as geological subsurface modelling or biomedical tissue analysis.

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Section: Required Point Density For a Reasonable Segmentation Of Treesmentioning

confidence: 99%

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confidence: 99%

Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds

Hamraz

Contreras

Zhang

2017

Sci Rep

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“…The score is based on the tree height difference, which should be less than 30%, and the leaning angle between the crown apex and the stem location, which should also be less than 15° from nadir. The set of pairs with the maximum total score where each crown or stem location appears not more than once is selected using the Hungarian assignment algorithm and is regarded as the matched trees [17]. The number of matched trees (MT) is an indication of the tree segmentation quality.…”

Section: Tree Segmentation Evaluationmentioning

confidence: 99%

Remote Sensing of Forests using Discrete Return Airborne LiDAR

Hamraz¹,

Contreras²

2018

Recent Advances and Applications in Remote Sensing

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Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attributes. Segmenting individual trees in natural deciduous forests however remained a challenge because of the complex and multi-layered canopy. In this chapter, we present (i) a robust segmentation method that avoids a priori assumptions about the canopy structure, (ii) a vertical canopy stratification procedure that improves segmentation of understory trees, (iii) an occlusion model for estimating the point density of each canopy stratum, and (iv) a distributed computing approach for efficient processing at the forest level. When applied to the University of Kentucky Robinson Forest, the segmentation method detected about 90% of overstory and 47% of understory trees with over-segmentation rates of 14% and 2%. Stratifying the canopy improved the detection rate of understory trees to 68% at the cost of increasing their over-segmentations to 16%. According to our occlusion model, a point density of ~170 pt/m² is needed to segment understory trees as accurately as overstory trees. Lastly, using the distributed approach, we segmented about two million trees in the 7,440-ha forest in 2.5 hours using 192 processors, which is 167 times faster than using a single processor.

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“…We adopted the tree segmentation algorithm presented by Hamraz et al (2016) as the single-processor building block to empirically assess the proposed distributed processing approach. The tree segmentation algorithm can efficiently be implemented such that T s (n) = O(n) (see Appendix A).…”

Section: Runtime and Scalabilitymentioning

confidence: 99%

“…Airborne LiDAR provides considerably less information about the mid-story trees due to decreased penetration of LiDAR points toward bottom canopy layers (Maguya et al, 2014;Reutebuch et al, 2003), hence detected tree rate is lower for mid-story trees (Duncanson et al, 2014;Hamraz et al, 2016). Also, the detected mid-story trees are likely biased to be smaller within the population of all existing mid-story trees because they are easier to detect when there is less canopy closure, which is associated with stand age and is minimal when stand is young and in general has smaller trees (Jules et al, 2008).…”

Section: Global Forest Parametersmentioning

confidence: 99%

A scalable approach for tree segmentation within small-footprint airborne LiDAR data

Hamraz

Contreras

Zhang

2017

Computers & Geosciences

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Abstract.This paper presents a distributed approach that scales up to segment tree crowns within a LiDAR point cloud representing an arbitrarily large forested area. The approach uses a single-processor tree segmentation algorithm as a building block in order to process the data delivered in the shape of tiles in parallel. The distributed processing is performed in a master-slave manner, in which the master maintains the global map of the tiles and coordinates the slaves that segment tree crowns within and across the boundaries of the tiles. A minimal bias was introduced to the number of detected trees because of trees lying across the tile boundaries, which was quantified and adjusted for.Theoretical and experimental analyses of the runtime of the approach revealed a near linear speedup. The estimated number of trees categorized by crown class and the associated error margins as well as the height distribution of the detected trees aligned well with field estimations, verifying that the distributed approach works correctly. The approach enables providing information of individual tree locations and point cloud segments for a forest-level area in a timely manner, which can be used to create detailed remotely sensed forest inventories. Although the approach was presented for tree segmentation within LiDAR point clouds, the idea can also be generalized to scale up processing other big spatial datasets.

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A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data

Cited by 75 publications

References 35 publications

Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds

Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds

Remote Sensing of Forests using Discrete Return Airborne LiDAR

A scalable approach for tree segmentation within small-footprint airborne LiDAR data

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