Strengths and limitations of assessing forest density and spatial configuration with aerial LiDAR

Richardson, J.; Moskal, L. Monika

doi:10.1016/j.rse.2011.05.020

Cited by 59 publications

(28 citation statements)

References 39 publications

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“…A ruleset was built to segment trees based on local maxima within the CHM using methods after Richardson and Moskal [20], which creates canopy objects belonging to four height classes (less than 10 m, 10-20 m, 20-30m, and greater than 30 m). If individual trees were not segmented, clumps of trees were segmented.…”

Section: Lwd Identification Automated Methodsmentioning

confidence: 99%

“…The high spatial precision of the mapped tree clumps (Figure 11) is the strength of the analysis, though, because it allows the interpreters of these data to know an estimated number of large trees within a specific distance of the river, which is pertinent to the recommendations below. Previous studies have shown much better accuracies when delineating and assessing the number of large trees in forested environments, suggesting that the results of this method could have been much more accurate given leaf-on LiDAR data [20][21][22][23].…”

Section: Strengths and Limitations Of The Individual Tree Analysismentioning

confidence: 99%

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An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris

Richardson

Moskal

2016

Remote Sensing

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Large woody debris (LWD) plays a critical structural role in riparian ecosystems, but it can be difficult and time-consuming to quantify and survey in the field. We demonstrate an automated method for quantifying LWD using aerial LiDAR and object-based image analysis techniques, as well as a manual method for quantifying LWD using image interpretation derived from LiDAR rasters and aerial four-band imagery. In addition, we employ an established method for estimating the number of individual trees within the riparian forest. These methods are compared to field data showing high accuracies for the LWD method and moderate accuracy for the individual tree method. These methods can be integrated to quantify the contemporary and recruitable LWD in a river system.

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Section: Lwd Identification Automated Methodsmentioning

confidence: 99%

Section: Strengths and Limitations Of The Individual Tree Analysismentioning

confidence: 99%

An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris

Richardson

Moskal

2016

Remote Sensing

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“…This kind of analysis utilizes more details of the ALS data together with the knowledge of the shapes and proportions of tree tops and tree crowns. However, it often fails to detect trees standing close together and trees below the tallest canopy layer [10,15].…”

Section: Introductionmentioning

confidence: 99%

Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods

Lindberg

Holmgren

Olofsson

et al. 2010

International Journal of Remote Sensing

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Individual tree crowns may be delineated from airborne laser scanning (ALS) data by segmentation of surface models or by 3D analysis. Segmentation of surface models benefits from using a priori knowledge about the proportions of tree crowns, which has not yet been utilized for 3D analysis to any great extent. In this study, an existing surface segmentation method was used as a basis for a new tree model 3D clustering method applied to ALS returns in 104 circular field plots with 12 m radius in pine-dominated boreal forest (64°14'N, 19°50'E). For each cluster below the tallest canopy layer, a parabolic surface was fitted to model a tree crown. The tree model clustering identified more trees than segmentation of the surface model, especially smaller trees below the tallest canopy layer. Stem attributes were estimated with k-Most Similar Neighbours (k-MSN) imputation of the clusters based on field-measured trees. The accuracy at plot level from the k-MSN imputation (stem density root mean square error or RMSE 32.7%; stem volume RMSE 28.3%) was similar to the corresponding results from the surface model (stem density RMSE 33.6%; stem volume RMSE 26.1%) with leave-one-out cross-validation for one field plot at a time. Three-dimensional analysis of ALS data should also be evaluated in multi-layered forests since it identified a larger number of small trees below the tallest canopy layer.

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“…There are known limitations to the use of discrete Lidar for forest mapping -in particular, smaller trees and understory are difficult to map reliably. In Washington state, Richardson and Moskal (2011) found unbiased density estimates for trees taller than 65 feet (20 meters) but underestimation of density in trees less tall than that. Similarly, Jakubowski, Guo, Collins et al (2013) found that the accuracy of stand structure metric predictions generally decreased with increased canopy penetration; measures at the top of the canopy (e.g., canopy cover, height) were more accurate than those near the forest floor (e.g., shrub height, fuel loads).…”

Section: Future Of Lidarmentioning

confidence: 96%

Mapping forests with Lidar provides flexible, accurate data with many uses

Kelly¹,

Tommaso²

2015

Cal Ag

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Strengths and limitations of assessing forest density and spatial configuration with aerial LiDAR

Cited by 59 publications

References 39 publications

An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris

An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris

Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods

Mapping forests with Lidar provides flexible, accurate data with many uses

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