A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in  Boreal Ontario

Penner, Margaret; Woods, Murray; Pitt, Doug

doi:10.3390/f6114034

Cited by 32 publications

(28 citation statements)

References 26 publications

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“…Our first observation agreed with that of other studies [36,37] in that lidar and nearest neighbor methods were able to provide meaningful predictive power for plot level dbh distributions. We were also able to identify patterns in the behavior of prediction performance with respect to the number of neighbors, k, the nearest neighbor distance type, use of strata in prediction, and the selection of variables used for imputing dbh distributions at the plot level.…”

Section: K-nn Imputation Strategiessupporting

confidence: 81%

An Examination of Diameter Density Prediction with k-NN and Airborne Lidar

Strunk

Gould

Packalén

et al. 2017

Forests

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While lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination (R 2 ), and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km 2 Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.

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Section: K-nn Imputation Strategiessupporting

confidence: 81%

An Examination of Diameter Density Prediction with k-NN and Airborne Lidar

Strunk

Gould

Packalén

et al. 2017

Forests

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“…The area sampled per time unit for these sensors depend of many factors, such as overlap percentage, flight speed, altitude, weather conditions and aim of the study [22,90,91]. UAV-imagery as well as photographs captured by manned aircrafts works differently from LiDAR technologies-whether it be the process of data extraction or be generation of metrics for assessing forest structure [92][93][94][95][96]. Even so, herein we are showing that the algorithm designed for LiDAR data processing can be also used for the purpose of processing UAV-SfM data ITD.…”

Section: Discussionmentioning

confidence: 99%

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Mohan

Silva

Klauberg

et al. 2017

Forests

325

232

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Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM) algorithm for automatic individual tree detection (ITD) using a local-maxima based algorithm on UAV-derived Canopy Height Models (CHMs). This study was conducted in a private forest at Cache Creek located east of Jackson city, Wyoming. Based on the UAV-imagery, we allocated 30 field plots of 20 m × 20 m. For each plot, the number of trees was counted manually using the UAV-derived orthomosaic for reference. A total of 367 reference trees were counted as part of this study and the algorithm detected 312 trees resulting in an accuracy higher than 85% (F-score of 0.86). Overall, the algorithm missed 55 trees (omission errors), and falsely detected 46 trees (commission errors) resulting in a total count of 358 trees. We further determined the impact of fixed tree window sizes (FWS) and fixed smoothing window sizes (SWS) on the ITD accuracy, and detected an inverse relationship between tree density and FWS. From our results, it can be concluded that ITD can be performed with an acceptable accuracy (F > 0.80) from UAV-derived CHMs in an open canopy forest, and has the potential to supplement future research directed towards estimation of above ground biomass and stem volume from UAV-imagery.

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“…In this study, a two-parameter Weibull density function (PDF) was used to describe CHD on each plot. As a Weibull model is highly adaptive, ranging from an inversed J-shape to unimodal skewed and unimodal symmetrical curve, the Weibull model has flexibility in characterizing distributions of a range of forest attributes [50,51]. The two parameters, i.e., Weibull scale (α 1 ) and Weibull shape (β 1 ), were derived by the maximum likelihood estimation method.…”

Section: Weibull Fitting Approachmentioning

confidence: 99%

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

2017

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Accurate and timely estimation of forest structural parameters plays a key role in the management of forest resources, as well as studies on the carbon cycle and biodiversity. Light Detection and Ranging (LiDAR) is a promising active remote sensing technology capable of providing highly accurate three dimensional and wall-to-wall forest structural characteristics. In this study, we evaluated the utility of standard metrics and canopy metrics derived from airborne LiDAR data for estimating plot-level forest structural parameters individually and in combination, over a subtropical forest in Yushan forest farm, southeastern China. Standard metrics, i.e., height-based and density-based metrics, and canopy metrics extracted from canopy vertical profiles, i.e., canopy volume profile (CVP), canopy height distribution (CHD), and foliage profile (FP), were extracted from LiDAR point clouds. Then the standard metrics and canopy metrics were used for estimating forest structural parameters individually and in combination by multiple regression models, including forest type-specific (coniferous forest, broad-leaved forest, mixed forest) models and general models. Additionally, the synergy of standard metrics and canopy metrics for estimating structural parameters was evaluated using field measured data. Finally, the sensitivity of vertical and horizontal resolution of voxel size for estimating forest structural parameters was assessed. The results showed that, in general, the accuracies of forest type-specific models (Adj-R 2 = 0.44-0.88) were relatively higher than general models (Adj-R 2 = 0.39-0.77). For forest structural parameters, the estimation accuracies of Lorey's mean height (Adj-R 2 = 0.61-0.88) and aboveground biomass (Adj-R 2 = 0.54-0.81) models were the highest, followed by volume (Adj-R 2 = 0.42-0.78), DBH (Adj-R 2 = 0.48-0.74), basal area (Adj-R 2 = 0.41-0.69), whereas stem density (Adj-R 2 = 0.39-0.64) models were relatively lower. The combination models (Adj-R 2 = 0.45-0.88) had higher performance compared with models developed using standard metrics (only) (Adj-R 2 = 0.42-0.84) and canopy metrics (only) (Adj-R 2 = 0.39-0.83). The results also demonstrated that the optimal voxel size was 5 × 5 × 0.5 m 3 for estimating most of the parameters. This study demonstrated that canopy metrics based on canopy vertical profiles can be effectively used to enhance the estimation accuracies of forest structural parameters in subtropical forests.

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A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

Cited by 32 publications

References 26 publications

An Examination of Diameter Density Prediction with k-NN and Airborne Lidar

An Examination of Diameter Density Prediction with k-NN and Airborne Lidar

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

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