Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests

Fire-tolerant eucalypt forests of south eastern Australia are assumed to fully recover from even the most intense fires but surprisingly very few studies have quantitatively assessed that recovery. Accurate assessment of horizontal and vertical attributes of tree crowns after fire is essential to understand the fire’s legacy effects on tree growth and on forest structure. In this study, we quantitatively assessed individual tree crowns 8.5 years after a 2009 wildfire that burnt extensive areas of eucalypt forest in temperate Australia. We used airborne lidar data validated with field measurements to estimate multiple metrics that quantified the cover, density, and vertical distribution of individual-tree crowns in 51 plots of 0.05 ha in fire-tolerant eucalypt forest across four wildfire severity types (unburnt, low, moderate, high). Significant differences in the field-assessed mean height of fire scarring as a proportion of tree height, and in the proportions of trees with epicormic (stem) resprouts were consistent with the gradation in fire severity. Linear mixed-effects models indicated persistent effects of both moderate- and high-severity wildfire on tree crown architecture. Trees at high-severity sites had significantly less crown projection area and live crown width as a proportion of total crown width than those at unburnt and low-severity sites. Significant differences in lidar-based metrics (crown cover, evenness, leaf area density profiles) indicated that tree crowns at moderate- and high-severity sites were comparatively narrow and more evenly distributed down the tree stem. These conical-shaped crowns contrasted sharply with the rounded crowns of trees at unburnt and low-severity sites, and likely influenced both tree productivity and the accuracy of biomass allometric equations for nearly a decade after the fire. Our data provide a clear example of the utility of airborne lidar data for quantifying the impacts of disturbances at the scale of individual trees. Quantified effects of contrasting fire severities on the structure of resprouter tree crowns provide a strong basis for interpreting post-fire patterns in forest canopies and vegetation profiles in lidar and other remotely-sensed data at larger scales.

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Detection of Individual Tree Crowns in Airborne Lidar Data

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.72.4.357 ◽

2006 ◽

Vol 72 (4) ◽

pp. 357-363 ◽

Cited By ~ 283

Author(s):

Barbara Koch ◽

Ursula Heyder ◽

Holger Weinacker

Keyword(s):

Airborne Lidar ◽

Lidar Data ◽

Individual Tree ◽

Tree Crowns ◽

Airborne Lidar Data

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Assessing Legacy Effects of Wildfires on the Crown Structure of Fire-Tolerant Eucalypt Trees Using Airborne LiDAR Data

Remote Sensing ◽

10.3390/rs11202433 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2433 ◽

Cited By ~ 6

Author(s):

Yogendra K. Karna ◽

Trent D. Penman ◽

Cristina Aponte ◽

Lauren T. Bennett

Keyword(s):

Airborne Lidar ◽

Lidar Data ◽

Legacy Effects ◽

Individual Tree ◽

Tree Crowns ◽

High Severity ◽

Eucalypt Forest ◽

Crown Width ◽

In Fire ◽

Airborne Lidar Data

The fire-tolerant eucalypt forests of south eastern Australia are assumed to fully recover from even the most intense fires; however, surprisingly, very few studies have quantitatively assessed that recovery. The accurate assessment of horizontal and vertical attributes of tree crowns after fire is essential to understand the fire’s legacy effects on tree growth and on forest structure. In this study, we quantitatively assessed individual tree crowns 8.5 years after a 2009 wildfire that burnt extensive areas of eucalypt forest in temperate Australia. We used airborne LiDAR data validated with field measurements to estimate multiple metrics that quantified the cover, density, and vertical distribution of individual-tree crowns in 51 plots of 0.05 ha in fire-tolerant eucalypt forest across four wildfire severity types (unburnt, low, moderate, high). Significant differences in the field-assessed mean height of fire scarring as a proportion of tree height and in the proportions of trees with epicormic (stem) resprouts were consistent with the gradation in fire severity. Linear mixed-effects models indicated persistent effects of both moderate and high-severity wildfire on tree crown architecture. Trees at high-severity sites had significantly less crown projection area and live crown width as a proportion of total crown width than those at unburnt and low-severity sites. Significant differences in LiDAR -based metrics (crown cover, evenness, leaf area density profiles) indicated that tree crowns at moderate and high-severity sites were comparatively narrow and more evenly distributed down the tree stem. These conical-shaped crowns contrasted sharply with the rounded crowns of trees at unburnt and low-severity sites and likely influenced both tree productivity and the accuracy of biomass allometric equations for nearly a decade after the fire. Our data provide a clear example of the utility of airborne LiDAR data for quantifying the impacts of disturbances at the scale of individual trees. Quantified effects of contrasting fire severities on the structure of resprouter tree crowns provide a strong basis for interpreting post-fire patterns in forest canopies and vegetation profiles in Light Detection and Ranging (LiDAR) and other remotely-sensed data at larger scales.

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Extraction of individual tree crowns from airborne LiDAR data in human settlements

Mathematical and Computer Modelling ◽

10.1016/j.mcm.2011.10.071 ◽

2013 ◽

Vol 58 (3-4) ◽

pp. 524-535 ◽

Cited By ~ 24

Author(s):

Jiping Liu ◽

Jing Shen ◽

Rong Zhao ◽

Shenghua Xu

Keyword(s):

Airborne Lidar ◽

Lidar Data ◽

Human Settlements ◽

Individual Tree ◽

Tree Crowns ◽

Airborne Lidar Data

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Individual Tree Position Extraction and Structural Parameter Retrieval Based on Airborne LiDAR Data: Performance Evaluation and Comparison of Four Algorithms

Remote Sensing ◽

10.3390/rs12030571 ◽

2020 ◽

Vol 12 (3) ◽

pp. 571 ◽

Cited By ~ 2

Author(s):

Chen ◽

Xiang ◽

Moriya

Keyword(s):

Template Matching ◽

Local Maximum ◽

Airborne Lidar ◽

Forest Monitoring ◽

Lidar Data ◽

Individual Tree ◽

Matching Algorithm ◽

Crown Width ◽

Individual Trees ◽

Airborne Lidar Data

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

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Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data

Remote Sensing ◽

10.3390/rs11010097 ◽

2019 ◽

Vol 11 (1) ◽

pp. 97 ◽

Cited By ~ 6

Author(s):

Lin Cao ◽

Zhengnan Zhang ◽

Ting Yun ◽

Guibin Wang ◽

Honghua Ruan ◽

...

Keyword(s):

Airborne Lidar ◽

Lidar Data ◽

Stem Density ◽

Shape Parameters ◽

Individual Tree ◽

Subtropical Forests ◽

Weibull Parameters ◽

Error Index ◽

Airborne Lidar Data ◽

Parameter Prediction

Accurate and reliable information on tree volume distributions, which describe tree frequencies in volume classes, plays a key role in guiding timber harvest, managing carbon budgets, and supplying ecosystem services. Airborne Light Detection and Ranging (LiDAR) has the capability of offering reliable estimates of the distributions of structure attributes in forests. In this study, we predicted individual tree volume distributions over a subtropical forest of southeast China using airborne LiDAR data and field measurements. We first estimated the plot-level total volume by LiDAR-derived standard and canopy metrics. Then the performances of three Weibull parameter prediction methods, i.e., parameter prediction method (PPM), percentile-based parameter recover method (PPRM), and moment-based parameter recover method (MPRM) were assessed to estimate the Weibull scale and shape parameters. Stem density for each plot was calculated by dividing the estimated plot total volume using mean tree volume (i.e., mean value of distributions) derived from the LiDAR-estimated Weibull parameters. Finally, the individual tree volume distributions were generated by the predicted scale and shape parameters, and then scaled by the predicted stem density. The results demonstrated that, compared with the general models, the forest type-specific (i.e., coniferous forests, broadleaved forests, and mixed forests) models had relatively higher accuracies for estimating total volume and stem density, as well as predicting Weibull parameters, percentiles, and raw moments. The relationship between the predicted and reference volume distributions showed a relatively high agreement when the predicted frequencies were scaled to the LiDAR-predicted stem density (mean Reynolds error index eR = 31.47–54.07, mean Packalén error index eP = 0.14–0.21). In addition, the predicted individual tree volume distributions predicted by PPRM of (average mean eR = 37.75) performed the best, followed by MPRM (average mean eR = 40.43) and PPM (average mean eR = 41.22). This study demonstrated that the LiDAR can potentially offer improved estimates of the distributions of tree volume in subtropical forests.

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