Comparative performances of new and existing indices of crown asymmetry: an evaluation using tall trees of Eucalyptus pilularis (Smith)

AbstractOver the past 50 years, crown asymmetry of forest trees has been evaluated through several indices constructed from the perspective of projected crown shape or displacement but often on an ad hoc basis to address specific objectives related to tree growth and competition, stand dynamics, stem form, crown structure and treefall risks. Although sharing some similarities, these indices are largely incoherent and non-comparable as they differ not only in the scale but also in the direction of their values in indicating the degree of crown asymmetry. As the first attempt at devising normative measures of crown asymmetry, we adopted a relative scale between 0 for perfect symmetry and 1 for extreme asymmetry. Five existing crown asymmetry indices (CAIs) were brought onto this relative scale after necessary modifications. Eight new CAIs were adapted from measures of circularity for digital images in computer graphics, indices of income inequality in economics, and a bilateral symmetry indicator in plant leaf morphology. The performances of the 13 CAIs were compared over different numbers of measured crown radii for 30 projected crowns of mature Eucalyptus pilularis trees through benchmarking statistics and rank order correlation analysis. For each CAI, the index value based on the full measurement of 36 evenly spaced radii of a projected crown was taken as the true value in the benchmarking process. The index (CAI13) adapted from the simple bilateral symmetry measure proved to be the least biased and most precise. Its performance was closely followed by that of three other CAIs. The minimum number of crown radii that is needed to provide at least an indicative measure of crown asymmetry is four. For more accurate and consistent measures, at least 6 or 8 crown radii are needed. The range of variability in crown morphology of the trees under investigation also needs to be taken into consideration. Although the CAIs are from projected crown radii, they can be readily extended to individual tree crown metrics that are now commonly extracted from LiDAR and other remotely sensed data. Adding a normative measure of crown asymmetry to individual tree crown metrics will facilitate the process of big data analytics and artificial intelligence in forestry wherever crown morphology is among the factors to be considered for decision making in forest management.

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ARBOR: A new framework for assessing the accuracy of individual tree crown delineation from remotely-sensed data

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111256 ◽

2019 ◽

Vol 231 ◽

pp. 111256

Author(s):

Jon Murray ◽

David Gullick ◽

George Alan Blackburn ◽

James Duncan Whyatt ◽

Christopher Edwards

Keyword(s):

Remotely Sensed ◽

Tree Crown ◽

Remotely Sensed Data ◽

Individual Tree ◽

Crown Delineation ◽

Tree Crown Delineation ◽

New Framework

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A Comparison of Forest Tree Crown Delineation from Unmanned Aerial Imagery Using Canopy Height Models vs. Spectral Lightness

Forests ◽

10.3390/f11060605 ◽

2020 ◽

Vol 11 (6) ◽

pp. 605 ◽

Cited By ~ 1

Author(s):

Jianyu Gu ◽

Heather Grybas ◽

Russell G. Congalton

Keyword(s):

Spectral Data ◽

Spatial Resolution ◽

Forest Type ◽

Canopy Height ◽

Aerial Imagery ◽

Tree Level ◽

Tree Crown ◽

Remotely Sensed Data ◽

Individual Tree ◽

Tree Crowns

Improvements in computer vision combined with current structure-from-motion photogrammetric methods (SfM) have provided users with the ability to generate very high resolution structural (3D) and spectral data of the forest from imagery collected by unmanned aerial systems (UAS). The products derived by this process are capable of assessing and measuring forest structure at the individual tree level for a significantly lower cost compared to traditional sources such as LiDAR, satellite, or aerial imagery. Locating and delineating individual tree crowns is a common use of remotely sensed data and can be accomplished using either UAS-based structural or spectral data. However, no study has extensively compared these products for this purpose, nor have they been compared under varying spatial resolution, tree crown sizes, or general forest stand type. This research compared the accuracy of individual tree crown segmentation using two UAS-based products, canopy height models (CHM) and spectral lightness information obtained from natural color orthomosaics, using maker-controlled watershed segmentation. The results show that single tree crowns segmented using the spectral lightness were more accurate compared to a CHM approach. The optimal spatial resolution for using lightness information and CHM were found to be 30 and 75 cm, respectively. In addition, the size of tree crowns being segmented also had an impact on the optimal resolution. The density of the forest type, whether predominately deciduous or coniferous, was not found to have an impact on the accuracy of the segmentation.

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FULLY AUTOMATED GIS-BASED INDIVIDUAL TREE CROWN DELINEATION BASED ON CURVATURE VALUES FROM A LIDAR DERIVED CANOPY HEIGHT MODEL IN A CONIFEROUS PLANTATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-563-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 563-569

Author(s):

R. J. L. Argamosa ◽

E. C. Paringit ◽

K. R. Quinton ◽

F. A. M. Tandoc ◽

R. A. G. Faelga ◽

...

Keyword(s):

Canopy Height ◽

Tree Crown ◽

M Cell ◽

Individual Tree ◽

Automated Method ◽

Coniferous Plantation ◽

Crown Shape ◽

Canopy Height Model ◽

Crown Delineation ◽

Height Model

The generation of high resolution canopy height model (CHM) from LiDAR makes it possible to delineate individual tree crown by means of a fully-automated method using the CHM’s curvature through its slope. The local maxima are obtained by taking the maximum raster value in a 3 m x 3 m cell. These values are assumed as tree tops and therefore considered as individual trees. Based on the assumptions, thiessen polygons were generated to serve as buffers for the canopy extent. The negative profile curvature is then measured from the slope of the CHM. The results show that the aggregated points from a negative profile curvature raster provide the most realistic crown shape. The absence of field data regarding tree crown dimensions require accurate visual assessment after the appended delineated tree crown polygon was superimposed to the hill shaded CHM.

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FULLY AUTOMATED GIS-BASED INDIVIDUAL TREE CROWN DELINEATION BASED ON CURVATURE VALUES FROM A LIDAR DERIVED CANOPY HEIGHT MODEL IN A CONIFEROUS PLANTATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-563-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 563-569

Author(s):

R. J. L. Argamosa ◽

E. C. Paringit ◽

K. R. Quinton ◽

F. A. M. Tandoc ◽

R. A. G. Faelga ◽

...

Keyword(s):

Canopy Height ◽

Tree Crown ◽

M Cell ◽

Individual Tree ◽

Automated Method ◽

Coniferous Plantation ◽

Crown Shape ◽

Canopy Height Model ◽

Crown Delineation ◽

Height Model

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Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8518441 ◽

2018 ◽

Author(s):

Daniele Marinelli ◽

Claudia Paris ◽

Lorenzo Bruzzone

Keyword(s):

Parameter Estimation ◽

Point Clouds ◽

Lidar Data ◽

Low Density ◽

Density Point ◽

Tree Crown ◽

Individual Tree

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Individual tree crown (ITC) delineation using watershed transformation algorithm for tropical lowland dipterocarp

2015 International Conference on Space Science and Communication (IconSpace) ◽

10.1109/iconspace.2015.7283795 ◽

2015 ◽

Cited By ~ 3

Author(s):

Nurul Ain Mohd Zaki ◽

Zulkiflee Abd Latif ◽

Mohd Zainee Zainal ◽

Khairulazhar Zainuddin

Keyword(s):

Tree Crown ◽

Individual Tree ◽

Watershed Transformation ◽

Tropical Lowland ◽

Transformation Algorithm

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A multi-band watershed segmentation method for individual tree crown delineation from high resolution multispectral aerial image

2014 IEEE Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2014.6945949 ◽

2014 ◽

Cited By ~ 5

Author(s):

Jian Yang ◽

Yuhong He ◽

John Caspersen

Keyword(s):

High Resolution ◽

Aerial Image ◽

Segmentation Method ◽

Watershed Segmentation ◽

Tree Crown ◽

Individual Tree ◽

Crown Delineation ◽

Tree Crown Delineation ◽

Multi Band

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Individual tree crown delineation and tree species classification with hyperspectral and LiDAR data

PeerJ ◽

10.7717/peerj.6227 ◽

2019 ◽

Vol 6 ◽

pp. e6227 ◽

Cited By ~ 12

Author(s):

Michele Dalponte ◽

Lorenzo Frizzera ◽

Damiano Gianelle

Keyword(s):

Remote Sensing ◽

Tree Species ◽

Data Science ◽

Remote Sensing Data ◽

Tree Crown ◽

Species Classification ◽

Individual Tree ◽

Sensing Data ◽

Tree Species Classification ◽

Selection Of

An international data science challenge, called National Ecological Observatory Network—National Institute of Standards and Technology data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: (1) individual tree crown (ITC) delineation, for identifying the location and size of individual trees; (2) alignment between field surveyed trees and ITCs delineated on remote sensing data; and (3) tree species classification. In this paper, the methods and results of team Fondazione Edmund Mach (FEM) are presented. The ITC delineation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the delineation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the delineated ITCs and the field surveyed trees was done using the Euclidean distance among the position, the height, and the crown radius of the ITCs and the field surveyed trees. The classification (Task 3) was performed using a support vector machine classifier applied to a selection of the hyperspectral bands and the canopy height model. The selection of the bands was done using the sequential forward floating selection method and the Jeffries Matusita distance. The results of the three tasks were very promising: team FEM ranked first in the data science competition in Task 1 and 2, and second in Task 3. The Jaccard score of the delineated crowns was 0.3402, and the results showed that the proposed approach delineated both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good (overall accuracy of 88.1%, kappa accuracy of 75.7%, and mean class accuracy of 61.5%), although the accuracy was biased toward the most represented species.

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