Tree crown segmentation and species classification in a wet eucalypt forest from airborne hyperspectral and LiDAR data

Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3&thinsp;&times;&thinsp;3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA&thinsp;=&thinsp;85.12&thinsp;%, Kc&thinsp;=&thinsp;0.90) performed better than LiDAR-metrics method (OA&thinsp;=&thinsp;79.86&thinsp;%, Kc&thinsp;=&thinsp;0.81) and spectral-metircs method (OA&thinsp;=&thinsp;71.26, Kc&thinsp;=&thinsp;0.69) in terms of classification accuracy, which indicated that the advanced method of data processing and sensitive feature selection are critical for improving the accuracy of crown-level tree species classification.

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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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Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD)

Remote Sensing of Environment ◽

10.1016/j.rse.2021.112857 ◽

2022 ◽

Vol 270 ◽

pp. 112857

Author(s):

Langning Huo ◽

Eva Lindberg ◽

Johan Holmgren

Keyword(s):

Detection Algorithm ◽

New Method ◽

Lidar Data ◽

Tree Crown ◽

Symmetrical Structure ◽

Structure Detection

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A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2015.11.010 ◽

2016 ◽

Vol 46 ◽

pp. 45-55 ◽

Cited By ~ 26

Author(s):

Yi Lin ◽

Juha Hyyppä

Keyword(s):

Tree Species ◽

Airborne Lidar ◽

Lidar Data ◽

Species Classification ◽

Tree Species Classification ◽

Feature Parameters ◽

Airborne Lidar Data

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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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A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

Remote Sensing ◽

10.3390/rs11242948 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2948 ◽

Cited By ~ 2

Author(s):

Hoang Minh Nguyen ◽

Begüm Demir ◽

Michele Dalponte

Keyword(s):

Tree Species ◽

Remote Sensing Data ◽

Classification Problem ◽

Learning Phase ◽

Support Vector ◽

Lidar Data ◽

Species Classification ◽

Individual Tree ◽

Tree Species Classification ◽

Training Samples

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

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