Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest

Brodić, N., Cvijetinović, Ž., Milenkovic, M., Kovačević, J., Stančić, N., Mitrović, M., & Mihajlović, D. (2022). Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest. Remote Sensing 14 (21) e5345. 10.3390/rs14215345.

[thumbnail of remotesensing-14-05345-v2.pdf]
remotesensing-14-05345-v2.pdf - Published Version
Available under License Creative Commons Attribution.

Download (3MB) | Preview


Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning-based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment-level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km × 4 km. The classification model’s training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient (κ) obtained from the ten-fold cross validation for the training data were 90.4% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0% and a κ = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests.

Item Type: Article
Uncontrolled Keywords: individual tree detection; airborne laser scanning; machine learning; Random Forest; Extreme Gradient Boosting; artificial neural network; Support Vector Machine
Research Programs: Advancing Systems Analysis (ASA)
Advancing Systems Analysis (ASA) > Novel Data Ecosystems for Sustainability (NODES)
Depositing User: Luke Kirwan
Date Deposited: 07 Nov 2022 09:45
Last Modified: 07 Nov 2022 09:45

Actions (login required)

View Item View Item