Forests

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Dr. Tuomas Yrttimaa

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Guest Editor

School of Forest Sciences, University of Eastern Finland, FI-80101 Joensuu, Finland
Interests: forest monitoring; remote sensing; laser scanning; point cloud processing

Special Issue Information

Dear Colleagues,

Close-range sensing techniques include but are not limited to sensors employing laser scanning (light detection and ranging, LiDAR) or digital photogrammetry, attached to static or mobile, terrestrial, or airborne platforms aiming to provide detailed 3D reconstruction of trees and forests. The use of terrestrial point clouds acquired using terrestrial laser scanning (TLS) or close-range photogrammetry has brought new approaches to detailed characterization of individual trees. Compared to conventional forest mensuration techniques, terrestrial point clouds enable non-destructive approaches to directly measure such attributes that have previously required destructive sampling or modeling. Attached to mobile platforms or using wearable sensors while employing simultaneous localization and mapping (SLAM) techniques, detailed terrestrial point clouds can be acquired on the move, which improves the cost-efficiency of point cloud data acquisition. The use of unmanned aerial vehicles (UAVs) as carrying platforms for acquiring close-range aerial point clouds provides a measurement geometry different than terrestrial point clouds, which enhances the characterization of tree crowns and enables covering entire forest stands. Regardless of the applied technology for point cloud acquisition, characterization of trees and forests requires point cloud processing techniques for semantic classification of different structures of interest and for quantification of their characteristics. Altogether, state-of-the-art sensor technology accompanied with different point cloud processing methods enables feasible observation tools for improving understanding of the functioning of trees and forests in general.

Dr. Tuomas Yrttimaa
Guest Editor

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Keywords

close-range laser scanning
close-range photogrammetry
forest characterization
forest monitoring
point cloud processing

Published Papers (2 papers)

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Research

16 pages, 7677 KiB

Open AccessArticle

A Comparison of Unpiloted Aerial System Hardware and Software for Surveying Fine-Scale Oak Health in Oak–Pine Forests

by Benjamin T. Fraser, Larissa Robinov, William Davidson, Shea O’Connor and Russell G. Congalton

Forests 2024, 15(4), 706; https://doi.org/10.3390/f15040706 - 17 Apr 2024

Viewed by 497

Abstract

Spongy moth (Lymantria dispar dispar) has caused considerable damage to oak trees across eastern deciduous forests. Forest management, post-outbreak, is resource intensive and typically focused on ecosystem restoration or resource loss mitigation. Some local forest managers and government partners are exploring developing technologies such as Unpiloted Aerial Systems (UASs, UAVs, or drones) to enhance their ability to gather reliable fine-scale information. However, with limited resources and the complexity of investing in hardware, software, and technical expertise, the decision to adopt UAS technologies has raised questions on their effectiveness. The objective of this study was to evaluate the abilities of two UAS surveying approaches for classifying the health of individual oak trees following a spongy moth outbreak. Combinations of two UAS multispectral sensors and two Structure from Motion (SfM)-based software are compared. The results indicate that the overall classification accuracy differed by as much as 3.8% between the hardware and software configurations. Additionally, the class-specific accuracy for ’Declining Oaks‘ differed by 5–10% (producer’s and user’s accuracies). The processing experience between open-source and commercial SfM software was also documented and demonstrated a 25-to-75-fold increase in processing duration. These results point out major considerations of time and software accessibility when selecting between hardware and software options for fine-scale forest mapping. Based on these findings, future stakeholders can decide between cost, practicality, technical complexity, and effectiveness. Full article

(This article belongs to the Special Issue Application of Close-Range Sensing in Forestry)

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19 pages, 20773 KiB

Open AccessArticle

Study on Individual Tree Segmentation of Different Tree Species Using Different Segmentation Algorithms Based on 3D UAV Data

by Yao Liu, Haotian You, Xu Tang, Qixu You, Yuanwei Huang and Jianjun Chen

Forests 2023, 14(7), 1327; https://doi.org/10.3390/f14071327 - 28 Jun 2023

Cited by 6 | Viewed by 1474

Abstract

Individual structural parameters of trees, such as forest stand tree height and biomass, serve as the foundation for monitoring of dynamic changes in forest resources. Individual tree structural parameters are closely related to individual tree crown segmentation. Although three-dimensional (3D) data have been successfully used to determine individual tree crown segmentation, this phenomenon is influenced by various factors, such as the (i) source of 3D data, (ii) the segmentation algorithm, and (iii) the tree species. To further quantify the effect of various factors on individual tree crown segmentation, light detection and ranging (LiDAR) data and image-derived points were obtained by unmanned aerial vehicles (UAVs). Three different segmentation algorithms (PointNet++, Li2012, and layer-stacking segmentation (LSS)) were used to segment individual tree crowns for four different tree species. The results show that for two 3D data, the crown segmentation accuracy of LiDAR data was generally better than that obtained using image-derived 3D data, with a maximum difference of 0.13 in F values. For the three segmentation algorithms, the individual tree crown segmentation accuracy of the PointNet++ algorithm was the best, with an F value of 0.91, whereas the result of the LSS algorithm yields the worst result, with an F value of 0.86. Among the four tested tree species, the individual tree crown segmentation of Liriodendron chinense was the best, followed by Magnolia grandiflora and Osmanthus fragrans, whereas the individual tree crown segmentation of Ficus microcarpa was the worst. Similar crown segmentation of individual Liriodendron chinense and Magnolia grandiflora trees was observed based on LiDAR data and image-derived 3D data. The crown segmentation of individual Osmanthus fragrans and Ficus microcarpa trees was superior according to LiDAR data to that determined according to image-derived 3D data. These results demonstrate that the source of 3D data, the segmentation algorithm, and the tree species all have an impact on the crown segmentation of individual trees. The effect of the tree species is the greatest, followed by the segmentation algorithm, and the effect of the 3D data source. Consequently, in future research on individual tree crown segmentation, 3D data acquisition methods should be selected based on the tree species, and deep learning segmentation algorithms should be adopted to improve the crown segmentation of individual trees. Full article

(This article belongs to the Special Issue Application of Close-Range Sensing in Forestry)

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