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Forests
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Panoptic Segmentation of Tree Scenes from Mobile LiDAR Data
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Panoptic Segmentation of Tree Scenes from Mobile LiDAR Data

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 5151

Special Issue Editors

Dr. Sheng Xu

E-Mail Website
Guest Editor
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: vegetation mapping; 3D spatial information processing; point cloud data analysis; remote sensing image processing; computer vision (object segmentation, detection, and recognition)
Dr. Shaobo Xia

E-Mail Website
Guest Editor
Department of Geomatics, Changsha University of Science and Technology, Changsha 410004, China
Interests: point cloud processing; multi-modal data processing; 3D vision; remote sensing and its applications in mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Di Wang

E-Mail Website
Guest Editor
Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, South Taibai Road 2, Xi'an 710071, China
Interests: LiDAR remote sensing; point cloud processing; 3D reconstruction; tree modeling; vegetation structure analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qiaolin Ye

E-Mail Website1 Website2
Guest Editor
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: pattern recognition, machine learning, and their applications in forestry; remote sensing image classification; tiny object detection recognition; robust feature extraction; distance metric learning; multi-view learning; artificial intelligence and forestry (forest fire prevention, vegetation classification, monitoring and prediction of combustible impact factors, etc.)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The spatial structure of forests is related to the management, optimization, and allocation of vegetation resources. The scientific evaluation of green quantity effectiveness has led to a trend toward precise research on forest spatial structure. Through intelligent information science technology, obtaining accurate tree instances and crown information to assess the green amount has become an interesting, yet complex, research topic.

LiDAR point clouds have better penetration than simple remote sensing images, with millimeter level processing accuracy, making them suitable for scientific greening and the fine evaluation of forests. Large-scale and individual-scale studies of 2D and 3D tree environments could be effectively performed through remote sensing data acquired from different sensor platforms and the use of computer vision and deep learning approaches.

This Special Issue focuses on the difficulties in analyzing the spatial structure of forests, using Mobile LiDAR point clouds as an input, or fusing multi-modal data to finely divide individual tree instances. By applying the panoptic segmentation of tree environments, results refine the forest tree models in real 3D scenarios, further serving the scientific greening, green assessment, and resource management of forests. Original research papers are expected to use the recently developed techniques to process a wide variety of remote sensing data for tree and vegetation mapping. High-quality contributions covering (but not limited to) the topics listed below are invited to submit to this Special Issue:

  • Classification, detection, and segmentation of trees;
  • Tree and vegetation inventory;
  • Fusion of multi-modal data in vegetation scenes;
  • Tree modeling;
  • Mapping and monitoring of forests;
  • Application of advanced image processing methodologies for mapping forest vegetation;
  • Vegetation structural characteristics;
  • Inversion of vegetation characteristics using mobile LiDAR data;
  • Early detection of forest disturbances;
  • Segmentation and reconstruction of non-tree objects in tree scenes.

Dr. Sheng Xu
Dr. Shaobo Xia
Dr. Di Wang
Prof. Dr. Qiaolin Ye
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • tree inventory and monitoring
  • vegetation mapping
  • tree structural phenotype analysis
  • forest management and protection
  • object segmentation
  • laser scanning
  • point cloud processing
  • point cloud registration
  • data and multi-modal fusion
  • classification and detection

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Published Papers (4 papers)

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Research

19 pages, 13917 KiB  
Article
TCSNet: A New Individual Tree Crown Segmentation Network from Unmanned Aerial Vehicle Images
by Yue Chi, Chenxi Wang, Zhulin Chen and Sheng Xu
Forests 2024, 15(10), 1814; https://doi.org/10.3390/f15101814 - 17 Oct 2024
Viewed by 193
Abstract
As the main area for photosynthesis in trees, the canopy absorbs a large amount of carbon dioxide and plays an irreplaceable role in regulating the carbon cycle in the atmosphere and mitigating climate change. Therefore, monitoring the growth of the canopy is crucial. [...] Read more.
As the main area for photosynthesis in trees, the canopy absorbs a large amount of carbon dioxide and plays an irreplaceable role in regulating the carbon cycle in the atmosphere and mitigating climate change. Therefore, monitoring the growth of the canopy is crucial. However, traditional field investigation methods are often limited by time-consuming and labor-intensive methods, as well as limitations in coverage, which may result in incomplete and inaccurate assessments. In response to the challenges encountered in the application of tree crown segmentation algorithms, such as adhesion between individual tree crowns and insufficient generalization ability of the algorithm, this study proposes an improved algorithm based on Mask R-CNN (Mask Region-based Convolutional Neural Network), which identifies irregular edges of tree crowns in RGB images obtained from drones. Firstly, it optimizes the backbone network by improving it to ResNeXt and embedding the SENet (Squeeze-and-Excitation Networks) module to enhance the model’s feature extraction capability. Secondly, the BiFPN-CBAM module is introduced to enable the model to learn and utilize features more effectively. Finally, it optimizes the mask loss function to the Boundary-Dice loss function to further improve the tree crown segmentation effect. In this study, TCSNet also incorporated the concept of panoptic segmentation, achieving the coherent and consistent segmentation of tree crowns throughout the entire scene through fine tree crown boundary recognition and integration. TCSNet was tested on three datasets with different geographical environments and forest types, namely artificial forests, natural forests, and urban forests, with artificial forests performing the best. Compared with the original algorithm, on the artificial forest dataset, the precision increased by 6.6%, the recall rate increased by 1.8%, and the F1-score increased by 4.2%, highlighting its potential and robustness in tree detection and segmentation. Full article
(This article belongs to the Special Issue Panoptic Segmentation of Tree Scenes from Mobile LiDAR Data)
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22 pages, 10564 KiB  
Article
YOLOTree-Individual Tree Spatial Positioning and Crown Volume Calculation Using UAV-RGB Imagery and LiDAR Data
by Taige Luo, Shuyu Rao, Wenjun Ma, Qingyang Song, Zhaodong Cao, Huacheng Zhang, Junru Xie, Xudong Wen, Wei Gao, Qiao Chen, Jiayan Yun and Dongyang Wu
Forests 2024, 15(8), 1375; https://doi.org/10.3390/f15081375 - 6 Aug 2024
Viewed by 1021
Abstract
Individual tree canopy extraction plays an important role in downstream studies such as plant phenotyping, panoptic segmentation and growth monitoring. Canopy volume calculation is an essential part of these studies. However, existing volume calculation methods based on LiDAR or based on UAV-RGB imagery [...] Read more.
Individual tree canopy extraction plays an important role in downstream studies such as plant phenotyping, panoptic segmentation and growth monitoring. Canopy volume calculation is an essential part of these studies. However, existing volume calculation methods based on LiDAR or based on UAV-RGB imagery cannot balance accuracy and real-time performance. Thus, we propose a two-step individual tree volumetric modeling method: first, we use RGB remote sensing images to obtain the crown volume information, and then we use spatially aligned point cloud data to obtain the height information to automate the calculation of the crown volume. After introducing the point cloud information, our method outperforms the RGB image-only based method in 62.5% of the volumetric accuracy. The AbsoluteError of tree crown volume is decreased by 8.304. Compared with the traditional 2.5D volume calculation method using cloud point data only, the proposed method is decreased by 93.306. Our method also achieves fast extraction of vegetation over a large area. Moreover, the proposed YOLOTree model is more comprehensive than the existing YOLO series in tree detection, with 0.81% improvement in precision, and ranks second in the whole series for mAP50-95 metrics. We sample and open-source the TreeLD dataset to contribute to research migration. Full article
(This article belongs to the Special Issue Panoptic Segmentation of Tree Scenes from Mobile LiDAR Data)
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18 pages, 7510 KiB  
Article
An Individual Tree Detection and Segmentation Method from TLS and MLS Point Clouds Based on Improved Seed Points
by Qiuji Chen, Hao Luo, Yan Cheng, Mimi Xie and Dandan Nan
Forests 2024, 15(7), 1083; https://doi.org/10.3390/f15071083 - 22 Jun 2024
Cited by 1 | Viewed by 964
Abstract
Individual Tree Detection and Segmentation (ITDS) is a key step in accurately extracting forest structural parameters from LiDAR (Light Detection and Ranging) data. However, most ITDS algorithms face challenges with over-segmentation, under-segmentation, and the omission of small trees in high-density forests. In this [...] Read more.
Individual Tree Detection and Segmentation (ITDS) is a key step in accurately extracting forest structural parameters from LiDAR (Light Detection and Ranging) data. However, most ITDS algorithms face challenges with over-segmentation, under-segmentation, and the omission of small trees in high-density forests. In this study, we developed a bottom–up framework for ITDS based on seed points. The proposed method is based on density-based spatial clustering of applications with noise (DBSCAN) to initially detect the trunks and filter the clusters by a set threshold. Then, the K-Nearest Neighbor (KNN) algorithm is used to reclassify the non-core clustered point cloud after threshold filtering. Furthermore, the Random Sample Consensus (RANSAC) cylinder fitting algorithm is used to correct the trunk detection results. Finally, we calculate the centroid of the trunk point clouds as seed points to achieve individual tree segmentation (ITS). In this paper, we use terrestrial laser scanning (TLS) data from natural forests in Germany and mobile laser scanning (MLS) data from planted forests in China to explore the effects of seed points on the accuracy of ITS methods; we then evaluate the efficiency of the method from three aspects: trunk detection, overall segmentation and small tree segmentation. We show the following: (1) the proposed method addresses the issues of missing segmentation and misrecognition of DBSCAN in trunk detection. Compared to using DBSCAN directly, recall (r), precision (p), and F-score (F) increased by 6.0%, 6.5%, and 0.07, respectively; (2) seed points significantly improved the accuracy of ITS methods; (3) the proposed ITDS framework achieved overall r, p, and F of 95.2%, 97.4%, and 0.96, respectively. This work demonstrates excellent accuracy in high-density forests and is able to accurately segment small trees under tall trees. Full article
(This article belongs to the Special Issue Panoptic Segmentation of Tree Scenes from Mobile LiDAR Data)
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20 pages, 7825 KiB  
Article
Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images
by Hao Zhong, Zheyu Zhang, Haoran Liu, Jinzhuo Wu and Wenshu Lin
Forests 2024, 15(2), 293; https://doi.org/10.3390/f15020293 - 3 Feb 2024
Cited by 2 | Viewed by 2224
Abstract
Automatic and accurate individual tree species identification is essential for the realization of smart forestry. Although existing studies have used unmanned aerial vehicle (UAV) remote sensing data for individual tree species identification, the effects of different spatial resolutions and combining multi-source remote sensing [...] Read more.
Automatic and accurate individual tree species identification is essential for the realization of smart forestry. Although existing studies have used unmanned aerial vehicle (UAV) remote sensing data for individual tree species identification, the effects of different spatial resolutions and combining multi-source remote sensing data for automatic individual tree species identification using deep learning methods still require further exploration, especially in complex forest conditions. Therefore, this study proposed an improved YOLOv8 model for individual tree species identification using multisource remote sensing data under complex forest stand conditions. Firstly, the RGB and LiDAR data of natural coniferous and broad-leaved mixed forests under complex conditions in Northeast China were acquired via a UAV. Then, different spatial resolutions, scales, and band combinations of multisource remote sensing data were explored, based on the YOLOv8 model for tree species identification. Subsequently, the Attention Multi-level Fusion (AMF) Gather-and-Distribute (GD) YOLOv8 model was proposed, according to the characteristics of the multisource remote sensing forest data, in which the two branches of the AMF Net backbone were able to extract and fuse features from multisource remote sensing data sources separately. Meanwhile, the GD mechanism was introduced into the neck of the model, in order to fully utilize the extracted features of the main trunk and complete the identification of eight individual tree species in the study area. The results showed that the YOLOv8x model based on RGB images combined with current mainstream object detection algorithms achieved the highest mAP of 75.3%. When the spatial resolution was within 8 cm, the accuracy of individual tree species identification exhibited only a slight variation. However, the accuracy decreased significantly with the decrease of spatial resolution when the resolution was greater than 15 cm. The identification results of different YOLOv8 scales showed that x, l, and m scales could exhibit higher accuracy compared with other scales. The DGB and PCA-D band combinations were superior to other band combinations for individual tree identification, with mAP of 75.5% and 76.2%, respectively. The proposed AMF GD YOLOv8 model had a more significant improvement in tree species identification accuracy than a single remote sensing sources and band combinations data, with a mAP of 81.0%. The study results clarified the impact of spatial resolution on individual tree species identification and demonstrated the excellent performance of the proposed AMF GD YOLOv8 model in individual tree species identification, which provides a new solution and technical reference for forestry resource investigation combined multisource remote sensing data. Full article
(This article belongs to the Special Issue Panoptic Segmentation of Tree Scenes from Mobile LiDAR Data)
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