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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 (12 September 2024) | Viewed by 13623

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Special Issue Editors

Prof. Dr. Huaiqing Zhang

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Research Institute of Forestry Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: artificial intelligence; visualization simulation and virtual reality for forestry
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Prof. Dr. Meili Wang

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College of Information and Engineering, Northwest A&F University, Xi’an 712100, China
Interests: computer graphics; computer vision; virtual reality
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Prof. Dr. Qiaolin Ye

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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.)
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Prof. Dr. Hua Sun

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College of Forestry, Central South University of Forestry & Technology, Changsha 410004, China
Interests: quantitative remote sensing in forestry; application of LiDAR in forestry; digital forest resource monitoring
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Prof. Dr. Wangfei Zhang

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College of Forestry, Southwest Forestry University, Kunming 650233, China
Interests: forest monitoring; forest scattering mechanisms at microwave bands; crop growth monitoring and identification; forest height inversion using PolInSAR technology
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Special Issue Information

Dear Colleagues,

Technologies such as deep learning (DL) can reproduce expert observations on every single tree in hundreds or thousands of hectares. In recent years, DL techniques have been applied in the field of forestry for aspects such as tree detection, tree species classification, and forest disturbance detection.

Considering the importance and critical requirement for computer application and DL in the monitoring of forests, this Special Issue focuses on collecting new insights, novel approaches and the most recent advances in the field of computer technology and DL application in forestry. We also welcome papers on tree detection, tree species classification, and forest disturbance detection using DL methods.

Prof. Dr. Huaiqing Zhang
Prof. Dr. Meili Wang
Prof. Dr. Qiaolin Ye
Prof. Dr. Hua Sun
Prof. Dr. Wangfei Zhang
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

deep learning
algorithm
tree detection
tree species classification
forest disturbance detection
remote sensing
satellite image analysis

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Published Papers (7 papers)

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Research

19 pages, 26836 KiB

Open AccessArticle

Single-Species Leaf Detection against Complex Backgrounds with YOLOv5s

by Ziyi Wang, Xiyou Su and Shiwei Mao

Forests 2024, 15(6), 894; https://doi.org/10.3390/f15060894 - 21 May 2024

Viewed by 772

Abstract

Accurate and rapid localization and identification of tree leaves are of significant importance for urban forest planning and environmental protection. Existing object detection neural networks are complex and often large, which hinders their deployment on mobile devices and compromises their efficiency in detecting plant leaves, especially against complex backgrounds. To address this issue, we collected eight common types of tree leaves against complex urban backgrounds to create a single-species leaf dataset. Each image in this dataset contains only one type of tree but may include multiple leaves. These leaves share similar shapes and textures and resemble various real-world background colors, making them difficult to distinguish and accurately identify, thereby posing challenges to model precision in localization and recognition. We propose a lightweight single-species leaf detection model, SinL-YOLOv5, which is only 15.7 MB. First, we integrated an SE module into the backbone to adaptively adjust the channel weights of feature maps, enhancing the expression of critical features such as the contours and textures of the leaves. Then, we developed an adaptive weighted bi-directional feature pyramid network, SE-BiFPN, utilizing the SE module within the backbone. This approach enhances the information transfer capabilities between the deep semantic features and shallow contour texture features of the network, thereby accelerating detection speed and improving detection accuracy. Finally, to enhance model stability during learning, we introduced an angle cost-based bounding box regression loss function (SIoU), which integrates directional information between ground-truth boxes and predicted boxes. This allows for more effective learning of the positioning and size of leaf edges and enhances the model’s accuracy in detecting leaf locations. We validated the improved model on the single-species leaf dataset. The results showed that compared to YOLOv5s, SinL-YOLOv5 exhibited a notable performance improvement. Specifically, SinL-YOLOv5 achieved an increase of nearly 4.7 percentage points in the [email protected] and processed an additional 20 frames per second. These enhancements significantly enhanced both the accuracy and speed of localization and recognition. With this improved model, we achieved accurate and rapid detection of eight common types of single-species tree leaves against complex urban backgrounds, providing technical support for urban forest surveys, urban forestry planning, and urban environmental conservation. Full article

(This article belongs to the Special Issue Computer Application and Deep Learning in Forestry)

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18 pages, 3927 KiB

Open AccessArticle

Interacting Sentinel-2A, Sentinel 1A, and GF-2 Imagery to Improve the Accuracy of Forest Aboveground Biomass Estimation in a Dry-Hot Valley

by Zihao Liu, Tianbao Huang, Xiaoli Zhang, Yong Wu, Xiongwei Xu, Zhenhui Wang, Fuyan Zou, Chen Zhang, Can Xu and Guanglong Ou

Forests 2024, 15(4), 731; https://doi.org/10.3390/f15040731 - 22 Apr 2024

Cited by 1 | Viewed by 1331

Abstract

Carbon absorption and storage in forests is one of the important ways to mitigate climate change. Therefore, it is essential to use a variety of remote-sensing resources to accurately estimate forest aboveground biomass (AGB) in dry-hot valley regions. In this study, satellite images from the Sentinel-1A, Sentinel-2A, and Gaofen-2 satellites were utilized to estimate the forest AGB in Yuanmou County, Yunnan Province, China. Different combinations of image data, based on selected variables of stepwise regression and their performance in constructing linear stepwise regression (LSR) and random forest (RF) models, were explored. The results showed that: (1) after adding the polarized values of the synthetic aperture radar backscatter coefficients, the combination fitting effect was significantly improved; (2) the fitting effect of the Sentinel-1A + Sentinel-2A + Gaofen-2 data combination was superior to the other combinations, indicating that the effective extraction of forest horizon and vertical information can improve the estimation effect of the forest AGB; and (3) the RF model exhibited superior fitting performance compared to the LSR model across all permutations of remotely sensed image datasets, with R² values of 0.71 and 0.65, and RMSE values of 30.67 and 33.79 Mg/ha, respectively. These findings lay the groundwork for enhancing the precision of AGB estimation in dry-hot valley areas by integrating Sentinel-2A, Sentinel-1A, and GF-2 imagery, providing valuable insights for future research and applications. Full article

(This article belongs to the Special Issue Computer Application and Deep Learning in Forestry)

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18 pages, 9218 KiB

Open AccessArticle

Detection of Pine-Wilt-Disease-Affected Trees Based on Improved YOLO v7

by Xianhao Zhu, Ruirui Wang, Wei Shi, Xuan Liu, Yanfang Ren, Shicheng Xu and Xiaoyan Wang

Forests 2024, 15(4), 691; https://doi.org/10.3390/f15040691 - 11 Apr 2024

Cited by 1 | Viewed by 1213

Abstract

Pine wilt disease (PWD) poses a significant threat to global pine resources because of its rapid spread and management challenges. This study uses high-resolution helicopter imagery and the deep learning model You Only Look Once version 7 (YOLO v7) to detect symptomatic trees in forests. Attention mechanism technology from artificial intelligence is integrated into the model to enhance accuracy. Comparative analysis indicates that the YOLO v7-SE model exhibited the best performance, with a precision rate of 0.9281, a recall rate of 0.8958, and an F1 score of 0.9117. This study demonstrates efficient and precise automatic detection of symptomatic trees in forest areas, providing reliable support for prevention and control efforts, and emphasizes the importance of attention mechanisms in improving detection performance. Full article

(This article belongs to the Special Issue Computer Application and Deep Learning in Forestry)

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22 pages, 10240 KiB

Open AccessArticle

Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI

by Chu Wang, Wangfei Zhang, Yongjie Ji, Armando Marino, Chunmei Li, Lu Wang, Han Zhao and Mengjin Wang

Forests 2024, 15(1), 215; https://doi.org/10.3390/f15010215 - 21 Jan 2024

Cited by 8 | Viewed by 3364 | Correction

Abstract

Forest aboveground biomass (AGB) is integral to the global carbon cycle and climate change study. Local and regional AGB mapping is crucial for understanding global carbon stock dynamics. NASA’s global ecosystem dynamics investigation (GEDI) and combination of multi-source optical and synthetic aperture radar (SAR) datasets have great potential for local and regional AGB estimation and mapping. In this study, GEDI L4A AGB data and ground sample plots worked as true AGB values to explore their difference for estimating forest AGB using Sentinel-1 (S1), Sentinel-2 (S2), and ALOS PALSAR-2 (PALSAR) data, individually and in their different combinations. The effects of forest types and different true AGB values for validation were investigated in this study, as well. The combination of S1 and S2 performed best in forest AGB estimation with R² ranging from 0.79 to 0.84 and RMSE ranging from 7.97 to 29.42 Mg/ha, with the ground sample plots used as ground truth data. While for GEDI L4A AGB product working as reference, R² values range from 0.36 to 0.47 and RMSE values range from 31.41 to 37.50 Mg/ha. The difference between using GEDI L4A and ground sample plot as reference shows obvious dependence on forest types. In summary, optical dataset and its combination with SAR performed better in forest AGB estimation when the average AGB is less than 150 Mg/ha. The AGB predictions from GEDI L4A AGB product used as reference underperformed across the different forest types and study sites. However, GEDI can work as ground truth data source for forest AGB estimation in a certain level of estimation accuracy. Full article

(This article belongs to the Special Issue Computer Application and Deep Learning in Forestry)

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22 pages, 5819 KiB

Open AccessArticle

Coniferous Forests Aboveground Biomass Inversion in Typical Regions of China with Joint Sentinel-1 and Sentinel-2 Remote Sensing Data Supported by Different Feature Optimizing Algorithms

by Fuxiang Zhang, Armando Marino, Yongjie Ji and Wangfei Zhang

Forests 2024, 15(1), 56; https://doi.org/10.3390/f15010056 - 28 Dec 2023

Viewed by 1227

Abstract

Multispectral remote sensing (RS) data and synthetic aperture radar (SAR) data can provide horizontal and vertical information about forest AGB under different stand conditions. With the abundance of RS features extracted from multispectral and SAR datasets, a key point for accurate forest AGB estimation is to use suitable feature optimization inversion algorithms. In this study, feature optimization inversion algorithms including multiple linear stepwise regression (MLSR), K-nearest neighbor with fast iterative feature selection (KNN-FIFS), and random forest (RF) were explored, with a total of 93 RS features working as inversion model input for forest AGB inversion. The results showed that KNN-FIFS with the combination of Sentinel-1 and Sentinel-2 performed best at both test sites (R² = 0.568 and RMSE = 15.05 t/hm² for Puer and R² = 0.511 and RMSE = 32.29 t/hm² for Genhe). Among the three feature optimization inversion algorithms, RF performed worst for forest AGB estimation with R² = 0.348 and RMSE = 18.06 t/hm² for Puer and R² = 0.345 and RMSE = 35.98 t/hm² for Genhe using the feature combination of Sentinel-1 and Sentinel-2. The results indicated that a combination of features extracted from Sentinel-1 and Sentinel-2 can improve the inversion accuracy of forest AGB, and the KNN-FIFS algorithm has robustness and transferability in forest AGB inversions. Full article

(This article belongs to the Special Issue Computer Application and Deep Learning in Forestry)

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17 pages, 5258 KiB

Open AccessArticle

Research on Forest Flame Detection Algorithm Based on a Lightweight Neural Network

by Yixin Chen, Ting Wang and Haifeng Lin

Forests 2023, 14(12), 2377; https://doi.org/10.3390/f14122377 - 5 Dec 2023

Cited by 3 | Viewed by 1349

Abstract

To solve the problem of the poor performance of a flame detection algorithm in a complex forest background, such as poor detection performance, insensitivity to small targets, and excessive computational load, there is an urgent need for a lightweight, high-accuracy, real-time detection system. This paper introduces a lightweight object-detection algorithm called GS-YOLOv5s, which is based on the YOLOv5s baseline model and incorporates a multi-scale feature fusion knowledge distillation architecture. Firstly, the ghost shuffle convolution bottleneck is applied to obtain richer gradient information through branching. Secondly, the WIoU loss function is used to address the issues of GIoU related to model optimization, slow convergence, and inaccurate regression. Finally, a knowledge distillation algorithm based on feature fusion is employed to further improve its accuracy. Experimental results based on the dataset show that compared to the YOLOv5s baseline model, the proposed algorithm reduces the number of parameters and floating-point operations by approximately 26% and 36%, respectively. Moreover, it achieved a 3.1% improvement in mAP_0.5 compared to YOLOv5s. The experiments demonstrate that GS-YOLOv5s, based on multi-scale feature fusion, not only enhances detection accuracy but also meets the requirements of lightweight and real-time detection in forest fire detection, commendably improving the practicality of flame-detection algorithms. Full article

(This article belongs to the Special Issue Computer Application and Deep Learning in Forestry)

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13 pages, 4285 KiB

Open AccessArticle

Automatic Detection and Counting of Stacked Eucalypt Timber Using the YOLOv8 Model

by Gianmarco Goycochea Casas, Zool Hilmi Ismail, Mathaus Messias Coimbra Limeira, Antonilmar Araújo Lopes da Silva and Helio Garcia Leite

Forests 2023, 14(12), 2369; https://doi.org/10.3390/f14122369 - 4 Dec 2023

Cited by 10 | Viewed by 3368

Abstract

The objective of this project was to automate the detection and counting process of stacked eucalypt (hybrid Eucalyptus urophylla x Eucalyptus grandis) timber in the forestry industry using the YOLOv8 model. The dataset consists of 230 diverse images of eucalypt roundwood, including images of roundwood separated on a rail and stacked timber. The annotations were made using LabelImg, ensuring accurate delineation of target objects on the log surfaces. The YOLOv8 model is customized with a CSPDarknet53 backbone, C2f module, and SPPF layer for efficient computation. The model was trained using an AdamW optimizer and implemented using Ultralytics YOLOv8.0.137, Python-3.10.12, and torch-2.0.1 + cu118 with CUDA support on NVIDIA T1000 (4096MiB). For model evaluation, the precision, recall, and mean Average Precision at a 50% confidence threshold (mAP50) were calculated. The best results were achieved at epoch 261, with a precision of 0.814, recall of 0.812, and mAP50 of 0.844 on the training set and a precision of 0.778, recall of 0.798, and mAP50 of 0.839 on the validation set. The model’s generalization was tested on separate images, demonstrating robust detection and accurate counting. The model effectively identified roundwood that was widely spaced, scattered, and overlapping. However, when applied to stacked timber, the automatic counting was not very accurate, especially when using images. In contrast, when using video, the relative percentage error for automatic counting significantly decreased to −12.442%. In conclusion, video proved to be more effective than images for counting stacked timber, while photographs should be reserved for the counting of individual roundwood pieces. Full article

(This article belongs to the Special Issue Computer Application and Deep Learning in Forestry)

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