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Forests
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Forest Ecology and Resource Monitoring Based on Sensors, Signal and...
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Forest Ecology and Resource Monitoring Based on Sensors, Signal and Image Processing

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: 30 April 2025 | Viewed by 8368

Special Issue Editors

Prof. Dr. Yili Zheng

E-Mail Website
Guest Editor
School of Technology, Beijing Forestry University, Beijing 10083, China
Interests: sensors and monitoring technologies
Prof. Dr. Xinwen Yu

E-Mail Website
Guest Editor
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 10091, China
Interests: internet of things; artificial intelligence; forest ecology and resource monitoring
Dr. Paul Sestras

E-Mail Website
Guest Editor
Department of Land Measurements and Cadastre, Faculty of Civil Engineering, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Interests: topography; land survey; construction surveying; mapping; cadastre; UAV photogrammetry; GIS
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yue Zhao

E-Mail Website
Guest Editor
School of Technology, Beijing Forestry University, Beijing 100083, China
Interests: artificial intelligence; machine learning; signal and image processing
Dr. Guang Deng

E-Mail Website
Guest Editor
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 10091, China
Interests: internet of things; artificial intelligence; forest ecology and resource monitoring
Dr. Ankur Srivastava

E-Mail Website
Guest Editor
College of Engineering, Science and Environment, School of Engineering, Callaghan, Australia
Interests: evapotranspiration; soil moisture; irrigation; hydrological modeling; ecohydrology; remote sensing of vegetation; solar radiation; landscape evolution; water resources; net radiation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

1. Background & history of this topic

Advanced sensors, signal, and image processing technologies have the potential to monitor the health and growth of forests, detecting wildlife and habitat changes, and to identify potential threats such as insect infestations or wildfires. This critical study field provides valuable insights into pressing forest ecology and resource challenges and helps us develop sustainable forest management and biodiversity conservation practices.

2. Aim and scope of the Special Issue:

This Special Issue aims to bring together researchers from various disciplines, including ecology, forestry, remote sensing, signal processing, and image analysis, to share their latest findings and insights.

  • Smart sensors and IoT for forest ecology and resource management.
  • Satellite or UAV remote sensing and image analysis for forest ecology and resource monitoring.
  • Artificial intelligence and machine learning for forest ecology and resource monitoring
  • Audio, image, and video processing for wildlife and plant monitoring.
  • Automatic monitoring of forest carbon stocks and fluxes, LiDAR technology for forest structure, and biomass estimation.
  • Other novel and practical technologies for forest ecology and resource monitoring.

3. Cutting-edge research

  • Development and application of novel forest environmental, soil, vegetation, and wildlife sensors.
  • Signal and image processing with data fusion, artificial intelligence, and machine learning.
  • Satellite or UAV remote sensing, light detection, and ranging (LiDAR) for forest resource monitoring.
  • Automatic monitoring of forest carbon stocks and fluxes.
  • Audio, image, and video processing for wild animal and plant monitoring.

4. What kind of papers we are soliciting
The papers submitted to this Special Issue should be focused on advancing our understanding of how advanced sensors, artificial intelligence, and signal and image processing can improve forest ecology and resource monitoring effectively and should present novel insights or approaches that advance the field.

Prof. Dr. Yili Zheng
Prof. Dr. Xinwen Yu
Dr. Paul Sestras
Prof. Dr. Yue Zhao
Dr. Guang Deng
Dr. Ankur Srivastava
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

  • sensors
  • remote sensing
  • LiDAR
  • artificial intelligence
  • machine learning
  • IoT (internet of things)
  • ecology monitoring
  • resource monitoring
  • carbon sequestration monitoring
  • wildlife and plant monitoring

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

17 pages, 1462 KiB  
Article
Extracting Individual Tree Positions in Closed-Canopy Stands Using a Multi-Source Local Maxima Method
by Guozhen Lai, Meng Cao, Chengchuan Zhou, Liting Liu, Xun Zhong, Zhiwen Guo and Xunzhi Ouyang
Forests 2025, 16(2), 262; https://doi.org/10.3390/f16020262 - 1 Feb 2025
Viewed by 208
Abstract
The accurate extraction of individual tree positions is key to forest structure quantification, and Unmanned Aerial Vehicle (UAV) visible light data have become the primary data source for extracting individual tree locations. Compared to deep learning methods, classical detection methods require lower computational [...] Read more.
The accurate extraction of individual tree positions is key to forest structure quantification, and Unmanned Aerial Vehicle (UAV) visible light data have become the primary data source for extracting individual tree locations. Compared to deep learning methods, classical detection methods require lower computational resources and have stronger interpretability and applicability. However, in closed-canopy forests, challenges such as crown overlap and uneven light distribution hinder extraction accuracy. To address this, the study improves the existing Revised Local Maxima (RLM) method and proposes a Multi-Source Local Maxima (MSLM) method, based on UAV visible light data, which integrates Canopy Height Models (CHMs) and Digital Orthophoto Mosaics (DOMs). Both the MSLM and RLM methods were used to extract individual tree positions from three different types of closed-canopy stands, and the extraction results of the two methods were compared. The results show that the MSLM method outperforms the RLM in terms of Accuracy Rate (85.59%), Overall Accuracy (99.09%), and F1 score (85.21%), with stable performance across different forest stand types. This demonstrates that the MSLM method can effectively overcome the challenges posed by closed-canopy stands, significantly improving extraction precision. These findings provide a cost-effective and efficient approach for forest resource monitoring and offer valuable insights for forest structure optimization and management. Full article
(This article belongs to the Special Issue Forest Ecology and Resource Monitoring Based on Sensors, Signal and Image Processing)
27 pages, 37085 KiB  
Article
A Method for Classifying Wood-Boring Insects for Pest Control Based on Deep Learning Using Boring Vibration Signals with Environment Noise
by Juhu Li, Xuejing Zhao, Xue Li, Mengwei Ju and Feng Yang
Forests 2024, 15(11), 1875; https://doi.org/10.3390/f15111875 - 25 Oct 2024
Viewed by 966
Abstract
Wood-boring pests are difficult to monitor due to their concealed lifestyle. To effectively control these wood-boring pests, it is first necessary to efficiently and accurately detect their presence and identify their species, which requires addressing the limitations of traditional monitoring methods. This paper [...] Read more.
Wood-boring pests are difficult to monitor due to their concealed lifestyle. To effectively control these wood-boring pests, it is first necessary to efficiently and accurately detect their presence and identify their species, which requires addressing the limitations of traditional monitoring methods. This paper proposes a deep learning-based model called BorerNet, which incorporates an attention mechanism to accurately identify wood-boring pests using the limited vibration signals generated by feeding larvae. Acoustic sensors can be used to collect boring vibration signals from the larvae of the emerald ash borer (EAB), Agrilus planipennis Fairmaire, 1888 (Coleoptera: Buprestidae), and the small carpenter moth (SCM), Streltzoviella insularis Staudinger, 1892 (Lepidoptera: Cossidae). After preprocessing steps such as clipping and segmentation, Mel-frequency cepstral coefficients (MFCCs) are extracted as inputs for the BorerNet model, with noisy signals from real environments used as the test set. BorerNet learns from the input features and outputs identification results. The research findings demonstrate that BorerNet achieves an identification accuracy of 96.67% and exhibits strong robustness and generalization capabilities. Compared to traditional methods, this approach offers significant advantages in terms of automation, recognition efficiency, and cost-effectiveness. It enables the early detection and treatment of pest infestations and allows for the development of targeted control strategies for different pests. This introduces innovative technology into the field of tree health monitoring, enhancing the ability to detect wood-boring pests early and making a substantial contribution to forestry-related research and practical applications. Full article
(This article belongs to the Special Issue Forest Ecology and Resource Monitoring Based on Sensors, Signal and Image Processing)
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17 pages, 4699 KiB  
Article
Analysis of the Effects of Different Nitrogen Application Levels on the Growth of Castanopsis hystrix from the Perspective of Three-Dimensional Reconstruction
by Peng Wang, Xuefeng Wang, Xingjing Chen and Mengmeng Shi
Forests 2024, 15(9), 1558; https://doi.org/10.3390/f15091558 - 4 Sep 2024
Viewed by 902
Abstract
Monitoring tree growth helps operators better understand the growth mechanism of trees and the health status of trees and to formulate more effective management measures. Computer vision technology can quickly restore the three-dimensional geometric structure of trees from two-dimensional images of trees, playing [...] Read more.
Monitoring tree growth helps operators better understand the growth mechanism of trees and the health status of trees and to formulate more effective management measures. Computer vision technology can quickly restore the three-dimensional geometric structure of trees from two-dimensional images of trees, playing a huge role in planning and managing tree growth. This study used binocular reconstruction technology to measure the height, canopy width, and ground diameter of Castanopsis hystrix and compared the growth differences under different nitrogen levels. In this research, we proposed a wavelet exponential decay thresholding method for image denoising. At the same time, based on the traditional semi-global matching (SGM) algorithm, a cost search direction is added, and a multi-line scanning semi-global matching (MLC-SGM) algorithm for stereo matching is proposed. The results show that the wavelet exponential attenuation threshold method can effectively remove random noise in red cone images, and the denoising effect is better than the traditional hard-threshold and soft-threshold denoising methods. The disparity images produced by the MLC-SGM algorithm have better disparity continuity and noise suppression than those produced by the SGM algorithm, with more minor measurement errors for C. hystrix growth factors. Medium nitrogen fertilization significantly promotes the height, canopy width, and ground diameter growth of C. hystrix. However, excessive fertilization can diminish this effect. Compared to tree height, excessive fertilization has a more pronounced impact on canopy width and ground diameter growth. Full article
(This article belongs to the Special Issue Forest Ecology and Resource Monitoring Based on Sensors, Signal and Image Processing)
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21 pages, 11127 KiB  
Article
Evaluation of the Functional Connectivity between the Mangomarca Fog Oasis and the Adjacent Urban Area Using Landscape Graphs
by Pedro Amaya, Violeta Vega, Doris Esenarro, Oscar Cuya and Vanessa Raymundo
Forests 2024, 15(6), 1003; https://doi.org/10.3390/f15061003 - 7 Jun 2024
Cited by 1 | Viewed by 989
Abstract
The present research aimed to measure the degree of connectivity and create a map of the ecological connectivity that highlights the real or potential presence of green, ecological, or ecotourism circuits integrating the green infrastructure of San Juan de Lurigancho and the Mangomarca [...] Read more.
The present research aimed to measure the degree of connectivity and create a map of the ecological connectivity that highlights the real or potential presence of green, ecological, or ecotourism circuits integrating the green infrastructure of San Juan de Lurigancho and the Mangomarca hills using graph theory applications implemented in the Graphab 2.8 software. Mangomarca and Huiracocha Park were selected for this study. In terms of the methodology, a simple approach based on landscape metrics, which are easy to interpret, was proposed to measure the connectivity of the mosaic of patches in the designated area. The IndiFrag software was used to obtain landscape metrics for the structural connectivity analysis. The Graphab software was employed for the functional connectivity analysis. Both tools proved effective in identifying vegetation gaps or the intensity of the greenery. Landsat 8 images from 8 July 2021 and 4 October 2021 were selected for this research due to the lower amount of cloud cover. Concerning the structural connectivity, the TMCl (patch size), NobCl (number of patches), and PerimCl (perimeter) metrics were effective in distinguishing the mosaic of urban landscape patches from the hill landscape. These indices confirm that the urban landscape patches have a higher number of fragments but are smaller in size compared to the hill landscape. Regarding the functional connectivity, it is evident that the patches are connected at lower-cost distances, averaging 7 cost units (210 m) during the wet season and 23 cost units (410 m) during the less humid season. However, these distances are too extensive and do not form ecological corridors. A survey of the population’s perception of the maximum separation distances between patches of vegetation cover that could still be considered a green corridor was included. The results indicate that a third of the sample (36%) prefer to walk down a hallway with a maximum separation distance of 10 m, while almost two-thirds (68%) would prefer a maximum separation distance of 50 m. Therefore, city planning should consider actions to reduce these distances and enable ecological connectivity in the area. It is recommended to continue researching the functional connectivity and determining the green corridors in the city to establish monitoring guidelines for the ecological connectivity of the city. Full article
(This article belongs to the Special Issue Forest Ecology and Resource Monitoring Based on Sensors, Signal and Image Processing)
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12 pages, 1895 KiB  
Article
Research on the Wood Density Measurement in Standing Trees through the Micro Drilling Resistance Method
by Jianfeng Yao, Yabin Zhao, Jun Lu, Hengyuan Liu, Zhenyang Wu, Xinyu Song and Zhuofan Li
Forests 2024, 15(1), 175; https://doi.org/10.3390/f15010175 - 15 Jan 2024
Cited by 1 | Viewed by 2176
Abstract
To achieve a micro-destructive and rapid measurement of the wood density of standing trees, this study investigated the possibility of the unified modeling of multiple tree species, the reliability of the micro drilling resistance method for measuring wood density, the relationship between drilling [...] Read more.
To achieve a micro-destructive and rapid measurement of the wood density of standing trees, this study investigated the possibility of the unified modeling of multiple tree species, the reliability of the micro drilling resistance method for measuring wood density, the relationship between drilling needle resistance and wood density, and whether moisture content has a significant impact on the model. First, 231 tree cores and drill resistance data were sampled from Pinus massoniana, Cunninghamia lanceolate, and Cryptomeria fortunei. The basic density and moisture content of each core were measured, and the average value of each resistance data record was calculated. Second, the average drill resistance, the natural logarithm of average drill resistance, and absolute moisture content were used as independent variables, while the basic wood density was used as the dependent variable. Third, the total model of the three tree species and sub-model for each tree species were established through a stepwise regression method. Finally, the accuracy of each model was compared and analyzed with that of using the average basic density of each tree species as an estimated density. The estimated accuracy of the total model, sub model, and average wood density modeling data were 90.070%, 93.865%, and 92.195%, respectively. The results revealed that the estimation accuracy of the sub-model was 1.670 percentage points higher than that of the average wood density modeling data, while the estimation accuracy of the total model was 2.125 percentage points lower than that of the average wood density modeling data. Additionally, except for Cryptomeria fortunei, the natural logarithm of drill resistance significantly influenced the wood density model at a significance level of 0.05. Moreover, moisture content significantly affected the total model and sub-models of Pinus massoniana at a significance level of 0.05. The results indicated the feasibility of using the micro-drilling resistance method to measure the wood density of standing trees. Moreover, the relationship between wood density and drill resistance did not follow a linear pattern, and moisture content slightly influenced the drill needle resistance. Furthermore, the establishment of a mathematical model for each tree species was deemed essential. This study provides valuable guidance for measuring the wood density of standing trees through the micro-drilling resistance method. Full article
(This article belongs to the Special Issue Forest Ecology and Resource Monitoring Based on Sensors, Signal and Image Processing)
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17 pages, 7732 KiB  
Article
Improved Tree Segmentation Algorithm Based on Backpack-LiDAR Point Cloud
by Dongwei Zhu, Xianglong Liu, Yili Zheng, Liheng Xu and Qingqing Huang
Forests 2024, 15(1), 136; https://doi.org/10.3390/f15010136 - 9 Jan 2024
Cited by 3 | Viewed by 2032
Abstract
For extracting tree structural data from LiDAR point clouds, individual tree segmentation is of great significance. Most individual tree segmentation algorithms miss segmentation and misrecognition, requiring manual post-processing. This study utilized a hierarchical approach known as segmentation based on hierarchical strategy (SHS) to [...] Read more.
For extracting tree structural data from LiDAR point clouds, individual tree segmentation is of great significance. Most individual tree segmentation algorithms miss segmentation and misrecognition, requiring manual post-processing. This study utilized a hierarchical approach known as segmentation based on hierarchical strategy (SHS) to improve individual tree segmentation. The tree point cloud was divided into the trunk layer and the canopy layer to carry out trunk detection and canopy segmentation, respectively. The effectiveness of SHS was evaluated on three mixed broadleaf forest plots. The segmentation efficacy of SHS was evaluated on three mixed broadleaf forest plots and compared with the point cloud segmentation algorithm (PCS) and the comparative shortest-path algorithm (CSP). In the three plots, SHS correctly identified all the trunk portion, had a recall (r) of 1, 0.98, and 1, a precision (p) of 1, and an overall segmentation rate (F) of 1, 0.99, and 1. CSP and PCS are less accurate than SHS. In terms of overall plots, SHS had 10%–15% higher F-scores than PCS and CSP. SHS extracted crown diameters with R2s of 0.91, 0.93, and 0.89 and RMSEs of 0.24 m, 0.23 m, and 0.30 m, outperforming CSP and PCS. Afterwards, we evaluate the three algorithms’ findings, examine the SHS algorithm’s parameters and constraints, and discuss the future directions of this research. This work offers an enhanced SHS that improves upon earlier research, addressing missed segmentation and misrecognition issues. It improves segmentation accuracy, individual tree segmentation, and provides both theoretical and data support for the LiDAR application in forest detection. Full article
(This article belongs to the Special Issue Forest Ecology and Resource Monitoring Based on Sensors, Signal and Image Processing)
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