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: 31 July 2024 | Viewed by 1583

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
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

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

Published Papers (2 papers)

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Research

12 pages, 1895 KiB  
Article
Research on the Wood Density Measurement in Standing Trees through the Micro Drilling Resistance Method
Forests 2024, 15(1), 175; https://doi.org/10.3390/f15010175 - 15 Jan 2024
Viewed by 700
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
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17 pages, 7732 KiB  
Article
Improved Tree Segmentation Algorithm Based on Backpack-LiDAR Point Cloud
Forests 2024, 15(1), 136; https://doi.org/10.3390/f15010136 - 09 Jan 2024
Viewed by 575
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
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