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Forests
Special Issues
New Tools for Forest Science
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New Tools for Forest Science

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 July 2024) | Viewed by 2353

Special Issue Editors

Dr. Paulo Eduardo Teodoro

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Guest Editor
Department of Agronomy, Universidade Federal de Mato Grosso do Sul, Chapadão do Sul 7956-000, Brazil
Interests: statistics; multivariate analysis; plant breending; biometrics; remote sensing; sensors; genomic selection; geostatistics; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Gileno Brito De Azevedo

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Guest Editor
Department de Forestry Engineering, Universidade Federal de Mato Grosso do Sul, Campus, Chapadão do Sul 7956-000, Brazil
Interests: forest measurement; modeling forest growth and production; forestry
Dr. Larissa Pereira Ribeiro Teodoro

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Guest Editor
Department of Agronomy, Universidade Federal de Mato Grosso do Sul, Chapadão do Sul 7956-000, Brazil
Interests: UAV; random forest; nitrogen; maize
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest ecosystems provide a series of essential products and services for humanity. To ensure the continuity of the benefits provided by forests, it is essential to promote their sustainable management, which ensures numerous environmental, social and economic benefits. In this context, the search for new tools and technologies applied to the various operational processes related to forest conservation and/or production is of fundamental importance for the sustainable development of Forestry Science.

Therefore, this Special Issue of Forests aims to publish research results that innovatively approach the use of new tools and technologies applied to Forestry Science. These innovations involve a broad scope and can be related to both the improvement of operational procedures and the analysis of data applied to different areas, such as plant breeding, neutralization of carbon emissions, forest measurement, modeling of forest growth and production, nature conservation, product use technologies forestry, forest protection, and computational tools, among others.

Dr. Paulo Eduardo Teodoro
Dr. Gileno Brito De Azevedo
Dr. Larissa Pereira Ribeiro Teodoro
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

  • forest breeding
  • forest measurement
  • forest inventory
  • forest growth and production
  • machine learning
  • data analysis
  • forestry
  • remote sensing
  • wood quality
  • forest protection

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

Published Papers (2 papers)

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20 pages, 9179 KiB  
Article
EIAGA-S: Rapid Mapping of Mangroves Using Geospatial Data without Ground Truth Samples
by Yuchen Zhao, Shulei Wu, Xianyao Zhang, Hui Luo, Huandong Chen and Chunhui Song
Forests 2024, 15(9), 1512; https://doi.org/10.3390/f15091512 - 29 Aug 2024
Viewed by 648
Abstract
Mangrove forests are essential for coastal protection and carbon sequestration, yet accurately mapping their distribution remains challenging due to spectral similarities with other vegetation. This study introduces a novel unsupervised learning method, the Elite Individual Adaptive Genetic Algorithm-Semantic Inference (EIAGA-S), designed for the [...] Read more.
Mangrove forests are essential for coastal protection and carbon sequestration, yet accurately mapping their distribution remains challenging due to spectral similarities with other vegetation. This study introduces a novel unsupervised learning method, the Elite Individual Adaptive Genetic Algorithm-Semantic Inference (EIAGA-S), designed for the high-precision semantic segmentation of mangrove forests using remote sensing images without the need for ground truth samples. EIAGA-S integrates an adaptive Genetic Algorithm with an elite individual’s evolution strategy, optimizing the segmentation process. A new Mangrove Enhanced Vegetation Index (MEVI) was developed to better distinguish mangroves from other vegetation types within the spectral feature space. EIAGA-S constructs segmentation rules through iterative rule stacking and enhances boundary information using connected component analysis. The method was evaluated using a multi-source remote sensing dataset covering the Hainan Dongzhai Port Mangrove Nature Reserve in China. The experimental results demonstrate that EIAGA-S achieves a superior overall mIoU (mean intersection over union) of 0.92 and an F1 score of 0.923, outperforming traditional models such as K-means and SVM (Support Vector Machine). A detailed boundary analysis confirms EIAGA-S’s ability to extract fine-grained mangrove patches. The segmentation includes five categories: mangrove canopy, other terrestrial vegetation, buildings and streets, bare land, and water bodies. The proposed EIAGA-S model offers a precise and data-efficient solution for mangrove semantic mapping while eliminating the dependency on extensive field sampling and labeled data. Additionally, the MEVI index facilitates large-scale mangrove monitoring. In future work, EIAGA-S can be integrated with long-term remote sensing data to analyze mangrove forest dynamics under climate change conditions. This innovative approach has potential applications in rapid forest change detection, environmental protection, and beyond. Full article
(This article belongs to the Special Issue New Tools for Forest Science)
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10 pages, 2080 KiB  
Technical Note
Eucalyptus Species Discrimination Using Hyperspectral Sensor Data and Machine Learning
by Larissa Pereira Ribeiro Teodoro, Rosilene Estevão, Dthenifer Cordeiro Santana, Izabela Cristina de Oliveira, Maria Teresa Gomes Lopes, Gileno Brito de Azevedo, Fábio Henrique Rojo Baio, Carlos Antonio da Silva Junior and Paulo Eduardo Teodoro
Forests 2024, 15(1), 39; https://doi.org/10.3390/f15010039 - 23 Dec 2023
Cited by 4 | Viewed by 1403
Abstract
The identification of tree species is very useful for the management and monitoring of forest resources. When paired with machine learning (ML) algorithms, species identification based on spectral bands from a hyperspectral sensor can contribute to developing technologies that enable accurate forest inventories [...] Read more.
The identification of tree species is very useful for the management and monitoring of forest resources. When paired with machine learning (ML) algorithms, species identification based on spectral bands from a hyperspectral sensor can contribute to developing technologies that enable accurate forest inventories to be completed efficiently, reducing labor and time. This is the first study to evaluate the effectiveness of classification of five eucalyptus species (E. camaldulensis, Corymbia citriodora, E. saligna, E. grandis, and E. urophyla) using hyperspectral images and machine learning. Spectral readings were taken from 200 leaves of each species and divided into three dataset sizes: one set containing 50 samples per species, a second with 100 samples per species, and a third set with 200 samples per species. The ML algorithms tested were multilayer perceptron artificial neural network (ANN), decision trees (J48 and REPTree algorithms), and random forest (RF). As a control, a conventional approach by logistic regression (LR) was used. Eucalyptus species were classified by ML algorithms using a randomized stratified cross-validation with 10 folds. After obtaining the percentage of correct classification (CC) and F-measure accuracy metrics, the means were grouped by the Scott–Knott test at 5% probability. Our findings revealed the existence of distinct spectral curves between the species, with the differences being more marked from the 700 nm range onwards. The most accurate ML algorithm for identifying eucalyptus species was ANN. There was no statistical difference for CC between the three dataset sizes. Therefore, it was determined that 50 leaves would be sufficient to accurately differentiate the eucalyptus species evaluated. Our study represents an important scientific advance for forest inventories and breeding programs with applications in both forest plantations and native forest areas as it proposes a fast, accurate, and large-scale species-level classification approach. Full article
(This article belongs to the Special Issue New Tools for Forest Science)
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