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Forests
Special Issues
Monitoring and Control of Forest Pest and Disease
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Monitoring and Control of Forest Pest and Disease

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Health".

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 1897

Special Issue Editor

Dr. Margarita Georgieva

E-Mail Website
Guest Editor
Department of Entomology, Phytopathology and Game Fauna, Forest Research Institute – Bulgarian Academy of Sciences, St. Kliment Ohridski Blvd. 132, 1756 Sofia, Bulgaria
Interests: forest health status; disturbances; invasive species
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As climatic changes and their effects on forest ecosystems have become more evident over recent years, forest monitoring has proven to be more relevant than ever. Climate change could alter the frequency and intensity of forest disturbances such as insect outbreaks, the occurrence of invasive species, and wildfires. Severe damage caused by invasive pests and pathogens can become devastating, covering vast areas of forests that pose a threat to economically important tree species. Remote sensing data and terrestrial observations could present information for the sizes of areas, deteriorated by biotic, abiotic, and fire damage, the health status of vegetation, and observation of the habitat in which pests and diseases are spreading out. The assessment of the harmful impact and spread of the most important insect pests and fungal pathogens is essential for making decisions about their control. It is necessary to take into account the severity of the infection and the prevalence of pests, as well as their specific impact on the affected forests. Research on the biological control of dangerous insect pests and pathogens is of particular importance as a prerequisite for the development of integrated pest management systems that are safer for humans and biodiversity. We encourage studies from all fields, including experimental studies and monitoring approaches to contribute to this Special Issue to promote knowledge and adaptation strategies for the assessment of health status and deterioration, preservation, and management of forest ecosystems.

Dr. Margarita Georgieva
Guest Editor

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

  • monitoring of forest ecosystems
  • forest health status
  • disturbances
  • forest pests and diseases
  • invasive species
  • remote sensing
  • forest protection
  • biological control

Published Papers (1 paper)

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Research

22 pages, 6920 KiB  
Article
Model-Based Identification of Larix sibirica Ledeb. Damage Caused by Erannis jacobsoni Djak. Based on UAV Multispectral Features and Machine Learning
by Lei Ma, Xiaojun Huang, Quansheng Hai, Bao Gang, Siqin Tong, Yuhai Bao, Ganbat Dashzebeg, Tsagaantsooj Nanzad, Altanchimeg Dorjsuren, Davaadorj Enkhnasan and Mungunkhuyag Ariunaa
Forests 2022, 13(12), 2104; https://doi.org/10.3390/f13122104 - 09 Dec 2022
Cited by 3 | Viewed by 1486
Abstract
While unmanned aerial vehicle (UAV) remote sensing technology has been successfully used in crop vegetation pest monitoring, a new approach to forest pest monitoring that can be replicated still needs to be explored. The aim of this study was to develop a model [...] Read more.
While unmanned aerial vehicle (UAV) remote sensing technology has been successfully used in crop vegetation pest monitoring, a new approach to forest pest monitoring that can be replicated still needs to be explored. The aim of this study was to develop a model for identifying the degree of damage to forest trees caused by Erannis jacobsoni Djak. (EJD). By calculating UAV multispectral vegetation indices (VIs) and texture features (TF), the features sensitive to the degree of tree damage were extracted using the successive projections algorithm (SPA) and analysis of variance (ANOVA), and a one-dimensional convolutional neural network (1D-CNN), random forest (RF), and support vector machine (SVM) were used to construct damage degree recognition models. The overall accuracy (OA), Kappa, Macro-Recall (Rmacro), and Macro-F1 score (F1macro) of all models exceeded 0.8, and the best results were obtained for the 1D-CNN based on the vegetation index sensitive feature set (OA: 0.8950, Kappa: 0.8666, Rmacro: 0.8859, F1macro: 0.8839), while the SVM results based on both vegetation indices and texture features exhibited the poorest performance (OA: 0.8450, Kappa: 0.8082, Rmacro: 0.8415, F1macro: 0.8335). The results for the stand damage level identified by the models were generally consistent with the field survey results, but the results of SVMVIs+TF were poor. Overall, the 1D-CNN showed the best recognition performance, followed by the RF and SVM. Therefore, the results of this study can serve as an important and practical reference for the accurate and efficient identification of the damage level of forest trees attacked by EJD and for the scientific management of forest pests. Full article
(This article belongs to the Special Issue Monitoring and Control of Forest Pest and Disease)
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