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Forests
Special Issues
Precise Forestry: Forest Dynamic Change Mapping, Monitoring and Modeling
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Precise Forestry: Forest Dynamic Change Mapping, Monitoring and Modeling

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: 5 August 2024 | Viewed by 3327

Special Issue Editors

Dr. Chunying Ren

E-Mail Website
Guest Editor
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: tree species classification; biomass estimation; forest fire; disturbances
Special Issues, Collections and Topics in MDPI journals
Dr. Chunyan Lu

E-Mail Website
Guest Editor
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Interests: remote sensing of environment and resources; eco-environment management; land cover change monitoring; landscape ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio J. Pérez-Luque

E-Mail Website
Guest Editor
Spanish National Research Council, Estación Experimental del Zaidín (EEZ-CSIC), Service of Evaluation, Restoration and Protection of Mediterranean Agrosystems, Profesor Albareda 1, ES-18008 Granada, Spain
Interests: remote sensing; forest ecology

Special Issue Information

Dear Colleagues,

Forests are precious resources and provide ecosystem services that are essential for human well-being. Similarly, the role of forests in climate regulation and carbon sequestration is of global importance. Under climate change and human interference, forest structure, function and related ecosystem services are changing, such as unreasonable structure, forest degradation and carbon loss. Thus, precisely and consistently mapping, monitoring and modelling forest status and dynamic changes in high spatial detail is urgently needed. The combination of field investigation, remote sensing, visualization technology and computer techniques is beneficial to the generation of updated and detailed information, which plays a critical role in supporting sustainable forest management and informing the development of policies. Given the growing interest in protecting forest diversity, multiple ecosystem services and promoting forest management, we encourage studies from all fields, including experimental studies, monitoring approaches and models, to contribute to this Special Issue in order to promote knowledge and adaptation strategies for the precise preservation, management, and future development of forest ecosystems.

Dr. Chunying Ren
Dr. Chunyan Lu
Dr. Antonio J. Pérez-Luque
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 monitoring
  • remote sensing
  • mapping
  • modeling
  • disturbance
  • dynamic changes
  • biomass estimation
  • forest structure

Published Papers (3 papers)

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Research

18 pages, 16786 KiB  
Article
MangroveSeg: Deep-Supervision-Guided Feature Aggregation Network for Mangrove Detection and Segmentation in Satellite Images
by Heng Dong, Yifan Gao, Riqing Chen and Lifang Wei
Forests 2024, 15(1), 127; https://doi.org/10.3390/f15010127 - 08 Jan 2024
Viewed by 805
Abstract
Mangrove forests are significant participants in coastal ecological environment systems. For the development of protection strategies, it is crucial to automatically and accurately detect the distribution and area of mangroves using satellite images. Although many deep-learning-based mangrove detection and segmentation algorithms have made [...] Read more.
Mangrove forests are significant participants in coastal ecological environment systems. For the development of protection strategies, it is crucial to automatically and accurately detect the distribution and area of mangroves using satellite images. Although many deep-learning-based mangrove detection and segmentation algorithms have made notable progress, the complex regional structures and the great similarity between mangroves and the surrounding environment, as well as the diversity of mangroves, render the task still challenging. To cover these issues, we propose a novel deep-supervision-guided feature aggregation network for mangrove detection and segmentation called MangroveSeg, which is based on a U-shaped structure with ResNet, combining an attention mechanism and a multi-scale feature extraction framework. We also consider the detection and segmentation of mangroves as camouflage detection problems for the improvement and enhancement of accuracy. To determine more information from extracted feature maps in a hidden layer, a deep supervision model is introduced in up-sampling to enhance feature representation. The spatial attention mechanism with attention gates is utilized to highlight significant regions and suppress task-independent feature responses. The feature fusion module can obtain multi-scale information by binding each layer to the underlying information and update feature mappings. We validated our framework for mangrove detection and segmentation using a satellite image dataset, which includes 4000 images comprising 256 × 256 pixels; we used 3002 for training and 998 for testing. The satellite images dataset was obtained from the Dongzhaigang National Nature Reserve located in Haikou City, Hainan Province, China. The proposed method achieved a 89.58% overall accuracy, 89.02% precision, and 80.7% mIoU. We also used the trained MangroveSeg model to detect mangroves on satellite images from other regions. We evaluated the statistical square measure of some mangrove areas and found that the evaluation accuracy can reach 96% using MangroveSeg. The proposed MangroveSeg model can automatically and accurately detect the distribution and area of mangroves from satellite images, which provides a method for monitoring the ecological environment. Full article
(This article belongs to the Special Issue Precise Forestry: Forest Dynamic Change Mapping, Monitoring and Modeling)
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12 pages, 7727 KiB  
Article
Development of a Real-Time Continuous Measurement System for Tree Radial Direction
by Qianjia Zhang, Yuanjing Sun, Xinyu Zheng, Shusheng Zhang and Luming Fang
Forests 2023, 14(9), 1876; https://doi.org/10.3390/f14091876 - 15 Sep 2023
Viewed by 741
Abstract
Tree diameter at breast height (DBH) is the most fundamental factor in modelling tree growth, but current DBH measurement instruments mainly focus on instantaneous acquisition, making it difficult to measure tree growth continuously and accurately. In this study, we propose a wireless sensing [...] Read more.
Tree diameter at breast height (DBH) is the most fundamental factor in modelling tree growth, but current DBH measurement instruments mainly focus on instantaneous acquisition, making it difficult to measure tree growth continuously and accurately. In this study, we propose a wireless sensing network that can transmit data in signal-free environments, and combine sensor and computer technologies to develop a real-time continuous measurement system for tree radials, which has the advantages of working in real-time, being low-cost and stable, and enabling high-precision. It can be applied to the DBH measurement of trees in the range of 50 mm–380 mm, with a measurement accuracy of 0.001 mm. Additionally, whole-point sampling, conducted 24 h per day, integrates DBH data measurement, transmission, storage and visualization analysis. After measuring in the field for a year, it initially reveals the change in DBH within the test area within that year. This study provides a scientific basis for researching the microscopic growth pattern of trees and establishing a tree growth model, which will be further optimised and improved in terms of appearance structure, communication and power supply in the future. Full article
(This article belongs to the Special Issue Precise Forestry: Forest Dynamic Change Mapping, Monitoring and Modeling)
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14 pages, 9797 KiB  
Article
Automatic 10 m Forest Cover Mapping in 2020 at China’s Han River Basin by Fusing ESA Sentinel-1/Sentinel-2 Land Cover and Sentinel-2 near Real-Time Forest Cover Possibility
by Xia Wang, Yihang Zhang and Kerong Zhang
Forests 2023, 14(6), 1133; https://doi.org/10.3390/f14061133 - 30 May 2023
Cited by 2 | Viewed by 1317
Abstract
Given the increasingly fragmented forest landscapes, it is necessary to map forest cover with fine spatial resolution in a large area. The European Space Agency (ESA) released the 10 m global land cover map in 2020 based on Sentinel-1 and Sentinel-2 images, and [...] Read more.
Given the increasingly fragmented forest landscapes, it is necessary to map forest cover with fine spatial resolution in a large area. The European Space Agency (ESA) released the 10 m global land cover map in 2020 based on Sentinel-1 and Sentinel-2 images, and Dynamic World provides near real-time possibilities of many land cover classes based on Sentinel-2 images, but they are not designed particularly for forest cover. In this research, we aimed to develop a method to automatically estimate an accurate 10 m forest cover map in 2020 by fusing the ESA forest cover map and Dynamic World near real-time forest cover possibilities. The proposed method includes three main steps: (1) generating stable forest samples, (2) determining the threshold T and (3) producing the fused forest cover map. China’s Han River Basin, dominated by complex subtropical forests, was used as the study site to validate the performance of the proposed method. The results show that the proposed method could produce a forest cover map with the best overall accuracy of 98.02% ± 1.20% and more accurate spatial details compared to using only one of the two data sources. The proposed method is thus superior in mapping forest cover in complex forest landscapes. Full article
(This article belongs to the Special Issue Precise Forestry: Forest Dynamic Change Mapping, Monitoring and Modeling)
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