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Forests
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New IoT Technologies for Monitoring Forests and Their Ecosystem Services
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New IoT Technologies for Monitoring Forests and Their Ecosystem Services

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 (12 December 2021) | Viewed by 13515

Special Issue Editors

Prof. Dr. Riccardo Valentini

E-Mail Website
Guest Editor
Department for Innovation in Biological, Agrifood and Forestry Systems, Tuscia University, Viterbo, Italy
Interests: forests; biology; ecosystems; livestock products; floods; droughts; gross primary productivity; urbanization; greenhouse gas emission; life cycle assessments; soil organic carbon; eddy covariance; net ecosystem exchange; citizen science; satellite imagery; mathematical modeling; artificial neural networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Thomas Nauss

E-Mail Website
Guest Editor
Environmental Informatics, Department of Geography, Philipp University of Marburg, 35037 Marburg, Germany
Interests: environmental informatics; geography; climatology

Special Issue Information

Dear Colleagues,

In the era of Industry 4.0, artificial intelligence and wireless technologies are increasingly being implemented in many different sectors of society, mainly to optimize production processes and automation. However, a few applications are still addressing environmental challenges, in particular forest monitoring and ecosystem services. The scale of forest services is continuously growing, from new materials production to carbon sequestration, air quality, health, biodiversity, land protection, and hydrologic cycle regulation. Urban trees and their services are also becoming a prominent need of the new urban life. The proposed Special Issue will accept papers that contribute to develop new sensors and technologies for monitoring forest ecosystems components and services, including trees, wildlife, soil, and atmosphere. Papers that integrate individual monitoring components with large-scale Earth observation technologies and/or artificial intelligence/big data analysis are also encouraged.

Prof. Dr. Riccardo Valentini
Prof. Dr. Thomas Nauss
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

  • Internet of Things
  • IoT
  • forest monitoring
  • forest ecology
  • ecosystem services
  • climate change
  • urban forestry
  • earth observation
  • wireless technologies
  • edge computing
  • artificial intelligence
  • remote sensing

Published Papers (3 papers)

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Research

19 pages, 2499 KiB  
Article
A Proposal for a Forest Digital Twin Framework and Its Perspectives
by Luca Buonocore, Jim Yates and Riccardo Valentini
Forests 2022, 13(4), 498; https://0-doi-org.brum.beds.ac.uk/10.3390/f13040498 - 23 Mar 2022
Cited by 21 | Viewed by 5671
Abstract
The increasing importance of forest ecosystems for human society and planetary health is widely recognized, and the advancement of data collection technologies enables new and integrated ways for forest ecosystems monitoring. Therefore, the target of this paper is to propose a framework to [...] Read more.
The increasing importance of forest ecosystems for human society and planetary health is widely recognized, and the advancement of data collection technologies enables new and integrated ways for forest ecosystems monitoring. Therefore, the target of this paper is to propose a framework to design a forest digital twin (FDT) that, by integrating different state variables at both tree and forest levels, creates a virtual copy of the forest. The integration of these data sets could be used for scientific purposes, for reporting the health status of forests, and ultimately for implementing sustainable forest management practices on the basis of the use cases that a specific implementation of the framework would underpin. Achieving such outcomes requires the twinning of single trees as a core element of the FDT by recording the physical and biotic state variables of the tree and of the near environment via real–virtual digital sockets. Following a nested approach, the twinned trees and the related physical and physiological processes are then part of a broader twinning of the entire forest realized by capturing data at forest scale from sources such as remote sensing technologies and flux towers. Ultimately, to unlock the economic value of forest ecosystem services, the FDT should implement a distributed ledger-based on blockchain and smart contracts to ensure the highest transparency, reliability, and thoroughness of the data and the related transactions and to sharpen forest risk management with the final goal to improve the capital flow towards sustainable practices of forest management. Full article
(This article belongs to the Special Issue New IoT Technologies for Monitoring Forests and Their Ecosystem Services)
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19 pages, 4943 KiB  
Article
Forest Total and Component Above-Ground Biomass (AGB) Estimation through C- and L-band Polarimetric SAR Data
by Peng Zeng, Wangfei Zhang, Yun Li, Jianmin Shi and Zhanhui Wang
Forests 2022, 13(3), 442; https://0-doi-org.brum.beds.ac.uk/10.3390/f13030442 - 11 Mar 2022
Cited by 14 | Viewed by 3593
Abstract
Forest biomass plays an essential role in forest carbon reservoir studies, biodiversity protection, forest management, and climate change mitigation actions. Synthetic Aperture Radar (SAR), especially the polarimetric SAR with the capability of identifying different aspects of forest structure, shows great potential in the [...] Read more.
Forest biomass plays an essential role in forest carbon reservoir studies, biodiversity protection, forest management, and climate change mitigation actions. Synthetic Aperture Radar (SAR), especially the polarimetric SAR with the capability of identifying different aspects of forest structure, shows great potential in the accurate estimation of total and component forest above-ground biomass (AGB), including stem, bark, branch, and leaf biomass. This study aims to fully explore the potential of polarimetric parameters at the C- and L-bands to achieve high estimation accuracy and improve the estimation of AGB saturation levels. In this study, the backscattering coefficients at different polarimetric channels and polarimetric parameters extracted from Freeman2, Yamaguchi3, H-A-Alpha, and Target Scattering Vector Model (TSVM) decomposition methods were optimized by a random forest algorithm, first, and then inputted into linear regression models to estimate the total forest AGB and biomass components of two test sites in China. The results showed that polarimetric observations had great potential in total and component AGB estimation in the two test sites; the best performances were for leaves at test site I, with R2 = 0.637 and RMSE = 1.27 t/hm2. The estimation of biomass components at both test sites showed obvious saturation phenomenon estimation according to their scatter plots. The results obtained at both test sites demonstrated the potential of polarimetric parameters in total and component biomass estimation. Full article
(This article belongs to the Special Issue New IoT Technologies for Monitoring Forests and Their Ecosystem Services)
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14 pages, 45073 KiB  
Article
Failure Detection in Eucalyptus Plantation Based on UAV Images
by Huanxin Zhao, Yixiang Wang, Zhibin Sun, Qi Xu and Dan Liang
Forests 2021, 12(9), 1250; https://0-doi-org.brum.beds.ac.uk/10.3390/f12091250 - 15 Sep 2021
Cited by 4 | Viewed by 2562
Abstract
The information of the locations and numbers of failures is crucial to precise management of new afforestation, especially during seedling replanting in young forests. In practice, foresters are more accustomed to determining the locations of failures according to their rows than based on [...] Read more.
The information of the locations and numbers of failures is crucial to precise management of new afforestation, especially during seedling replanting in young forests. In practice, foresters are more accustomed to determining the locations of failures according to their rows than based on their geographical coordinates. The relative locations of failures are more difficult to collect than the absolute geographic coordinates which are available from an orthoimage. This paper develops a novel methodology for obtaining the relative locations of failures in rows and counting the number of failures in each row. The methodology contains two parts: (1) the interpretation of the direction angle of seedlings rows on an unmanned aerial vehicle (UAV) orthoimage based on the probability statistical theory (called the grid-variance (GV) method); (2) the recognition of the centerline of each seedling rows using K-means and the approach to counting failures in each row based on the distribution of canopy pixels near the centerline of each seedling row (called the centerline (CL) method). The experimental results showed that the GV method can accurately interpret the direction angle of rows (45°) in an orthoimage and the CL method can quickly and accurately obtain the numbers and relative locations of failures in rows. The failure detection rates in the two experimental areas were 91.8% and 95%, respectively. These research findings can provide technical support for the precise cultivation of planted seedling forests. Full article
(This article belongs to the Special Issue New IoT Technologies for Monitoring Forests and Their Ecosystem Services)
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