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Sensors and Forest Research
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Sensors and Forest Research

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 8771

Special Issue Editors

Dr. Barmpoutis Panagiotis

E-Mail Website
Guest Editor
Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece
Interests: forest informatics; forest monitoring; natural hazards detection and assessment; computer vision; pattern analysis; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Vasiliki Kamperidou

E-Mail Website
Guest Editor
Department of Forestry and Natural Environment, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Interests: heat treatment; wood; biomass; forest products; biomaterials; biofuels; building materials; cement; lime; roughness; adhesion; modification; coating
Special Issues, Collections and Topics in MDPI journals
Assoc. Prof. Dr. Nina Dobrinkova

E-Mail Website
Guest Editor
Institute of Information and Communication Technologies- Bulgarian Academy of Sciences (Department Cyber Physical Systems), Sofia, Bulgaria
Interests: decision support systems; fire behavior propagation systems; fire behavior fuel modeling; flood risks mapping; open source GIS tools; GIS systems; civil protection contingency planning and related exercises
Dr. Nikos Grammalidis

E-Mail Website
Guest Editor
Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece
Interests: computer vision; machine learning and artificial intelligence; multi-dimensional signal processing; intelligent systems and applications; environmental informatics and remote sensing; ICT for civil protection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an attempt to prevent and mitigate the impact of climate change, the conservation and protection of forest ecosystems, which constitute a storage of carbon dioxide, is proving to be one of the most significant factors. In addition, the constantly increasing population of our planet further increases the demand for forest biomass products, the resultant stress and pressure that forests receive because of human intervention, as well as the risk of disasters, floods and fires, encroachments, poaching and deforestation.

In the frame of protection of the precious natural resource of forests, addressing all the aforementioned risks and difficulties, the roles of sensors and artificial intelligence in the field of forest research have been highlighted as multilateral and highly critical, since they provide in shorter time, with less effort and lower cost, accurate, and in many cases real-time, data and information; these data are crucial for the proper development of trees, cultivation of the forest, maintenance of the fragile balances and mechanisms that interact in such an ecosystem, maintenance of biodiversity and the management and rational utilization of forest products.

Contributions in the Special Issue entitled: “Sensors and Forest Research” may cover a broad range of approaches, ranging from research and application-oriented papers to more theoretical studies discussing recommendations for more effective solutions to these challenges now and in the future. Some of the prospective/encouraged topics for this Special Issue include:

Utilization of sensors technology in forest monitoring, internet of things (IoT) for forest monitoring, satellites and forest research, unmanned aerial vehicles (UAVs) for forestry applications, machine learning, deep learning and artificial intelligence (AI) in forestry, light detection and ranging (LiDAR) in forest environments, forestry applications in geographic information system (GIS), environmental instrumentation, products density and quality prediction, biomass recording, harvesting processes, dendrochronology, forest management, forest roads, hydrological processes and flood monitoring in a forest ecosystem, wildfire modeling/analysis, detection of fires, diseases, natural disasters monitoring, biodiversity issues, wildlife species mobility and habitats monitoring, forest bioengineering works, protective constructions and measures assessment.

Dr. Barmpoutis Panagiotis
Dr. Kamperidou Vasiliki
Assoc. Prof. Dr. Nina Dobrinkova
Dr. Grammalidis Nikos
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. Sensors is an international peer-reviewed open access semimonthly 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
  • Forest management
  • Forest protection
  • Forest surveillance
  • Biomass monitoring
  • Natural disasters
  • Internet of things (IoT)
  • Forest sensors
  • Satellite
  • Unmanned aerial vehicles (UAVs)
  • Light detection and ranging (LiDAR)
  • Geographic information system (GIS)
  • Machine learning
  • Deep learning
  • Artificial intelligence

Published Papers (2 papers)

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Research

25 pages, 10501 KiB  
Article
Study on the Estimation of Forest Volume Based on Multi-Source Data
by Tao Hu, Yuman Sun, Weiwei Jia, Dandan Li, Maosheng Zou and Mengku Zhang
Sensors 2021, 21(23), 7796; https://doi.org/10.3390/s21237796 - 23 Nov 2021
Cited by 11 | Viewed by 2593
Abstract
We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olgensis, Pinus koraiensis, and Pinus [...] Read more.
We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olgensis, Pinus koraiensis, and Pinus sylvestris plantations in Mengjiagang forest farms as the research object, based on the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system, we extracted the visible vegetation index, texture features, terrain factors, and point cloud feature variables, respectively. Random forest (RF), support vector regression (SVR), and an artificial neural network (ANN) were used to estimate forest volume. In the small-scale space, the estimation of sample plot volume is influenced by the surrounding environment as well as the neighboring observed data. Based on the residuals of these three machine learning models, OK interpolation was applied to construct new hybrid forest volume estimation models called random forest Kriging (RFK), support vector machines for regression Kriging (SVRK), and artificial neural network Kriging (ANNK). The six estimation models of forest volume were tested using the leave-one-out (Loo) cross-validation method. The prediction accuracies of these six models are better, with RLoo2 values above 0.6, and the prediction accuracy values of the hybrid models are all improved to different extents. Among the six models, the RFK hybrid model had the best prediction effect, with an RLoo2 reaching 0.915. Therefore, the machine learning method based on multi-source remote sensing factors is useful for forest volume estimation; in particular, the hybrid model constructed by combining machine learning and the OK method greatly improved the accuracy of forest volume estimation, which, thus, provides a fast and effective method for the remote sensing inversion estimation of forest volume and facilitates the management of forest resources. Full article
(This article belongs to the Special Issue Sensors and Forest Research)
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14 pages, 2381 KiB  
Communication
Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas
by Zhijun Zhen, Shengbo Chen, Tiangang Yin, Eric Chavanon, Nicolas Lauret, Jordan Guilleux, Michael Henke, Wenhan Qin, Lisai Cao, Jian Li, Peng Lu and Jean-Philippe Gastellu-Etchegorry
Sensors 2021, 21(6), 2115; https://doi.org/10.3390/s21062115 - 17 Mar 2021
Cited by 31 | Viewed by 5077
Abstract
Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) [...] Read more.
Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs’ saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI. Full article
(This article belongs to the Special Issue Sensors and Forest Research)
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