Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.5
Journals
Forests
Special Issues
Advances in Remote Sensing for Forest Resource Monitoring and Management
share announcement

Advances in Remote Sensing for Forest Resource Monitoring and Management

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: 31 July 2026 | Viewed by 3257

Special Issue Editors

Prof. Dr. Saygin Abdikan

E-Mail Website
Guest Editor
Department of Geomatics Engineering, Hacettepe University, Ankara 06800, Türkiye
Interests: remote sensing; synthetic aperture radar; vegetation monitoring; land use land cover; image classification
Dr. Aliihsan Sekertekin

E-Mail Website
Guest Editor
Department of Architecture and Town Planning, Igdir University, Igdir, Türkiye
Interests: thermal infrared remote sensing; surface heat island; microwave remote sensing for soil moisture estimation; spectral indexes; air quality/pollution monitoring; albedo retrieval
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests are vital ecosystems that provide carbon storage, conserve biodiversity, regulate water, and offer socioeconomic benefits. However, they face increasing pressures from deforestation, climate change, and natural disturbances such as fires, storms, and pests. These challenges underscore the importance of accurate, consistent, and large-scale monitoring to inform sustainable management and policy development. Remote sensing has emerged as a key tool for forest observation, delivering spatially and temporally continuous data across local to global scales. Advances in satellite, airborne, and unmanned aerial vehicle (UAV) platforms—along with the use of multispectral, hyperspectral, LiDAR, and synthetic aperture radar (SAR) sensors—are enabling more detailed assessments of forest structure, composition, and dynamics. The integration of multi-source data with machine learning, artificial intelligence, and cloud-based platforms further enhances the capacity to detect change, estimate biomass, and evaluate forest health. This Special Issue, “Advances in Remote Sensing for Forest Resource Monitoring and Management”, compiles research highlighting novel algorithms, applications, and comprehensive reviews. Together, these contributions demonstrate how recent innovations are advancing the precision, scalability, and applicability of remote sensing for sustainable forest monitoring and management in a rapidly changing world.

Prof. Dr. Saygin Abdikan
Dr. Aliihsan Sekertekin
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 250 words) can be sent to the Editorial Office for assessment.

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 management
  • forest parameter extraction
  • tree identification
  • deep learning
  • machine learning
  • forest fire
  • carbon and biomass mapping
  • LiDAR altimetry
  • SAR imagery
  • optical imagery

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

23 pages, 2119 KB  
Article
Airborne LiDAR for Basal Area Estimation: Accuracy Assessment and Improvement in Eastern Canada’s Mixed Temperate Forests
by David Normandeau, Daniel Beaudoin, Martin Riopel and Hakim Ouzennou
Forests 2026, 17(4), 406; https://doi.org/10.3390/f17040406 - 25 Mar 2026
Viewed by 359
Abstract
Sustainable forest management requires current, territory-wide data, which is difficult to obtain in vast regions like Quebec, Canada. To complement ground inventories and photo-interpretation, the province developed an airborne laser scanning (ALS)-based model that performs well in coniferous stands, but its accuracy in [...] Read more.
Sustainable forest management requires current, territory-wide data, which is difficult to obtain in vast regions like Quebec, Canada. To complement ground inventories and photo-interpretation, the province developed an airborne laser scanning (ALS)-based model that performs well in coniferous stands, but its accuracy in hardwood stands remains untested. This study aims to evaluate the accuracy of the ALS-based prediction of stand basal area and then test new approaches to increase its performance. Airborne LiDAR data from 2011 to 2020 and 12,506 validation plots from sample plots were used. The ALS model accuracy was initially compared across the stand types, revealing lower accuracy in shade-tolerant deciduous stands. Three inputs were found to increase prediction accuracy: proportion of each species basal area in the stand, geographical coordinates, and meteorological data associated with location. Parametric and auto machine learning (AutoML) methods were employed using those inputs to improve accuracy, with AutoML achieving the highest improvement with initial R2 of 0.27, 0.47 and 0.54 and after correction R2 of 0.31, 0.56 and 0.67, respectively, for shade-tolerant deciduous, shade-intolerant deciduous, and coniferous stand. Even with the advancements made, further improvements will be necessary to consider using an ALS-based model for shade-tolerant deciduous species. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Forest Resource Monitoring and Management)
►▼ Show Figures

Figure 1

24 pages, 12588 KB  
Article
Effects of Highway Construction on Landscape Patterns, Ecosystem Service Value, Habitat Connectivity and Their Associations in Zhejiang, China
by Jieyong Zhan, Yuhang Chen, Yanbo Yang and Wenjie Wang
Forests 2026, 17(3), 338; https://doi.org/10.3390/f17030338 - 8 Mar 2026
Viewed by 407
Abstract
Highway construction is a major driver of landscape transformation, yet its integrated effects on ecological functions in forested regions under strong ecological governance remain poorly quantified. This study examines spatiotemporal changes in land use, landscape patterns, ecosystem service value (ESV), and habitat connectivity [...] Read more.
Highway construction is a major driver of landscape transformation, yet its integrated effects on ecological functions in forested regions under strong ecological governance remain poorly quantified. This study examines spatiotemporal changes in land use, landscape patterns, ecosystem service value (ESV), and habitat connectivity within 1–5 km buffer zones along three highways in Zhejiang, China, from 2000 to 2023. Results indicate that highway-induced fragmentation was land-use-specific: cropland and construction land became more fragmented, while forests maintained high spatial cohesion due to protective policies. ESV per hectare increased over time and with distance from highways, driven by forest expansion and economic revaluation. In contrast, habitat connectivity for reptiles, amphibians, mammals, and birds declined, revealing a decoupling between ESV enhancement and connectivity conservation. These findings underscore the context-dependent impacts of highways and highlight the need for integrated management strategies that preserve forest integrity to balance ecological functions in rapidly developing regions. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Forest Resource Monitoring and Management)
►▼ Show Figures

Figure 1

19 pages, 17253 KB  
Article
ALS and SfM Field Data Survey as a Basis of Forest Road Design
by Ivica Papa, Luka Hodak, Maja Popović, Andreja Đuka, Tibor Pentek and Mihael Lovrinčević
Forests 2026, 17(2), 265; https://doi.org/10.3390/f17020265 - 16 Feb 2026
Viewed by 491
Abstract
Field data of high accuracy and precision is the basis for creating the high-quality design of a forest road. In this study, three survey methods for collecting field data were tested: ALS UAV, LiDAR data of the Republic of Croatia, collected by airplane, [...] Read more.
Field data of high accuracy and precision is the basis for creating the high-quality design of a forest road. In this study, three survey methods for collecting field data were tested: ALS UAV, LiDAR data of the Republic of Croatia, collected by airplane, and UAV SfM. A total of three detailed forest road projects were created based on the collected data. The designed forest roads had the same horizontal and vertical development, thus eliminating the human factor from the design process. Four important forest road parameters were tested: earthwork cut and fill volume, cross-terrain slope, and carriageway value. No significant statistical difference was found for any of the tested parameters between designs. The design based on ALS data had a total number of earthworks of 1026.03 m3, the amount was 1449.56 m3 for SfM design, and the number of earthworks for the State Geodetic Administration LiDAR data was 889.02 m3. The calculated amount of cut volume was significantly affected by the error of the carriageway value for the State Geodetic Administration LiDAR data-based design. The results indicate the possibility of using all used methods on terrain with a moderate slope, but there is a need for further testing on different terrain slope classes. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Forest Resource Monitoring and Management)
►▼ Show Figures

Figure 1

Review

Jump to: Research

32 pages, 1256 KB  
Review
Internet of Things (IoT)-Based Applications in Smart Forestry: A Conceptual and Technological Analysis
by Iulia Diana Arion, Irina M. Morar, Alina M. Truta, Ioan Aurel Chereches, Vlad Ilie Isarie and Felix H. Arion
Forests 2026, 17(1), 44; https://doi.org/10.3390/f17010044 - 28 Dec 2025
Cited by 1 | Viewed by 1558
Abstract
In the context of green transition and digital transformation, forestry is becoming a strategic area of application of current modern technologies. The Internet of Things (IoT), artificial intelligence (AI), big data analysis (Big Data) and Digital Twins define the basic infrastructure of smart [...] Read more.
In the context of green transition and digital transformation, forestry is becoming a strategic area of application of current modern technologies. The Internet of Things (IoT), artificial intelligence (AI), big data analysis (Big Data) and Digital Twins define the basic infrastructure of smart forestry. By connecting sensors, drones and satellites, IoT allows for continuous monitoring of forest ecosystems, risk anticipation and decision optimization in real-time. The purpose of this study is to perform a comprehensive narrative analysis of the relevant scientific literature from the recent period (2020–2025) regarding the application of IoT in forestry, highlighting the conceptual, technological and institutional developments. Based on a selection of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (29 full-text articles), four major axes are analyzed: (A) forest fire detection and prevention; (B) climate-smart forestry and carbon accounting; (C) forest digitalization through the concepts of Forest 4.0, Forest 5.0 and Digital Twins; (D) sustainability and digital forest policies. The results show that IoT is a catalyst for the sustainable transformation of the forest sector, supporting carbon accounting, climate-risk reduction and data-driven governance. The analysis highlights four major developments: the consolidation of IoT–AI architectures, the integration of IoT and remote sensing, the emergence of Forest 4.0/5.0 and Digital Twins and the growing role of governance and data standards. These findings align with the objectives of the EU Forest Strategy 2030 and the European Green Deal. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Forest Resource Monitoring and Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop