Challenges and Prospects of Remote Sensing Application for Forest Resources 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 May 2025 | Viewed by 817

Special Issue Editor


E-Mail Website
Guest Editor
Department of Science and Technology of Agriculture and Environment (DISTAL), University of Bologna, 40126 Bologna, Italy
Interests: forest mapping; remote sensing; forestry; modeling

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the innovative applications and emerging challenges of remote sensing in forest resources management. Forests play a pivotal role in maintaining ecological balance, supporting biodiversity, and mitigating climate change. However, the effective management of forest resources is becoming increasingly complex due to global changes, such as deforestation, climate variability, and resource exploitation. Remote sensing technologies offer significant potential to address these challenges by providing scalable, cost-effective, and detailed spatial and temporal data. Contributions are invited on topics including, but not limited to, forest mapping, biomass estimation, carbon stock assessments, monitoring forest health, and integrating remote sensing with machine learning and geospatial analytics for decision support. This Special Issue will also explore the limitations and opportunities for enhancing the utility of remote sensing tools in operational forest management.

Dr. Saverio Francini
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

  • remote sensing
  • forest resources management
  • forest mapping
  • biomass estimation
  • machine learning

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 5687 KiB  
Article
Mapping of Dominant Tree Species in Yunnan Province Based on Sentinel-2 Time-Series Data and Assessment of the Influence of Understory Background on Mapping Accuracy
by Yihao Sun, Jingyuan Zhu, Ben Yang and Haodong Liu
Forests 2025, 16(2), 272; https://doi.org/10.3390/f16020272 - 5 Feb 2025
Viewed by 597
Abstract
Accurate information on the location of dominant tree species is essential for scientific forest management. However, factors like changes in forest phenology, stand conditions, and mixed understory backgrounds introduce uncertainties in remote sensing-based species mapping. To address these challenges, this study maps dominant [...] Read more.
Accurate information on the location of dominant tree species is essential for scientific forest management. However, factors like changes in forest phenology, stand conditions, and mixed understory backgrounds introduce uncertainties in remote sensing-based species mapping. To address these challenges, this study maps dominant tree species using time series Sentinel-2 data combined with environmental context data. To quantify the impact of understory background on mapping accuracy, this study applied a random forest inversion model to estimate the canopy cover across the study area. Binary contour plots and Pearson’s correlation coefficient were used to quantify the relationship between canopy cover and classification uncertainty at both the grid and pixels. A 10 m resolution map of dominant tree species in Yunnan Province, featuring eight species, was produced with an overall accuracy of 83.52% and a Kappa coefficient of 0.8115. The R2 value between the predicted and actual tree area proportions was greater than 0.93, with RMSEs consistently below 2.6. In addition, we observed strong negative correlations between different canopy cover classes. The correlations were −0.67 for low-cover areas, −0.40 for medium-cover areas, and −0.73 for high-cover areas. Our mapping framework enables the accurate identification of regional dominant species, and the established relationship between understory context and classification uncertainty provides valuable insights for analyzing potential mapping errors. Full article
Show Figures

Figure 1

Back to TopTop