Remote Sensing of Forest Land-Cover Change and Microclimate Conditions

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 (31 October 2024) | Viewed by 1974

Special Issue Editors


E-Mail Website
Guest Editor
Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, Madrid, Spain
Interests: remote sensing; GIS; forests; wildfire; land-cover change
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Forestry and Environmental Engineering and Management, Technical University of Madrid (UPM), Madrid, Spain
Interests: remote sensing; satellite time series; GIS; forestry; wildfire

E-Mail Website
Guest Editor Assistant
Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, Madrid, Spain
Interests: remote sensing; GIS; forests; machine learning

Special Issue Information

Dear Colleagues,

Forests are a highly complex ecosystem that covers almost a third of the Earth's land surface. They offer numerous benefits, which have the potential to help mitigate climate change. However, these benefits are being threatened by climate change, human activity and forest disturbances.

Changes in forest land-cover and land-use represent one way that humans modify the natural landscape, though changes can also occur as a result of climate effects. Urban expansion or significant disturbance can cause some changes that result in permanent destruction. Conversely, some changes like forest restoration or cropland abandonment, may aim to or lead to the restoration of previous damage.

Land use and land cover change mapping can play an important role in planning and management. Remote sensing data can be obtained in different spatial, spectral, and temporal resolutions for a variety of purposes. Satellite images have traditionally been employed for detecting and monitoring changes in land cover, mapping changes in landscapes, identifying disturbances, and modeling microclimate changes and impacts.

This Special Issue aims to collect studies covering different uses of different sensors and platforms in forest and landscape sciences.  Articles may address, but are not limited to, the following topics:

  • New advances in mapping of land cover and land use change;
  • Modeling and measuring forest ecosystems’ structure and landscape scaling issues;
  • Algorithms for retrievals of biophysical (landcover, LAI, NPP, chlorophyll content, GSV and biomass) and radiometric (surface reflectance, VI, albedo and energy budget) parameters;
  • Monitoring ecosystem change including, clear-cuts, post-fire and infestation recovery, forest succession and climate-driven vegetation cover changes;
  • Image classification and change detection;
  • Machine learning in forest remote sensing;
  • Vegetation dynamic and time series techniques for extracting climate change indicators;
  • Modeling and measuring the effect of microclimatic conditions on forests;
  • Applications of latest remote sensing methods to detect disturbance events, severity and their impacts on forest structure and function.

Prof. Dr. Carlos José Novillo Camacho
Dr. Silvia Merino De Miguel
Guest Editors

Adrián García Bruzón
Guest Editor Assistant

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 remote sensing
  • land cover-change
  • monitoring of climate conditions
  • GIS
  • satellite 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.
  • 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

25 pages, 32127 KiB  
Article
Deep Learning Approach for Studying Forest Types in Restored Karst Rocky Landscapes: A Case Study of Huajiang, China
by Jiaxue Wan, Zhongfa Zhou, Meng Zhu, Jiale Wang, Jiajia Zheng, Changxiang Wang, Xiaopiao Wu and Rongping Liu
Forests 2024, 15(12), 2122; https://doi.org/10.3390/f15122122 - 1 Dec 2024
Viewed by 1209
Abstract
Forest restoration landscapes are vital for restoring native habitats and enhancing ecosystem resilience. However, field monitoring (lasting months to years) in areas with complex surface habitats affected by karst rocky desertification is time-consuming. To address this, forest structural parameters were introduced, and training [...] Read more.
Forest restoration landscapes are vital for restoring native habitats and enhancing ecosystem resilience. However, field monitoring (lasting months to years) in areas with complex surface habitats affected by karst rocky desertification is time-consuming. To address this, forest structural parameters were introduced, and training samples were optimized by excluding fragmented samples and those with a positive case ratio below 30%. The U-Net instance segmentation model in ArcGIS Pro was then applied to classify five forest restoration landscape types: intact forest, agroforestry, planted forest, unmanaged, and managed naturally regenerated forests. The optimized model achieved a 2% improvement in overall accuracy, with unmanaged and intact forests showing the highest increases (7%). Incorporating tree height and age improved the model’s accuracy by 3.5% and 1.9%, respectively, while biomass reduced it by 2.9%. RGB imagery combined with forest height datasets was most effective for agroforestry and intact forests, RGB imagery with aboveground biomass was optimal for unmanaged naturally regenerated forests, and RGB imagery with forest age was most suitable for managed naturally regenerated forests. These findings provide a practical and efficient method for monitoring forest restoration and offer a scientific basis for sustainable forest management in regions with complex topography and fragile ecosystems. Full article
Show Figures

Figure 1

Back to TopTop