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Forests
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Advances in Remote Sensing and GIS Utilization in Monitoring of...
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Advances in Remote Sensing and GIS Utilization in Monitoring of Forest Ecosystems

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 October 2025 | Viewed by 3277

Special Issue Editors

Dr. Hang Li

E-Mail Website
Guest Editor
Department of Ecosystem Science and Management, University of Northern British Columbia, Prince George, BC V2N 4Z9, Canada
Interests: optical remote sensing; time series analysis; forest management; dendrochronology
Dr. Aleksandar Dj Valjarević

E-Mail Website
Guest Editor
Faculty of Geography, University of Belgrade, Studentski Trg 3/3, 11000 Belgrade, Serbia
Interests: GIS; remote sensing; water science; meteorology; climatology; environment; digital cartography; atmosphere; statistics; numerical analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Menglin Qin

E-Mail Website
Guest Editor
Department of Architecture and Planning, College of Civil Engineering and Architecture, Guangxi University, Nanning 530003, China
Interests: low-carbon design and planning; territorial spatial carbon metabolism; land heat island (LHI); urban carbon pool engineering technology
Prof. Dr. Giorgos Mallinis

E-Mail Website
Guest Editor
Laboratory of Photogrammetry and Remote Sensing (PERS Lab), School of Rural and Surveying Engineering, The Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
Interests: land use/land cover (LULC) mapping; forests; classification development and comparison; geographic object-based image analysis; natural disasters; UAS; ecosystem services
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global forests lost 28.3 million hectares in 2023 and continue to be threatened by substantial natural and human disturbances. In the temporal dimension (recent decades or even recent centuries), we could witness the spatial–temporal forest change that includes carbon storage change, forest landscape change, and forest diversity change and explore its possible drivers. In the spatial dimension, we expect to detect extreme events (deforestation, forest fire, forest insects, forest drought, and other forest disturbances) and collect new forest ecological data (tree canopy height, forest aboveground biomass, and forest soil biochemistry) for management and planning. Remote sensing (RS) and geographic information systems (GISs) are cost-effective tools to monitor forest change in the long term and detect extreme events on a large spatial scale, which could be very helpful for forest management and planning. Novel applications with remote sensing and GIS could be entire new methods such as advanced techniques (deep learning or machine learning) or high-quality sensors. They could also be updated versions of classical techniques/sensor applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Forest cover or diversity change detection;
  • Forest disturbances (fire, insect, drought and others);
  • Forest carbon sequestration;
  • Environmental impact assessment;
  • Forest dynamics (biomass, humidity, temperature and soil);
  • Forest policy and management with remote sensing or GIS;
  • Dendrochronology with remote sensing or GIS.

Dr. Hang Li
Dr. Aleksandar Dj Valjarević
Dr. Menglin Qin
Prof. Dr. Giorgos Mallinis
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

  • remote sensing
  • GIS
  • forest management
  • change detection
  • forest disturbances
  • carbon sequestration

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

Published Papers (4 papers)

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Research

35 pages, 9355 KiB  
Article
Early Response of Post-Fire Forest Treatments Across Four Iberian Ecoregions: Indicators to Maximize Its Effectiveness by Remote Sensing
by Javier Pérez-Romero, Manuel Esteban Lucas-Borja, Demetrio Antonio Zema, Rocío Soria, Isabel Miralles, Laura Blanco-Cano, Cristina Fernández and Antonio D. del Campo García
Forests 2025, 16(7), 1154; https://doi.org/10.3390/f16071154 - 12 Jul 2025
Viewed by 212
Abstract
Remote sensing techniques that use spectral indices (SIs) are essential for monitoring vegetation recovery after wildfires. However, there is a critical gap in the comparability of SI responses across ecoregions due to ecological variability. In this study, a meta-analysis was conducted to evaluate [...] Read more.
Remote sensing techniques that use spectral indices (SIs) are essential for monitoring vegetation recovery after wildfires. However, there is a critical gap in the comparability of SI responses across ecoregions due to ecological variability. In this study, a meta-analysis was conducted to evaluate the capacity of different SIs (GCI, MSI, NBR, NDVI, NDII, and EVI2) to reflect the effect of post-wildfire emergency works on early recovery of vegetation in four Spanish ecoregions. It compared vegetation regrowth between treated and untreated sites, identifying the most sensitive SI for monitoring this recovery. All indices except EVI2 detected significantly better recovery in treated areas; among these, GCI was the most sensitive and NDII the least. The effect of treatment on recovery measured through SI is influenced by site covariates (fire severity, physiography, post-fire action period, post-fire climate, and edaphic characteristics). Finally, random mixed models showed that annual precipitation lower than 700 mm, diurnal temperature over 21 °C, soils with finer texture, and water content under 33% are quantitative limits of the treatment effectiveness on vegetation recovery. Overall, the study highlighted the importance of immediate interventions after fires, especially in the first six months, and advocated context-specific management strategies based on fire severity, ecoregion, soil properties, and climate to optimize vegetation recovery. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS Utilization in Monitoring of Forest Ecosystems)
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25 pages, 20862 KiB  
Article
GIS-Based Multi-Criteria Analysis for Urban Afforestation Planning in Semi-Arid Cities
by Halil İbrahim Şenol, Abdurahman Yasin Yiğit and Ali Ulvi
Forests 2025, 16(7), 1064; https://doi.org/10.3390/f16071064 - 26 Jun 2025
Viewed by 354
Abstract
Urban forests are very important for the environment and for people, especially in semi-arid cities where there is not much greenery. This makes heat stress worse and makes the city less livable. This paper presents a comprehensive geospatial methodology for selecting afforestation sites [...] Read more.
Urban forests are very important for the environment and for people, especially in semi-arid cities where there is not much greenery. This makes heat stress worse and makes the city less livable. This paper presents a comprehensive geospatial methodology for selecting afforestation sites in the expanding semi-arid urban area of Şanlıurfa, Turkey, characterized by minimal forest cover, rapid urbanization, and extreme weather conditions. We identified nine ecological and infrastructure criteria using high-resolution Sentinel-2 images and features from the terrain. These criteria include slope, aspect, topography, land surface temperature (LST), solar radiation, flow accumulation, land cover, and proximity to roads and homes. After being normalized to make sure they were ecologically relevant and consistent, all of the datasets were put together into a GIS-based Multi-Criteria Decision Analysis (MCDA) tool. The Analytic Hierarchy Process (AHP) was then used to weight the criteria. A deep learning-based semantic segmentation model was used to create a thorough classification of land cover, primarily to exclude unsuitable areas such as dense urban fabric and water bodies. The final afforestation suitability map showed that 151.33 km2 was very suitable and 192.06 km2 was suitable, mostly in the northeastern and southeastern urban fringes. This was because the terrain and subclimatic conditions were good. The proposed methodology illustrates that urban green infrastructure planning can be effectively directed within climate adaptation frameworks through the integration of remote sensing and spatial decision-support tools, especially in ecologically sensitive and rapidly urbanizing areas. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS Utilization in Monitoring of Forest Ecosystems)
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16 pages, 4037 KiB  
Article
Classification of Tree Species in Poland Using CNNs Tabular-to-Pseudo Image Approach Based on Sentinel-2 Annual Seasonality Data
by Łukasz Mikołajczyk, Paweł Hawryło, Paweł Netzel, Jakub Talaga, Nikodem Zdunek and Jarosław Socha
Forests 2025, 16(7), 1039; https://doi.org/10.3390/f16071039 - 20 Jun 2025
Viewed by 268
Abstract
Tree species classification provides invaluable information across various sectors, from forest management to conservation. This task is most commonly performed using remote sensing; however, this method is prone to classification errors, which modern computational approaches aim to minimize. Convolutional neural networks (CNNs) used [...] Read more.
Tree species classification provides invaluable information across various sectors, from forest management to conservation. This task is most commonly performed using remote sensing; however, this method is prone to classification errors, which modern computational approaches aim to minimize. Convolutional neural networks (CNNs) used to model tabular data have recently gained popularity as a highly efficient classification tool. In the present study, a variation of this method is used to classify satellite multispectral data from the Sentinel-2 mission to distinguish between 18 common Polish tree species. The novel model is trained and tested on data from species-homogeneous forest stands. The data form a multi-seasonal time series and cover five years of observations. The model achieved an overall accuracy of 80% and Cohen Kappa of 0.80 of the raw output and increased to 93% with post-processing procedures. Considering the large number of species classified, this is a promising and encouraging result. The presented results indicate the importance of early vegetation season reflectance data in model training. The spectral bands representing the infrared, red-edge and green wavelengths had the greatest impact on the model. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS Utilization in Monitoring of Forest Ecosystems)
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25 pages, 19380 KiB  
Article
GIS-Based Spatial Modeling of Soil Erosion and Wildfire Susceptibility Using VIIRS and Sentinel-2 Data: A Case Study of Šar Mountains National Park, Serbia
by Uroš Durlević, Tanja Srejić, Aleksandar Valjarević, Bojana Aleksova, Vojislav Deđanski, Filip Vujović and Tin Lukić
Forests 2025, 16(3), 484; https://doi.org/10.3390/f16030484 - 10 Mar 2025
Cited by 5 | Viewed by 1730
Abstract
Soil erosion and wildfires are frequent natural disasters that threaten the environment. Identifying and zoning susceptible areas are crucial for the implementation of preventive measures. The Šar Mountains are a national park with rich biodiversity and various climate zones. Therefore, in addition to [...] Read more.
Soil erosion and wildfires are frequent natural disasters that threaten the environment. Identifying and zoning susceptible areas are crucial for the implementation of preventive measures. The Šar Mountains are a national park with rich biodiversity and various climate zones. Therefore, in addition to protecting the local population from natural disasters, special attention must be given to preserving plant and animal species and their habitats. The first step in this study involved collecting and organizing the data. The second step applied geographic information systems (GIS) and remote sensing (RS) to evaluate the intensity of erosion using the erosion potential model (EPM) and the wildfire susceptibility index (WSI). The EPM involved the analysis of four thematic maps, and a new index for wildfires was developed, incorporating nine natural and anthropogenic factors. This study introduces a novel approach by integrating the newly developed WSI with the EPM, offering a comprehensive framework for assessing dual natural hazards in a single region using advanced geospatial tools. The third step involved obtaining synthetic maps and comparing the final results with satellite images and field research. For the Šar Mountains (Serbia), high and very high susceptibility to wildfires was identified in 21.3% of the total area. Regarding soil erosion intensity, about 8.2% of the area is affected by intensive erosion, while excessive erosion is present in 2.2% of the study area. The synthetic hazard maps provide valuable insights into the dynamics of the erosive process and areas susceptible to wildfires. The final results can be useful for decision-makers, spatial planners, and emergency management services in implementing anti-erosion measures and improving forest management in the study area. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS Utilization in Monitoring of Forest Ecosystems)
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