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Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling...
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Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling (2nd Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1560

Special Issue Editors

Dr. Jan Komarek

E-Mail Website
Guest Editor
Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
Interests: multispectral remote sensing; UAV data analysis; vegetation inventory; landscape modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Marlena Kycko

E-Mail Website
Guest Editor
Chair of Geomatics and Information Systems, Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, Poland
Interests: vegetation monitoring; vegetation condition; biophysical remote sensing; hyperspectral and multispectral remote sensing; geostatistics; image analysis; classification; algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests, covering almost a third of the terrestrial land surface, representing one of the most sophisticated ecosystems, and they provide countless ecosystem services, potentially mitigating ongoing climate change. However, those services suffer from increasing anthropogenic pressure and forest disturbances. To properly evaluate the effects, scientists worldwide are working to improve their abilities to monitor forest ecosystems and how they undergo change. Beyond forests, networks of small landscape elements (groves, hedgerows, tree avenues, agroforestry, urban greenery, etc.) are not only highly important in terms of biodiversity conservation and restoration but also contribute to the quality of our cultural landscapes.

Remotely sensed data may be acquired over various spatial, spectral, and temporal resolutions for numerous purposes. Satellite imagery is traditionally used, for example, to track land cover change, for the mapping of landscape dynamics, the detection and monitoring of disturbances, to derive vegetation parameters and structures, and to model (micro)climate change and effects. This imagery may then be fused with lidar or radar data to provide a 3D forest structure. The acquisition of such 3D structures has become even easier and more accessible with the improved capabilities and availability of unmanned aerial systems.

This Special Issue aims to collect studies covering the different uses of a variety of sensors and platforms in the forest sciences. Multi-source data fusion and integration (e.g., multispectral, hyperspectral, thermal, microwave) and multi-scale and multi-temporal approaches, among others, are welcome. Studies focusing on the use of consumer-grade and low-cost solutions, as well as those focused on high mountain areas, are welcome.

Articles may address:

  • Tree and vegetation inventories;
  • Forest structural characteristics;
  • Forest biodiversity;
  • Forest changes;
  • Forest microclimate modelling;
  • Phenological vegetation traits and trends.

Dr. Jan Komarek
Dr. Marlena Kycko
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. Remote Sensing 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 2700 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 inventory
  • landscape modeling
  • feature and pattern detection
  • long-term monitoring
  • image analysis
  • point cloud analysis
  • data fusion

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Related Special Issue

Published Papers (2 papers)

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Research

30 pages, 3489 KiB  
Article
Assessing the Robustness of Multispectral Satellite Imagery with LiDAR Topographic Attributes and Ancillary Data to Predict Vertical Structure in a Wet Eucalypt Forest
by Bechu K. V. Yadav, Arko Lucieer, Gregory J. Jordan and Susan C. Baker
Remote Sens. 2025, 17(10), 1733; https://doi.org/10.3390/rs17101733 - 15 May 2025
Viewed by 277
Abstract
Remote sensing approaches can be cost-effective for estimating forest structural attributes. This study aims to use airborne LiDAR data to assess the robustness of multispectral satellite imagery and topographic attributes derived from DEMs to predict the density of three vegetation layers in a [...] Read more.
Remote sensing approaches can be cost-effective for estimating forest structural attributes. This study aims to use airborne LiDAR data to assess the robustness of multispectral satellite imagery and topographic attributes derived from DEMs to predict the density of three vegetation layers in a wet eucalypt forest in Tasmania, Australia. We compared the predictive capacity of medium-resolution Landsat-8 Operational Land Imager (OLI) surface reflectance and three pixel sizes from high-resolution WorldView-3 satellite imagery. These datasets were combined with topographic attributes extracted from resampled LiDAR-derived DEMs and a geology layer and validated with vegetation density layers extracted from high-density LiDAR. Using spectral bands, indices, texture features, a geology layer, and topographic attributes as predictor variables, we evaluated the predictive power of 13 data schemes at three different pixel sizes (1.6 m, 7.5 m, and 30 m). The schemes of the 30 m Landsat-8 (OLI) dataset provided better model accuracy than the WorldView-3 dataset across all three pixel sizes (R2 values from 0.15 to 0.65) and all three vegetation layers. The model accuracies increased with an increase in the number of predictor variables. For predicting the density of the overstorey vegetation, spectral indices (R2 = 0.48) and texture features (R2 = 0.47) were useful, and when both were combined, they produced higher model accuracy (R2 = 0.56) than either dataset alone. Model prediction improved further when all five data sources were included (R2 = 0.65). The best models for mid-storey (R2 = 0.46) and understorey (R2 = 0.44) vegetation had lower predictive capacity than for the overstorey. The models validated using an independent dataset confirmed the robustness. The spectral indices and texture features derived from the Landsat data products integrated with the low-density LiDAR data can provide valuable information on the forest structure of larger geographical areas for sustainable management and monitoring of the forest landscape. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling (2nd Edition))
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18 pages, 1501 KiB  
Article
Tree Species Classification at the Pixel Level Using Deep Learning and Multispectral Time Series in an Imbalanced Context
by Florian Mouret, David Morin, Milena Planells and Cécile Vincent-Barbaroux
Remote Sens. 2025, 17(7), 1190; https://doi.org/10.3390/rs17071190 - 27 Mar 2025
Cited by 2 | Viewed by 672
Abstract
This paper investigates tree species classification using the Sentinel-2 multispectral satellite image time series (SITS). Despite its importance for many applications and users, such mapping is often unavailable or outdated. The value of using SITS to classify tree species on a large scale [...] Read more.
This paper investigates tree species classification using the Sentinel-2 multispectral satellite image time series (SITS). Despite its importance for many applications and users, such mapping is often unavailable or outdated. The value of using SITS to classify tree species on a large scale has been demonstrated in numerous studies. However, many methods proposed in the literature still rely on a standard machine learning algorithm, usually the random forest (RF) algorithm. Our analysis shows that the use of deep learning (DL) models can lead to a significant improvement in classification results, especially in an imbalanced context where the RF algorithm tends to predict the majority class. In our case study in central France with 10 tree species, we obtained an overall accuracy (OA) of around 95% and an F1-macro score of around 80% using three different benchmark DL architectures (fully connected, convolutional, and attention-based networks). In contrast, using the RF algorithm, the OA and F1 scores obtained were 92% and 60%, indicating that the minority classes are poorly classified. Our results also show that DL models are robust to imbalanced data, although small improvements can be obtained by specifically addressing this issue. Validation on independent in situ data shows that all models struggle to predict in areas not well covered by training data, but even in this situation, the RF algorithm is largely outperformed by deep learning models for minority classes. The proposed framework can be easily implemented as a strong baseline, even with a limited amount of reference data. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Forest Ecosystem Monitoring and Spatial Modeling (2nd Edition))
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