Special Issue "Advances in the Remote Sensing of Forest Cover Change"
Deadline for manuscript submissions: 15 February 2022.
Tel. +30 2310 992699; Fax: +30 2310 998897
Interests: remote sensing; GIS; forest management; forest fires
Interests: remote sensing; GIS; forest management; forest fires; time series analysis; land cover change
Forests are considered one of the Earth’s most diverse ecosystems, and as such the conservation of biodiversity from local to global scale is entirely dependent on the protection and management of these ecosystems. Forests provide valuable ecosystem services and a very wide variety of benefits related to climate and water regulation, food security, human health, and energy resources, to name just a few. At the same time, forest ecosystems are constantly exposed to a significant number of environmental, economic, and social threats and pressures. Climate-driven pressures (which are foreseen to increase), coupled with growing demands on natural resources, are challenging the health and resilience of these ecosystems.
Existing initiatives and established strategies by national and international organizations and agencies provide extensive reports on the current status of forests worldwide, and emphasize the continuation and improvement of monitoring approaches, among other practices. Remote sensing methods and Earth observation datasets are valuable tools for providing spatially explicit information on past and on-going forest cover changes and for assessing future risks. The availability of large archives of satellite imagery and the scheduled data continuity missions, as implemented from the Landsat mission and the Copernicus programme, are now fostering new approaches in the fields of spatio-temporal data analysis and forest management.
This Special Issue aims to gather the latest research related to the use of advanced remote-sensing-based methods and strategies for the detailed monitoring and assessment of changes in forest ecosystems. We therefore invite contributions that will provide further insight into the way forests respond to pressures at local, regional, and global scales. We welcome the submission of manuscripts covering topics including but not limited to the following:
- Exploitation of Landsat archives and recent Sentinel-2 imagery;
- Research on data fusion approaches (multispectral, hyperspectral and microwave data sources);
- Detection and characterization of rapid and gradual changes in forest cover;
- Development of operational solutions for mitigating degradation impacts on forests;
- Advances in Big Data applications;
- Time series methods for the detection of disturbances;
- Evaluation of satellite products related to forest biophysical parameters.
Dr. Ioannis Gitas
Dr. Thomas Katagis
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 papers will be 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 2400 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.
- forest ecosystems
- forest change monitoring
- vegetation cover
- remote sensing
- satellite sensors
- time series
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Combining Landsat and Sentinel-2 observations for characterizing forest vegetation dynamics at local scale
Authors: Katagis Thomas; Grammalidis Nikolaos; Papaiordanidis Stefanos; Papaioannou Periklis; Kontopoulos Christos; Gitas Ζ. Ioannis; Charalampopoulou Vasiliki
Affiliation: Laboratory of Forest Management and Remote Sensing, AUTH; Centre for Research and Technology Hellas – CERTH; Geosystems Hellas S.A
Abstract: Continuous monitoring of natural ecosystems is of crucial importance for assessing past and current conditions of vegetation dynamics, for detection of disturbances as well as for management of natural resources. In the past decade, due to the free access to large datasets of finer resolution remote sensing imagery, new algorithms and methods have been developed for monitoring of natural resources at various scales. Indeed, advanced techniques for processing dense satellite time series have been developed, with emerging cloud-based technologies supporting the computational efforts of the users. The combination of reflectance measurements from Landsat and Sentinel-2 sensors provides nowadays new opportunities for multi-temporal classification processes or for reliable extraction of spatial and temporal trajectories. In this work, we investigate the capabilities of the combined use of Landsat 7 and 8 with Sentinel 2 optical datasets to create a recent history of observations over selected forest occupied sites. Although various studies have shown that there are subtle differences in the spectral characteristics among these sensors, additional calibration should be considered when these are used across various biomes and landcapes and under different climate conditions. Therefore, various methodological steps are implemented for providing a consistent set of frequent, comparable reflectance values, and spectral indices. A ten year period is initially examined in order to extract recent vegetation trajectories in selected Mediterranean study areas, with implications for detecting subtle changes and aiming at establishing a basis for reliable forest health monitoring. In addition, recent advances in machine learning and artificial intelligence will be used to model these vegetation trajectories and predict future trends, based on self-attention mechanisms, allowing the model to focus on relevant parts of the time-series to improve prediction efficiency.