Advances in Remote Sensing and GIS Utilization in Monitoring of Forest Ecosystems: 2nd Edition

Dear Colleagues,

Global forests are experiencing unprecedented pressures from climate change and anthropogenic activities. In 2023 alone, the world lost approximately 28.3 million hectares of forest, highlighting the urgent need for advanced approaches to monitor, understand, and manage forest ecosystems. Across temporal scales ranging from recent decades to centuries, forests exhibit complex spatial–temporal dynamics involving changes in carbon storage, biodiversity, ecosystem structure, and landscape configuration. At the same time, increasing frequencies of extreme disturbances—including deforestation, wildfire, insect outbreaks, drought, storms, and land-use conversion—pose major challenges to global sustainability and ecological resilience.

Recent advances in remote sensing (RS), geographic information systems (GISs), artificial intelligence (AI), and cloud computing have created unprecedented opportunities for forest observation and analysis. Multi-source Earth observation datasets derived from satellite, airborne, UAV, and LiDAR platforms now enable high-resolution monitoring of forest dynamics at regional to global scales. In particular, deep learning, machine learning, and time-series analysis techniques have rapidly transformed forest research by improving the detection, classification, prediction, and interpretation of complex ecological processes. Long-term remote sensing time series provide valuable insights into forest succession, disturbance recovery, vegetation phenology, and climate–forest interactions, while advanced AI frameworks offer powerful tools for extracting information from increasingly large and heterogeneous datasets.

This Special Issue aims to bring together cutting-edge research exploring innovative applications of RS and GIS in forest science, ecology, management, and policy. We welcome original research articles, methodological developments, case studies, and comprehensive reviews addressing both theoretical advances and practical applications. Contributions employing emerging technologies—such as deep learning architectures, spatiotemporal modeling, data fusion, explainable AI, digital twins, and cloud-based geospatial platforms—are especially encouraged.

Topics of interest include, but are not limited to application of cutting-edge research RS and GIS tools and methods for the following:

• Forest cover, structure, and biodiversity change detection;
• Time-series analysis of forest dynamics and ecosystem processes;
• Deep learning and machine learning applications in forestry;
• Forest disturbance monitoring (fire, drought, insects, storms, and degradation);
• Forest carbon sequestration and climate change mitigation;
• Biomass estimation, canopy height retrieval, and vegetation productivity;
• Environmental impact assessment and ecological risk analysis;
• Forest humidity, temperature, soil, and hydrological dynamics;
• Multi-source data fusion using optical, SAR, LiDAR, UAV, and hyperspectral data;
• Spatiotemporal modeling and prediction of forest ecosystem change;
• Forest policy, restoration, and sustainable management supported by RS/GIS;
• Explainable AI and uncertainty analysis in forest remote sensing;
• Dendrochronology integrated with remote sensing and GIS techniques.

Through this Special Issue, we aim to advance interdisciplinary collaboration and promote innovative methodologies that improve our understanding of forest ecosystems under global environmental change. We hope that this collection will provide valuable scientific insights and practical guidance for researchers, policymakers, and forest managers working toward sustainable forest conservation and management worldwide.

Dr. Hang Li
Prof. Dr. Aleksandar Dj Valjarević
Prof. 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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.

• remote sensing
• GIS
• geospatial analysis
• forest monitoring
• forest change detection
• land cover/land use
• disturbance monitoring
• tree classification
• satellite imagery
• LiDAR
• Landsat
• sentinel
• SAR
• UAV/drone
• machine learning
• low-carbon
• forest carbon sequestration

• Advances in Remote Sensing and GIS Utilization in Monitoring of Forest Ecosystems in Forests (10 articles)