Dynamic Monitoring of Forest Resources Based on Multi-source Remote Sensing Data

Dear Colleagues,

The 21st century has seen the development of countless new remote sensors that can be used to monitor both forests and forest plantations. These sensors use various technologies to capture meaningful and valuable forest information (e.g., LiDAR, SAR, multispectral, and hyperspectral imagery). Furthermore, and depending on the scale of the work, these remote sensors can be used from different platforms (terrestrial, UAV-borne, airborne, and spaceborne remote sensors), which makes it necessary to develop methodologies that allow for the efficient management and effective integration of what we will hereafter call "Multi-Source Remote Sensing Data”.

The dynamic monitoring of forest resources has become a trending research topic not only because of the pivotal socioeconomic importance of forests as providers of ecosystem services (wildlife habitat, supply of wood and non-wood products, recreational opportunities) but also due to the urgent need to collect accurate, timely, and large-scale information related to aboveground biomass and carbon stocks fixed by forests. This comprehensive monitoring is considered crucial within the United Nations framework convention on climate change strategy. In this sense, the growing need for data on the distribution and temporal dynamics of carbon sequestration in forests is likely to drive new research in multi-source remote sensing data fusion in the coming years.

On the other hand, the 21st century has also witnessed the rapid development of powerful tools for the automatic extraction of information from multi-source remote sensing data. We are referring to advances related to object-based image analysis (OBIA), machine learning, and, more recently, the promising contributions of deep learning, which serve as a melting pot from which to extract the best remote sensing data. In our view, this is another line of research that, together with the aforementioned boost in available multi-source remote sensing data, will greatly contribute to the development of an ongoing strategy to support decision making within what could be described as “Integrated and Sustainable Management Policy for Forest Areas”.

This Special Issue will report the latest advances and trends in the field of the dynamic monitoring of forest resources based on multi-source remote sensing, addressing original developments, new applications, and practical solutions to open questions. Topics for this Special Issue include but are not limited to the following:

• The application of radar and/or optical satellite sensors (multispectral and hyperspectral) of very high to medium spatial resolution to extract forest dasometric features and classify tree species (area-based approaches).
• Portable terrestrial LiDAR (PTL) and terrestrial laser scanning (TLS) to digitize forest at centimeter level. Automatic methods for dendrometric features extraction (tree-centric approaches).
• UAV LiDAR- and image-based (multispectral and hyperspectral) data for accurate and agile forest mapping.
• The dynamic monitoring of forest from airborne and space-borne LiDAR sensors.
• The data fusion of multi-source remote sensing data for the dynamic monitoring of forest resources. The integration of data collected from terrestrial and space-borne remote sensors.
• Object-based image analysis (OBIA) for multi-source remote sensing data fusion in forest resources monitoring.
• Time series analysis of multi-source remote sensing data in forest resources monitoring.
• Machine learning and deep learning approaches for extracting meaningful information from multi-source remote sensing data in forest resources monitoring.
• Near Real-Time (NRT) forest monitoring.

Papers must be original contributions that have not been previously published or submitted to other journals. Submissions based on previously published or submitted conference papers may be considered provided they are considerably improved and extended.

Prof. Dr. Fernando José Aguilar
Prof. Dr. Flor Álvarez-Taboada
Prof. Dr. Manuel Ángel Aguilar
Guest Editors

Muhammad Yasir
Guest Editor Assistant
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Email: [email protected]
Webpage: https://scholar.google.com/citations?user=jDB7QpwAAAAJ&hl=en
Interests: remote sensing; deep learning; image processing

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• multi-source remote sensing data
• dynamic monitoring of forest resources
• data fusion
• machine learning
• deep learning
• OBIA
• precision forestry
• multi-temporal remote sensing data
• aboveground biomass and carbon stock
• near real-time (NRT) forest monitoring