Dear Colleagues,

Forests are complex and highly dynamic ecosystems. Their structure depends for instance on species composition, age, density, growth conditions, natural disturbances (fires, storms, drought, flooding, pests …) and human impacts (logging, degradation, plantation, natural re-growth etc.). In forestry the assessment of forest structure has a long history starting from ground based measurements (e.g., DBH, height, density, species composition, stand volume, etc.) for forest planning. But since the beginning of the last century this field work was supported by remote sensing means.

In the meantime besides the characterization of single trees or forest stands from their spectral response (i.e., from optical passive remote sensing), 3-D measurements of tree height, density, crown diameter etc. are nowadays state of the art deploying photogrammetry, LIDAR and SAR interferometry.

Nevertheless, as forests on all levels are endangered by impacts of global warming and increasing human activities the dynamics of forest structure changes and their impact to the forest ecosystems require more detailed and more frequent monitoring. Here, for instance the assessment and monitoring of above ground biomass (AGB) of tropical forests for the reduction of deforestation and degradation to mitigate global warming has become a major driver to improve measurements with respect to accuracy, cost and frequency.

We are pleased to invite you to contribute to a special issue “Forest Structure Monitoring Based on Remote Sensing” where new and innovative methods for the assessment and the regular monitoring of forest structures shall be described.

This special issue shall represent the state of the art of forest structure assessment and monitoring by remote sensing to improve forest planning, management and protection on all levels.

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

• new data analysis means from passive and active sensors on from global to local scales,
• integrated inventory schemes to combine remote sensing with in-situ measurements for improving the level of detail and the quality of results,
• large data volume processing for monitoring forest structure changes in time series,
• Use of artificial intelligence (AI) methods for improved data analysis in 2- and 3-D.

We look forward to receiving your contributions.

Dr. Steffen Kuntz
Dr. Ioannis Gitas
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.

• forest structure
• forest inventory
• remote sensing
• spectral response
• 3-D measurements
• structure development monitoring
• large data processing
• artificial intelligence for data analysis