Dear Colleagues,

Forests absorb carbon dioxide in the atmosphere through photosynthesis and are characterized by a large carbon sink, low cost, and high ecological value-added. Forests are the largest source of carbon storage in the terrestrial ecosystem. Accurate estimations of forest biomass/carbon stocks and monitoring carbon dynamics are essential for modeling the global carbon cycle, quantifying carbon fluxes, and accomplishing carbon neutrality targets. In recent years, numerous remote sensing data (e.g., multispectral, hyperspectral, LiDAR, and SAR) with various platforms (e.g., satellite, airborne, unmanned aerial vehicle, and ground-based) and advanced artificial intelligence (e.g., machine learning, deep learning, and transfer learning) have been established and provided us with powerful tools to accurately estimate forest biomass/carbon stock and to monitor carbon dynamic.

For this Special Issue, we invite scientists actively applying remote sensing and related technology to assess forest biomass and monitor carbon dynamics in their research to submit their papers. Well-prepared, unpublished submissions that address one or more of the following topics (or related topics) are welcome:

• The advantages of remote sensing in forest biomass estimation and carbon dynamics monitoring;
• The estimation of forest biomass using remote sensing across scales;
• The monitoring and modeling of the dynamics of forest biomass/carbon;
• Deep learning or innovative artificial intelligence algorithms for forest biomass estimation;
• The estimation of biophysical, biochemical, and physiological properties that are significant for forest biomass;
• The impact of climate change on the carbon dynamics of forests;
• The response of forest carbon dynamics to extremes (e.g., heavy precipitation, drought, heat, wildfire, insects) and its legacy effects;
• The impact of forest mortality on carbon dynamics;
• Forest growth modeling based on remote sensing techniques;
• The combination of in situ observation and remote sensing data across scales;
• Uncertainties and error analysis for the estimation of forest biomass.

Dr. Qian Zhang
Dr. Weiliang Fan
Dr. Hui Yang
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.

• multi-source remote sensing
• different remote sensing platforms
• multiple modeling methods
• artificial intelligence
• biomass/carbon stock
• carbon dynamic