Estimating Above-Ground Biomass and Above-Ground Carbon by Remote Sensing Data
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 16 July 2024 | Viewed by 917
Special Issue Editors
2. GeoLAB—Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Via San Bonaventura 13, 50145 Firenze, Italy
Interests: forest modeling through remote sensing data; spatialization of environmental variables, particularly in growing stock volume trends; carbon cycle
Interests: application of geomatics to forestry; remote sensing; forest inventories and monitoring; sustainable forest management; land planning; landscape ecology; biodiversity; forest fires and climate change; bio-geo-chemical models; decision support systems; forest ecology
Special Issues, Collections and Topics in MDPI journals
Interests: forest modeling; climate change; climate change impacts; forest management scenario; carbon cycle; nitrogen cycle; climate change adaptation; climate change mitigation; forest ecology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Today, forests stand as a powerful ally in the battle against climate change and, if managed properly, they can prevent emissions from deforestation and forest degradation and act as critical carbon sinks. The urgency of safeguarding these ecosystems has never been greater and accurately measuring their above-ground biomass (AGB) and carbon storage (AGC) is a major step toward this goal. While traditional field-based methods for quantifying AGB and AGC pose certain challenges, recent advancements in remote sensing and computational capabilities have come to offer efficient and innovative new alternatives, enabling increasingly accurate estimates at a wide range of spatial and temporal scales. Recent estimation methods rely on data fusion from different sensors and advanced machine learning algorithms; yet, these new possibilities raise questions regarding accuracy and precision. In a framework where greater and greater precision is required, we can no longer rely on “static” metrics to assess the performance of models and maps. Understanding spatial uncertainties is paramount and imperative to making remote sensing estimates more robust and reliable, and our continued efforts should focus on complementing maps of forest variables with the related error maps.
This Special Issue aims to bring together scientists and specialists developing and applying new remote sensing approaches in an effort to improve our understanding of the biomass and carbon dynamics of forest ecosystems. The topics covered in this Special Issue include, but are not exclusive to, the following:
- New methods to assess biomass and carbon in forest ecosystems using remote sensing;
- New sensors and new data fusion approaches;
- Temporal assessment of biomass and carbon dynamics;
- Biomass and carbon assessment via proximal sensing, i.e., TLS, photogrammetry, etc.;
- Spatial and temporal uncertainty assessments;
- Impact of forest disturbances on biomass and carbon balance;
- Large-scale monitoring of biomass and carbon dynamics;
- Cloud computing approaches.
Dr. Elia Vangi
Prof. Dr. Gherardo Chirici
Dr. Alessio Collalti
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. 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 2700 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.
Keywords
- above-ground biomass
- carbon stock
- remote sensing
- machine learning
- climate change
- forest modeling
- spatial modeling
