Estimating Carbon Stocks in Forest Ecosystems: From Allometric Equations to Remote Sensing-Based Methods

Dear Colleagues,

The determination of carbon stocks in forest ecosystems is a relevant input of global relevance. Many countries have signed the United Nations Framework Convention on Climate Change (UNFCCC) agreement, where initiatives related to reducing emissions from deforestation and forest degradation (REDD+) have been implemented. Considering such factors, rigorous methodologies regarding the quantification of carbon stocks are of paramount importance. Traditional approaches using allometric equations have been used for a long time in forest sciences. However, the accurate definition of allometric equations requires a destructive approach, which is also expensive, time-consuming, and in some cases inappropriate, considering that some species are protected by international organizations (e.g., CITES, IUCN).

Novel technologies that have become established and new ones that are being tested and validated can validly support, complement and in some cases substitute traditional methods. Remote sensing techniques and geomatics techniques in general i.e., measuring Earth data from satellite image analysis, photogrammetry, laser scanning etc.…, can be integrated by local and general allometric equations. However, even if the technologies are quite advanced, field data collection is always required, for training and/or validation.

The focus of this Special Issue is on documenting developing methodologies using existing site- and species-specific allometric equations to estimate carbon stocks and explore advanced and novel remote sensing technologies that allow measuring these data. Since allometric equations are specific to a species and to an environmental site’s condition, it will be relevant to include local equations as ground truth values. On the other hand, developing local and general allometric equations through photogrammetry, laser scanning and mobile applications can be a valid solution to the lack of allometric equations in the case of protected species which cannot undergo destructive analyses.

Suitable research papers for this Special Issue can be related to the following topics:

  • Active and passive earth-observation sensors;
  • Error propagation;
  • Photogrammetry;
  • Close-range sensing;
  • Airborne and terrestrial laser scanning;
  • SLAM-based hand-carried mobile laser scanning;
  • Forest inventory;
  • Forest biometry and modeling;
  • 3D modeling and augmented reality from remote sensing data;
  • Uncertainty metrics

Deadline for abstract submissions: 31 December 2021.
Deadline for manuscript submissions: 31 May 2022.

Topic Board

Prof. Dr. Francesco Pirotti
Website
Topic Editor-in-Chief
Department of Land, Environment, Agriculture and Forestry, University of Padova, Padova, Italy
Interests: laser scanning; remote sensing; machine learning; geomatics engineering; photogrammetry
Special Issues and Collections in MDPI journals
Dr. L. Monika Moskal
Website
Topic Associate Editor-in-Chief
School of Environmental and Forest Sciences, College of the Environment, University of Washington (UW), Director, UW Precision Forestry Cooperative and Remote Sensing and Geospatial Analysis Laboratory, Washington, Box 352100, Seattle WA 98195-2100, USA
Interests: ALT; TLS; MLS; lidar precision forestry; hyper-resolution (spatial, temporal, spectral) remote sensing; ecosystem services
Special Issues and Collections in MDPI journals
Dr. H. Jaime Hernández Palma
Website1 Website2
Topic board member
Facultad de Ciencias Forestales y de la Conservación de la Naturaleza, Universidad de Chile, Santiago, Chile
Interests: remote sensing; forestry; spatial analysis
Dr. Gaia Vaglio Laurin
Website
Topic board member
Department of Innovation in Biological, Agri-food and Forestry systems, University of Tuscia, Viterbo, Italy
Interests: forest; biodiversity and vegetation monitoring using multiple remote sensors
Special Issues and Collections in MDPI journals
Dr. Erico Kutchartt
Website
Topic board member
Department of Land, Environment, Agriculture and Forestry TESAF, University of Padova, Padua, Italy
Interests: remote sensing; geomatics; forest inventory; forest biometry and modeling; allometric equations

Keywords

  • active/passive earth observation
  • photogrammetry
  • close-range sensing
  • error propagation
  • 3D modelling
  • augmented and virtual reality AR/VR
  • laser scanning / lidar

Relevant Journals List

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Remote Sensing
remotesensing
4.509 6.1 2009 16.36 Days 2400 CHF Submit
Forests
forests
2.221 2.7 2010 16.73 Days 1800 CHF Submit
Sensors
sensors
3.275 5.0 2001 15.2 Days 2200 CHF Submit
Geomatics
geomatics
- - 2021 11.51 Days 1000 CHF Submit

Published Papers

This Topic is now open for submission, see below for planned papers.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Mike Salazar, PhD student at Institute of Photogrammetry and Remote Sensing, Faculty of Environmental Sciences. Technische Universität Dresden, Germany, “Integration of sentinel SAR and MSI data using machine learning for enhancing AGB estimation in Tropical Secondary Dry Forest, Colombia
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