Estimation of Forest Biomass from High and Medium Spatial Resolution Satellite Imagery

Dear Colleagues,

Forest biomass estimation is currently used with several approaches, namely for sustainable forest management, biodiversity, conservation, carbon sequestration, climate change mitigation, and environmental monitoring. Accurate data and innovative methodologies are essential for making informed decisions that balance human needs and the maintenance of ecosystems. Remote sensing data have brought about significant advancements in the estimation of forest biomass. Combining data from different remote sensing sensors (such as LiDAR, SAR, and optical sensors) provides the characterization of several forest parameters. Each sensor contributes unique information, leading to more accurate biomass estimates by capturing various aspects of stand structures. These data can be quantitatively analyzed to derive biomass estimates using both parametric and non-parametric regression models. These models relate remote sensing data, such as canopy height, horizontal crown projection, or spectral reflectance (such as original spectral data, vegetation indices, principal components, and texture indices) to ground-based measurements of biomass. The application of machine learning algorithms and artificial intelligence that can automatically learn complex relationships in data also contributes towards enhancing biomass models’ accuracies.

Remote sensing data have developed rapidly in recent decades, with more varied and higher spatial, radiometric, and temporal resolutions enabling the periodic monitoring of spatiotemporal changes in forest areas at different scales (local, regional, continental, and global). Forest biomass estimation is crucial at local and regional scales due to forests’ impacts on communities, ecosystems, and sustainable development. For this purpose, new satellites have appeared with high and medium spatial resolution data, allowing for forest areas to be defined in more detail and consequently improving biomass models’ accuracies.

The goal of this Special Issue is to collect papers (original research articles and review papers) which provide insights into biomass modeling at tree and area levels with high and medium spatial resolution satellite data.

This Special Issue welcomes the submission of manuscripts that link the following themes:

• Remote sensing;
• Satellite image processing;
• Geographic information systems;
• Biomass modeling at tree and area level;
• Model uncertainties;
• Decision support systems;
• Forestry;
• Active/passive sensors.

We look forward to receiving your original research articles and reviews.

Dr. Adélia Sousa
Dr. Ana Cristina Gonçalves
Guest Editors

Manuscript Submission Information

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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.

• image classification
• spectral indices
• sustainability
• stand structure
• datasets
• multiple scale
• modeling
• forest biomass
• carbon sequestration