Quantifying Forest Biomass Carbon Stocks From Space
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This review presents cutting-edge methods and current and forthcoming satellite remote sensing technologies to map aboveground biomass (AGB). The monitoring of carbon stored in living AGB of forest is of key importance to understand the global carbon cycle and for the functioning of international economic mechanisms aiming to protect and enhance forest carbon stocks. The main challenge of monitoring AGB lies in the difficulty of obtaining field measurements and allometric models in several parts of the world due to geographical remoteness, lack of capacity, data paucity or armed conflicts. Space-borne remote sensing in combination with ground measurements is the most cost-efficient technology to undertake the monitoring of AGB. These approaches face several challenges: lack of ground data for calibration/validation purposes, signal saturation in high AGB, coverage of the sensor, cloud cover persistence or complex signal retrieval due to topography. New space-borne sensors to be launched in the coming years will allow accurate measurements of AGB in high biomass forests (>200 t ha−1) for the first time across large areas.
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ML can be used to predict the LiDAR's outcome from satellite imagery, and the learned estimator can perform predictions at scale.
“To address this challenge, ML can be used to predict the LiDAR's outcome from satellite imagery [48,599,693].From there, the learned estimator can perform predictions at scale.”
From Tackling climate change with machine learning (2023) · cites this paper as [693]
Since most of a forest's carbon is stored in above-ground biomass, tree species and heights are a good indicator of the carbon stock.
“Since most of a forest's carbon is stored in above-ground biomass [693], tree species and heights are a good indicator of the carbon stock.”
From Tackling climate change with machine learning (2023) · cites this paper as [693]
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Tackling climate change with machine learning
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- Authors
- Rodríguez-Veiga, P., Wheeler, J., Louis, V., Tansey, K., & Balzter, H.
- Affiliations
- University of Leicester (GB)
- Venue
- Current Forestry Reports
- Published
- 2017-02-09
- Volume
- 3
- Issue
- 1
- Pages
- 1-18
- ISSN
- 2198-6436 (ISSN-L)
- OpenAlex
- W2584181319
- Cited by
- 177
- References
- 134
- Citations by year
- 2026: 15 · 2025: 31 · 2024: 27 · 2023: 22 · 2022: 29 · 2021: 14 · 2020: 17 · 2019: 15 · 2018: 5 · 2017: 2
- FWCI
- 5.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Current Forestry Reports (publishedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Leicester Research Archive (University of Leicester) (submittedVersion, cc-by)
- Funders
- University of Leicester
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Remote Sensing and LiDAR Applications
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Forest ecology and management
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- aboveground biomass, forest carbon stocks, satellite remote sensing, satellite sensors, biomass mapping, allometric equations, field measurements, calibration and validation, cloud cover, topographic effects, carbon cycle, forest carbon monitoring
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