Modelling forest carbon stocks on the Canary Islands

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🔬研究者:Provides a methodological framework for high-resolution carbon mapping using integrated remote sensing and ML, applicable to other island ecosystems.

🏢実務担当者:Enables baseline carbon stock assessment and monitoring in topographically complex areas for forest management and carbon projects.

🏛政策担当者:Offers evidence of high carbon densities in island forests, supporting regional climate policy and conservation strategies towards carbon neutrality.

Abstract Background Forest carbon mapping is crucial for sustainable forest management, climate mitigation, biodiversity conservation, ecosystem service provision and land-use planning. Carbon stocks have been studied at regional to global scales and across various biomes. However, island-wide studies of carbon stocks and carbon mapping remain limited. Here, we present the first high-resolution (50 m) spatial mapping of forest carbon for the Canary Archipelago. Combining structural field data from the Spanish National Forest Inventory plots with airborne laser scanning, Sentinel-2 multispectral satellite data and fine-scale interpolated climatic variables, we modeled total, aboveground and belowground carbon density of 18 forest types, using machine learning approaches, including Boosted Regression Tree and Random Forest models. Results Forests across the Canary Islands store an estimated 10.26 Tg of carbon. Canarian pine forests contain the largest carbon pool (57%) due to their extensive distribution area, whereas mature laurel forests exhibit exceptional carbon densities. In humid laurel forests, average carbon densities reached 413.2 ± 149.5 Mg C ha⁻¹, exceeding previous regional estimates and approaching levels of primary tropical forests. The high spatial heterogeneity of carbon densities across forest types and islands was best explained by structural stand attributes, such as tree canopy cover and volume, and climatic factors with carbon stocks being more strongly associated with moisture than with temperature. Both statistical modelling approaches performed similarly in terms of efficiency, accuracy and error statistics, and the selection of the best model depended on the specific island. Conclusions Our approach integrates field data with advanced remote sensing tools and machine learning algorithms to produce a high-resolution and accurate carbon map of topographically complex oceanic islands. We show that the Canary Islands contain exceptionally high total carbon densities, particularly within the mature, humid laurel forests of La Gomera, and identify structural attributes and water availability as the main drivers of spatial variation in carbon stocks. This assessment provides a baseline for biodiversity conservation, nature-based forest management, ecological restoration and regional climate policy towards carbon neutrality in island ecosystems.

• openalex https://doi.org/10.1186/s13021-026-00488-4first seen 2026-07-29 05:15:11