Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/182522
Título: Spatially explicit assessment of carbon storage and sequestration in forest ecosystems
Autor: Almeida, Bruna
Monteiro, Luís
Tiengo, Rafaela
Gil, Artur
Cabral, Pedro
Palavras-chave: Climate regulation
Vegetation dynamics
Sustainable Development Goals
Geographical Information Systems
Machine learning
Geography, Planning and Development
Computers in Earth Sciences
SDG 13 - Climate Action
Data: Abr-2025
Resumo: Forests play an important role in the global carbon cycle, making accurate assessments of carbon dynamics essential for effective forest management and climate change mitigation strategies. This research examines the spatiotemporal patterns of carbon storage and sequestration (CSS) in forests' aboveground biomass using satellite data, machine learning (Support Vector Machines), carbon modeling and spatial statistics. The methodology follows a two-step classification process: (i) binary forest classification and (ii) forest type classification, mapping seven forest types within two main categories - Broadleaves (Quercus suber, Quercus ilex, Eucalyptus sp., and other species) and Coniferous (Pinus pinaster, Pinus pinea, and other species). We analyzed the relationship between forest type and CSS at the Nomenclature of Territorial Units for Statistics (NUTS) III level and identified spatial clusters, outliers, and hot and cold spots of carbon sequestration at the municipal level across mainland Portugal. The broadleaved category demonstrated the highest classification accuracy in both years, decreasing slightly from 90.3% in 2018 to 89% in 2022, while the Coniferous group had the lowest accuracy, declining from 84.1% in 2018 to 83.6% in 2022. Anselin's Local Moran's I identified clusters of carbon sequestration, while the Getis-Ord Gi analysis confirmed these findings, revealing statistically significant hotspots of carbon sequestration in the northern and central regions and cold spots in the southern region. By providing insights at the sub-regional and municipal levels, this study offers a robust framework to support sustainable forest management and climate change mitigation strategies. Moreover, it can assist decision-makers in prioritizing natural capital, and developing nature-based solutions to tackle climate change and biodiversity loss.
Descrição: Almeida, B., Monteiro, L., Tiengo, R., Gil, A., & Cabral, P. (2025). Spatially explicit assessment of carbon storage and sequestration in forest ecosystems. Remote Sensing Applications: Society and Environment, 38, 1-19. Article 101544. https://doi.org/10.1016/j.rsase.2025.101544 --- This work was supported by the research project MaSOT – Mapping Ecosystem Services from Earth Observations, funded by the Portuguese Science Foundation – FCT [EXPL/CTA-AMB/0165/2021], and by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS). We are grateful to the anonymous reviewers for their comments and recommendations which improved the manuscript.
Peer review: yes
URI: http://hdl.handle.net/10362/182522
DOI: https://doi.org/10.1016/j.rsase.2025.101544
ISSN: 2352-9385
Aparece nas colecções:NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)

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