RT info:eu-repo/semantics/article
T1 Mapping Seagrass Meadows and Assessing Blue Carbon Stocks Using Sentinel-2 Satellite Imagery: A Case Study in the Canary Islands, Spain
A1 Arbelo Pérez, Manuel Imeldo
A1 Veiras-Yanes, Jorge
A1 Martín-García, Laura
A1 Casas Más, Enrique José
A2 Física
K1 Cymodocea nodosa
K1 machine learning algorithms
K1 classification
K1 ecosystem services
K1 InVEST
AB This research evaluates the capability of Sentinel-2 satellite imagery for mappingCymodocea nodosa meadows in El Médano (Tenerife, Canary Islands, Spain). A Level-1C image from27 October 2022 was used. Atmospheric correction was addressed using the Sen2Cor tool, whileLyzenga’s method was employed to account for the water column effect. Three supervised classifications were performed using Random Forest, K-Nearest Neighbors (KNN) and KDTree-KNNalgorithms. These classifications were complemented by an unsupervised classification and in situdata. Additionally, the amount of blue carbon sequestered by the C. nodosa in the study area wasalso estimated. Among the classifiers, the Random Forest algorithm produced the highest F1 scores,ranging from 0.96 to 0.99. The results revealed an average area of 237 ± 5 ha occupied by C. nodosa inthe study region, translating to an average sequestration of 111,000 ± 2000 Mg CO2. Notably, theseagrass meadows in this study area have the potential to offset the CO2 emissions produced by theindustrial combustion plant sector throughout the Canary Islands. This research represents a significant step forward in the protection and understanding of these invaluable ecosystems. It effectivelyunderlines the potential of Sentinel-2 satellite data to map seagrass meadows and highlights theircrucial role in achieving net zero carbon emissions on our planet.
YR 2023
FD 2023
LK http://riull.ull.es/xmlui/handle/915/36911
UL http://riull.ull.es/xmlui/handle/915/36911
LA en
NO https://doi.org/10.3390/ECRS2023-15856
DS Repositorio institucional de la Universidad de La Laguna
RD 19-may-2024