Identification and mapping of Algerian island vegetation using high-resolution images (Pléiades and SPOT 6/7) and random forest modeling
| Autor: | Mohamed Hamimeche, Antoine Billey, Simona Niculescu, Riadh Moulaï |
|---|---|
| Přispěvatelé: | Faculté des Sciences, Université de Jijel, Littoral, Environnement, Télédétection, Géomatique (LETG - Brest), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Mediterranean climate
010504 meteorology & atmospheric sciences Management Monitoring Policy and Law 01 natural sciences Normalized Difference Vegetation Index 14. Life underwater Ecosystem ComputingMilieux_MISCELLANEOUS 0105 earth and related environmental sciences General Environmental Science geography.geographical_feature_category Plant community 04 agricultural and veterinary sciences General Medicine Vegetation [SHS.GEO]Humanities and Social Sciences/Geography Plants 15. Life on land Pollution Random forest Geography Habitat Archipelago 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Pleiades Cartography Environmental Monitoring |
| Zdroj: | Environmental Monitoring and Assessment Environmental Monitoring and Assessment, Springer Verlag (Germany), 2021, 193 (9), ⟨10.1007/s10661-021-09429-9⟩ |
| ISSN: | 0167-6369 1573-2959 |
| DOI: | 10.1007/s10661-021-09429-9⟩ |
| Popis: | Despite their proximity to the coast, few studies have focused on identifying and mapping the vegetation of Algerian islands and islets. To fill this lacuna, our work, using satellite images and machine learning methods, is mainly aimed at identifying and mapping the main vegetation groups on a few islands, while evaluating the effectiveness of the random forest classifier, which is effectively used in the study of the vegetation of large areas. However, despite the high heterogeneity of their vegetation cover, the use of very high-resolution images (Pleaides and SPOT 6/7), through the fusion bands and derived bands (NDVI), has allowed the elaboration of a fairly precise vegetation map that can be used for the preparation of management and protection plans for these habitats. Our methodological approach revealed very satisfactory results, having allowed the identification of the plant communities inventoried in the field, while showing high accuracy values, ranging from 0.642 for the halophilic group of Asteriscus to 1 for the endemic Chasmophyte group of the Habibas archipelago (Pleiades images). The groups identified from SPOT 6/7 images show accuracy values between 0.67 for the Mediterranean cliff formations on Garlic Islet and 1 for the two formations (shrubby and herbaceous) of the Skikda islands. Our methodological approach, and notwithstanding the great heterogeneity and the very small surface areas of our islands and islets, has led to very satisfactory results, reflected with good overall accuracy and kappa index values (for Pleiades: overall accuracy > 92% and kappa index > 0.90; for SPOT 6/7: overall accuracy > 83% and kappa index > 0.80). |
| Databáze: | OpenAIRE |
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