Differential Morphological Profile on remote sensing images for vegetation mapping in a semi-arid region of the Algerian Saharan Atlas

Autor: Samir L'haddad, Akila Kemmouche, Aude Nuscia Taïbi, Thouraya Merazi-Meksen
Přispěvatelé: Espaces et Sociétés (ESO), Le Mans Université (UM)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-AGROCAMPUS OUEST-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), Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université d'Angers (UA)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Le Mans Université (UM)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of Arid Environments
Journal of Arid Environments, Elsevier, 2021, 188, pp.104463. ⟨10.1016/j.jaridenv.2021.104463⟩
ISSN: 0140-1963
1095-922X
DOI: 10.1016/j.jaridenv.2021.104463⟩
Popis: In this paper, a new approach for mapping polygenic depressions colonised by vegetation is presented. These spatially periodic vegetation patterns situated in the arid areas of North Africa are known locally as "Dayas". The mapping of these structures is an important component in monitoring their evolution which can be regarded as an indicator of socio-environmental conditions. For this purpose, a method based on satellite image analysis is proposed. First, Landsat Thematic Mapper image inspection is performed to highlight the vegetation spots using the Normalized Difference Vegetation Index (NDVI). Then, a Differential Morphological Profile (DMP) which includes both spectral and morphological information, is built for each NDVI pixel. We adapt the F-Score technique combined with the SVM classifier, to select and classify the most effective DMP images in order to extract the Daya's signature. Using feature selection, overall classification accuracy reached 97.8% with a kappa value of 95.61 but also reduced the dimension of the DMP vector. Dayas' morphological and vegetation characteristics are related to their degree of evolution. Furthermore, we categorise categorise the 170 extracted Dayas into three classes according to their size. On the resulting map, each class corresponds to a specific morphological and vegetation evolution stage.
Databáze: OpenAIRE