Machine learning-based multi-sensor approach for mapping mangrove disturbance state in the Parc Marin des Mangroves (PMM, DR Congo) : Abstract)

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environment and energygeospatial

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NGE

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This study presents a developped prediction of mangroves disturbance state in the Parc Marin des Mangroves, DR Congo, based on a multi-sensor approach combining Radar, optical and terrain data. This is a conference presentation file.

Mapping mangrove disturbance state using decision tree classification based on Sentinel-2 surface reflectance spectral indices, elevation, and salinity data in the Parc Marin des Mangroves (Moanda, Democratic Republic of Congo)

Description of the subject. Despite increased human pressures, few studies have characterized distur

Canopy density spatial model and binary classification of mangrove presence using the fusion of Landsat composite and Top of Atmosphere Sentinel-2 images in the Parc Marin des Mangroves (Moanda, Democratic Republic of Congo)

Description of the subject. The Parc Marin des Mangroves (PMM), a protected coastal area in the Cong

Machine learning-based multi-sensor approach for mapping mangrove disturbance state in the Parc Marin des Mangroves (PMM, DR Congo) : Abstract)

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Mapping mangrove disturbance state using decision tree classification based on Sentinel-2 surface reflectance spectral indices, elevation, and salinity data in the Parc Marin des Mangroves (Moanda, Democratic Republic of Congo)

Canopy density spatial model and binary classification of mangrove presence using the fusion of Landsat composite and Top of Atmosphere Sentinel-2 images in the Parc Marin des Mangroves (Moanda, Democratic Republic of Congo)