Machine and deep learning methods for detection and mapping of coastal wetlands of Crozon Peninsula (Brittany, France) used metric and sub-metric spatial resolution - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Proceedings of SPIE, the International Society for Optical Engineering Année : 2023

Machine and deep learning methods for detection and mapping of coastal wetlands of Crozon Peninsula (Brittany, France) used metric and sub-metric spatial resolution

Résumé

Wetlands are one of the most important ecosystems in the world. Today, however, their fate is under serious threat, and their alarming decline highlights the urgent need to preserve these areas rich in biodiversity. The aim of this work is to spatially map and mapping the wetlands of the Crozon peninsula in Brittany France. The methodology is divided into two parts; the first part is to map the wetlands as a whole, while the second part is to mapping the wetlands using a adapted typology. Several databases were used to spatialise the wetlands: 12 Sentinel-2 images in L3A format, 23 Sentinel-1 VV and VH images and the RGE Alti (DTM at 1 metre resolution). The images were processed and stacked alone or in synergy. A random forest (RF) machine learning algorithm was then trained to predict wetlands in our study area using binary training data. The training data were obtained from a wetland inventory conducted in Brittany, distributed at the scale of a Sentinel-2 tile (30UUU). Post-processing was then carried out on the best result: binary morphological erosion and thresholding based on the DTM to remove outliers. We carried out two classifications, which we later merged. The classifications were carried out using a Pleiades time series (5 dates) to achieve a very fine scale classification. A classification of 13 land cover classes with 6 different wetland types (mudflats, salt marshes, coastal lagoons, wet meadows, wet forest, swamps/bogs) was performed using three methods: pixel-by-pixel random forest, object-based random forest and convolutional neural network (CNN). The best results obtained was for the pixel-based classification: kappa = 0.89, overall accuracy = 0.90, F1-score = 0.90.
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Dates et versions

hal-04257951 , version 1 (26-10-2023)

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Adrien Le Guillou, Simona Niculescu, Christiane Schmullius. Machine and deep learning methods for detection and mapping of coastal wetlands of Crozon Peninsula (Brittany, France) used metric and sub-metric spatial resolution. Proceedings of SPIE, the International Society for Optical Engineering, 2023, Earth Resources and Environmental Remote Sensing/GIS Applications XIV, 12734, ⟨10.1117/12.2678006⟩. ⟨hal-04257951⟩
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