摘要：Mapping the fine-grained pattern of vegetation is critical for assessing the functions and conservation status of wetlands. Although satellite time-series images can accurately model vegetation, the spatial resolution of these data is generally too coarse (> 6 m) to capture the fine-grained pattern of wetland vegetation. SPOT-7 satellite sensors address this issue since they acquire images at very high spatial resolution (1.5 m) with a potential high frequency revisit. While the ability of SPOT-7 images to discriminate wetland vegetation has yet to be assessed, this study investigates the contribution of SPOT-7 multi-temporal images for mapping the fine-grained pattern of 11 vegetation classes in a 470 ha fresh marsh (France). Random forest modeling, calibrated and validated using 170 vegetation plots, was conducted on four SPOT-7 pan-sharpened images collected from April-July 2017. The results highlight that (1) the wetland vegetation was accurately modeled (F1 score 0.88), (2) near-infrared spectral bands acquired in the spring are the most discriminating features, (3) the fine-grained pattern of vegetation plant communities is mapped well, and (4) model uncertainties reflect floristic transition, unconsidered classes or areas of shadow.
关键词：Grasslands ; natural habitats ; phytosociology ; Natura 2000 ; random forest ; uncertainty
