ABSTRACT

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.

摘要

绘制植被的细粒度图对于评估湿地的功能和保护状况至关重要。尽管卫星时间序列图像可以准确地对植被进行建模，但是这些数据的空间分辨率通常过于粗糙（> 6 m），无法捕获湿地植被的细粒度模式。SPOT-7卫星传感器解决了这个问题，因为它们以非常高的空间分辨率（1.5 m）采集图像，并具有潜在的高频重访能力。虽然尚待评估SPOT-7影像区分湿地植被的能力，但本研究调查了SPOT-7多时相影像对470公顷新鲜沼泽中11种植被类别的细粒度模式的绘制的贡献（法国）。随机森林建模，使用170个植被图进行校准和验证，