基于Landsat像元级时间序列的海岸带盐沼植被分类
Classification of salt marsh vegetation based on pixel-level time series from Landsat images
- 2023年27卷第6期 页码：1400-1413
纸质出版日期： 2023-06-07
DOI： 10.11834/jrs.20232461
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摘要
盐沼是最具生态价值且最为脆弱的生态系统之一，及时、精确地监测盐沼植被分布对于海岸带生态管理和保护尤为重要。随着多源遥感数据不断积累，时间序列方法日益成为海岸带资源监测的重要手段。然而，由于云雨天气频发，海岸带影像可用性较差，如何有效构建时间序列仍存在较大挑战。本研究耦合多源Landsat影像，以长三角典型滨海湿地为研究区，构建像元级时间序列的XGBoost分类模型，探讨盐沼植被精细识别的可行性与稳定性。研究结果表明：（1）通过相互定标耦合多源影像成效显著，不但提高了影像可用性，还减小了不同传感器之间的光谱反射率差异。（2）基于像元级时间序列方法的盐沼植被分类效果较好，研究区内盐沼植被平均总体分类精度可达81.50%，平均Kappa系数为0.758，对于长三角区域分布广泛的海三棱藨草和互花米草尤为优良。（3）相较于单一时相分类方法，像元级时间序列分类方法的年际绝对均值误差保持小于3.88%，稳定性较好，有望应用在盐沼植被动态变化监测中，为中国海岸带资源高效管理提供遥感技术支持。
Abstract
Salt marshes are the world’s most valuable and vulnerable ecosystem. Thus
accurate and timely monitoring of the distribution of salt marsh vegetation is essential. With the accumulation of multi-source remote sensing imagery
the time-series method has increasingly become important for monitoring coastal areas. However
effectively constructing time series is still challenging as the number of available observations is relatively low owing to the frequent cloudy weather in the coastal areas. In this study
we coupled multi-sourced Landsat images and constructed a pixel-level time series with XGBoost. Based on this
the feasibility and stability of classifying salt marsh vegetation were tested using the three typical sites in the Yangtze River Delta. Results showed that (1) Inter-calibration for multi-sourced images was necessary for not only improving the availability of images but also reducing the spectral differences among sensors. (2) The performance of salt marsh vegetation classification based on the pixel-level time series was favorable
reflected by 81.50% as the mean overall accuracy and 0.755 as the Kappa coefficient. The classification results were excellent
particularly for the widely distributed
Suaeda salsa
and
Spartina alterniflora
in the Yangtze River Delta. (3) Compared with the single-phrase classifications
the pixel-level time series–based classifications were stable
evidenced by an inter-annual absolute mean error lower than 3.27%. Therefore
our proposed method is expected for dynamic monitoring of salt marsh vegetation
which facilitates managing coastal resources and implementing ecological conservation effectively of China’s coasts.
关键词
遥感分类盐沼植被Landsat影像像元级时间序列XGBoost长三角丹顶鹤自然保护区九段沙湿地杭州湾南岸湿地
Keywords
remote sensing classificationsalt marsh vegetationLandsatpixel-level time-seriesXGBoostYangtze River DeltaRed-crowned Crane Nature ReserveJiuduansha WetlandSouthern Coastal Wetland in Hangzhou Bay
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