Wu Hong-Wei, Gong Cheng-Ao, Gong Zhao-Ning, Zhao Yu-Xin, Qiu Hua-Chang, Chen An-Kang
Beijing Key Laboratory of Resource Environment and GIS, Capital Normal University, Beijing 100048, China.
Beijing Laboratory of Water Resource Security, Capital Normal University, Beijing 100048, China.
Ying Yong Sheng Tai Xue Bao. 2025 Jun;36(6):1759-1769. doi: 10.13287/j.1001-9332.202506.029.
The spatial distribution of salt marsh vegetation in Yellow River Delta are highly heterogeneous. Accurate information on the historical distribution of salt marsh is of great significance for regional ecological stability and sustainable development. We constructed a long-series temporal-spatial-spectral multidimensional elicitation based on multi-source data, and accurately extracted information on the spatial distribution of typical salt marsh in the Yellow River Delta from 1996 to 2022 using a random forest (RF) model with recursive feature elimination, and further analyzed the succession of the native/invasive salt marsh communities since the diversion of the Yellow River in 1996. Compared to the single temporal spectral feature, the use of a temporal-spatial-spectral multidimensional feature set for extraction improved the overall accuracy of salt marsh vegetation classification by 8.4%. The classification effect of the sparse and the mixed area of and was optimized based on the temporal and spatial features of optical and SAR images. The distribution of salt marsh on the tidal flats after the Yellow River was diverted was obvious. The cover area of communities decreased from 91.67 km in 1996 to 38.11 km in 2022, with the successional trend being influenced by the invasion of was rapidly expanded and then distributed in large areas on the tidal flats on both sides of the current river channel since 2008. The area of the community reached the maximum (51.25 km) in 2020. The invasion and expansion of had a certain impact on the habitat pattern of the tidal flats.
黄河三角洲盐沼植被的空间分布具有高度异质性。准确掌握盐沼历史分布信息对区域生态稳定和可持续发展具有重要意义。我们基于多源数据构建了一个长时间序列的时空谱多维数据集,并使用带有递归特征消除的随机森林(RF)模型,准确提取了1996年至2022年黄河三角洲典型盐沼的空间分布信息,并进一步分析了自1996年黄河改道以来原生/入侵盐沼群落的演替情况。与单一时间光谱特征相比,使用时空谱多维特征集进行提取,提高了盐沼植被分类的总体精度8.4%。基于光学和合成孔径雷达(SAR)图像的时空特征,优化了光滩和混合区域的稀疏区域的分类效果。黄河改道后潮滩上盐沼分布明显。某群落的覆盖面积从1996年的91.67平方千米减少到2022年的38.11平方千米,其演替趋势受某入侵影响,某自2008年起迅速扩张,然后在当前河道两侧的潮滩上大面积分布。某群落面积在2020年达到最大(51.25平方千米)。某的入侵和扩张对潮滩的栖息地格局产生了一定影响。 (注:原文中部分物种名缺失,用“某”代替)