| 结合多源遥感数据和面向对象的旱区湿地信息提取 |
| 摘要点击 223 全文点击 15 投稿时间：2024-05-31 修订日期：2024-07-17 |
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| 中文关键词 干旱区湿地 宁夏沿黄城市群 Sentinel-1影像 Sentinel-2影像 RF-Pearson模型 随机森林（RF） |
| 英文关键词 wetlands in arid areas Ningxia Yellow River Basin urban agglomeration Sentinel-1 imagery Sentinel-2 imagery RF-Pearson model random forest （RF） |
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| 中文摘要 |
| 湿地是西北干旱半干旱区域绿洲的心脏,在气候调节、水源供给、蓄洪防旱、保护生物多样性和维系干旱区生态稳定等方面至关重要.进行旱区湿地信息提取,为旱区生态环境监测提供快速准确手段,维持旱区生物多样性,防治荒漠化和土地退化.以宁夏沿黄城市群为研究区,以Sentinel-1合成孔径雷达（SAR）影像、Sentinel-2光学影像以及地形数据为数据源,应用面向对象技术的湿地信息特征提取方法,探讨红边波段、雷达波束以及地形特征对干旱区湿地提取的重要性,验证了利用RF-Pearson模型筛选旱区湿地最优特征组合的可行性,结合随机森林算法和BP神经网络对2021年宁夏沿黄城市群湿地进行提取.结果表明：①利用Sentinel-2影像的红边波段、Sentinel-1影像的雷达波束和地形数据,可以有效促进对旱区湿地特征的识别与获取,相较光谱指数和几何特征湿地的总体精度分别提高了3.27%、2.14%和1.83%；②基于RF-Pearson模型特征优选方法的分类精度最高,大小为：光谱特征>几何特征>红边特征>雷达特征>地形特征.③基于特征优选的随机森林模型（RF）干旱区流域湿地分类效果最佳,总精度为89.79%,Kappa系数为0.842 3,高于BP神经网络（BP）分类方法,表明利用该方法提取干旱区流域湿地信息具有一定的可靠性.④宁夏沿黄城市群湿地,主要包含河流、湖泊、滩涂沼泽、水库坑塘和沟渠等这5种类型.主要集中在银川市区、平罗县、沙坡头区、灵武市和中宁县五地.河流湿地在旱区湿地中占主导型地位,是宁夏沿黄城市群突出的湿地类型,分类结果中天然湿地（河流、湖泊和滩涂沼泽）面积为1 076.65 km2,人工湿地（水库坑塘、沟渠）面积为108.18 km2,分别占研究区总面积的90.86%和9.14%.研究结果可为旱区生态本底环境监测和黄河流域生态保护和高质量发展提供科学依据. |
| 英文摘要 |
| Wetlands are the heart of oases in the arid and semi-arid regions of Northwest China, playing a crucial role in climate regulation, water supply, flood storage and drought prevention, biodiversity conservation, and maintaining ecological stability in arid areas. The extraction of wetland information in arid regions provides a rapid and accurate means for monitoring the ecological environment, maintaining biodiversity, and preventing desertification and land degradation. Taking the Ningxia Yinchuan metropolitan area along the Yellow River as the study area, this research uses Sentinel-1 synthetic aperture radar （SAR） imagery, Sentinel-2 optical imagery, and topographic data as data sources. It applies object-oriented wetland information feature extraction methods to explore the importance of red edge, radar, and topographic features in extracting wetlands in arid areas. The feasibility of using the RF-Pearson model to select the optimal combination of features for wetlands in arid regions is verified, combined with the random forest algorithm and BP neural network to extract wetlands in the Ningxia Yinchuan metropolitan area in 2021. The results show that： ① Using the red edge band of Sentinel-2 imagery, the radar beam of Sentinel-1 imagery, and topographic data could effectively promote the identification and acquisition of wetland characteristics in arid regions, improving the overall accuracy of wetlands by 3.27%, 2.14%, and 1.83% compared to spectral indices and geometric features, respectively. ② The classification accuracy of the RF-Pearson model feature selection method was the highest, with the order of importance being： spectral features > geometric features > red edge features > radar features > topographic features. ③ The random forest model （RF） based on feature selection had the best classification effect on wetlands in arid region basins, with an overall accuracy of 89.79% and a Kappa coefficient of 0.842 3, which was higher than that of the BP neural network （BP） classification method, indicating that this method had certain reliability in extracting wetland information in arid regions. ④ Wetlands in the Ningxia Yinchuan metropolitan area mainly included five types： rivers, lakes, tidal flats and marshes, reservoir ponds, and ditches. They were mainly concentrated in Yinchuan City, Pingluo County, Shapotou District, Lingwu City, and Zhongning County. River wetlands dominated the wetlands in arid regions and were a prominent type of wetland in the Ningxia Yinchuan metropolitan area. In the classification results, the area of natural wetlands （rivers, lakes, and tidal flats and marshes） was 1 076.65 km2, and the area of artificial wetlands （reservoir ponds, ditches） was 108.18 km2, accounting for 90.86% and 9.14% of the total area of the study area, respectively. The research results can provide a scientific basis for monitoring the ecological background environment in arid regions and for ecological protection and high-quality development in the Yellow River Basin. |
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