[Combining Multi-source Remote Sensing Data and Object-oriented Information Extraction for Arid Wetlands].

Q2 Environmental Science
Hong-Xia Li, Yun Shi, Zhong-Jie Ding, Lin Huang, Jun Dong, Zhi-Gang Liang, Xiao-Wen Zhu, Yi-Ting Ma, Tong Wang
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引用次数: 0

Abstract

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.

结合多源遥感数据与面向对象的干旱湿地信息提取[j]。
湿地是西北干旱半干旱区绿洲的心脏,在干旱区调节气候、供水、蓄洪抗旱、保护生物多样性、维护生态稳定等方面发挥着至关重要的作用。干旱区湿地信息的提取为监测生态环境、维护生物多样性、预防荒漠化和土地退化提供了快速、准确的手段。本研究以宁夏银川黄河沿线都市圈为研究区域,采用Sentinel-1合成孔径雷达(SAR)影像、Sentinel-2光学影像和地形数据作为数据源。应用面向对象的湿地信息特征提取方法,探讨红边特征、雷达特征和地形特征在干旱区湿地信息提取中的重要性。验证利用RF-Pearson模型选择干旱区湿地最优特征组合的可行性,结合随机森林算法和BP神经网络对宁夏银川都市圈2021年湿地进行提取。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地促进干旱区湿地特征的识别和获取,与光谱指数和几何特征相比,湿地的整体精度分别提高了3.27%、2.14%和1.83%。②RF-Pearson模型特征选择方法的分类精度最高,重要程度依次为:光谱特征>;几何特征>;红边特征>;雷达特性>;地形特征。③基于特征选择的随机森林模型(RF)对干旱区流域湿地的分类效果最好,总体准确率为89.79%,Kappa系数为0.842 3,高于BP神经网络(BP)分类方法,说明该方法在提取干旱区湿地信息方面具有一定的可靠性。④宁夏银川都市圈湿地主要包括河流、湖泊、滩地沼泽、水库池塘和沟渠5种类型。主要集中在银川市、平罗县、沙坡头区、灵武市和中宁县。河流湿地在干旱区湿地中占主导地位,是宁夏银川都市圈湿地的突出类型。分类结果显示,自然湿地(河流、湖泊、滩涂和沼泽)面积为1 076.65 km2,人工湿地(水库池塘、沟渠)面积为108.18 km2,分别占研究区总面积的90.86%和9.14%。研究成果可为干旱区生态背景环境监测、黄河流域生态保护和高质量发展提供科学依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
环境科学
环境科学 Environmental Science-Environmental Science (all)
CiteScore
4.40
自引率
0.00%
发文量
15329
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