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遥感技术与应用  2021, Vol. 36 Issue (4): 760-776    DOI: 10.11873/j.issn.1004-0323.2021.4.0760
湿地遥感专栏     
基于Sentinel-1,2和Landsat 8时序影像的鄱阳湖湿地连续变化监测研究
姚杰鹏1,2(),杨磊库1,陈探2,宋春桥2,1()
1.河南理工大学 测绘与国土信息工程学院,河南 焦作 454000
2.中国科学院南京地理与湖泊研究所流域地理学重点实验室,江苏 南京 210008
Consecutive Monitoring of the Poyang Lake Wetland by Integrating Sentinel-2 with Sentinel-1 and Landsat 8 Data
Jiepeng Yao1,2(),Leiku Yang1,Tan Chen2,Chunqiao Song2,1()
1.College of Surveying and Geotechnical Engineering,Henan Polytechnic University,Jiaozuo 454000,China
2.Key Laboratory of Watershed Geographic Sciences,Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences,Nanjing 210008,China
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摘要:

湿地具有季节性特征,高时间分辨率遥感监测能够更为客观精准地认识其时空变化规律。选择季节性变化显著、我国第一大淡水湖生态湿地——鄱阳湖湿地为典型案例,利用Sentinel-1,2和Landsat 8卫星的2017~2019年所有可以获取的不同时相影像,采用随机森林分类(Random Forest,RF)方法,对研究区的湿地进行遥感分类和信息提取,发挥海量遥感影像在湿地宏观连续监测的优势,解析鄱阳湖湿地的年际、年内时空动态变化特征。研究结果表明:Sentinel-2影像为鄱阳湖湿地动态变化监测提供良好的数据基础,随机森林分类总体分类精度高于90%,提取效果具有比较优势。对3 a分类结果进行统计分析,各湿地类型在年内均呈现出动态变化的特点,在每年2月泥滩和草洲面积到达年内最大,水体面积为年内最小;每年6、7月份水域面积达到年内最大,泥滩和草洲面积最小,季节性变化明显;月度时间序列的分类结果,能更准确地说明湿地类型的月度和季度变化。因此,结合Seninel-1,2以及Landsat 8数据,基于RF算法,能及时、有效地对鄱阳湖等季节性变化强烈的湿地进行动态监测,对开展湿地资源高效调查工作具有重要意义。

关键词: SentinelLandsat遥感监测随机森林分类时间序列鄱阳湖湿地    
Abstract:

Wetlands are usually featured by evident seasonality, and thus high temporal-resolution remote sensing monitoring of their consecutive changes would greatly benefit to more objectively and accurately detecting the characteristics of spatial-temporal changes. The Poyang Lake wetland, as the largest freshwater lake in China, which shows significant intra-annual variability, was selected as the demonstrative case in this study. By collecting all available remote sensing images of Sentinel-1 & 2 and Landsat-8 from 2017 to 2019 based on the Google Earth Engine platform, we adopted the Random Forest (RF) method to map various types of wetlands of the Poyang Lake. It aims to demonstrate the capacity of Sentinel-2 optical images integrated with Sentinel-1 SAR and Landsat-8 data applicable to monitor wetland variations at both the inter-annual and intra-annual timescales. Results show that the Sentinel-2 images enable to provide a powerful data base for monitoring the dynamics of Poyang Lake wetland, and the overall classification accuracy was higher than 90%. the areas of the classification results were statistically analyzed in the 3 years, in February of each year, mudflat and vegetation reach the maximum area, while water area is the minimum.In June and July of each year, the water area reaches the largest in the year, while the mudflat and vegetation area is the smallest. All types of wetlands in the Poyang Lake show evidently seasonal changes, and the monthly classification results can more accurately illustrate the intra-annual changes characteristics of various types. Overall, the integration of Seninel-2 data with Sentinel 1 and Landsat-8 images, can effectively monitor the wetland changes at fine timescale, which is crucial for timely and costly management of wetland resources.

Key words: Sentinel    Landsat    Remote sensing    Random Forest    Time series    Poyang Lake wetland
收稿日期: 2020-10-20 出版日期: 2021-09-26
ZTFLH:  P237  
基金资助: 国家重点研发计划(2018YFD1100101);国家“人才引进项目”青年项目(Y7QR011001);中国科学院战略性先导科技专项(A类)(XDA23100102);国家自然科学基金项目(41971403)
通讯作者: 宋春桥     E-mail: JPYao2020@163.com;cqsong@niglas.ac.cn
作者简介: 姚杰鹏(1992-),男,山西运城人,硕士研究生,主要从事环境遥感方面的研究。E?mail: JPYao2020@163.com
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引用本文:

姚杰鹏,杨磊库,陈探,宋春桥. 基于Sentinel-1,2和Landsat 8时序影像的鄱阳湖湿地连续变化监测研究[J]. 遥感技术与应用, 2021, 36(4): 760-776.

Jiepeng Yao,Leiku Yang,Tan Chen,Chunqiao Song. Consecutive Monitoring of the Poyang Lake Wetland by Integrating Sentinel-2 with Sentinel-1 and Landsat 8 Data. Remote Sensing Technology and Application, 2021, 36(4): 760-776.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.4.0760        http://www.rsta.ac.cn/CN/Y2021/V36/I4/760

图1  鄱阳湖地理位置
图2  2017~2019年鄱阳湖湿地研究区的月尺度不同来源遥感影像数量统计分布图
湿地类型描述
水体深浅不一的蓝色,蓝紫色,形状各异,大小不一,边界清晰
沙地呈现亮黄色,多成细长的条带分布,在水体或者泥滩附近
草洲深浅不一的棕色,绿色,黄绿色,形状大小各异,边界有些模糊
泥滩浅灰色,沿水体呈条带状,或者环湖水体,或江心片状,大小不一,边界清晰
农田淡绿色、土黄色, 形状规则,呈长方形或正方形,纹理清晰
其他非湿地, 深紫色或银白色、城市成片区分布、农村呈片区分布、部分房屋建筑成零星点分布
表1  鄱阳湖湿地采用的分类系统
水体沙地草洲泥滩农田其他
样本点3721023843695449
验证点3641043863745947
表2  样本点和验证点的数量
随机森林参数输入变量
决策树个数每个节点选择的特征数量具体特征
404Sentinel-2(15个波段)
405Sentinel-1,2(22个特征)
404Landsat8,Seninel-1(17个特征)
表3  随机森林的参数和输入汇总
时间总分类精度 /%Kappa系数时间总分类精度 /%Kappa系数时间总分类精度 /%Kappa系数
20170190.960.8520180191.110.8920190186.670.83
20170291.040.8820180291.010.8920190290.620.89
20170392.120.9020180390.950.8820190392.170.88
20170491.880.8720180490.230.8820190493.330.92
20170590.970.8720180589.420.8720190592.150.90
20170690.620.8720180690.850.8820190683.210.78
20170791.440.8920180791.060.8820190791.890.90
20170892.540.9020180891.740.8920190893.480.92
20170990.290.8820180991.850.9020190993.260.91
20171092.330.9120181091.880.8720191093.750.92
20171192.130.9020181191.030.8920191190.360.88
20171291.240.8920181290.670.8820191293.260.91
表4  鄱阳湖湿地2017~2019年逐月分类结果评价的总体精度
月份123456789101112
平均分类精度/%89.690.991.791.890.888.2291.592.691.892.791.291.7
表5  鄱阳湖湿地分类3 a平均的各月份分类精度
水体沙地草洲泥滩农田其他
总体分类精度=91.74% Kappa系数=0.890 7
用户精度/%97.5085.6492.8593.7584.6889.86
制图精度/%95.1296.4389.6588.2385.7188.89
表6  以2018年8月的Sentinel-2影像分类结果为例的各湿地类型精度评价
图3  以2017年度为例鄱阳湖在枯水期和丰水期的不同类型湿地分布情况
图4  2017~2019年鄱阳湖天然湿地各类型分布面积占比
图5  2018年年内鄱阳湖湿地分布图
图6  蚌湖2018年内的时空动态变化
图7  2017~2019年蚌湖主要湿地类型面积变化
图8  鄱阳湖湿地2017~2019年不同类型面积变化
图9  2019年8月到11月鄱阳湖湿地信息分类提取结果
卫星Landsat 8Sentinel-2Sentinel-1
时间2019-9-202019-9-242019-9-18
表7  用于分类的影像采集时间
图10  2019年9月鄱阳湖湿地信息分类提取结果
图11  各方案中的特征重要性分布
分类 类别Sentinel-2Sentinel-1&2Landsat8 & Sentinel-1
用户精度/%制图精度/%用户精度/%制图精度/%用户精度/%制图精度/%
水体96.2597.3794.5094.5095.6595.65
沙地9798.2496.4796.4792.1092.10
草洲92.8592.8195.3195.3196.9796.97
泥滩8685.7190.4490.4485.7185.71
农田100100100100100100
其他90.0090.001001008080
总分类精度/%93.2694.3692.91
Kappa系数0.912 30.931 20.907 5
表8  不同影像组合分类精度评价
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