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遥感技术与应用  2021, Vol. 36 Issue (4): 777-790    DOI: 10.11873/j.issn.1004-0323.2021.4.0777
湿地遥感专栏     
基于多季相Sentinel-2影像的白洋淀湿地信息提取
梁爽1,2,3(),宫兆宁1,2,3(),赵文吉1,2,3,关鸿亮1,2,3,梁亚囡1,2,3,陆丽1,2,3,赵雪1,2,3
1.首都师范大学资源环境与旅游学院,北京 100048
2.三维信息获取与应用教育部重点实验室,北京 100048
3.资源环境与地理信息系统北京市重点实验室,北京 100048
Information Extraction of Baiyangdian Wetland based on Multi-season Sentinel-2 Images
Shuang Liang1,2,3(),Zhaoning Gong1,2,3(),Wenji Zhao1,2,3,Hongliang Guan1,2,3,Yanan Liang1,2,3,Li Lu1,2,3,Xue Zhao1,2,3
1.College of Resources Environment &Tourism,Capital Normal University,Beijing 100048,China
2.Key Laboratory of 3D Information Acquisition and Application of Ministry,Beijing 100048,China
3.Key Laboratory of Resources Environment and GIS of Beijing Municipal,Beijing 100048,China
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摘要:

白洋淀湿地是华北平原上重要的浅水湖泊湿地,对雄安新区绿色发展具有重要的生态价值。对白洋淀高度异质化的景观格局进行分类,能够为白洋淀湿地资源的遥感监测提供指导意义。针对湿地季节变化的特点,对白洋淀每个季节选取一期具有代表性的Sentinel-2影像,采用分类与回归树(CART)、支持向量机(SVM)、随机森林(RF)3种常用的机器学习分类器对15种季相组合实验方案进行分类,分析不同季相遥感影像及其组合对白洋淀湿地信息提取的优劣。结果表明:相较于使用单一季相影像分类,多季相影像的组合能够显著提高分类精度,春&夏季相组合能够得到最优的分类效果,相对单季影像总体分类精度提高了10.9%~25.5%,Kappa系数提高了0.09~0.29;SVM分类器的分类表现较为稳定,能够得到最高的平均分类精度,CART分类器在处理高维特征的能力不如随机森林和SVM;不同特征类型对湿地信息提取的贡献度从高到底依次是红边光谱特征、传统光谱特征、缨帽变换特征、主成分分析特征、纹理特征。实验成果能为湿地信息的遥感识别提供依据。

关键词: 白洋淀湿地季相特征组合红边波段信息提取Sentinel?2    
Abstract:

Baiyangdian is an important shallow lake wetland in the North China Plain, which has important ecological value for the green development of Xiong’an New Area. Wetland mapping of the highly heterogeneous landscape pattern of Baiyangdian can provide guidance for the remote sensing monitoring of Baiyangdian Lake wetland resources. In view of the seasonal changes of wetlands, a representative Sentinel-2 image is selected for each season of Baiyangdian in 2019. Three commonly used machine learning classifiers, including Classification and Regression Tree (CART), Support Vector Machine (SVM) and Random Forest (RF), were used to classify 15 classification scenario. The advantages and disadvantages of different seasonal remote sensing images and their combinations for extracting Baiyangdian wetland information were analyzed. The results showed that the combination of multi-seasonal images can significantly improve the classification accuracy. The combination of spring and summer images obtained the optimal classification accuracy. Compared with the single seasonal images, the overall accuracy was improved by 10.9%~25.5% and the kappa coefficient was improved by 0.09~0.29. The classification performance of the SVM classifier was relatively stable, and the highest classification accuracy can be obtained. The ability of CART classifier in processing high-dimensional features was not as good as that of random forest and SVM. The contribution of different features to the wetland information extraction was described as follows: red-edge spectral feature > traditional spectral feature > tasselled cap transformation feature > principal component analysis feature > texture feature. The research results can provide a basis for the remote sensing mapping of Baiyangdian wetland.

Key words: Baiyangdian wetland    Seasonal features combination    Red-edge band    Information extraction    Sentinel-2
收稿日期: 2020-11-02 出版日期: 2021-09-26
ZTFLH:  P237  
基金资助: 国家自然科学基金项目(41971381);北京市水务局重点项目(TAHP?2018?ZB?YY?490S)
通讯作者: 宫兆宁     E-mail: liangsh1010@163.com;gongzhn@cnu.edu.cn
作者简介: 梁爽(1997-),男,江苏宿迁人,硕士研究生,主要从事遥感技术在湿地领域的应用研究。E?mail: liangsh1010@163.com
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引用本文:

梁爽,宫兆宁,赵文吉,关鸿亮,梁亚囡,陆丽,赵雪. 基于多季相Sentinel-2影像的白洋淀湿地信息提取[J]. 遥感技术与应用, 2021, 36(4): 777-790.

Shuang Liang,Zhaoning Gong,Wenji Zhao,Hongliang Guan,Yanan Liang,Li Lu,Xue Zhao. Information Extraction of Baiyangdian Wetland based on Multi-season Sentinel-2 Images. Remote Sensing Technology and Application, 2021, 36(4): 777-790.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.4.0777        http://www.rsta.ac.cn/CN/Y2021/V36/I4/777

图1  白洋淀湿地位置与样本点分布图(底图为2019年4月3日Sentinel-2影像)
影像日期波段数量代表季节
2019-01-0813
2019-04-0313
2019-07-0213
2019-10-3013
表1  Sentinel-2影像获取时间
波段中心波长/nm波谱宽度/nm分辨率/m
B2-blue4906510
B3-green5603510
B4-red6653010
B5-Red Edge 17051520
B6-Red Edge 27401520
B7-Red Edge 37832020
B8-NIR84211510
B8a-Red Edge 48652020
B11-SWIR11 6109020
B12-SWIR22 19018020
表2  Sentinel-2波段参数
图2  多年平均气温和降水变化
一级类型二级类型三级类型类型说明

湿地

水体常年积水,包含河流、湖泊、鱼塘
水田种植水稻
挺水植物台地芦苇台田上的芦苇
滩地芦苇浅滩上的芦苇
浮水植物主要为荷花、芡实
耕地旱地夏季种植玉米、冬季种植小麦
林地包括有林地、疏林地、灌木林
裸地未利用地
建设用地包括城镇、乡村、交通等建设用地
表3  白洋淀湿地土地覆被分类方案
指数简称指数全称计算公式类型
NDVINormalized Difference Vegetation Index(B8-B4)/(B8+B4)传统光谱指数
EVIEnhanced Vegetation Index2.5×(B8-B4)/(B8+6.0×B4-7.5×B2+1)传统光谱指数
MNDWIModified Normalized Difference Water Index(B3-B11)/(B3+B11)传统光谱指数
LSWILand Surface Water Index(B8-B11)/(B8+B11)传统光谱指数
MSAVIModified Soil Adjusted Vegetation Index[2×B8+1-2×B8+12-8×(B8-B4)]/2传统光谱指数
NDI45Normalized Difference Index(B5-B4)/(B5+B4)红边光谱指数
MCARIModified Chlorophyll Absorption Ratio Index[(B5-B4)-0.2×(B5-B3)] × (B5-B4)红边光谱指数
PSSRIPigment Specific Simple Ratio (chlorophyll) IndexB7/B4红边光谱指数
NDre1Normalized Difference red-edge 1(B6-B5)/(B6+B5)红边光谱指数
NDre2Normalized Difference red-edge 2(B7-B5)/(B7+B5)红边光谱指数
表4  Sentinel-2光谱指数描述
实验方案季相组合实验方案季相组合
19夏、冬
210秋、冬
311春、夏、秋
412夏、秋、冬
5春、夏13秋、冬、春
6春、秋14冬、春、夏
7春、冬15春、夏、秋、冬
8夏、秋
表5  分类方案
CARTSVMRF
实验方案MOAMKCMOAMKCMOAMKC
170.20.6575.10.7070.80.65
275.30.7383.40.7975.70.71
364.80.5573.90.7168.80.61
460.60.5365.50.5864.50.54
5春、夏84.80.8090.80.8691.50.86
6春、秋77.10.7483.80.7982.00.75
7春、冬69.40.6576.70.7274.90.68
8夏、秋76.60.7587.40.8386.20.81
9夏、冬76.70.7688.50.8588.60.85
10秋、冬71.90.6581.10.7680.70.74
11春、夏、秋81.60.7789.90.8689.90.86
12春、夏、冬79.30.7891.40.8788.60.85
13夏、秋、冬75.40.7388.00.8287.10.81
14春、秋、冬73.10.7186.40.7987.50.82
15春、夏、秋、冬83.80.7990.90.8689.80.86
表6  不同分类方案的的分类精度对比
图3  不同季相的9种土地覆被平均地表反射率光谱曲线
地物类型
PAUAPAUAPAUAPAUA
水体79.281.397.898.994.095.172.770.6
水田73.064.178.576.971.764.961.558.5
台地芦苇87.182.896.294.286.677.191.170.8
滩地芦苇68.182.196.285.759.777.939.581.3
浮水植物64.656.884.489.077.972.769.950.3
旱地91.598.086.382.688.376.885.788.8
林地82.970.980.481.460.979.566.858.0
裸地72.860.285.978.152.243.743.938.6
建筑用地86.192.898.494.296.995.292.189.9
OA(%)75.183.473.965.5
Kappa0.70.790.710.58
表7  单一季相影像的分类精度
图4  春季MNDWI的频率直方图
地物类型春、夏春、秋春、冬夏、秋夏、冬秋、冬
PAUAPAUAPAUAPAUAPAUAPAUA
水体97.699.493.390.962.970.297.395.992.596.674.481.5
水田93.191.775.372.746.360.082.790.885.993.663.567.6
台地芦苇93.787.789.381.687.776.493.888.192.987.187.481.7
滩地芦苇83.792.660.091.048.986.285.487.581.190.559.684.7
浮水植物89.292.683.075.062.348.388.193.390.593.569.257.8
旱地90.994.991.494.391.694.492.793.992.593.284.291.7
林地92.177.887.984.388.970.484.585.891.886.384.088.2
裸地76.285.957.461.583.366.075.477.764.478.878.368.4
建筑用地94.295.090.193.889.992.094.695.093.594.790.583.3
OA(%)90.883.876.787.488.581.1
Kappa0.860.810.720.830.850.76
表8  双季相影像组合不同地物的分类精度
地物类型春、夏、秋春、夏、冬夏、秋、冬春、秋、冬春、夏、秋、冬
PAUAPAUAPAUAPAUAPAUA
水体97.098.797.798.397.998.197.999.297.797.8
水田92.686.992.496.092.496.094.395.289.196.0
台地芦苇95.292.495.287.595.287.595.091.095.291.2
滩地芦苇90.584.578.186.578.188.582.484.586.992.9
浮水植物91.894.891.495.491.495.491.794.692.595.1
旱地88.195.893.095.993.095.991.793.990.595.2
林地92.986.393.184.393.184.388.393.991.280.6
裸地73.766.166.568.792.381.983.486.581.284.4
建筑用地95.796.094.795.994.795.995.795.795.595.7
OA(%)89.991.48886.490.9
Kappa0.860.870.820.790.86
表9  三季节及以上季相特征组合不同地物的分类精度
图5  白洋淀湿地SVM分类结果图
CARTSVMRF
实验方案MOAMKCMOAMKCMOAMKC
5春、夏84.80.8090.80.8691.50.86
5a原始波段78.30.7487.50.8288.00.83
5b传统波段77.80.7486.40.8185.90.80
5c红边波段74.70.7080.40.7580.40.74
5dTCT特征81.70.7885.90.8089.00.84
5e纹理特征30.90.1831.20.1433.80.15
5f主成分分析72.00.6779.70.7379.10.72
表10  不同特征子集分类的精度
图6  方案5的特征变量重要性(前30个)(NDre1_2表示为夏季的NDre1指数,以此类推,橘红色填充代表具有红边参数的特征)
图7  特征变量所占比重统计
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