基于连续统去除法的水生植物提取及其时空变化分析——以官厅水库库区为例

doi:10.17521/cjpe.2017.0240

植物生态学报 ›› 2018, Vol. 42 ›› Issue (6): 640-652.DOI: 10.17521/cjpe.2017.0240

所属专题：生态遥感及应用

基于连续统去除法的水生植物提取及其时空变化分析——以官厅水库库区为例

汪星,宫兆宁(),井然,张磊,金点点

首都师范大学资源环境与旅游学院, 北京 100048; 三维信息获取与应用教育部重点实验室, 北京 100048; 资源环境与地理信息系统北京市重点实验室, 北京 100048; 北京市城市环境过程与数字模拟国家重点实验室培育基地, 北京 100048

收稿日期:2017-09-13 修回日期:2018-04-04 出版日期:2018-06-20 发布日期:2018-06-20
通讯作者: 宫兆宁
基金资助:
国家国际科技合作专项资助项目(2014DFA21620)

Extraction of aquatic plants based on continuous removal method and analysis of its temporal and spatial changes—A case study of Guanting Reservoir

WANG Xing,GONG Zhao-Ning(),JING Ran,ZHANG Lei,JIN Dian-Dian

College of Resource Environment & Tourism, Capital Normal University, Beijing 100048, China; Key Laboratory of 3D Information Acquisition and Application,Ministry of Education, Beijing 100048, China; Key Laboratory of Resources Environment and GIS of Beijing Municipal, Beijing 100048, China; Base of the State Laboratory of Urban Environmental Processes and Digital Modeling, Beijing 100048, China

Received:2017-09-13 Revised:2018-04-04 Online:2018-06-20 Published:2018-06-20
Contact: Zhao-Ning GONG
Supported by:
Supported by the International Science & Technology Cooperation Program of China(2014DFA21620)

摘要/Abstract

摘要：

光谱特征变量的筛选作为水生植物识别的重要手段之一, 在水生植物种类识别研究中应用广泛。该研究将实测光谱特征提取与多时相Landsat 8 OLI影像数据分析相结合, 找到一种有效识别不同种类水生植物的特征变量。在水生植物反射光谱特征分析中引入矿质分析中普遍使用的连续统去除法, 对光谱重采样结果作连续统去除处理后提取光谱吸收深度特征。采用单因素方差分析法对比7个光谱重采样波段和3个连续统去除吸收深度敏感波段, 发现经连续统去除处理的短波红外1波段(SWIR1CR)对于不同类型的水生植物区分效果最佳。将连续统去除法应用到遥感影像处理上, 发现SWIR1CR波段能较好区分沉水植物和挺水植物; 结合影像归一化植被指数和SWIR1CR波段可较好区分三类水生植物。结合特征波段筛选结果采用支持向量机分类方法, 得到水生植物的分类结果精度为86.33%, 对比全生长期12期影像提取的水生植物分布图, 发现水生植物主要分布于官厅水库库区南北岸浅水区, 水生植物面积最大时约占库区总面积的35.13%; 其中沉水植物年内生长分布变化幅度较大, 6月上旬开始迅速生长; 10月份水生植物开始衰减; 11月份水生植物占库区面积的20%, 沉水、浮水植物大幅衰减消失。

关键词: 连续统去除, 光谱吸收深度, 单因素方差分析, 时空变化, 短波红外1波段

Abstract:

Aims Screening of spectral characteristic variables is one of the important means for aquatic plant recognition, and it is widely applied in aquatic plant species identification. In this paper, a method for identifying aquatic plants species was constructed by combining extracted spectral feature information with the multi-temporal Landsat 8 OLI image data analysis.

Methods In analyzing reflectance spectra of aquatic plants, the method of continuum removal for mineral analysis was introduced. The spectral resampling was performed on the measured spectral curve, and the spectral absorption depth was characterized by the continuous removal of the spectral resampling results. One-way ANOVA method was used to compare the seven spectral resampling bands and the three continuum removal absorption depth sensitive bands. Then the characteristic bands with significant differentiation of different aquatic plants were selected. The continuum removal was applied on remote sensing image processing. The results of the spectroscopic analysis were used to guide the identification of aquatic plants in using Landsat 8 OLI. The classification of aquatic plants was carried out by using support vector machine (SVM) classification.

Important findings The results of the measured spectrum resampling are similar to the atmospheric calibration of Landsat 8 OLI in the same position, and the results of the measured spectral curves can be used to guide the classification of Landsat 8 OLI. The one-way ANOVA method was used to compare seven spectral resampling bands and three continuous systems in absorbing sensitive wavelengths. The results showed that the short wave infrared 1 band, which was processed by continuum removal (SWIR1CR), was the best in distinguishing different types of aquatic plants. In this paper, the continuum removal was applied on remote sensing image processing, and it was found that the SWIR1CR band can better distinguish the submerged plants and the emergent plants. The normalized differential vegetation index and SWIR1CR band were well capable of identifying submerged plants, floating plants and emergent plants. Based on the SVM classification method, the classification accuracies of aquatic plants were 86.33%. The distribution of aquatic plants showed that the aquatic plants were mainly distributed in shallow water areas of the south north bank of Guanting reservoir. When the aquatic plant distribution area reached the peak, it accounted for about 35.13% of the total area of the reservoir. The growth distribution of submerged plants changed significantly during a year. The stem and leaves of submerged plants began to emerge in early June. Aquatic plants began to wither in October, and aquatic plants accounted for only 20% of the total area in November.

Key words: continuous removal, spectral absorption depth, one-way ANOVA, spatiotemporal variation, the short wave infrared I band

汪星, 宫兆宁, 井然, 张磊, 金点点. 基于连续统去除法的水生植物提取及其时空变化分析——以官厅水库库区为例. 植物生态学报, 2018, 42(6): 640-652. DOI: 10.17521/cjpe.2017.0240

WANG Xing, GONG Zhao-Ning, JING Ran, ZHANG Lei, JIN Dian-Dian. Extraction of aquatic plants based on continuous removal method and analysis of its temporal and spatial changes—A case study of Guanting Reservoir. Chinese Journal of Plant Ecology, 2018, 42(6): 640-652. DOI: 10.17521/cjpe.2017.0240

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2017.0240

https://www.plant-ecology.com/CN/Y2018/V42/I6/640

图/表 14

图1 研究区概况图(2016-7-7 Landsat 8 OLI影像)。

Fig. 1 Map of the study area (2016-7-7 Landsat 8 OLI images).

表1 官厅水库湿地植物类型及光谱数

Table 1 Wetland vegetation types and spectra numbers of Guanting Reservoir

植物类型 Vegetation types	典型湿地植物 Typical wetland plant	光谱数 Number of spectrum
沉水植物 Submerged plants	篦齿眼子菜 Potamogeton pectinatus	9
	穗状狐尾藻 Myriophyllum spicatum	8
	狸藻 Utricularia vulgaris	3
浮水植物 Floating plants	两栖蓼 Polygonum amphibium	20
挺水植物 Emergent plants	芦苇 Phragmites australis	10
挺水植物 Emergent plants	香蒲 Typha angustifolia	10

表2 Landsat 8 OLI 波段参数设置

Table 2 Spectral bands setting of Landsat 8 OLI

波段名称 Band names	波长 Wavelength (μm)	空间分辨率 Spatial resolution (m)
海岸 Coastal	0.433-0.453	30
蓝 Blue	0.450-0.515	30
绿 Green	0.525-0.600	30
红 Red	0.630-0.680	30
近红外 NIR	0.845-0.885	30
短波红外1 SWIR1	1.560-1.660	30
短波红外2 SWIR2	2.100-2.300	30
全色 Pan	0.500-0.680	15
卷云 Cirrus	1.360-1.390	30

图2 A, 实测水体样点光谱曲线图。B, 典型代表性沉水植物、浮水植物、挺水植物和水体样点光谱曲线对比图。C, 官厅水库典型水生植物种类光谱曲线图。D, 水生植物光谱重采样结果图。

Fig. 2 A, Measured water sample spectral curve. B, Typical representative submerged plants, floating plants, emergent plants, and waters comparison of sample points spectrum curves. C, Guanting Reservoir typical aquatic plant species spectral curve. D, Aquatic plant spectrum resampling results.

图3 连续统去除结果。A, 高光谱辐射仪实测光谱。B, 光谱重采样结果。

Fig. 3 Continuous removal result. A, Spectroradiometer measured spectrum. B, The result of spectral resampling.

表3 官厅水库3种湿地植物类型吸收特征参数统计

Table 3 Statistics of parameters of absorption characteristic in 3 types of wetland vegetation types in Guanting Reservoir

植物类型 Vegetation type	光谱数 Number of spectra	Blue处吸收深度平均值 Blue absorption depth average	Red处吸收深度平均值 Red absorption depth average	SWIR1处吸收深度平均值 SWIR1 absorption depth average
沉水植物 Submerged plants	20	0.3202	0.3579	0.7977
浮水植物 Floating plants plants	20	0.5955	0.7740	0.2167
挺水植物 Emergent plants	20	0.7272	0.8466	0.3542

图4 光谱特征变量单因素方差分析结果。

Fig. 4 One-way ANOVA results of spectral characteristic variables.

图5 Landsat 8 OLI影像反射率光谱和实测光谱重采样结果对比。

Fig. 5 Comparison between the reflectance spectra of Landsat 8 and the measured spectral resampling.

图6 不同类型水生植物影像灰度直方图统计结果。A, 归一化植被指数(NDVI)灰度影像。B, 连续统去除后短波红外1波段(SWIR1CR)灰度影像。

Fig. 6 The results of histogram of the gray histogram of different types of aquatic plants. A, Normalized differential vegetation index (NDVI) grey image. B, Short wave infrared 1 band, which was processed by continuum removal (SWIR1CR) grey image.

图7 多时相遥感影像3种水生植物样点的归一化植被指数(NDVI)(A)和连续统去除后短波红外1波段(SWIR1CR)时间序列曲线(B)。

Fig. 7 Time series curves of normalized differential vegetation index (NDVI)(A) and short wave infrared 1 band, which was processed by continuum removal (SWIR1CR)(B) for three types of aquatic plants in multitemporal remote sensing images.

表4 分类精度验证

Table 4 Classification accuracy test

地物类型 Category	水体 Water body	沉水植物 Submerged plants	浮水植物 Floating plants	挺水植物 Emergent plants
水体 Water body	820	312	186	15
沉水植物 Submerged plants	47	2969	9	86
浮水植物 Floating plants	11	0	76	8
挺水植物 Emergent plants	39	14	1	731
总体精度 Overall accuracy	86.33%	Kappa系数 Kappa coefficient	0.76

图8 官厅水库库区WorldView_2影像图和局部水生植物影像特征展示。A, 挺水植物。B, 沉水植物。C, 浮水植物。

Fig. 8 The WorldView_2 image of the reservoir area and the display of local aquatic plant image features. A, Emergent plants. B, Submerged plants. C, Floating plants.

图9 不同生长期官厅水库库区水生植物分类结果图。

Fig. 9 Aquatic plant classification results of different growth stage in Guanting Reservoir.

图10 官厅水库2016年水位统计结果(A)和年内水生植物空间分布面积变化统计(B)。

Fig. 10 The statistical results of the water level in Guanting Reservoir in 2016 (A) and the spatial distribution of aquatic plants in the year (B).

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基于连续统去除法的水生植物提取及其时空变化分析——以官厅水库库区为例

Extraction of aquatic plants based on continuous removal method and analysis of its temporal and spatial changes—A case study of Guanting Reservoir

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