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

doi:10.17521/cjpe.2017.0240

Abstract

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.

http://jtp.cnki.net/bilingual/detail/html/ZWSB201806004

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

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[J]. Chin J Plant Ecol, 2018, 42(6): 640-652.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2017.0240

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

Figures/Tables 14

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

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

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

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.

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

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

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

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

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.

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.

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

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.

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

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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