Distribution and invasion of Spartina alterniflora within the Jiaozhou Bay monitored by remote sensing image
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Abstract: Spartina alterniflora as an alien invasive plant, poses a serious threat to the ecological functions of the coastal wetland of the Jiaozhou Bay. As of 2019, the distribution area of S. alterniflora in the Jiaozhou Bay has reached more than 500 hm2. For this reason, combined with field surveys, remote sensing monitoring of the invasion S. alterniflora in the Jiaozhou Bay has been carried out. To accurately identify S. alterniflora within the Jiaozhou Bay coastal wetland, we used a new method which is an implement of deep convolutional neural network, and by which we got a higher accuracy than the traditional method. Based on distribution of S. alterniflora extracted by the proposed method, the temporal and spatial distribution characteristics of S. alterniflora were analyzed. And then combined with environmental factors, the invasion mechanism of S. alterniflora in the Jiaozhou Bay was analyzed in detail. From the monitoring results, it can be seen that S. alterniflora in Jiaozhou Bay is mainly distributed in the beaches near the Yanghe River Estuary and its southern side, the Dagu River Estuary and the Nügukou. Spartina alterniflora first broke out near the Yanghe River Estuary and gradually spread to the tidal flats near the Nügukou. The Dagu River Estuary is dominated by S. anglica, whose area has not changed much over the years, and a small amount of S. alterniflora has invaded later.
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Key words:
- Spartina alterniflora /
- remote sensing /
- coastal wetland /
- deep residual network
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Figure 2. Field survey sites (a, b; green dots) and vegetation photographs (c–g). c. Dense growth of Spartina alterniflora on the tidal flat; d. S. anglica mixed with S. alterniflora, which is mainly distributed in the Dagu River Estuary; e. densely distributed S. alterniflora blocking the river channel; f. S. alterniflora occupying the growing area of Phragmites australis; g. Suaeda salsa near S. alterniflora, mainly in the Yanghe River Estuary and the Moshui River Estuary.
Figure 3. Data preparation and experimental process. NDVI is the abbreviation of the normalized difference vegetation index; RVI, the ratio vegetation index; MSAVI, the modified soil-adjusted vegetation index; CNN, the convolutional neural network; DCNN, deep convolutional neural network; SVM, the support vector machine; RF, random forest.
Figure 5. Remote sensing monitoring results of Spartina alterniflora over time in the Jiaozhou Bay. a. Spartina alterniflora distribution in the Yanghe River Estuary and Dagu River Estuary in 2002, 2012, 2014 and 2019; b. S. alterniflora distribution in Nügukou in 2013, 2015, 2017 and 2019; c. S. alterniflora distribution in the Lianwan River Estuary in 1988, 2013, 2015 and 2019.
Figure 7. Bar chart of Spartina alterniflora area in different distribution areas in the Jiaozhou Bay. Green shows the area of S. alterniflora each year in the Yanghe River Estuary; black shows the area of S. alterniflora near Nügukou; blue and red represent the area of S. alterniflora and S. anglica in the Dagu River Estuary and Lianwan River Estuary, respectively.
Figure 9. Invasion process of Spartina alterniflora on the east tidal flat of Hongdao. a. The invasion of S. alterniflora through seed. The initial stage shows scattered S. alterniflora seedlings. b. Spartina alterniflora starts root propagation after the seed invasion and forms a large number of patches. The patches are almost circular and spaced apart from each other. c. Spartina alterniflora multiplies through seeds and roots to connect the patches and finally completes the occupation of the tidal flat.
Figure 10. Scattered Spartina anglica at the Dagu River Estuary and S. alterniflora invading in the S. anglica growing area. a. The Gaofen-1 WFV satellite image and the photographs of S. anglica invaded by S. alterniflora; b. a mixed area of S. anglica and S. alterniflora taken at the scene. The taller plants are S. alterniflora, the smaller plants are S. anglica.
Table 1. Details of satellite images used in this analysis
Image name Imaging time Space resolution/m Landsat 5 TM 1988–2008 30 Landsat 7 ETM+ 2012–2014 30 Landsat 8 OLI 2015–2019 15 Gaofen-1 WFV 2014–2019 16 Table 2. GF-1 data classification results (%) of the Jiaozhou Bay in 2019
Method Spartina altemiflora Surrounding OA Recall Precision F1-score DCNN 98.75 98.97 98.97 98.75 60.27 74.85 SVM 29.55 99.99 98.90 29.55 98.30 45.44 RF 98.55 98.44 98.39 95.63 49.21 64.99 Basic CNN 98.55 98.44 98.33 98.55 48.17 64.71 DCNN (no) 99.12 97.04 97.08 99.12 34.63 51.33 SVM (no) 29.53 99.99 98.90 29.53 98.30 45.42 RF (no) 95.57 98.51 98.46 95.57 50.28 66.57 Basic CNN (no) 98.29 98.29 98.29 98.29 47.65 64.18 Note: OA is the abbreviation of the overall accuracy; DCNN, deep convolutional neural network; SVM, the support vector machine; RF, the random forest; CNN, the convolutional neural network. “no” in bracket means that the vegetation index is not used. Table 3. Classification results (%) of the Jiaozhou Bay images from different satellites in different years
Method Spartina altemiflora Surrounding OA Recall Precision F1-score DCNN (R8 2017) 98.12 98.90 98.83 98.12 47.08 63.63 DCNN (L8 2017) 95.24 97.93 97.91 95.24 25.04 39.65 DCNN (L7 2017) 88.24 98.27 98.20 88.24 27.01 41.36 Note: OA is the abbreviation of the overall accuracy; DCNN, deep convolutional neural network. R8 refers to the image upsampled by Landsat 8 to a spatial resolution of 15 m. L8 and L7 refer to Landsat 8 and Landsat 7, respectively. -
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