Distribution and invasion of Spartina alterniflora within the Jiaozhou Bay monitored by remote sensing image
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 Yang River Estuary and its southern side, the Dagu River Estuary and the Nvgukou. S. alterniflora first broke out near the Yang River Estuary and gradually spread to the tidal flats near the Nvgukou. The Dagu River Estuary is dominated by Spartina anglica, whose area has not changed much over the years, and a small amount of S. alterniflora has invaded later.
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. Spartina 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 Yang River Estuary and the Moshui River Estuary.
Figure 5. Remote sensing monitoring results of Spartina alterniflora over time in the Jiaozhou Bay. a. S. alterniflora distribution in the Yang River Estuary and Dagu River Estuary in 2002, 2012, 2014 and 2019; b. S. alterniflora distribution in Nvgukou in 2013, 2015, 2017 and 2019; c. S. alterniflora distribution in the Lianwan River Estuary in 1988, 2013 and 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 Yang River Estuary; black shows the area of S. alterniflora near Nvgukou; 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 S. alterniflora on the east tidal flat of Hongdao. a. the invasion of Spartina alterniflora through seed. The initial stage shows scattered S. alterniflora seedlings. b. S. 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. S. 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, DCNN, SVM, RF, CNN blod represents; "no" 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, DCNN; R8 refers to the image upsampled by Landsat8 to a spatial resolution of 15 m. L8 and L7 refer to Landsat 8 and Landsat 7, respectively.
 Breiman L. 2001. Random forests. Machine Learning, 45(1): 5–32. doi: 10.1023/A:1010933404324  Brusati E D, Grosholz E D. 2007. Effect of native and invasive cordgrass on Macoma petalum density, growth, and isotopic signatures. Estuarine, Coastal and Shelf Science, 71(3–4): 517–522  Callaway J C, Josselyn M N. 1992. The introduction and spread of smooth cordgrass (Spartina alterniflora) in South San Francisco Bay. Estuaries, 15(2): 218–226. doi: 10.2307/1352695  Chung C H. 1993. Thirty years of ecological engineering with Spartina plantations in China. Ecological Engineering, 2(3): 261–289. doi: 10.1016/0925-8574(93)90019-C  Daehler C C, Strong D R. 1996. Status, prediction and prevention of introduced cordgrass Spartina spp. invasions in Pacific estuaries, USA. Biological Conservation, 78(1–2): 51–58. doi: 10.1016/0006-3207(96)00017-1  Davis M A, Thompson K. 2000. Eight ways to be a Colonizer; two ways to be an invader: a proposed nomenclature scheme for invasion ecology. Bulletin of the Ecological Society of America, 81(3): 226–230  Fan Jianyong. 2005. Monitoring dynamic changes of coastline around Qingdao and its adjacent coastal zone using remote sensing (in Chinese)[dissertation]. Qingdao: The Institute of Oceanology, Chinese Academy of Sciences  Hsu Chih–Wei, Chang Chih–Chang, Lin Chih–Jen. 2003. A practical guide to support vector classification. Taiwan, China: Department of Computer Science and Information Engineering, National Taiwan University  Huang Huamei, Zhang Liquan. 2007. A study of the population dynamics of Spartina alterniflora at Jiuduansha shoals, Shanghai, China. Ecological Engineering, 29(2): 164–172. doi: 10.1016/j.ecoleng.2006.06.005  Huete A R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295–309. doi: 10.1016/0034-4257(88)90106-X  Jordan C F. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology, 50(4): 663–666. doi: 10.2307/1936256  Li Yi, Chen Yining, Li Yan. 2017. Remote sensing analysis of the changes in the ecotone of mangrove forests and Spartina alterniflora saltmarshes. Marine Science Bulletin, 36(3): 348–360  Li Xiang, Li Wei, Xu Xiaodong, et al. 2018. CascadeNet: modified resnet with cascade blocks. In: 2018 24th International Conference on Pattern Recognition. Beijing: IEEE, 483–488  Li Jingmei, Wang Xiaoling. 2013. Wetland reclamation and habitat damage assessment in Jiaozhou bay. Resources Science, 35(1): 59–65  Li Hepeng, Zhang Liquan. 2008. An experimental study on physical controls of an exotic plant Spartina alterniflora in Shanghai, China. Ecological Engineering, 32(1): 11–21. doi: 10.1016/j.ecoleng.2007.08.005  Lin Wenpeng, Chen Guangsheng, Guo Pupu, et al. 2015. Remote-sensed monitoring of dominant plant species distribution and dynamics at Jiuduansha wetland in Shanghai, China. Remote Sensing, 2015, 7(8): 10227–10241  Lu Feng, Yang Junfang. 2018. Remote sensing monitoring and analysis of Spartina alterniflora based on Landsat 8 OLI satellite data—taken the Shandong Yellow River Delta National Nature Reserve as an example. Shandong Forestry Science and Technology, 48(1): 29–32  Ma Xu, Yan Jiaguo, Wang Fangfang, et al. 2019. Trait and density responses of Spartina alterniflora to inundation in the Yellow River Delta, China. Marine Pollution Bulletin, 146: 857–864. doi: 10.1016/j.marpolbul.2019.07.022  Maricle B R, Lee R W. 2002. Aerenchyma development and oxygen transport in the estuarine cordgrasses Spartina alterniflora and S. anglica. Aquatic Botany, 74(2): 109–120. doi: 10.1016/S0304-3770(02)00051-7  Meng Weiqing, Feagin R A, Innocenti R A, et al. 2020. Invasion and ecological effects of exotic smooth cordgrass Spartina alterniflora in China. Ecological Engineering, 143: 105670. doi: 10.1016/j.ecoleng.2019.105670  Pearson R L, Miller L D. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie. In: Proceedings of the Eighth International Symposium on Remote Sensing of Environment. Ann Arbor: ERIM International, 1357–1381  Qi J, Chehbouni A, Huete A R, et al. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2): 119–126. doi: 10.1016/0034-4257(94)90134-1  Qin Yingying, Jiang Xiaoxiao, Li Feng, et al. 2009. Morphological plasticity and biomass allocation of Spartina alterniflora lossel in different habitats. Marine Environmental Science, 28(6): 657–659,667  Qin P, Jing M D, Xie M. 1985. The comparison of three ecotypes of Sparina alterniflora in coastal marshes of Luoyuanwan, Fujian Province. Journal of Nanjing University: Natural Science, 40: 226–236  Ren Guangbo, Wang Jinjin, Wang Andong, et al. 2019. Monitoring the invasion of smooth cordgrass Spartina alterniflora within the modern Yellow River delta using remote sensing. Journal of Coastal Research, 90(S1): 135–145  Richardson A J, Weigand C L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12): 1541–1542  Shi Xiaoyu, Zhang Ridong, Zhang Wenjing, et al. 2018. Impact of Jiaozhou bay cross-sea bridge on winter ice formation in northern Jiaozhou Bay. Marine Science Bulletin, 37(6): 633–642  Silinski A, Van Belzen J, Fransen E, et al. 2016. Quantifying critical conditions for seaward expansion of tidal marshes: a transplantation experiment. Estuarine, Coastal and Shelf Science, 169: 227–237  Sun Samei. 2005. Monitoring of smooth cordgrass invasion by remote sensing in Sandu Bay, Fujian. Journal of Oceanography in Taiwan Strait, 24(2): 223–227  Tao Yancheng, Pan Lianghao, Fan Hangqing, et al. 2017. Remote sensing monitoring of Spartina alterniflora in coastal intertidal zone of Guangxi. Guangxi Sciences, 24(5): 483–489  Tian Yanlin, Jia Mingming, Wang Zongming, et al. 2020. Monitoring invasion process of Spartina alterniflora by seasonal Sentinel-2 imagery and an object-based random forest classification. Remote Sensing, 12(9): 1383. doi: 10.3390/rs12091383  Tucker C J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127–150. doi: 10.1016/0034-4257(79)90013-0  Wang Qing, An Shuqing, Ma Zhijun, et al. 2006. Invasive Spartina alterniflora: biology, ecology and management. Acta Phytotaxonomica Sinica, 44(5): 559–588. doi: 10.1360/aps06044  Yao Hongyan, Liu Pudong, Shi Runhe, et al. 2017. Extracting the transitional zone of Spartina alterniflora and Phragmites australis in the wetland using high-resolution remotely sensed images. Journal of Geo-information Science, 19(10): 1375–1381  Zhu Yuling, Wang Janbu, Wang Andong, et al. 2019. Remote-sensed monitoring of Spartina alterniflora using deep convolutional neural network method with fusion of shallow features. Marine Sciences, 43(7): 12–22  Zuo Ping, Liu Chang’an, Zhao Shuhe, et al. 2009. Distribution of Spartina plantations along the China's coast. Haiyang Xuebao (in Chinese), 31(5): 101–111  Zuo Ping, Zhao Shuhe, Liu Chang’an, et al. 2012. Distribution of Spartina spp. along China’s coast. Ecological Engineering, 40: 160–166. doi: 10.1016/j.ecoleng.2011.12.014
- 文章访问数: 34
- HTML全文浏览量: 6
- 被引次数: 0