Analyzing the spatio-temporal distribution of Eichhornia crassipes based on Sentinel-2 remote sensing

doi:10.13474/j.cnki.11-2246.2023.0166

Abstract

Abstract: The large-scale outbreak of Eichhornia crassipes dramatically influence river and lake flood control, water supply security, and water ecology. In this study, the Lixia River area of Jiangsu province is chosen as the research area. We applied the Sentinel-2 image data to filter the optimal classification method of Eichhornia crassipes by compare and analyze the classification accuracy of three machine learning algorithms. Then, analysis the inter-annual spatio-temporal (2017—2021) characteristics and their spreading trends of Eichhornia crassipes by inversion of remote sensing data. The results showed that, the classification performance based on support vector machine (SVM) (overall accuracy is 84.81%～94.30%, Kappa coefficient is 0.70～0.89) is better than neural network (NN) and random forest (RF), The annual outbreak area of Eichhornia crassipes showed a trend of “increase and then decrease” from 2017 to 2021, the outbreak area of Eichhornia crassipes reached its peak in 2019. The outbreak hotspots are mainly located in the junctions of administrative region and dense water networks,such as southern Funing county, central Baoying county, and southern Xinghua city. The average Eichhornia crassipes area during 2017—2021 in the five districts and counties of Xinghua, Baoying, Gaoyou, Jiangdu, and Funing is above 3 km². Especially, the annual average Eichhornia crassipes outbreak in Xinghua is the most frequent, with an area of 6.85 km².

Key words: remote sensing, Eichhornia crassipes, Sentinel-2, support vector machine, Lixia River area, spatio-temporal distribution

CLC Number:

P237

WANG Dongmei, WU Yongfeng, SHI Yifan, LIANG Wenguang, WANG Yihong, PAN Siyuan. Analyzing the spatio-temporal distribution of Eichhornia crassipes based on Sentinel-2 remote sensing[J]. Bulletin of Surveying and Mapping, 2023, 0(6): 44-49.

References

[1] HE C Y,LIU Z F,WU J G,et al. Future global urban water scarcity and potential solutions[J]. Nature Communications,2021,12(1):4667.
[2] DAI D,SUN M D,LÜ X B,et al. Comprehensive assessment of the water environment carrying capacity based on the spatial system dynamics model,a case study of Yongding River Basin in North China[J]. Journal of Cleaner Production,2022,344:131137.
[3] 蔡竟芳,孙凯,张羽,等. 凤眼莲入侵强度和水深对沉水植物生长及种间关系的影响[J]. 环境科学学报,2022,42(1):36-46.
[4] ALBRIGHT T P,MOORHOUSE T G,MCNABB T J. The rise and fall of Eichhornia crassipes in Lake Victoria and the Kagera River basin,1989-2001[J]. Journal of Aquatic Plant Management,2004,42(42):73-84.
[5] VERMA R,SINGH S P,RAJ K G. Assessment of changes in water-hyacinth coverage of water bodies in northern part of Bangalore city using temporal remote sensing data[J]. Current Science,2003,84:795-804.
[6] 蒋明,郭云开,朱佳明,等. 时序遥感影像滇池凤眼莲时空动态变化分析[J]. 遥感信息,2019,34(3):43-47.
[7] 杨静学,陈亮雄,李伟添,等. 多源卫星遥感数据和水色遥感技术在水库水葫芦覆盖监察中的应用[J]. 农业与技术,2019,39(3):5-7.
[8] 张覃雅基于Worldview影像的外来入侵植物提取方法对比及格局动态[D].北京:中国地质大学,2019.
[9] 秦雁,陈亮雄,杨静学,等. 东莞市水葫芦分布遥感动态监测分析[J]. 广东水利水电,2020(7):11-15.
[10] MUKARUGWIRO J A,NEWETE S W,ADAM E,et al. Mapping distribution of Eichhornia crassipes (Eichhornia crassipes) in Rwanda using multispectral remote sensing imagery[J]. African Journal of Aquatic Science,2019,44(4):339-348.
[11] SINGH G,REYNOLDS C,BYRNE M,et al.A remote sensing method to monitor water,aquatic vegetation,and invasive eichhornia crassipes at national extents[J]. Remote Sensing,2020,12(24):4021.
[12] 徐勇,郑志威,郭振东,等. 2000-2020年长江流域植被NDVI动态变化及影响因素探测[J].环境科学,2022,43(7):3730-3740.
[13] 宁津生.测绘工程专业和测绘学[J]. 测绘工程,2000,9(2):70-74.
[14] 李瑶,张立福,黄长平,等. 基于MODIS植被指数时间谱的太湖2001年-2013年蓝藻暴发监测[J]. 光谱学与光谱分析,2016,36(5):1406-1411.
[15] 张圳,张弥,肖薇,等. 太湖水生植被NDVI的时空变化特征分析[J]. 遥感学报,2018,22(2):324-334.
[16] 汪星,宫兆宁,井然,等. 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[J]. 植物生态学报,2018,42(6):640-652.
[17] 张妮娜,张珂,李运平,等. 中国南方典型湿润山区植被类型的无人机多光谱遥感机器学习分类研究[J]. 遥感技术与应用,2022,37(4):816-825.
[18] NOI P T,KUCH V,LEHNERT L. Land cover classification using Google Earth Engine and random forest classifier-the role of image composition[J]. Remote Sensing,2020,12(15):2411.
[19] PRATICÒ S,SOLANO F,DI FAZIO S,et al. Machine learning classification of Mediterranean forest habitats in Google Earth Engine based on seasonal Sentinel-2 time-series and input image composition optimisation[J]. Remote Sensing,2021,13(4):586.
[20] YE N,JUSTIN M,XU C,et al. Improving neural network classification of indigenous forest in New Zealand with phenological features[J]. Journal of Environmental Management,2022,13(4):586.
[21] MAUNG W S,SASAKI J. Assessing the natural recovery of mangroves after human disturbance using neural network classification and Sentinel-2 imagery in wunbaik mangrove forest,Myanmar[J]. Remote Sensing,2020,13(1):52.
[22] ZHANG M L,QU H,XIE X R,et al. Supervised learning in spiking neural networks with noise-threshold[J]. Neurocomputing,2017,219:333-349.
[23] LIANG X,ZHU L,HUANG D S. Multi-task ranking SVM for image cosegmentation[J]. Neurocomputing,2017,247:126-136.
[24] CERVANTES J,GARCIA-LAMONT F,RODRÍGUEZ-MAZAHUA L,et al. A comprehensive survey on support vector machine classification:applications,challenges and trends[J]. Neurocomputing,2020,408:189-215.