当前位置: X-MOL 学术Front. Marine Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Vegetation changes in Yellow River Delta wetlands from 2018 to 2020 using PIE-Engine and short time series Sentinel-2 images
Frontiers in Marine Science ( IF 3.7 ) Pub Date : 2022-08-10 , DOI: 10.3389/fmars.2022.977050
Dong Chang, Zhiyong Wang, Xiaogang Ning, Zhenjin Li, Long Zhang, Xiaotong Liu

Vegetation is the functional subject in the wetland ecosystem and plays an irreplaceable role in biodiversity conservation. It is of great significance to monitor wetland vegetation for scientific assessment of the impact of vegetation on ecological environment and biodiversity. In this paper, a method for extracting wetland vegetation based on short time series Normalized Difference Vegetation Index (NDVI) data set was constructed. First, time series NDVI data were constructed using Sentinel-2 images. Then, the Support Vector Machine (SVM) classifier was used to classify the wetland vegetation types. The distributions of the main wetland vegetation in the study area in 2018 and 2020 were got. Finally, the land cover transfer matrix was calculated to analyze the spatial pattern and change of wetland vegetation emphatically from 2018 to 2020. Based on 46 Sentinel-2 images acquired in 2018 and 2020, the spatial pattern and change of vegetation in the Yellow River Delta wetlands were extracted and analyzed in this paper. The results show that: (1) The method for extracting wetland vegetation in estuary delta based on PIE-Engine platform and short time series NDVI data constructed in this paper can effectively extract the wetland vegetation information. The overall accuracy of the classification results reached 90.47% in 2018 and 80.30% in 2020. The Kappa coefficient of the classification results are 0.874 in 2018 and 0.739 in 2020 respectively. Compared with the results from the random forest classification method and the maximum likelihood classification method, the accuracy is improved by 6.40% and 13.04%, and the Kappa coefficient is improved by 0.055 and 0.069. (2) There were significant changes in vegetation coverage in the Yellow River Delta wetlands from 2018 to 2020. The Spartina alterniflora increased by 3.74km2. The Suaeda salsa degraded seriously, and the total area decreased by 20.38km2. In addition, the increase of Spartina alterniflora effectively guaranteed the stability of the coastline in the study area. This study can provide a theoretical basis for wetlands vegetation classificaton, and the classificaton results can provide scientific reference for protecting the ecological environment of wetlands and maintaining ecological stability.



中文翻译:

2018-2020年黄河三角洲湿地植被变化PIE-Engine和短时间序列Sentinel-2图像

植被是湿地生态系统的功能主体,在生物多样性保护中发挥着不可替代的作用。湿地植被监测对于科学评估植被对生态环境和生物多样性的影响具有重要意义。本文构建了一种基于短时间序列归一化植被指数(NDVI)数据集的湿地植被提取方法。首先,使用 Sentinel-2 图像构建时间序列 NDVI 数据。然后,使用支持向量机(SVM)分类器对湿地植被类型进行分类。得到研究区2018年和2020年主要湿地植被分布。最后,通过计算土地覆被转移矩阵,重点分析了2018-2020年湿地植被的空间格局和变化。本文基于2018年和2020年采集的46张Sentinel-2影像,提取分析黄河三角洲湿地植被的空间格局和变化情况。结果表明:(1)本文构建的基于PIE-Engine平台和短时间序列NDVI数据的河口三角洲湿地植被提取方法能够有效提取湿地植被信息。分类结果的整体准确率在2018年达到90.47%,在2020年达到80.30%。分类结果的Kappa系数分别在2018年和2020年分别为0.874和0.739。与随机森林分类法和最大似然分类法的结果相比,准确率分别提高了6.40%和13.04%,Kappa系数分别提高了0.055和0.069。互花米草增加了3.74km 2。这碱蓬退化严重,总面积减少20.38km 2。此外,增加互花米草有效保障了研究区海岸线的稳定性。本研究可为湿地植被分类提供理论依据,分类结果可为保护湿地生态环境、维护生态稳定提供科学参考。

更新日期:2022-08-10
down
wechat
bug