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
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。