The effect of season on Spartina alterniflora identification and monitoring

文献类型： 外文期刊

第一作者： Li, Yanzhou

作者： Li, Yanzhou;Qin, Feng;He, Yanzhou;Qiao, Xi;Qin, Feng;He, Yanzhou;Liu, Bo;Liu, Conghui;Pu, Xuejiao;Wan, Fanghao;Qiao, Xi;Qian, Wanqiang

作者机构：

关键词： Spartina alterniflora; UAV; image analysis; invasive plant; deep learning

期刊名称：FRONTIERS IN ENVIRONMENTAL SCIENCE （ 影响因子：5.411； 五年影响因子：6.313 ）

ISSN：

年卷期： 2022 年 10 卷

页码：

收录情况： SCI

摘要： The invasion of Spartina alterniflora (S. alterniflora) has resulted in significant losses in the diversity of coastal ecosystems. However, the impact of seasonal changes on the accurate identification of S. alterniflora remains to be explored, which is of great significance due to its early monitoring and warning. In this study, S. alterniflora in Beihai, Guangxi, was selected as the research object. Unmanned aerial vehicles (UAVs) and deep convolutional neural networks (CNNs) were used to explore the identification of S. alterniflora in different seasons. Through comparative analysis, the ResNet50 model performed well in identifying S. alterniflora, with an F1-score of 96.40%. The phenological characteristics of S. alterniflora differ in different seasons. It is difficult to accurately monitor the annual S. alterniflora using only single-season data. For the monitoring of S. alterniflora throughout the year, the autumn-winter two-season model was selected from the perspective of time cost, the four-season model was selected from the perspective of identification performance, and the three-season model of summer, autumn and winter was selected from the perspective of time cost and identification performance. In addition, a method was developed to predict and evaluate the diffusion trend of S. alterniflora based on time series UAV images. Using the spring data to predict the diffusion trend of S. alterniflora in summer and autumn, the results showed that the highest recall reached 84.28%, the F1-score was higher than 70%, and most of the diffusion of S. alterniflora was correctly predicted. This study demonstrates the feasibility of distinguishing S. alterniflora from native plants in different seasons based on UAV and CNN recognition algorithms. The proposed diffusion early warning method reflects the actual diffusion of S. alterniflora to a certain extent, which is of great significance for the early management of invasive plants in coastal wetlands.

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