Dynamic Analysis of Spartina alterniflora in Yellow River Delta Based on U-Net Model and Zhuhai-1 Satellite

Coastal wetlands are critical for global biodiversity and ecological stability, yet the invasive Spartina alterniflora (S. alterniflora) poses severe threats to these ecosystems. This study evaluates the effectiveness of management efforts targeting S. alterniflora in the Yellow River Delta (YRD) using Zhuhai-1 hyperspectral imagery and the U-Net method. The U-Net model, coupled with the Relief-F algorithm, achieved a superior extraction accuracy (Kappa > 0.9 and overall accuracy of 93%) compared to traditional machine learning methods. From 2019 to 2021, S. alterniflora expanded rapidly, increasing from 4055.06 hm2 to 6105.50 hm2, primarily in tidal flats and water bodies. A clearing project reduced its extent to 5063.62 hm2 by 2022, and by 2023, only 0.55 hm2 remained. These results underscore the effectiveness of Shandong’s management policies but highlight the risk of regrowth due to the species’ resilience. Continuous monitoring and maintenance are essential to prevent its resurgence and ensure wetland restoration. This study offers critical insights into dynamic vegetation monitoring and informs conservation strategies for wetland health.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2025-01-09
DOI
https://doi.org/10.3390/rs17020226
Citations
4
Primary Topic
Environmental Changes in China
Type
article
Field-Weighted Citation Impact
4.42

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dynamic Analysis of Spartina alterniflora in Yellow River Delta Based on U-Net Model and Zhuhai-1 Satellite

Guangxu Cui, Dehua Mao, Haojie Liu, Zhiyong Lv et al.
4 citations
Remote Sensing
Environmental Changes in China
4.42
article

Dynamic Analysis of Spartina alterniflora in Yellow River Delta Based on U-Net Model and Zhuhai-1 Satellite

Guangxu Cui, Dehua Mao, Haojie Liu, Zhiyong Lv, Xiao Huang, Zongming Wang, Mingming Jia, Sheng Zhao, Qi Wang, Rong Zhang, Huiying Li, Hao Yu
article en
4 citations

Abstract

Coastal wetlands are critical for global biodiversity and ecological stability, yet the invasive Spartina alterniflora (S. alterniflora) poses severe threats to these ecosystems. This study evaluates the effectiveness of management efforts targeting S. alterniflora in the Yellow River Delta (YRD) using Zhuhai-1 hyperspectral imagery and the U-Net method. The U-Net model, coupled with the Relief-F algorithm, achieved a superior extraction accuracy (Kappa > 0.9 and overall accuracy of 93%) compared to traditional machine learning methods. From 2019 to 2021, S. alterniflora expanded rapidly, increasing from 4055.06 hm2 to 6105.50 hm2, primarily in tidal flats and water bodies. A clearing project reduced its extent to 5063.62 hm2 by 2022, and by 2023, only 0.55 hm2 remained. These results underscore the effectiveness of Shandong’s management policies but highlight the risk of regrowth due to the species’ resilience. Continuous monitoring and maintenance are essential to prevent its resurgence and ensure wetland restoration. This study offers critical insights into dynamic vegetation monitoring and informs conservation strategies for wetland health.

Remote SensingVol. 17(2)
Qingdao University of Science and Technology (CN), Emory University (US), Chinese Academy of Sciences (CN), Jilin Jianzhu University (CN), Northeast Institute of Geography and Agroecology (CN), Xi'an University of Technology (CN), Qingdao University of Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province, Wuyi University
Openalex Percentile: Top 7%
Environmental Changes in China
4.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.