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Assessing the effectiveness of Spartina alterniflora control in Dafeng Milu National Nature Reserve (2022–2025) using satellite imagery from Sentinel-2

Hu, Yang LU (2026) In Student thesis series INES NGEM21 20261
Department of Earth and Environmental Sciences (MGeo)
Abstract
Spartina alterniflora is a highly invasive plant that has extensively colonized Chinese coastal wetlands, degrading critical habitat for migratory waterbirds. In response, the Chinese government launched the Special Action Plan for Spartina alterniflora Prevention and Control (2022–2025), aiming to remove over 90% of the invaded area nationwide. This study quantitatively assessed the effectiveness of this campaign in Dafeng Milu National Nature Reserve (DMNNR), a UNESCO World Heritage site on the Yellow Sea coast, using satellite imagery from Sentinel-2 and a dual-method framework combining Random Forest (RF) classification with Spartina alterniflora Index (SAI) threshold extraction. Cloud-free Sentinel-2 scenes from... (More)
Spartina alterniflora is a highly invasive plant that has extensively colonized Chinese coastal wetlands, degrading critical habitat for migratory waterbirds. In response, the Chinese government launched the Special Action Plan for Spartina alterniflora Prevention and Control (2022–2025), aiming to remove over 90% of the invaded area nationwide. This study quantitatively assessed the effectiveness of this campaign in Dafeng Milu National Nature Reserve (DMNNR), a UNESCO World Heritage site on the Yellow Sea coast, using satellite imagery from Sentinel-2 and a dual-method framework combining Random Forest (RF) classification with Spartina alterniflora Index (SAI) threshold extraction. Cloud-free Sentinel-2 scenes from December 2022 and December 2025 were analyzed to map S. alterniflora distribution before and after control. A total of 640 reference samples were collected through multi-source image interpretation and split spatially into a western subset for training and eastern subset for independent testing, which were used to train an RF classifier and derive site-specific SAI thresholds (−0.790 ≤ SAI ≤ −0.388). The RF model achieved an overall accuracy of 94.97% on the independent test set. High spatial agreement between the RF-based and SAI-based maps, quantified by the Intersection over Union (IoU = 0.916) validated the SAI approach, which was applied to 2025 imagery for sample-free remnant patch detection. Results show that the invaded area declined from 3,147.86 ha to 22.84 ha, representing a 99.27% removal rate that substantially exceeds the 90% national target. Of the baseline extent, 3,141.92 ha (99.81%) was cleared, with only 5.95 ha persisting and 16.89 ha of new growth detected. Remnant patches exhibited strong spatial clustering (Moran's I = 0.95, p < 0.001) in three hotspots along the seaward fringe. The complete disappearance of S. alterniflora spectral signatures in SAI and NDVI histograms confirmed large-scale removal attributable to the management. The restoration of over 3,000 ha of tidal flat supports UNESCO conservation objectives and maintains critical foraging habitat for globally threatened shorebirds. The transferable dual-method framework addresses key limitations in post-eradication monitoring where field access is constrained, providing a practical tool for evaluating control effectiveness across coastal reserves. (Less)
Popular Abstract
Every year, millions of migratory shorebirds stop along the Yellow Sea coast during their long journeys between breeding and wintering grounds. These coastal wetlands provide food and resting areas that are essential for their survival. However, many of these habitats have been threatened by an invasive plan, Spartina alterniflora.
Over the past few decades, Spartina alterniflora has spread rapidly along the Chinese coast. It forms dense stands that replace open mudflats, making it harder for shorebirds to find food. To restore coastal ecosystems, China launched a nationwide campaign to remove the species between 2022 and 2025. Yet it remains unclear how successful these efforts have been in individual protected areas.
This study... (More)
Every year, millions of migratory shorebirds stop along the Yellow Sea coast during their long journeys between breeding and wintering grounds. These coastal wetlands provide food and resting areas that are essential for their survival. However, many of these habitats have been threatened by an invasive plan, Spartina alterniflora.
Over the past few decades, Spartina alterniflora has spread rapidly along the Chinese coast. It forms dense stands that replace open mudflats, making it harder for shorebirds to find food. To restore coastal ecosystems, China launched a nationwide campaign to remove the species between 2022 and 2025. Yet it remains unclear how successful these efforts have been in individual protected areas.
This study examined the situation in Dafeng Milu National Nature Reserve in Jiangsu Province, a UNESCO World Natural Heritage site that supports many migratory bird species. Because the tidal flats are large and difficult to access, satellite images were used instead of traditional field surveys.
Using freely available Sentinel-2 imagery, I mapped the distribution of Spartina alterniflora before and after the main control period. By combining machine learning techniques with a simple vegetation index, I was able to identify where the plant remained and measure how much had been removed.
The results show that the control program was highly successful. In 2022, Spartina alterniflora covered more than 3,100 hectares of the reserve’s intertidal zone. By 2025, only about 23 hectares remained. This means that more than 99% of the invasion had been removed. The few remaining patches were concentrated in three small areas near major tidal channels, making future monitoring and management more targeted and efficient.
The recovery of over 3,000 hectares of open tidal flats is good news for migratory shorebirds. These habitats are important feeding grounds during migration and winter. The study also shows how satellite imagery can provide a fast and cost-effective way to track invasive species and evaluate large-scale conservation projects.
This thesis can help managers understand where progress has been made and where further action is still needed. (Less)
Please use this url to cite or link to this publication:
author
Hu, Yang LU
supervisor
organization
course
NGEM21 20261
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Geographical Information Science, Remote Sensing, Spartina alterniflora, invasive species control, Sentinel-2, coastal wetlands, Random Forest
publication/series
Student thesis series INES
report number
784
language
English
id
9239163
date added to LUP
2026-06-16 11:07:58
date last changed
2026-06-16 13:48:26
@misc{9239163,
  abstract     = {{[i]Spartina alterniflora[/i] is a highly invasive plant that has extensively colonized Chinese coastal wetlands, degrading critical habitat for migratory waterbirds. In response, the Chinese government launched the Special Action Plan for [i]Spartina alterniflora[/i] Prevention and Control (2022–2025), aiming to remove over 90% of the invaded area nationwide. This study quantitatively assessed the effectiveness of this campaign in Dafeng Milu National Nature Reserve (DMNNR), a UNESCO World Heritage site on the Yellow Sea coast, using satellite imagery from Sentinel-2 and a dual-method framework combining Random Forest (RF) classification with [i]Spartina alterniflora[/i] Index (SAI) threshold extraction. Cloud-free Sentinel-2 scenes from December 2022 and December 2025 were analyzed to map [i]S. alterniflora[/i] distribution before and after control. A total of 640 reference samples were collected through multi-source image interpretation and split spatially into a western subset for training and eastern subset for independent testing, which were used to train an RF classifier and derive site-specific SAI thresholds (−0.790 ≤ SAI ≤ −0.388). The RF model achieved an overall accuracy of 94.97% on the independent test set. High spatial agreement between the RF-based and SAI-based maps, quantified by the Intersection over Union (IoU = 0.916) validated the SAI approach, which was applied to 2025 imagery for sample-free remnant patch detection. Results show that the invaded area declined from 3,147.86 ha to 22.84 ha, representing a 99.27% removal rate that substantially exceeds the 90% national target. Of the baseline extent, 3,141.92 ha (99.81%) was cleared, with only 5.95 ha persisting and 16.89 ha of new growth detected. Remnant patches exhibited strong spatial clustering (Moran's I = 0.95, p < 0.001) in three hotspots along the seaward fringe. The complete disappearance of [i]S. alterniflora[/i] spectral signatures in SAI and NDVI histograms confirmed large-scale removal attributable to the management. The restoration of over 3,000 ha of tidal flat supports UNESCO conservation objectives and maintains critical foraging habitat for globally threatened shorebirds. The transferable dual-method framework addresses key limitations in post-eradication monitoring where field access is constrained, providing a practical tool for evaluating control effectiveness across coastal reserves.}},
  author       = {{Hu, Yang}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Student thesis series INES}},
  title        = {{Assessing the effectiveness of Spartina alterniflora control in Dafeng Milu National Nature Reserve (2022–2025) using satellite imagery from Sentinel-2}},
  year         = {{2026}},
}