{"title":"利用LANDSAT时间序列和机器学习监测布列塔尼(法国)沿海湿地生态系统的动态","authors":"Adrien Le Guillou , Simona Niculescu","doi":"10.1016/j.ecoinf.2025.103303","DOIUrl":null,"url":null,"abstract":"<div><div>Coastal wetlands protect against erosion, reduce flood risks, and maintain watercourses during periods of drought, which can mitigate global warming and its effects on humans. This study aims to analyse the spatio-temporal dynamics of coastal wetland ecosystems in relation to the main drivers of change urbanisation and coastal erosion in a specific region of Brittany (France) between 1990 and 2020. The study exploits the potential of satellite image time series (SITS), machine learning (ML), and Random Forest (RF) algorithms.</div><div>These algorithms enable the software to learn autonomously from multiple datasets, including Landsat 4/5 and 8 SITS archive images. The different elements or ecosystems within a dataset are classified into categories using the Corine Biotopes classification system and multi-scalar analyses. The study proposes two scales of analysis: at the scale of coastal wetlands throughout Brittany and at the local scale of two RAMSAR coastal wetlands (Mont-Saint-Michel Bay and Audierne Bay). The results revealed contrasting spatial patterns. The Audierne Bay has experienced significant urban expansion, with a 24% increase upstream, as well as coastal erosion reaching 1.63 m/year locally, with a retreat of approximately 50 m in the most affected areas during the period 1990–2020. The wetlands in this region are receding in parallel with the coastline and have slightly decreased in area over the last 30 years, with a reduction of 8%. In contrast, the Bay of Mont-Saint-Michel has seen an expansion of salt marshes. Using Landsat archives and an automated coastline detection method, we found that the area of salt marshes has increased by 36% over the last 30 years. In both nested spatial scale approaches, the proposed spatial methodology generates spatial statistics on the dynamics of key ecosystems at the designated time scale. The results provide useful information for stakeholders. These results highlight the diversity of coastal dynamics in Brittany.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103303"},"PeriodicalIF":5.8000,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Monitoring the dynamics of coastal wetlands ecosystems in Brittany (France) using LANDSAT time series and machine learning\",\"authors\":\"Adrien Le Guillou , Simona Niculescu\",\"doi\":\"10.1016/j.ecoinf.2025.103303\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Coastal wetlands protect against erosion, reduce flood risks, and maintain watercourses during periods of drought, which can mitigate global warming and its effects on humans. This study aims to analyse the spatio-temporal dynamics of coastal wetland ecosystems in relation to the main drivers of change urbanisation and coastal erosion in a specific region of Brittany (France) between 1990 and 2020. The study exploits the potential of satellite image time series (SITS), machine learning (ML), and Random Forest (RF) algorithms.</div><div>These algorithms enable the software to learn autonomously from multiple datasets, including Landsat 4/5 and 8 SITS archive images. The different elements or ecosystems within a dataset are classified into categories using the Corine Biotopes classification system and multi-scalar analyses. The study proposes two scales of analysis: at the scale of coastal wetlands throughout Brittany and at the local scale of two RAMSAR coastal wetlands (Mont-Saint-Michel Bay and Audierne Bay). The results revealed contrasting spatial patterns. The Audierne Bay has experienced significant urban expansion, with a 24% increase upstream, as well as coastal erosion reaching 1.63 m/year locally, with a retreat of approximately 50 m in the most affected areas during the period 1990–2020. The wetlands in this region are receding in parallel with the coastline and have slightly decreased in area over the last 30 years, with a reduction of 8%. In contrast, the Bay of Mont-Saint-Michel has seen an expansion of salt marshes. Using Landsat archives and an automated coastline detection method, we found that the area of salt marshes has increased by 36% over the last 30 years. In both nested spatial scale approaches, the proposed spatial methodology generates spatial statistics on the dynamics of key ecosystems at the designated time scale. The results provide useful information for stakeholders. These results highlight the diversity of coastal dynamics in Brittany.</div></div>\",\"PeriodicalId\":51024,\"journal\":{\"name\":\"Ecological Informatics\",\"volume\":\"90 \",\"pages\":\"Article 103303\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2025-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecological Informatics\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1574954125003127\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574954125003127","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
Monitoring the dynamics of coastal wetlands ecosystems in Brittany (France) using LANDSAT time series and machine learning
Coastal wetlands protect against erosion, reduce flood risks, and maintain watercourses during periods of drought, which can mitigate global warming and its effects on humans. This study aims to analyse the spatio-temporal dynamics of coastal wetland ecosystems in relation to the main drivers of change urbanisation and coastal erosion in a specific region of Brittany (France) between 1990 and 2020. The study exploits the potential of satellite image time series (SITS), machine learning (ML), and Random Forest (RF) algorithms.
These algorithms enable the software to learn autonomously from multiple datasets, including Landsat 4/5 and 8 SITS archive images. The different elements or ecosystems within a dataset are classified into categories using the Corine Biotopes classification system and multi-scalar analyses. The study proposes two scales of analysis: at the scale of coastal wetlands throughout Brittany and at the local scale of two RAMSAR coastal wetlands (Mont-Saint-Michel Bay and Audierne Bay). The results revealed contrasting spatial patterns. The Audierne Bay has experienced significant urban expansion, with a 24% increase upstream, as well as coastal erosion reaching 1.63 m/year locally, with a retreat of approximately 50 m in the most affected areas during the period 1990–2020. The wetlands in this region are receding in parallel with the coastline and have slightly decreased in area over the last 30 years, with a reduction of 8%. In contrast, the Bay of Mont-Saint-Michel has seen an expansion of salt marshes. Using Landsat archives and an automated coastline detection method, we found that the area of salt marshes has increased by 36% over the last 30 years. In both nested spatial scale approaches, the proposed spatial methodology generates spatial statistics on the dynamics of key ecosystems at the designated time scale. The results provide useful information for stakeholders. These results highlight the diversity of coastal dynamics in Brittany.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.