{"title":"遥感时间序列揭示的绵卡莱湿地水体与植被40年时空变化趋势","authors":"Nima Arij , Hooman Latifi , Arvin Fakhri , Rohollah Esmaili","doi":"10.1016/j.ecoinf.2025.103374","DOIUrl":null,"url":null,"abstract":"<div><div>Coastal wetlands offer essential ecosystem services, but are increasingly threatened by anthropogenic activities and climate change. These disrupt regional patterns, necessitating time series analyses to inform their long-term trends. Remote sensing provides cost-effective alternatives to demanding traditional wetland monitoring. Here, we employed a 40-year time series of Landsat data, supplemented by Sentinel-1 SAR imagery and Sentinel-2 multispectral data for enhanced recent-period analysis, and applied non-parametric trend analysis to examine changes in water bodies, vegetation, and climatic conditions in Miankaleh peninsula, encompassing an extensive Ramsar site in Iran. We utilized spectral indices and random forest classification to derive the area of water bodies and vegetation, followed by identifying significant trends using various trend analysis methods: Mann-Kendall (MK), Modified Mann-Kendall (MMK), Sequential Mann-Kendall (SeqMK), Seasonal Mann-Kendall (SMK), Sen’s Slope (SS), and LOcally Estimated Scatterplot Smoothing (LOESS). Findings showed a significant reduction in water area (30,700 ha, SS = -1.074) and an increase in vegetation cover (31,400 ha, SS = 1.365) from baseline levels. Among climatic factors, groundwater levels (SS = -0.214) and evaporation (SS = -0.312) were most influential on the wetland. The MMK, accounting for data autocorrelation, provided more accurate results compared to MK, while SeqMK detected important trend change points that were mostly missed by MMK. LOESS visualized local, nonlinear changes and identify subtle trend shifts. The results underscore significant ecological shifts, particularly the reduction of water bodies, which threaten the wetland's functionality. We provide general and case-specific considerations on the sole and complementary application of non-parametric trend analysis approaches, expanding insights into ecological processes in coastal wetlands with broader implications for similar ecosystems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"91 ","pages":"Article 103374"},"PeriodicalIF":7.3000,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Four decades of spatio-temporal trends in Miankaleh Wetland´s water body and vegetation as revealed by remote sensing time series\",\"authors\":\"Nima Arij , Hooman Latifi , Arvin Fakhri , Rohollah Esmaili\",\"doi\":\"10.1016/j.ecoinf.2025.103374\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Coastal wetlands offer essential ecosystem services, but are increasingly threatened by anthropogenic activities and climate change. These disrupt regional patterns, necessitating time series analyses to inform their long-term trends. Remote sensing provides cost-effective alternatives to demanding traditional wetland monitoring. Here, we employed a 40-year time series of Landsat data, supplemented by Sentinel-1 SAR imagery and Sentinel-2 multispectral data for enhanced recent-period analysis, and applied non-parametric trend analysis to examine changes in water bodies, vegetation, and climatic conditions in Miankaleh peninsula, encompassing an extensive Ramsar site in Iran. We utilized spectral indices and random forest classification to derive the area of water bodies and vegetation, followed by identifying significant trends using various trend analysis methods: Mann-Kendall (MK), Modified Mann-Kendall (MMK), Sequential Mann-Kendall (SeqMK), Seasonal Mann-Kendall (SMK), Sen’s Slope (SS), and LOcally Estimated Scatterplot Smoothing (LOESS). Findings showed a significant reduction in water area (30,700 ha, SS = -1.074) and an increase in vegetation cover (31,400 ha, SS = 1.365) from baseline levels. Among climatic factors, groundwater levels (SS = -0.214) and evaporation (SS = -0.312) were most influential on the wetland. The MMK, accounting for data autocorrelation, provided more accurate results compared to MK, while SeqMK detected important trend change points that were mostly missed by MMK. LOESS visualized local, nonlinear changes and identify subtle trend shifts. The results underscore significant ecological shifts, particularly the reduction of water bodies, which threaten the wetland's functionality. We provide general and case-specific considerations on the sole and complementary application of non-parametric trend analysis approaches, expanding insights into ecological processes in coastal wetlands with broader implications for similar ecosystems.</div></div>\",\"PeriodicalId\":51024,\"journal\":{\"name\":\"Ecological Informatics\",\"volume\":\"91 \",\"pages\":\"Article 103374\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2025-08-08\",\"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/S1574954125003838\",\"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/S1574954125003838","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}
Four decades of spatio-temporal trends in Miankaleh Wetland´s water body and vegetation as revealed by remote sensing time series
Coastal wetlands offer essential ecosystem services, but are increasingly threatened by anthropogenic activities and climate change. These disrupt regional patterns, necessitating time series analyses to inform their long-term trends. Remote sensing provides cost-effective alternatives to demanding traditional wetland monitoring. Here, we employed a 40-year time series of Landsat data, supplemented by Sentinel-1 SAR imagery and Sentinel-2 multispectral data for enhanced recent-period analysis, and applied non-parametric trend analysis to examine changes in water bodies, vegetation, and climatic conditions in Miankaleh peninsula, encompassing an extensive Ramsar site in Iran. We utilized spectral indices and random forest classification to derive the area of water bodies and vegetation, followed by identifying significant trends using various trend analysis methods: Mann-Kendall (MK), Modified Mann-Kendall (MMK), Sequential Mann-Kendall (SeqMK), Seasonal Mann-Kendall (SMK), Sen’s Slope (SS), and LOcally Estimated Scatterplot Smoothing (LOESS). Findings showed a significant reduction in water area (30,700 ha, SS = -1.074) and an increase in vegetation cover (31,400 ha, SS = 1.365) from baseline levels. Among climatic factors, groundwater levels (SS = -0.214) and evaporation (SS = -0.312) were most influential on the wetland. The MMK, accounting for data autocorrelation, provided more accurate results compared to MK, while SeqMK detected important trend change points that were mostly missed by MMK. LOESS visualized local, nonlinear changes and identify subtle trend shifts. The results underscore significant ecological shifts, particularly the reduction of water bodies, which threaten the wetland's functionality. We provide general and case-specific considerations on the sole and complementary application of non-parametric trend analysis approaches, expanding insights into ecological processes in coastal wetlands with broader implications for similar ecosystems.
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.