{"title":"Evaluation of machine learning algorithm capability for Bosten Lake Wetland classification based on multi-temporal Sentinel-2 data","authors":"Feiying Xia , Guanghui Lv","doi":"10.1016/j.ecoinf.2024.102839","DOIUrl":null,"url":null,"abstract":"<div><div>As crucial carbon sinks within terrestrial ecosystems, wetlands have been extensively studied in terms of spatio-temporal distributions. However, existing methods for classifying wetlands are of limited accuracy, and it is difficult to acquire consistent samples over time. Therefore, precise classification methods are required to facilitate wetland conservation and ecological restoration. In this study, multiple machine learning (ML) algorithms in combination with feature sets based on Sentinel-2 data were used to accurately classify the land-use types (LUTs) of the Bosten Lake Wetland (BLW) in Xinjiang, China. The enhanced water index (EWI), modified normalised difference water index (MNDWI), and normalised difference water index (NDWI) were selected to extract water information and distinguish water bodies from land surfaces in the BLW. Three classification plans based on vegetation indices, water indices, and textural features were developed using artificial neural network (ANN), support vector machine (SVM), random forest (RF) algorithms. Plan 9 combined vegetation water and texture with the highest overall accuracy (OA) 91.02 % and kappa coefficient (KC) 0.89. This plan obtained a producer accuracy of over 90 % for lake wetlands, river wetlands, grassland wetlands, mud flats, and farmland and > 83 % for construction land and bareland. According to Plan 9, the wetland area during 2018–2023 showed noticeable seasonal fluctuations but stable interannual changes. Conversely, non-wetland areas demonstrated significant interannual fluctuations, particularly in bareland and farmland, which might have been influenced by urbanisation and ecological policies. This study provides insights into the data sources, feature selection, and methodological approaches for wetland information extraction in arid regions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102839"},"PeriodicalIF":5.8000,"publicationDate":"2024-09-30","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/S1574954124003819","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}
引用次数: 0
Abstract
As crucial carbon sinks within terrestrial ecosystems, wetlands have been extensively studied in terms of spatio-temporal distributions. However, existing methods for classifying wetlands are of limited accuracy, and it is difficult to acquire consistent samples over time. Therefore, precise classification methods are required to facilitate wetland conservation and ecological restoration. In this study, multiple machine learning (ML) algorithms in combination with feature sets based on Sentinel-2 data were used to accurately classify the land-use types (LUTs) of the Bosten Lake Wetland (BLW) in Xinjiang, China. The enhanced water index (EWI), modified normalised difference water index (MNDWI), and normalised difference water index (NDWI) were selected to extract water information and distinguish water bodies from land surfaces in the BLW. Three classification plans based on vegetation indices, water indices, and textural features were developed using artificial neural network (ANN), support vector machine (SVM), random forest (RF) algorithms. Plan 9 combined vegetation water and texture with the highest overall accuracy (OA) 91.02 % and kappa coefficient (KC) 0.89. This plan obtained a producer accuracy of over 90 % for lake wetlands, river wetlands, grassland wetlands, mud flats, and farmland and > 83 % for construction land and bareland. According to Plan 9, the wetland area during 2018–2023 showed noticeable seasonal fluctuations but stable interannual changes. Conversely, non-wetland areas demonstrated significant interannual fluctuations, particularly in bareland and farmland, which might have been influenced by urbanisation and ecological policies. This study provides insights into the data sources, feature selection, and methodological approaches for wetland information extraction in arid regions.
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.