{"title":"Zayandeh-rud河流域植被覆盖变化检测的最优指标:融合方法","authors":"S. Gholinejad, S. B. Fatemi","doi":"10.1080/19479832.2019.1601642","DOIUrl":null,"url":null,"abstract":"ABSTRACT In this study, vegetation changes in the Zayandeh-rud river basin in the period 2001 to 2016 have been investigated based on the combining of the 15 different vegetation indices. Two main plans were applied and tested to produce the final vegetation change map. In the first plan, change maps were produced by differencing the original vegetation indices individually. The second plan, which was a fusion perspective, included two algorithms. In the first one, change maps, obtained from vegetation indices, were fused at the decision level using the Majority Voting Method. The second algorithm included a particle swarm optimisation (PSO) based weighted combination of the different vegetation maps. The results show that the high correlation between vegetation indices does not necessarily provide the same results and combination of all them can be done automatically by applying PSO. Although PSO-based combination could not significantly improve the change detection, it solved the problem of finding optimal threshold value for the change detection process. However, from the vegetation change point of view, they all indicate that during the years of study, the vegetation cover has increased in the study area due to irregular water usage of the Zayandeh-rud river in its western part.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"10 1","pages":"199 - 216"},"PeriodicalIF":1.8000,"publicationDate":"2019-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/19479832.2019.1601642","citationCount":"4","resultStr":"{\"title\":\"Optimum indices for vegetation cover change detection in the Zayandeh-rud river basin: a fusion approach\",\"authors\":\"S. Gholinejad, S. B. Fatemi\",\"doi\":\"10.1080/19479832.2019.1601642\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT In this study, vegetation changes in the Zayandeh-rud river basin in the period 2001 to 2016 have been investigated based on the combining of the 15 different vegetation indices. Two main plans were applied and tested to produce the final vegetation change map. In the first plan, change maps were produced by differencing the original vegetation indices individually. The second plan, which was a fusion perspective, included two algorithms. In the first one, change maps, obtained from vegetation indices, were fused at the decision level using the Majority Voting Method. The second algorithm included a particle swarm optimisation (PSO) based weighted combination of the different vegetation maps. The results show that the high correlation between vegetation indices does not necessarily provide the same results and combination of all them can be done automatically by applying PSO. Although PSO-based combination could not significantly improve the change detection, it solved the problem of finding optimal threshold value for the change detection process. However, from the vegetation change point of view, they all indicate that during the years of study, the vegetation cover has increased in the study area due to irregular water usage of the Zayandeh-rud river in its western part.\",\"PeriodicalId\":46012,\"journal\":{\"name\":\"International Journal of Image and Data Fusion\",\"volume\":\"10 1\",\"pages\":\"199 - 216\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2019-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/19479832.2019.1601642\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Image and Data Fusion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/19479832.2019.1601642\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Image and Data Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/19479832.2019.1601642","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"REMOTE SENSING","Score":null,"Total":0}
Optimum indices for vegetation cover change detection in the Zayandeh-rud river basin: a fusion approach
ABSTRACT In this study, vegetation changes in the Zayandeh-rud river basin in the period 2001 to 2016 have been investigated based on the combining of the 15 different vegetation indices. Two main plans were applied and tested to produce the final vegetation change map. In the first plan, change maps were produced by differencing the original vegetation indices individually. The second plan, which was a fusion perspective, included two algorithms. In the first one, change maps, obtained from vegetation indices, were fused at the decision level using the Majority Voting Method. The second algorithm included a particle swarm optimisation (PSO) based weighted combination of the different vegetation maps. The results show that the high correlation between vegetation indices does not necessarily provide the same results and combination of all them can be done automatically by applying PSO. Although PSO-based combination could not significantly improve the change detection, it solved the problem of finding optimal threshold value for the change detection process. However, from the vegetation change point of view, they all indicate that during the years of study, the vegetation cover has increased in the study area due to irregular water usage of the Zayandeh-rud river in its western part.
期刊介绍:
International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).