{"title":"Long term monitoring of seagrass distribution in Moreton Bay, Australia, from 1972–2010 using Landsat MSS, TM, ETM+","authors":"M. Lyons, S. Phinn, C. Roelfsema","doi":"10.1109/IGARSS.2010.5651878","DOIUrl":null,"url":null,"abstract":"Seagrass ecosystems are well studied and seagrass is recognised as a vital contributor to overall ecosystem health and productivity. However, a significant gap in knowledge exists in terms of the large scale temporal and spatial dynamics of cover level and distribution of seagrass communities. Remotely sensed satellite imagery offers a means to map seagrass cover and distribution over large temporal and spatial scales. At present, no operational methods have been produced to map seagrass on large spatio-temporal scales (> 100km2). This study presents a combined per-pixel/object-based method to rapidly map seagrass cover and distribution from a full Landsat archive, from 1972–2010 (MSS, TM and ETM+), with no in-situ data and at accuracies as good or better than existing mapping methods. The products provide management agencies with a baseline assessment as well as the capacity to continue to map seagrass distribution and predict changes in the future.","PeriodicalId":406785,"journal":{"name":"2010 IEEE International Geoscience and Remote Sensing Symposium","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS.2010.5651878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Seagrass ecosystems are well studied and seagrass is recognised as a vital contributor to overall ecosystem health and productivity. However, a significant gap in knowledge exists in terms of the large scale temporal and spatial dynamics of cover level and distribution of seagrass communities. Remotely sensed satellite imagery offers a means to map seagrass cover and distribution over large temporal and spatial scales. At present, no operational methods have been produced to map seagrass on large spatio-temporal scales (> 100km2). This study presents a combined per-pixel/object-based method to rapidly map seagrass cover and distribution from a full Landsat archive, from 1972–2010 (MSS, TM and ETM+), with no in-situ data and at accuracies as good or better than existing mapping methods. The products provide management agencies with a baseline assessment as well as the capacity to continue to map seagrass distribution and predict changes in the future.