{"title":"1972-2010年Landsat MSS、TM、ETM+对澳大利亚Moreton湾海草分布的长期监测","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":"{\"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}","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}
Long term monitoring of seagrass distribution in Moreton Bay, Australia, from 1972–2010 using Landsat MSS, TM, ETM+
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.