Mariane S. Reis, Lucélia S. de Barros, Manoel R. Rodrigues Neto, Dayane Rafaela V. de Moraes, Noeli Aline P. Moreira, Gabriel Mikael R. Alves, Bruno V. Adorno, Cassiano Gustavo Messias, Luciano V. Dutra, Camilo D. Rennó, Sidnei João S. Sant’Anna, Maria Isabel S. Escada
{"title":"评估从亚马逊地区大地遥感卫星历史时间序列中收集土地覆被参考数据时判读员的意见分歧","authors":"Mariane S. Reis, Lucélia S. de Barros, Manoel R. Rodrigues Neto, Dayane Rafaela V. de Moraes, Noeli Aline P. Moreira, Gabriel Mikael R. Alves, Bruno V. Adorno, Cassiano Gustavo Messias, Luciano V. Dutra, Camilo D. Rennó, Sidnei João S. Sant’Anna, Maria Isabel S. Escada","doi":"10.1080/01431161.2024.2373340","DOIUrl":null,"url":null,"abstract":"Land cover information, derived from the classification of Remote Sensing images, is only useful if accompanied by a rigorous accuracy assessment, usually dependent on reference samples. As field d...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"18 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing interpreter’s disagreements in land cover reference data collection from historical Landsat time series in Amazon\",\"authors\":\"Mariane S. Reis, Lucélia S. de Barros, Manoel R. Rodrigues Neto, Dayane Rafaela V. de Moraes, Noeli Aline P. Moreira, Gabriel Mikael R. Alves, Bruno V. Adorno, Cassiano Gustavo Messias, Luciano V. Dutra, Camilo D. Rennó, Sidnei João S. Sant’Anna, Maria Isabel S. Escada\",\"doi\":\"10.1080/01431161.2024.2373340\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Land cover information, derived from the classification of Remote Sensing images, is only useful if accompanied by a rigorous accuracy assessment, usually dependent on reference samples. As field d...\",\"PeriodicalId\":14369,\"journal\":{\"name\":\"International Journal of Remote Sensing\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/01431161.2024.2373340\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/01431161.2024.2373340","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY","Score":null,"Total":0}
Assessing interpreter’s disagreements in land cover reference data collection from historical Landsat time series in Amazon
Land cover information, derived from the classification of Remote Sensing images, is only useful if accompanied by a rigorous accuracy assessment, usually dependent on reference samples. As field d...
期刊介绍:
The International Journal of Remote Sensing ( IJRS) is concerned with the theory, science and technology of remote sensing and novel applications of remotely sensed data. The journal’s focus includes remote sensing of the atmosphere, biosphere, cryosphere and the terrestrial earth, as well as human modifications to the earth system. Principal topics include:
• Remotely sensed data collection, analysis, interpretation and display.
• Surveying from space, air, water and ground platforms.
• Imaging and related sensors.
• Image processing.
• Use of remotely sensed data.
• Economic surveys and cost-benefit analyses.
• Drones Section: Remote sensing with unmanned aerial systems (UASs, also known as unmanned aerial vehicles (UAVs), or drones).