Land Cover Mapping from Remotely Sensed and Auxiliary Data for Harmonized Official Statistics. Costa, H., Almeida, D., Vala, F., Marcelino, F., & Caetano, M. ISPRS International Journal of Geo-Information, 7(4):157, April, 2018. ![Land Cover Mapping from Remotely Sensed and Auxiliary Data for Harmonized Official Statistics [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper describes a general framework alternative to the traditional surveys that are commonly performed to estimate, for statistical purposes, the areal extent of predefined land cover classes across Europe. The framework has been funded by Eurostat and relies on annual land cover mapping and updating from remotely sensed and national GIS-based data followed by area estimation. Map production follows a series of steps, namely data collection, change detection, supervised image classification, rule-based image classification, and map updating/generalization. Land cover area estimation is based on mapping but compensated for mapping error as estimated through thematic accuracy assessment. This general structure was applied to continental Portugal, successively updating a map of 2010 for the following years until 2015. The estimated land cover change was smaller than expected but the proposed framework was proved as a potential for statistics production at the national and European levels. Contextual and structural methodological challenges and bottlenecks are discussed, especially regarding mapping, accuracy assessment, and area estimation.
@article{costa_land_2018,
title = {Land {Cover} {Mapping} from {Remotely} {Sensed} and {Auxiliary} {Data} for {Harmonized} {Official} {Statistics}},
volume = {7},
issn = {2220-9964},
url = {http://www.mdpi.com/2220-9964/7/4/157},
doi = {10.3390/ijgi7040157},
abstract = {This paper describes a general framework alternative to the traditional surveys that are commonly performed to estimate, for statistical purposes, the areal extent of predefined land cover classes across Europe. The framework has been funded by Eurostat and relies on annual land cover mapping and updating from remotely sensed and national GIS-based data followed by area estimation. Map production follows a series of steps, namely data collection, change detection, supervised image classification, rule-based image classification, and map updating/generalization. Land cover area estimation is based on mapping but compensated for mapping error as estimated through thematic accuracy assessment. This general structure was applied to continental Portugal, successively updating a map of 2010 for the following years until 2015. The estimated land cover change was smaller than expected but the proposed framework was proved as a potential for statistics production at the national and European levels. Contextual and structural methodological challenges and bottlenecks are discussed, especially regarding mapping, accuracy assessment, and area estimation.},
number = {4},
journal = {ISPRS International Journal of Geo-Information},
author = {Costa, Hugo and Almeida, Diana and Vala, Francisco and Marcelino, Filipe and Caetano, Mário},
month = apr,
year = {2018},
keywords = {NALCMS},
pages = {157},
}
Downloads: 0
{"_id":"HutpED5h4vgS6X9mg","bibbaseid":"costa-almeida-vala-marcelino-caetano-landcovermappingfromremotelysensedandauxiliarydataforharmonizedofficialstatistics-2018","authorIDs":[],"author_short":["Costa, H.","Almeida, D.","Vala, F.","Marcelino, F.","Caetano, M."],"bibdata":{"bibtype":"article","type":"article","title":"Land Cover Mapping from Remotely Sensed and Auxiliary Data for Harmonized Official Statistics","volume":"7","issn":"2220-9964","url":"http://www.mdpi.com/2220-9964/7/4/157","doi":"10.3390/ijgi7040157","abstract":"This paper describes a general framework alternative to the traditional surveys that are commonly performed to estimate, for statistical purposes, the areal extent of predefined land cover classes across Europe. The framework has been funded by Eurostat and relies on annual land cover mapping and updating from remotely sensed and national GIS-based data followed by area estimation. Map production follows a series of steps, namely data collection, change detection, supervised image classification, rule-based image classification, and map updating/generalization. Land cover area estimation is based on mapping but compensated for mapping error as estimated through thematic accuracy assessment. This general structure was applied to continental Portugal, successively updating a map of 2010 for the following years until 2015. The estimated land cover change was smaller than expected but the proposed framework was proved as a potential for statistics production at the national and European levels. Contextual and structural methodological challenges and bottlenecks are discussed, especially regarding mapping, accuracy assessment, and area estimation.","number":"4","journal":"ISPRS International Journal of Geo-Information","author":[{"propositions":[],"lastnames":["Costa"],"firstnames":["Hugo"],"suffixes":[]},{"propositions":[],"lastnames":["Almeida"],"firstnames":["Diana"],"suffixes":[]},{"propositions":[],"lastnames":["Vala"],"firstnames":["Francisco"],"suffixes":[]},{"propositions":[],"lastnames":["Marcelino"],"firstnames":["Filipe"],"suffixes":[]},{"propositions":[],"lastnames":["Caetano"],"firstnames":["Mário"],"suffixes":[]}],"month":"April","year":"2018","keywords":"NALCMS","pages":"157","bibtex":"@article{costa_land_2018,\n\ttitle = {Land {Cover} {Mapping} from {Remotely} {Sensed} and {Auxiliary} {Data} for {Harmonized} {Official} {Statistics}},\n\tvolume = {7},\n\tissn = {2220-9964},\n\turl = {http://www.mdpi.com/2220-9964/7/4/157},\n\tdoi = {10.3390/ijgi7040157},\n\tabstract = {This paper describes a general framework alternative to the traditional surveys that are commonly performed to estimate, for statistical purposes, the areal extent of predefined land cover classes across Europe. The framework has been funded by Eurostat and relies on annual land cover mapping and updating from remotely sensed and national GIS-based data followed by area estimation. Map production follows a series of steps, namely data collection, change detection, supervised image classification, rule-based image classification, and map updating/generalization. Land cover area estimation is based on mapping but compensated for mapping error as estimated through thematic accuracy assessment. This general structure was applied to continental Portugal, successively updating a map of 2010 for the following years until 2015. The estimated land cover change was smaller than expected but the proposed framework was proved as a potential for statistics production at the national and European levels. Contextual and structural methodological challenges and bottlenecks are discussed, especially regarding mapping, accuracy assessment, and area estimation.},\n\tnumber = {4},\n\tjournal = {ISPRS International Journal of Geo-Information},\n\tauthor = {Costa, Hugo and Almeida, Diana and Vala, Francisco and Marcelino, Filipe and Caetano, Mário},\n\tmonth = apr,\n\tyear = {2018},\n\tkeywords = {NALCMS},\n\tpages = {157},\n}\n\n","author_short":["Costa, H.","Almeida, D.","Vala, F.","Marcelino, F.","Caetano, M."],"key":"costa_land_2018","id":"costa_land_2018","bibbaseid":"costa-almeida-vala-marcelino-caetano-landcovermappingfromremotelysensedandauxiliarydataforharmonizedofficialstatistics-2018","role":"author","urls":{"Paper":"http://www.mdpi.com/2220-9964/7/4/157"},"keyword":["NALCMS"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/NAAtlas","creationDate":"2020-07-03T22:46:17.962Z","downloads":0,"keywords":["nalcms"],"search_terms":["land","cover","mapping","remotely","sensed","auxiliary","data","harmonized","official","statistics","costa","almeida","vala","marcelino","caetano"],"title":"Land Cover Mapping from Remotely Sensed and Auxiliary Data for Harmonized Official Statistics","year":2018,"dataSources":["5S2zj2hKW8TWTkuMq","yRJdyJoax6GFubwNS","WChDenW3ipDLTib3G","wszmZbW485BrvbZff","3fS6y29xitMxSyCmw","SmeabbQSEFozkXJuC","3cx6m8LrzQx6SsjF2"]}