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ISPRS Int. J. Geo-Inf. 2018, 7(4), 157; https://doi.org/10.3390/ijgi7040157

Land Cover Mapping from Remotely Sensed and Auxiliary Data for Harmonized Official Statistics

1
Direção-Geral do Território, 1099-052 Lisbon, Portugal
2
European Commission, Joint Research Centre (JRC), Directorate Space, Security and Migration, Disaster Risk Management Unit, 21027 Ispra (VA), Italy
3
Statistics Portugal, 1000-043 Lisbon, Portugal
4
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Received: 16 February 2018 / Revised: 11 April 2018 / Accepted: 14 April 2018 / Published: 21 April 2018
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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. View Full-Text
Keywords: change detection; expert knowledge; GIS; Landsat; LUCAS survey; rule-based classification change detection; expert knowledge; GIS; Landsat; LUCAS survey; rule-based classification
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Costa, H.; Almeida, D.; Vala, F.; Marcelino, F.; Caetano, M. Land Cover Mapping from Remotely Sensed and Auxiliary Data for Harmonized Official Statistics. ISPRS Int. J. Geo-Inf. 2018, 7, 157.

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