Show simple item record

Authordc.contributor.authorZhao, Yuanyuan 
Authordc.contributor.authorFeng, Duole 
Authordc.contributor.authorYu, Le 
Authordc.contributor.authorWang, Xiaoyi 
Authordc.contributor.authorChen, Yanlei 
Authordc.contributor.authorBai, Yuqi 
Authordc.contributor.authorHernández Palma, Héctor 
Authordc.contributor.authorGalleguillos Torres, Mauricio 
Authordc.contributor.authorEstades Marfán, Cristián 
Authordc.contributor.authorBiging, Gregory 
Authordc.contributor.authorRadke, John 
Authordc.contributor.authorGong, Peng 
Admission datedc.date.accessioned2017-01-05T18:47:11Z
Available datedc.date.available2017-01-05T18:47:11Z
Publication datedc.date.issued2016
Cita de ítemdc.identifier.citationRemote Sensing of Environment 183 (2016) 170–185es_ES
Identifierdc.identifier.other10.1016/j.rse.2016.05.016
Identifierdc.identifier.urihttps://repositorio.uchile.cl/handle/2250/142288
Abstractdc.description.abstractStretching over 4300 km north to south, Chile is a special country with complicated landscapes and rich biodiversity. Accurate and timely updated land cover map of Chile in detailed classification categories is highly demanded for many applications. A conclusive land cover map integrated from multi-seasonal mapping results and a seasonal dynamic map series were produced using Landsat 8 imagery mainly acquired in 2013 and 2014, supplemented by MODIS Enhanced Vegetation Index data, high resolution imagery on Google Earth, and Shuttle Radar Topography Mission DEM data. The overall accuracy is 80% for the integrated map at level 1 and 73% for level 2 based on independent validation data. Accuracies for seasonal map series were also assessed, which is around 70% for each season, greatly improved by integrated use of seasonal information. The importance of growing season imagery was proved in our analysis. The analysis of the spatial variation of accuracies among various ecoregions indicates that the accuracy for land cover mapping decreases gradually from central Chile to both north and south. More mapping efforts for those ecoregions are needed. In addition, the training dataset includes sample points spatially distributed in the whole country, temporally distributed throughout the year, and categorically encompassing all land cover types. This training dataset constitutes a universal sample set allowing us to map land cover from any Landsat 8 image acquired in Chile without additional ad hoc training sample collection. (C) 2016 Elsevier Inc. All rights reservedes_ES
Patrocinadordc.description.sponsorshipproject Development of advanced remote sensing methods for mapping and managing plant species diversity in Mediterranean Forest of Chile in UC Berkeley-Chile Seed Funds 77477 project Development of advanced remote sensing methods for mapping and managing plant species diversity in Mediterranean Forest of California in UC Berkeley-Chile Seed Funds 77477 Meteorological Public Benefit project of China GYHY201506010es_ES
Lenguagedc.language.isoenes_ES
Publisherdc.publisherElsevieres_ES
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile*
Link to Licensedc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
Sourcedc.sourceChile--Mapases_ES
Keywordsdc.subjectCartografía--Chile--Percepción remotaes_ES
Keywordsdc.subjectLandsates_ES
Keywordsdc.subjectes_ES
Keywordsdc.subjectes_ES
Keywordsdc.subjectes_ES
Títulodc.titleDetailed dynamic land cover mapping of Chile: Accuracy improvement by integrating multi-temporal dataes_ES
Document typedc.typeArtículo de revista
Catalogueruchile.catalogadorapces_ES
Indexationuchile.indexArtículo de publicación ISIes_ES


Files in this item

Icon

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 Chile
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile