Detailed dynamic land cover mapping of Chile: Accuracy improvement by integrating multi-temporal data
Author
dc.contributor.author
Zhao, Yuanyuan
Author
dc.contributor.author
Feng, Duole
Author
dc.contributor.author
Yu, Le
Author
dc.contributor.author
Wang, Xiaoyi
Author
dc.contributor.author
Chen, Yanlei
Author
dc.contributor.author
Bai, Yuqi
Author
dc.contributor.author
Hernández Palma, Héctor
Author
dc.contributor.author
Galleguillos Torres, Mauricio
Author
dc.contributor.author
Estades Marfán, Cristián
Author
dc.contributor.author
Biging, Gregory
Author
dc.contributor.author
Radke, John
Author
dc.contributor.author
Gong, Peng
Admission date
dc.date.accessioned
2017-01-05T18:47:11Z
Available date
dc.date.available
2017-01-05T18:47:11Z
Publication date
dc.date.issued
2016
Cita de ítem
dc.identifier.citation
Remote Sensing of Environment 183 (2016) 170–185
es_ES
Identifier
dc.identifier.other
10.1016/j.rse.2016.05.016
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/142288
Abstract
dc.description.abstract
Stretching 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 reserved
es_ES
Patrocinador
dc.description.sponsorship
project 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 GYHY201506010