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Article: A multiple dataset approach for 30-m resolution land cover mapping: a case study of continental Africa

TitleA multiple dataset approach for 30-m resolution land cover mapping: a case study of continental Africa
Authors
Issue Date2018
Citation
International Journal of Remote Sensing, 2018, v. 39, n. 12, p. 3926-3938 How to Cite?
Abstract© 2018 Informa UK Limited, trading as Taylor & Francis Group. Recent developments in global land-cover mapping have focused on spatial resolution improvement with more heterogeneous features to integrate spatial, spectral and temporal information. In this study, hundreds of features derived from four seasonal Landsat 8 OLI (Operational Land Imager) spectral bands, Moderate Resolution Imaging Spectroradiometer (MODIS) time series vegetation index (VI) data, night-time light (NTL), digital elevation models (DEM) and climatic variables were used for land cover mapping with a target 30-m resolution for the whole African continent. In total, 49,007 training samples (from 11,231 locations) and 23,803 validation samples (from 5,414 locations) interpreted from seasonal Landsat, MODIS Normalized Difference Vegetation Index (NDVI) time series and high-resolution images in Google Earth were used for classifier training (Random Forest) and map validation. Overall accuracy was 76% at 30-m spatial resolution, which is better than previous land cover mapping for the African continent. Besides, accuracies for cropland were improved dramatically by more than 10%. Our method also addressed many remaining issues for 30-m mapping (e.g. boundary effects and declines in resolution). This framework is promising for automatic and efficient global land cover mapping resulting in better visual effects and classification accuracy.
Persistent Identifierhttp://hdl.handle.net/10722/296847
ISSN
2020 Impact Factor: 3.151
2020 SCImago Journal Rankings: 0.918
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorFeng, Duole-
dc.contributor.authorYu, Le-
dc.contributor.authorZhao, Yuanyan-
dc.contributor.authorCheng, Yuqi-
dc.contributor.authorXu, Yidi-
dc.contributor.authorLi, Congcong-
dc.contributor.authorGong, Peng-
dc.date.accessioned2021-02-25T15:16:48Z-
dc.date.available2021-02-25T15:16:48Z-
dc.date.issued2018-
dc.identifier.citationInternational Journal of Remote Sensing, 2018, v. 39, n. 12, p. 3926-3938-
dc.identifier.issn0143-1161-
dc.identifier.urihttp://hdl.handle.net/10722/296847-
dc.description.abstract© 2018 Informa UK Limited, trading as Taylor & Francis Group. Recent developments in global land-cover mapping have focused on spatial resolution improvement with more heterogeneous features to integrate spatial, spectral and temporal information. In this study, hundreds of features derived from four seasonal Landsat 8 OLI (Operational Land Imager) spectral bands, Moderate Resolution Imaging Spectroradiometer (MODIS) time series vegetation index (VI) data, night-time light (NTL), digital elevation models (DEM) and climatic variables were used for land cover mapping with a target 30-m resolution for the whole African continent. In total, 49,007 training samples (from 11,231 locations) and 23,803 validation samples (from 5,414 locations) interpreted from seasonal Landsat, MODIS Normalized Difference Vegetation Index (NDVI) time series and high-resolution images in Google Earth were used for classifier training (Random Forest) and map validation. Overall accuracy was 76% at 30-m spatial resolution, which is better than previous land cover mapping for the African continent. Besides, accuracies for cropland were improved dramatically by more than 10%. Our method also addressed many remaining issues for 30-m mapping (e.g. boundary effects and declines in resolution). This framework is promising for automatic and efficient global land cover mapping resulting in better visual effects and classification accuracy.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Remote Sensing-
dc.titleA multiple dataset approach for 30-m resolution land cover mapping: a case study of continental Africa-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/01431161.2018.1452073-
dc.identifier.scopuseid_2-s2.0-85044020265-
dc.identifier.volume39-
dc.identifier.issue12-
dc.identifier.spage3926-
dc.identifier.epage3938-
dc.identifier.eissn1366-5901-
dc.identifier.isiWOS:000427866600009-
dc.identifier.issnl0143-1161-

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