Circa 2014 African land-cover maps compatible with FROM-GLC and GLC2000 classification schemes based on multi-seasonal Landsat data. Issue 19 (1st October 2016)

• Record Type: Journal Article
• Authors: Feng, Duole; Zhao, Yuanyuan; Yu, Le; Li, Congcong; Wang, Jie; Clinton, Nicholas; Bai, Yuqi; Belward, Alan; Zhu, Zhiliang; Gong, Peng
• Is Part Of: International journal of remote sensing. Volume 37:Issue 19(2016)
• Page Start: 4648
• Page End: 4664
• Publication Date: 2016-10-01
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1218090
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: A new African land-cover data set has been developed using multi-seasonal Landsat Operational Land Imager (OLI) imagery mainly acquired around 2014, supplemented by Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+). Each path/row location was covered by five images, including one in the growing season of vegetation and the others in four meteorological seasons (i.e. spring, summer, autumn, and winter), choosing the image with the least cloud coverage. The data set has two classification schemes, i.e. Finer Resolution Observation and Monitoring – Global Land Cover (FROM-GLC) and Global Land Cover 2000 (GLC2000), providing greater flexibility in product comparisons and applications. Random forest was used as the classifier in this project. Overall accuracies were 71% and 67% for the maps in the FROM-GLC classification scheme at level 1 and level 2, respectively, and 70% for the map in the GLC2000 classification scheme. The newly developed African land-cover map achieved a greater improvement in accuracy compared to previous products in the FROM-GLC project. Multi-seasonal imagery helped increase the mapping accuracy by better differentiating vegetation types with similar spectral features in one specific season and identifying vegetation with a shorter growing season. Night light data with 1 km resolution was used to identify the potential area of impervious surfaces to avoid overestimating the distribution of impervious surfaces without decreasing the spatial resolution. Stacking multi-seasonal mapping results could adequately minimize the disturbance of cloud and shade.