Seasonal Accuracy Assessments of Three Land Cover Datasets in Pakistan

Global land cover datasets play an important role in the fields of ecology, climate and resources. GlobeLand30-2010 (30 m), FROM-GLC-2010 (30 m) and GlobCover-2009 (300 m) are three global high-precision land cover datasets with a wide range of applications. In order to judge whether these data sets are sufficient to describe the real situation of land cover in different seasons, the inter-seasonal accuracy of the aforementioned three global land cover datasets using Pakistan as a representative study area was evaluated. A total of 1 000 land cover sample points were selected from 122 Landsat-5, Landsat-7 multi-spectral remote sensing images during 2009~2011 to generate summer and winter land cover classifications.The results show that the accuracies of summer and winter land cover classifications are different in Pakistan. The overall land cover classification accuracies of GlobeLand30-2010 (65.6% vs. 63.9%) and FROM-GLC-2010 (61.2% vs. 59.0%) in summer are slightly higher than those in winter. The overall accuracy of GlobCover-2009 (59.5% vs. 59.1%) in winter is slightly higher than that in summer. GlobeLand30-2010 performs best in classifying cropland, impervious surface, and water body, FROM-GLC-2010 performs best in classifying vegetation, glaciers, and snow, and GlobCover-2009 performs best in classifying bare land. The classification of cropland, bare land, glaciers, and snow in the three datasets is more in line with the real situation in winter than in summer; the classification of vegetation and water bodies is more in line with the real situation in summer; there is no obvious seasonal difference in impervious surface. There should be at least one sample point per 1 000 square kilometers.