Abstract
This paper presents a rigorous validation of five widely used global land cover products, i.e., GLCC (Global Land Cover Characterization), UMd (University of Maryland land cover product), GLC2000 (Global Land Cover 2000 project data), MODIS LC (Moderate Resolution Imaging Spectro-radiometer Land Cover product) and GlobCover (GLOBCOVER land cover product), and a national land cover map GLCD-2005 (Geodata Land Cover Dataset for year 2005) against an independent reference data set over China. The land cover reference data sets in three epochs (1990, 2000, and 2005) were collected on a web-based prototype system using a sampling-based labeling approach. Results show that, in China, the highest overall accuracy is observed in GLCD-2005 (72.3%), followed by MODIS LC (68.9%), GLC2000 (65.2%), GlobCover (57.7%) and GLCC (57.2%), while UMd has the lowest accuracy (48.6%); all of the products performed best in representing “Trees” and “Others”, well with “Grassland” and “Cropland”, but problematic with “Water” and “Urban” across China in general. Moreover, in respect of GLCD-2005, there are significant accuracy differences across seven geographical locations of China, ranging from 46.3% in the Southwest, 77.5% in the South, 79.2% in the Northwest, 80.8% in the North, 81.8% in the Northeast, 82.6% in the Central, to 89.0% in the East. This study indicates that a regionally focused land cover map would in fact be more accurate than extracting the same region from a globally produced map.
Highlights
Land cover is a crucial parameter of the needed ecosystem-based information within the global change framework, which has been placed at the top of international scientific and political agendas by an increasing number of multilateral environmental agreements [1,2,3]
A summary of error matrixes between each map pair and land cover samples is given in Tables 2–4 separately
This study reveals that there were significant accuracy differences among the one national and five global land cover maps when compared in China
Summary
Land cover is a crucial parameter of the needed ecosystem-based information within the global change framework, which has been placed at the top of international scientific and political agendas by an increasing number of multilateral environmental agreements [1,2,3]. Remote sensing offers a practical and economical means to acquire land cover information over large areas on account of its capacity for systematic observations at various scales [6]; it has been identified as one of the major data sources for the generation of land cover products. The general approach of land cover mapping is to produce temporal, usually monthly composites from daily or weekly mosaics to minimize cloud cover and data noise [7]. In conjunction with other ancillary data sets, monthly composites are used to produce land cover categories according to a defined classification scheme at a regional, continental or global scale. The first global land cover map was produced using the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) [8,9,10]. Moderate resolution satellite sensors have emerged, i.e., Systeme
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