Abstract
Comparisons of the accuracy and consistency of different remote-sensing land cover datasets are important for the rational application of multi-source land cover datasets to regional development, or to studies of global or local environmental change. Existing comparisons of accuracy or spatial consistency among land cover datasets primarily use confusion or transfer matrices and focus on the type and area consistency of land cover. However, less attention has been paid to the consistency of spatial patterns, and quantitative analyses of spatial pattern consistency are rare. However, when proportions of land cover types are similar, spatial patterns are essential for studies of the ecological functions of a landscape system. In this study, we used classical landscape indices that quantifies spatial patterns to analyze the spatial pattern consistency among different land cover datasets, and chose three datasets (GlobeLand30-2010, FROM-GLC2010, and SERVIR MEKONG2010) in northern Laos as a case study. We also analyzed spatial pattern consistency at different scales after comparing the landscape indices method with the confusion matrix method. We found that the degree of consistency between GlobeLand30-2010 and SERVIR MEKONG2010 was higher than that of GlobeLand30-2010 and FROM-GLC2010, FROM-GLC2010, and SERVIR MEKONG2010 based on the confusion matrix, mainly because of the best forest consistency and then water. However, the spatial consistency results of the landscape indices analysis show that the three datasets have large differences in the number of patches (NP), patch density (PD), and landscape shape index (LSI) at the original scale of 30 m, and decrease with the increase of the scale. Meanwhile, the aggregation index (AI) shows different changes, such as the changing trend of the forest aggregation index increasing with the scale. Our results suggested that, when using or producing land cover datasets, it is necessary not only to ensure the consistency of landscape types and areas, but also to ensure that differences among spatial patterns are minimized, especially those exacerbated by scale. Attention to these factors will avoid larger deviations and even erroneous conclusions from these data products.
Highlights
Land cover change is closely related to global environmental change, human survival, material circulation, and global ecosystem energy cycles [1,2,3,4]
Using the area comparison and confusion matrix analysis methods, Tchuente et al [23] compared the accuracy of the GLC2000, GLOBCOVER, MODIS, and ECOCLIMAP datasets on the African continent; the results showed that the consistency percent among the four land cover products was 56–69%
The experimental results show that Globeland30-2010 and SERVIR MEKONG2010 have the highest overall consistency among land cover data, while Globeland30-2010 and FROM-GLC2010 have the lowest overall consistency
Summary
Land cover change is closely related to global environmental change, human survival, material circulation, and global ecosystem energy cycles [1,2,3,4]. The most commonly used datasets are IGBP DISCOVER, produced by the US geological survey [9]; GLC2000, produced by the European union joint research center [10]; MODIS, produced by Boston University [11]; GLOBCOVER, produced by the European space agency [12,13]; LandLand, produced by the national geomatics center of China [14]; FROM-GLC, produced by Tsinghua University [15]; and SERVIR MEKONG, produced jointly by several teams, including the United States agency for international development (USAID), the national aeronautics and space administration (NASA), and the Asian disaster preparedness center (ADPC) data As these land cover datasets have different classification systems, classification methods, production processes, and spatial resolutions, they yield different results when applied to research questions at regional or global scales. It is necessary to analyze the accuracy and consistency of these multi-source remote sensing land cover datasets in order to provide a reference for the effective use of land cover data in environmental monitoring studies
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