A Comparison of Four Global Land Cover Maps on a Provincial Scale Based on China’s 30 m GlobeLand30

Uncertainties in Classification System Conversion and an Analysis of Inconsistencies in Global Land Cover Products

“…These land cover types were the dominant types in our study area, illustrating that the different percentages of dominant types among land cover products can greatly influence areal inconsistencies. In addition, the areal inconsistency of croplands (17.89% in mountainous area) between the FROM-GLC and the GlobCover 2009 using IGBP-9 is slightly higher than the inconsistent result (15.36%) from paper [21], but the areal inconsistencies for croplands (4.13% and 0.23% in oasis areas and desert areas, respectively) are far less than the result in the paper mentioned above. Overall areal inconsistencies between pairs of the FROM-GLC, GlobCover 2009 and MODISLC in four common classification systems are shown in the Figure 2.…”

“…Previous comparisons of land cover products have been made only at global [19,20], continental [18], national [16], or provincial scales [21], since they focused on general patterns of inconsistencies or indirect validation accuracy of the products, which is meaningful to large scale studies. To the knowledge of the authors, this is the first study to compare the inconsistency of three recent land cover products (the MODISLC, GlobCover 2009, and FROM-GLC) from a terrain perspective in a complex mountain-oasis-desert area.…”

“…A specific way to compare datasets is to perform a relative comparison of various land cover maps, first reconciling their thematic classification systems into more aggregated categories after resampling the datasets to be into the same spatial resolution [15]. Using common classification systems based on the definition of each class in the original land cover products [16,17] or standards in reference to FAO [18], IGBP [12], or other dataset, some previous studies have highlighted general patterns of agreement, inconsistencies and accuracy among different land cover products at global [19,20], continental [18], national [16], and provincial scales [21]. Other studies not only demonstrated the compatibility and discrepancies between different datasets, but also qualitatively discussed the impacts of landscape inhomogeneity, thematic resolution, spatial resolution and mis-registration errors on product accuracy [20,22].…”

Contrasting Changes in Vegetation Growth due to Different Climate Forcings over the Last Three Decades in the Selenga-Baikal Basin

“…We used the Global Land Cover (GLC) 30-m dataset (GlobeLand30), which is derived from a Chinese GLC mapping project (www.globeland30.org) that provides GLC data at a spatial resolution of 30 m in 2000 and 2010 [40]. The GlobeLand30 classification utilized multispectral images from the American Land Resources Satellite (Landsat) TM5/ETM+ and the China Environmental Disaster Alleviation Satellite (HJ-1) [41]. The total classification accuracy of GlobeLand30 was 83.5%, and the products have been widely used for studies of environmental change, land resource management, sustainable development, etc.…”

Assessing inconsistency in global land cover products and synthesis of studies on land use and land cover dynamics during 2001 to 2017 in the southeastern region of Bangladesh

“…Using common classification systems based on the definition of each class in the original land cover products [22][23] or standards in reference to FAO [24], IGBP [11], or other dataset, some previous studies have highlighted general patterns of agreement, inconsistencies and accuracy among different land cover products at global [25][26], continental [24], national [22], and provincial scales [27]. Other studies not only demonstrated the compatibility and discrepancies between different datasets, but also qualitatively discussed the impacts of landscape in homogeneity, thematic resolution, spatial resolution, and mis-registration errors on product accuracy [26,28].…”