Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Abstract

Global land cover maps have been widely employed as the base layer for a number of applications including climate change, food security, water quality, biodiversity, change detection and environmental planning. Due to the importance of land cover, there is a pressing need to increase the temporal and spatial resolution of global land cover maps. A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery, which has been developed by the National Geomatics Center of China (NGCC). Although overall accuracy is greater than 80%, the NGCC would like help in assessing the accuracy of the product in different regions of the world. To assist in this process, this study compares the GlobeLand30 product with existing public and online datasets, i.e. CORINE, Urban Atlas, OpenStreetMap and ATKIS for Germany in order to assess overall and per class agreement. The results of the analysis reveal high agreement of up to 92% between these datasets and GlobeLand30 but that large disagreements for certain classes are evident, in particular wetlands. However, overall, GlobeLand30 is shown to be a useful product for characterizing land cover in Germany, and paves the way for further regional and national validation efforts.

Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

“…For artificial surfaces, it was also compared to other global land cover products for eight test areas around the world and accuracies ranged from 79% to 97%, outperforming both CLC and the FROM-GLC product of Gong et al [4]. There have been a few other studies that have independently compared GL30 to authoritative products in Italy [30], Germany [31] and water bodies in Scandinavian countries [32] and more recently, land cover in Iran [33], with high agreements, i.e., greater than 78%. Other than the comparison for Iran, there has been little validation of this product outside Europe, and particularly in less developed countries, which may benefit from such a product if no other land cover maps are available.…”

Analysis and Applications of GlobeLand30: A Review

“…Yang et al [31] evaluated the accuracy of seven land cover products over China, namely, International Geosphere-Biosphere Program Data and Information System's land cover dataset (IGBP DISCover), The University of Maryland land cover dataset (UMD), Global Land Cover 2000 dataset (GLC2000) from the European Commission's Joint Research Center (JRC), Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products MOD12Q1 and MCD12Q1, Global Map-Global LC (GLCNMO) dataset from the International Steering Committee for Global Mapping, Climate Change Initiative land cover dataset (CCI-LC) from European Space Agency (ESA), and GlobeLand30; they found that GlobeLand30-2010 has the highest overall accuracy (82.4%). Arsanjani et al [28] reported that GlobeLand30 has high agreements with CORINE (92.52%), Urban Atlas (85.43%), OpenStreetMap (74.24%), and ATKIS (85.23%) in Germany. Mozak [34] found that GlobeLand30 product overlap with a degree of 77% with GLC-Share product in Continental Portugal.…”

“…Grekousis et al [7] summarized the reported accuracy of 23 global and 41 regional LC products, in which GlobeLand30 products have relatively high overall accuracy (78.6% and 80.3% for 2000 and 2010, respectively) compared with GLC2000 (68.6%), GlobCover2005 (73.1%), GlobeCover2009 (67.5%), UMD (65.0%), MODIS (71.6%), GLCNMO V2 (77.9%), FROM-GLC (63.69%), GeoWiki hybrid 1 (87.9%) and so on. GlobeLand30 is then recommended for diverse applications, including climate change studies, land change research, and ecosystem analysis [28,88]. In particular, GlobeLand30 is considered a useful product for developing countries or regions in which good land cover maps are difficult to find [14,89].…”

“…The accuracy of GlobeLand30 had been evaluated by third-party researchers from more than ten countries for its all classes or one single class via sample-based validation or comparison with existing land cover products [28][29][30]. At the country/region level, a satisfactory overall accuracy was estimated as 82.4% for China [31], 80% for Italy [29], 77.90% for all of Iran [14], 80.1% for Nepal [32], and 89.7% for Kyiv Oblast, Ukraine [33].…”

Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas

“…However, most of these data products have limited temporal coverage and present limited usefulness for a dynamic analysis at large scales. For example, although the class accuracy of each land-cover classification of GlobeLand30 data is generally at a high level [35], this product is still limited by time constraints (i.e., it only contains two terms of data with benchmark years of 2000 and 2010); thus, it cannot meet the practical requirements for monitoring urban spatial change within a long-term time series. More importantly, problems related to the same objects using different spectra and different objects using similar spectra remain unresolved, which increases the difficulty of distinguishing between urban and bare lands simply based on the spectral information obtained from several bands of surface reflectance data.…”