Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Arsanjani, Jamal Jokar; See, Linda; Tayyebi, Amin

doi:10.1080/17538947.2016.1151956

Cited by 53 publications

(41 citation statements)

References 54 publications

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“…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.…”

Section: Globeland30mentioning

confidence: 99%

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

Fonte

Minghini

Patriarca

et al. 2017

IJGI

Self Cite

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Abstract:With the opening up of the Landsat archive, global high resolution land cover maps have begun to appear. However, they often have only a small number of high level land cover classes and they are static products, corresponding to a particular period of time, e.g., the GlobeLand30 (GL30) map for 2010. The OpenStreetMap (OSM), in contrast, consists of a very detailed, dynamically updated, spatial database of mapped features from around the world, but it suffers from incomplete coverage, and layers of overlapping features that are tagged in a variety of ways. However, it clearly has potential for land use and land cover (LULC) mapping. Thus the aim of this paper is to demonstrate how the OSM can be converted into a LULC map and how this OSM-derived LULC map can then be used to first update the GL30 with more recent information and secondly, enhance the information content of the classes. The technique is demonstrated on two study areas where there is availability of OSM data but in locations where authoritative data are lacking, i.e., Kathmandu, Nepal and Dar es Salaam, Tanzania. The GL30 and its updated and enhanced versions are independently validated using a stratified random sample so that the three maps can be compared. The results show that the updated version of GL30 improves in terms of overall accuracy since certain classes were not captured well in the original GL30 (e.g., water in Kathmandu and water/wetlands in Dar es Salaam). In contrast, the enhanced GL30, which contains more detailed urban classes, results in a drop in the overall accuracy, possibly due to the increased number of classes, but the advantages include the appearance of more detailed features, such as the road network, that becomes clearly visible.

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Section: Globeland30mentioning

confidence: 99%

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

Fonte

Minghini

Patriarca

et al. 2017

IJGI

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“…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.…”

Section: Accuracy Analysismentioning

confidence: 99%

“…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].…”

Section: Comprehensive Validationmentioning

confidence: 99%

“…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].…”

Section: Accuracy Analysismentioning

confidence: 99%

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Analysis and Applications of GlobeLand30: A Review

Chen

Cao

Peng

et al. 2017

IJGI

157

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GlobeLand30, donated to the United Nations by China in September 2014, is the first wall-to-wall 30 m global land cover (GLC) data product. GlobeLand30 is widely used by scientists and users around the world. This paper provides a review of the analysis and applications of GlobeLand30 based on its data-downloading statistics and published studies. An average accuracy of 80% for full classes or one single class is achieved by third-party researchers from more than 10 countries through sample-based validation or comparison with existing data. GlobeLand30 has users from more than 120 countries on five continents, and from all five Social Benefit Areas. The significance of GlobeLand30 is demonstrated by a number of published papers dealing with land-cover status and change analysis, cause-and-consequence analysis, and the environmental parameterization of Earth system models. Accordingly, scientific data sharing in the field of geosciences and Earth observation is promoted, and fine-resolution GLC mapping and applications worldwide are stimulated. The future development of GlobeLand30, including comprehensive validation, continuous updating, and monitoring of sustainable development goals, is also discussed.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Tong

Luo

et al. 2017

Remote Sensing

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Abstract:The accuracy of training samples used for data classification methods, such as support vector machines (SVMs), has had a considerable positive impact on the results of urban area extractions. To improve the accuracy of urban built-up area extractions, this paper presents a sample-optimized approach for classifying urban area data using a combination of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) for nighttime light data, Landsat images, and GlobeLand30, which is a 30-m global land cover data product. The proposed approach consists of three main components: (1) initial sample generation and data classification into built-up and non-urban built-up areas based on the maximum and minimum intervals of digital numbers from the DMSP-OLS data, respectively; (2) refined sample selection and optimization by the probability threshold of each pixel based on vegetation-cover, using the Landsat-derived normalized differential vegetation index (NDVI) and artificial surfaces extracted from the GlobeLand30 product as the constraints; (3) iterative classification and urban built-up area data extraction using the relationship between these three aspects of data collection together with the training sets. Experiments were conducted for several cities in western China using this proposed approach for the extraction of built-up areas, which were classified using urban construction statistical yearbooks and Landsat images and were compared with data obtained from traditional data collection methods, such as the threshold dichotomy method and the improved neighborhood focal statistics method. An analysis of the empirical results indicated that (1) the sample training process was improved using the proposed method, and the overall accuracy (OA) increased from 89% to 96% for both the optimized and non-optimized sample selection; (2) the proposed method had a relative error of less than 10%, as calculated by an accuracy assessment; (3) the overall and individual class accuracy were higher for artificial surfaces in GlobeLand30; and (4) the average OA obviously improved and the Kappa coefficient in the case of Chengdu increased from 0.54 to 0.80. Therefore, the experimental results demonstrated that our proposed approach is a reliable solution for extracting urban built-up areas with a high degree of accuracy.

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Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Cited by 53 publications

References 54 publications

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

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

Analysis and Applications of GlobeLand30: A Review

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

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