The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results

Brovelli, M. A.; Molinari, Monia Elisa; Hussein, Eman Hussein Mohamed; Chen, Jun; Li, Ran

doi:10.3390/rs70404191

Cited by 134 publications

(98 citation statements)

References 26 publications

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“…The updated GL30 map (generated with the approach described in Section 4.3) is used as the starting layer. The non-urban classes, i.e., cultivated land (10), forest (20), grassland (30), shrubland (40), wetland (50) and water bodies (60), remain unchanged. Four new classes, which correspond to the UA level 2 nomenclature as listed in Table 2, are added to the GL30, replacing the single artificial surfaces class with these four new ones (Table 4).…”

Section: Enhancing Globeland30 Through the Use Of More Detailed Osm-dmentioning

confidence: 99%

“…As an example, Figure 1 provides a meaningful comparison between Google Maps and OSM for the Kibera slum in Nairobi (Kenya) obtained using the Map Compare tool from Geofabrik [29]. While this slum is totally absent in the former, the latter shows the impressively high level of detail that has resulted from the work of the local volunteers in the frame of the Map Kibera project [30]. Situations like this one clearly show the potential of using OSM data to derive accurate map products like LULC maps, as discussed in Section 4.…”

Section: Humanitarian Applications Of Osmmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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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: Enhancing Globeland30 Through the Use Of More Detailed Osm-dmentioning

confidence: 99%

Section: Humanitarian Applications Of Osmmentioning

confidence: 99%

Section: Globeland30mentioning

confidence: 99%

See 1 more Smart Citation

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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“…This information will allow replication of weighted error calculations). Although related recommendations have been made before [34,35], it is not hard to find accuracy assessments that do not report any error matrices [36]. This information allows for potential map users to follow all of the recommendations below, and to calculate accuracy metrics in a way that is tailored to their particular needs.…”

Section: Recommendationsmentioning

confidence: 99%

An Exploration of Some Pitfalls of Thematic Map Assessment Using the New Map Tools Resource

et al. 2018

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A variety of metrics are commonly employed by map producers and users to assess and compare thematic maps' quality, but their use and interpretation is inconsistent. This problem is exacerbated by a shortage of tools to allow easy calculation and comparison of metrics from different maps or as a map's legend is changed. In this paper, we introduce a new website and a collection of R functions to facilitate map assessment. We apply these tools to illustrate some pitfalls of error metrics and point out existing and newly developed solutions to them. Some of these problems have been previously noted, but all of them are under-appreciated and persist in published literature. We show that binary and categorical metrics, including information about true-negative classifications, are inflated for rare categories, and more robust alternatives should be chosen. Most metrics are useful to compare maps only if their legends are identical. We also demonstrate that combining land-cover classes has the often-neglected consequence of apparent improvement, particularly if the combined classes are easily confused (e.g., different forest types). However, we show that the average mutual information (AMI) of a map is relatively robust to combining classes, and reflects the information that is lost in this process; we also introduce a modified AMI metric that credits only correct classifications. Finally, we introduce a method of evaluating statistical differences in the information content of competing maps, and show that this method is an improvement over other methods in more common use. We end with a series of recommendations for the meaningful use of accuracy metrics by map users and producers.

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“…Furthermore, the overall accuracy (OA) of GlobeLand30-2010 data is 83.50% and the Kappa coefficient (K) is 0.78. Some research on the analysis and application of the GlobeLand30 data has been made and their results have shown good performance [39,40]. In the present study, the GlobeLand30 product for the 2010 reference year is selected, with the time period ranging from 1 January 2010 to 31 December 2010, and map numbers N50_25 and N50_30.…”

Section: Globeland30 Datamentioning

confidence: 99%

Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China

Liang

Zuo

Huang

et al. 2015

IJGI

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Abstract:Land cover plays an important role in the climate and biogeochemistry of the Earth system. It is of great significance to produce and evaluate the global land cover (GLC) data when applying the data to the practice at a specific spatial scale. The objective of this study is to evaluate and validate the consistency of the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) at a provincial scale (Anhui Province, China) based on the Chinese 30 m GLC product (GlobeLand30). A harmonization method is firstly used to reclassify the land cover types between five classification schemes (International Geosphere Biosphere Programme (IGBP) global vegetation classification, University of Maryland (UMD), MODIS-derived Leaf Area Index and Fractional Photosynthetically Active Radiation (LAI/FPAR), MODIS-derived Net Primary Production (NPP), and Plant Functional Type (PFT)) of MCD12Q1 and ten classes of GlobeLand30, based on the knowledge rule (KR) and C4.5 decision tree (DT) classification algorithm. A total of five harmonized land cover types are derived including woodland, grassland, cropland, wetland and artificial surfaces, and four evaluation indicators are selected including the area consistency, spatial consistency, classification accuracy and landscape diversity in the three sub-regions of Wanbei, Wanzhong and Wannan. The results indicate that the consistency of IGBP is the best among the five schemes of MCD12Q1 according to the correlation coefficient (R). The "woodland" LAI/FPAR is the worst, with a spatial OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 2520 similarity (O) of 58.17% due to the misclassification between "woodland" and "others". The consistency of NPP is the worst among the five schemes as the agreement varied from 1.61% to 56.23% in the three sub-regions. Furthermore, with the biggest difference of diversity indices between LAI/FPAR and GlobeLand30, the consistency of LAI/FPAR is the weakest. This study provides a methodological reference for evaluating the consistency of different GLC products derived from multi-source and multi-resolution remote sensing datasets on various spatial scales.

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The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results

Cited by 134 publications

References 26 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

An Exploration of Some Pitfalls of Thematic Map Assessment Using the New Map Tools Resource

Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China

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