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

Abstract

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.

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

“…The accuracy of artificial surfaces was tested in eight areas around the world, which ranged from 79% to 97%, showing improvements when compared with CORINE Land Cover [54] and the FROM-GLC product [55]. A more recent validation of GlobeLand30 for Dar Es Salaam, Tanzania, and Kathmandu, Nepal, yielded overall accuracies of 61% and 54%, respectively [56]. Other independent validations of GlobeLand30 include comparisons with authoritative products in Italy [57], Germany [58], Iran [59], and water bodies in Scandinavian countries [60], with agreements of greater than 78%.…”

A global assessment of gross and net land change dynamics for current conditions and future scenarios

“…for Africa (see Fig. 2) where accuracies from regional case studies were also reported to be lower (Fonte et al, 2017;See et al, 2017). Nonetheless, despite greater challenges for a consistent land cover classification at a global level and its inherent complexity to compete with regional and continental datasets, global datasets are able to provide us with a more comprehensive picture over the entire globe by also providing information for areas that are currently underrepresented by regional and continental datasets, e.g.…”

“…Regional accuracy assessments of Globeland30 yielded lower accuracies, e.g. for Dar Es Salaam (Tanzania) and Kathmandu (Nepal) accuracies were 61 and 54 %, respectively (Fonte et al, 2017), while in Kenya it was found to be 56-64 % . Accuracies for Italy (Brovelli et al, 2015), Germany (Jokar Arsanjani et al, 2016a), and Iran (Jokar Arsanjani et al, 2016b) were greater than 78 % .…”

An Open Source Approach for the Intrinsic Assessment of the Temporal Accuracy, Up-to-Dateness and Lineage of Openstreetmap

“…Hecht et al, 2013;Fan et al, 2014;Brovelli et al, 2016b), land use (see e.g. Jokar Arsanjani et al, 2015b;Fonte et al, 2017) and points of interest (see e.g. Girres and Touya, 2010;Jackson et al, 2013).…”