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.
Highlights
Land cover is an essential climate variable [1] as it strongly influences current and future climate, with rapid changes in the landscape due to human activities [2]
GL30 exists at the global scale but it only has a small number of high level classes, e.g., only one urban class
The results showed that, for the chosen study areas of Kathmandu, Nepal and Dar es Salaam, Tanzania, the conversion of OSM data into the GL30 classes, and the integration of the resultant maps with the original GL30, produced more up-to-date maps, with a higher overall accuracy (OAc)
Summary
Land cover is an essential climate variable [1] as it strongly influences current and future climate, with rapid changes in the landscape due to human activities [2]. The FROM-GLC (Finer Resolution Observation and Monitoring of Global Land Cover) product has been created using fully automated approaches [4] while the GlobeLand (GL30) product has been developed using a combination of automated and manual methods [5]. The availability of detailed OSM data in these areas represents a source of up-to-date information that could be used to generate LULC maps that are more accurate or detailed than currently available global products. The aim of this paper is to present a methodology that can answer these questions, i.e., demonstrate how to create two LULC maps from OSM using the nomenclatures from the GL30, produced by the National Geomatic Center of China, and the Urban Atlas (UA), produced by the European Environment Agency The former is used to produce an updated version of GL30 with more up-to-date information. The procedure is tested on two study areas in places where OSM coverage is good but where detailed authoritative LULC maps are not readily available
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