Leveraging OSM and Geobia to Create and Update Forest Type Maps
Bibliographic Data
| ID | 22032508 |
|---|---|
| Authors | Melanie Brauchler (0000-0002-1013-9597, Universität Trier, corresponding author), Johannes Stoffels (0000-0003-3509-7773, Universität Trier) |
| Year | 2020 |
| Volume | 9 |
| Issue | 9 |
| Pages | 499 |
| Publication date | 2020-08-21 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9090499 |
| OpenAlex | W3081353520 |
| Language | EN |
| References cited | 56 |
Up-to-date information about the type and spatial distribution of forests is an essential element in both sustainable forest management and environmental monitoring and modelling. The OpenStreetMap (OSM) database contains vast amounts of spatial information on natural features, including forests (landuse=forest). The OSM data model includes describing tags for its contents, i.e., leaf type for forest areas (i.e., leaf_type=broadleaved). Although the leaf type tag is common, the vast majority of forest areas are tagged with the leaf type mixed, amounting to a total area of 87% of landuse=forests from the OSM database. These areas comprise an important information source to derive and update forest type maps. In order to leverage this information content, a methodology for stratification of leaf types inside these areas has been developed using image segmentation on aerial imagery and subsequent classification of leaf types. The presented methodology achieves an overall classification accuracy of 85% for the leaf types needleleaved and broadleaved in the selected forest areas. The resulting stratification demonstrates that through approaches, such as that presented, the derivation of forest type maps from OSM would be feasible with an extended and improved methodology. It also suggests an improved methodology might be able to provide updates of leaf type to the OSM database with contributor participation
Cartography · Geography · Land use · Remote sensing · Segmentation · Spatial analysis · Computer Science · Land Use and Ecosystem Services · Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture · Artificial Intelligence · Ecology
| Citation velocity | historical |
|---|---|
| Highly cited | No |