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<pre>Hello to all list members,
I am writing to share with you our recently published paper on forest type stratification titled ""Leveraging OSM and GEOBIA to Create and Update Forest Type Maps". We use OSM landuse=forest + leaf_type=mixed information as bounding areas for region growing segmentation on aerial imagery and the main idea is to derive additional leaf type information on forests, which are tagged with leaf_type=mixed, especially on those which have a large area. We also classify the segments based on training data derived from OSM. We therefore only use OSM and open data from the Grand Duchy of Luxembourg as well as GRASS GIS and R to carry out the workflow, which makes the approach reproducible and transferable.
Brauchler, M.; Stoffels, J. Leveraging OSM and GEOBIA to Create and Update Forest Type Maps. ISPRS Int. J. Geo-Inf. 2020, 9(9), 499; https://doi.org/10.3390/ijgi9090499
<a href="https://www.mdpi.com/2220-9964/9/9/499">https://www.mdpi.com/2220-9964/9/9/499</a>
As this is my first scientific contribution, I am happy to start a discussion and to get in touch with others who might be interested in the topic or who are working on similar projects.<br />Kind regards,<br />Melanie<br /><br />Abstract<br />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 (<span class="html-italic">landuse=forest</span>). The OSM data model includes describing tags for its contents, i.e., leaf type for forest areas (i.e., <span class="html-italic">leaf_type=broadleaved</span>). Although the leaf type tag is common, the vast majority of forest areas are tagged with the leaf type <span class="html-italic">mixed</span>, amounting to a total area of 87% of <span class="html-italic">landuse=forests</span> 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 <span class="html-italic">needleleaved</span> and <span class="html-italic">broadleaved</span> 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.<br /><br /><br /></pre>
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