Accurate large-scale mapping of coastal dune types is critical for coastal management but remains challenging due to the dunes’ high spatial heterogeneity and the spectral complexity in coastal environments. Therefore, this study aims to develop a coastal dune mapping approach and demonstrate its applicability across Australia and New Zealand. Firstly, we leveraged geological maps to delineate the spatial extent of coastal dune fields, effectively masking out non-dune areas. Secondly, we applied an object-based image analysis (OBIA), followed by a random forest classifier that integrated multi-source features to classify six coastal dune types within the delineated coastal dune fields, including active dunes, foredunes, sandy beaches, semi-stabilized dunes, stabilized dunes, and wetlands (dune slacks). Based on this approach, we generated a 10 m fine classification map of coastal dune types across Australia and New Zealand, covering a total area of 27,564 km 2 . Accuracy assessment yielded an overall classification accuracy of 90.68 %. Most categories achieved satisfactory performance, with stabilized dunes showing the highest accuracy (96.81 %). We also found that approximately 498 km 2 of coastal dune area has been converted to urban development land. However, this development pressure was mitigated by conservation efforts, as 15,781.80 km 2 (59.44 %) of the total coastal dune area was situated within protected areas. This study provides a replicable methodology for large-scale fine classification of coastal dune types. The resulting map offers scientific support for monitoring coastal dune systems state and evolution under human intervention.
Zheng et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: