Habitat mapping of coastal dunes with deep learning
Lansu, E.; Reijers, V.C.; Daniëls, F.; James, R.K.; Christianen, M.J.A.; van der Heide, T. (2025). Habitat mapping of coastal dunes with deep learning. Ecological Informatics 92: 103444. https://dx.doi.org/10.1016/j.ecoinf.2025.103444
About one-third of the world's shoreline is defined by sandy coasts with developed dune ecosystems. These ecosystems have drastically degraded due to anthropogenic pressures. To develop strategic management that counteracts this degradation, it is essential to closely monitor ongoing habitat changes. Traditionally, coastal dune monitoring is based on field observations, which are labour intensive and costly. While automated analyses of aerial imagery could reduce monitoring efforts and enhance spatial coverage, to date, its application has remained limited to a single small-scale trial (<2 km2). Here, we trained a Convolutional Neural Network to map the Dutch coastal dunes (562 km2) at 25 cm resolution using six habitat classes: bare sand, shrubs, fresh water, grass, broadleaf trees, and needleleaf trees. Training the network on only RGB imagery resulted in predictions with 92 % accuracy, 80 % average recall and 70 % precision. Model performance increased when the network was trained on all available data – RGB imagery, near-infrared, distance to sea, digital surface model, and canopy height - resulting in 95 % accuracy, 88 % averaged recall and 80 % precision. Finally, we compared the predictions with 499 in-field observations across the Dutch coastal dunes and found 88 % accuracy, 74 % averaged recall and 62 % precision. We used this model to create a map of the entire Dutch coastal dunes, which enables rapid and precise assessments of habitat diversity and extent. As habitat and species diversity are intrinsically linked, our results showcase how automated image analysis can enable biodiversity monitoring on a national scale.
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