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Published December 23, 2025 | Version v1
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Wetland classification and revitalisation monitoring by using drone data

Authors/Creators

  • 1. Slovak Academy of Sciences, Bratislava, Slovakia|Slovak University of Technology in Bratislava, Bratislava, Slovakia|Algoritmy:SK s.r.o., Bratislava, Slovakia
  • 2. Slovak Academy of Sciences, Bratislava, Slovakia
  • 3. BROZ – Conservation Association, Bratislava, Slovakia
  • 4. Algoritmy:SK s.r.o., Bratislava, Slovakia|Slovak University of Technology in Bratislava, Bratislava, Slovakia
  • 5. Slovak University of Technology in Bratislava, Bratislava, Slovakia|Algoritmy:SK s.r.o., Bratislava, Slovakia

Description

Wetlands are essential ecosystems increasingly threatened by human activities and climate change. This study presents a method for classifying and monitoring wetland habitats in the Čiližská Radvaň protected area (Slovak Republic) using RGB drone imagery and the Natural Numerical Network (NatNet), a mathematically based supervised deep learning approach. The primary aim was to evaluate the effectiveness of NatNet in identifying target habitat types and to assess the impact of ongoing revitalisation efforts. Habitat types were classified using RGB drone imagery and ground-truth training polygons that represented the dominant vegetation communities in Čiližská Radvaň wetland. The NatNet achieved the training classification success rate exceeding 97%, allowing the creation of relevancy maps successfully identifying spatial habitat distribution. Relevancy maps verified in the field reached classification accuracy of 0.88 and F1 score of 0.90 across all habitats together. Results showed observable shifts in habitat extent and structure after one year of restoration, confirming the suitability of the method for detecting ecological changes in wetland environments.

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Cites
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Publication: 10.1016/j.isprsjprs.2021.01.012 (DOI)
Publication: 10.1007/978-94-007-6173-5 (DOI)
Publication: 10.1007/s10750-025-05809-1 (DOI)
Publication: 10.3390/drones5020045 (DOI)
Publication: 10.1023/A:1009800327334 (DOI)
Publication: 10.1002/rse2.363 (DOI)
Publication: 10.3390/rs11192210 (DOI)
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Publication: 10.1007/s10530-016-1259-2 (DOI)
Publication: 10.1371/journal.pone.0200288 (DOI)
Publication: 10.1016/j.ecolind.2022.108949 (DOI)
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Publication: 10.1016/j.gecco.2020.e01027 (DOI)
Publication: 10.3390/w15132410 (DOI)
Publication: 10.1046/j.1526-100x.1999.07108.x (DOI)
Has part
Figure: 10.3897/ved.175765.figure6 (DOI)
Figure: 10.3897/ved.175765.figure1 (DOI)
Figure: 10.3897/ved.175765.figure2 (DOI)
Figure: 10.3897/ved.175765.figure3 (DOI)
Figure: 10.3897/ved.175765.figure4 (DOI)
Figure: 10.3897/ved.175765.figure5 (DOI)
Figure: 10.3897/ved.175765.figure7 (DOI)
Other: 10.3897/ved.175765.suppl1 (DOI)

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