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Dryad

Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs

Cite this dataset

Nieuwenhuis, Brian Owain et al. (2023). Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs [Dataset]. Dryad. https://doi.org/10.5061/dryad.6m905qg2p

Abstract

Very shallow coral reefs (< 5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV-imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products to ecologically relevant habitat maps are still relatively underdeveloped. Here we demonstrate that incorporating geomorphometric variables (the DEM and its derivatives) in addition to spectral information (the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM, and classified through Object-Based Image Analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV’s DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset.

Methods

This dataset contains the Supplementary Data to:

 

Nieuwenhuis, B.O.; Marchese, F.; Casartelli, M.; Sabino, A.; van der Meij, S.E.T.; Benzoni, F. 

Integrating a UAV-Derived DEM in Object-Based Image Analysis Increases Habitat Classification Accuracy on Coral Reefs.

Remote Sensing. 2022, 14, 5017

 

There are 4 datasets stored here.

  • Dataset 1. UAV orthomosaics and DEMs
    • File folder with the UAV orthomosaics and DEMs as .tiff files.
    • Orthomosaics and DEMs were constructed by processing DJI Phantom 4 RTK images with the Structure-from-Motion Software Pix4D.
    • The file folder also contains a README.txt file with further explanation.
  • Dataset 2. Snorkel transect orthomosaics
    • File folder with the orthomosaics of the snorkel transects as .tiff files.
    • Orthomosaics were created from GoPro Hero 9 imagery processed with the Structure-from-Motion Software Agisoft Metashape.
    • The file folder also contains a README.txt file with further explanation.
  • Dataset 3. Habitat classifications
    • File folder with the habitat classifications generated in eCognition as 'shapefiles'.
    • Please note that a shapefile consists of multiple files with the extensions: .shp, .shx, .dbf, .cpg, .prj, and .shp.xml.
    • All these files should be kept in the same directory.
    • The file folder also contains a README.txt file with further explanation.
  • Dataset 4. Manual classifications for accuracy assessment
    • File folder with the manual classifications that were used for the accuracy assessment of the automatic classifications in Dataset 3.
    • Manual classifications were constructed based on a on-screen interpretation of Dataset 1 and 2 and are stored as 'shapefiles'.
    • Please note that a shapefile consists of multiple files with the extensions: .shp, .shx, and .dbf, .cpg, .prj, and .shp.xml.
    • All these files should be kept in the same directory.
    • The file folder also contains a README.txt file with further explanation.

Usage notes

GIS software is necessary to use and view the data. We advise the use of either ArcGis or QGIS (open-source).

Funding

King Abdullah University of Science and Technology, Award: BAS11090-01-01

Groninger Universiteitsfonds, Award: 2021AU050