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Optimizing the scale of observation for intertidal habitat classification through multiscale analysis

Espriella Michael C. et Lecours Vincent. (2022). Optimizing the scale of observation for intertidal habitat classification through multiscale analysis. Drones, 6, (6), p. 140.

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Monitoring intertidal habitats, such as oyster reefs, salt marshes, and mudflats, is logistically challenging and often cost- and time-intensive. Remote sensing platforms, such as unoccupied aircraft systems (UASs), present an alternative to traditional approaches that can quickly and inexpensively monitor coastal areas. Despite the advantages offered by remote sensing systems, challenges remain concerning the best practices to collect imagery to study these ecosystems. One such challenge is the range of spatial resolutions for imagery that is best suited for intertidal habitat monitoring. Very fine imagery requires more collection and processing times. However, coarser imagery may not capture the fine-scale patterns necessary to understand relevant ecological processes. This study took UAS imagery captured along the Gulf of Mexico coastline in Florida, USA, and resampled the derived orthomosaic and digital surface model to resolutions ranging from 3 to 31 cm, which correspond to the spatial resolutions achievable by other means (e.g., aerial photography and certain commercial satellites). A geographic object-based image analysis (GEOBIA) workflow was then applied to datasets at each resolution to classify mudflats, salt marshes, oyster reefs, and water. The GEOBIA process was conducted within R, making the workflow open-source. Classification accuracies were largely consistent across the resolutions, with overall accuracies ranging from 78% to 82%. The results indicate that for habitat mapping applications, very fine resolutions may not provide information that increases the discriminative power of the classification algorithm. Multiscale classifications were also conducted and produced higher accuracies than single-scale workflows, as well as a measure of uncertainty between classifications.

Type de document:Article publié dans une revue avec comité d'évaluation
Pages:p. 140
Version évaluée par les pairs:Oui
Date:7 Juin 2022
Nombre de pages:1
Identifiant unique:10.3390/drones6060140
Sujets:Sciences naturelles et génie > Sciences appliquées > Océanographie
Sciences naturelles et génie > Sciences naturelles > Sciences de la terre (géologie, géographie)
Département, module, service et unité de recherche:Départements et modules > Département des sciences fondamentales
Unités de recherche > Centre de recherche sur la Boréalie (CREB)
Mots-clés:UAS, multiscale, geographic object-based image analysis, oyster, habitat mapping, scale, drone, UAV, Florida, coastal
Déposé le:27 oct. 2023 18:05
Dernière modification:27 oct. 2023 18:05
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