Automatic extraction of large-scale aquaculture encroachment areas using Canny Edge Otsu algorithm in Google earth engine – the case study of Kolleru Lake, South India

Kolli, Meena Kumari, Opp, Christian, Karthe, Daniel and Pradhan, Biswajeet, (2022). Automatic extraction of large-scale aquaculture encroachment areas using Canny Edge Otsu algorithm in Google earth engine – the case study of Kolleru Lake, South India. Geocarto International, 1-17

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  • Sub-type Journal article
    Author Kolli, Meena Kumari
    Opp, Christian
    Karthe, Daniel
    Pradhan, Biswajeet
    Title Automatic extraction of large-scale aquaculture encroachment areas using Canny Edge Otsu algorithm in Google earth engine – the case study of Kolleru Lake, South India
    Appearing in Geocarto International
    Publication Date 2022-03-10
    Place of Publication London
    Publisher Taylor & Francis
    Start page 1
    End page 17
    Language eng
    Abstract The aquaculture expansion has made significant contributions toglobal food security, socio-economic development and, if imple-mented sustainably, can help preserve stable coastal environ-ments. This study explicitly details the rapid expansion of large-scale aquaculture growth across the Kolleru and Upputeru regionsof South India. We developed a novel classification method forautomated extraction of aquaculture ponds in the Kolleru zoneusing the Canny Edge-Otsu algorithm to segment and extract theponds applied to SAR-VV images in Google Earth Engine. Thisapproach enables the area estimation of dense aquaculture pondsare essential for monitoring and management purposes. Theresults indicated that this method could effectively map the aqua-culture ponds and the overall accuracy achieved in 2020 for theKolleru and Upputeru areas by 90.6% and 95.7%, respectively. Theaquaculture maps of this study can help government organiza-tions, resource managers, stakeholders, and decision-makersunderstand the dynamics and plan sustainable measures inthis area.
    Keyword Google Earth Engine
    CannyEdge-Otsu threshold
    Sentinel-1
    remote sensing
    image segmentation
    Copyright Holder Informa UK Limited
    Copyright Year 2022
    Copyright type All rights reserved
    DOI 10.3390/resources11100093
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    Created: Thu, 12 Jan 2023, 22:11:23 JST by María Eugenia de la Garza Leal on behalf of UNU FLORES