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dc.contributor.authorDube, Timothy
dc.contributor.authorMazvimavi, Dominic
dc.contributor.authorSibanda, Mbulisi
dc.contributor.authorMutanga, Onisimo
dc.date.accessioned2023-06-19T08:02:59Z
dc.date.available2023-06-19T08:02:59Z
dc.date.issued2022
dc.identifier.citationSibanda, M., Mutanga, O., Chimonyo, V.G., Clulow, A.D., Shoko, C., Mazvimavi, D., Dube, T. and Mabhaudhi, T., 2022. Correction: Sibanda et al. Application of drone technologies in surface water resources monitoring and assessment: A systematic review of progress, challenges, and opportunities in the Global South. Drones 2021, 5, 84. Drones, 6(5), p.131.en_US
dc.identifier.urihttps://doi.org/10.3390/drones6050131
dc.identifier.urihttp://hdl.handle.net/10566/9106
dc.description.abstractIn the original publication [1], “Fahad Alawadi. Detection of surface algal blooms using the newly developed algorithm surface algal bloom index (SABI)”, “Proc. SPIE 7825, Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2010, 782506 (18 October 2010); https://doi.org/10.1117/12.862096” [2] was not cited. The citation has now been inserted in “3.5. The Role of Drone Data Derived Vegetation Indices and Machine Algorithms in Remote Sensing Water Quality and Quantity” as reference [60] and should read: “Numerous vegetation indices were derived from drone remotely sensed data for characterizing surface water quality and quantity. The most widely used sections of the electromagnetic spectrum in detecting water quality parameters were the visible section (blue and green) and the NIR wavebands. In this regard, vegetation indices such as the red and near-infrared (NIR), Surface Algal Bloom Index (SABI) [60], two-band algorithm (2BDA) [26], NDVI, and Green NDV [33], as well as band combinations and differencing such as (R+NIR/G) were used mostly in characterizing chlorophyll content as well as TSS.en_US
dc.language.isoenen_US
dc.publisherDronesen_US
dc.subjectDrone Technologiesen_US
dc.subjectSurface Water Resourcesen_US
dc.subjectGlobal Southen_US
dc.subjectWateren_US
dc.subjectAlgorithmen_US
dc.titleCorrection: sibanda et al. application of drone technologies in surface water resources monitoring and assessment: a systematic review of progress, challenges, and opportunities in the global south. drones 2021, 5, 84en_US
dc.typeArticleen_US


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