Abstract:Using airborne drones to monitor water quality in inland, transitional or coastal surface waters is an emerging research field. Airborne drones can fly under clouds at preferred times, capturing data at cm resolution, filling a significant gap between existing in situ, airborne and satellite remote sensing capabilities. Suitable drones and lightweight cameras are readily available on the market, whereas deriving water quality products from the captured image is not straightforward; vignetting effects, georefer… Show more
“…Variants of the algorithm involving its applicability on red to NIR wavelengths were reported in various studies, where [4,21] used 660 nm and 668 nm, and [41] chose 710 nm instead. [39] suggested 681 nm, which has the lowest error rate for suspended particle matter (SPM) retrieval.…”
Section: Assessment Of Pre-processing Methods With Turbidity Retrievalmentioning
confidence: 99%
“…Turbidity retrieval from UAV imagery with machine learning (ML) models appears to show promise. In another UAV study by [50], where various ML models were evaluated for the retrieval of suspended solids (SS), SS prediction in ranges from R 2 = 0.91 to 0.99, which performed significantly better than some studies using semi-analytical algorithms, e.g., [4,21]. With the development of CoastalWQL, its application, together with ML models, was used to extensively monitor turbidity plumes associated with land reclamation activities with an R 2 = 0.75 (see [5]).…”
Section: Retrieval Of Turbiditymentioning
confidence: 99%
“…For instance, the results obtained in this study using Nechad et al's (2010) [39] semi-analytical algorithm at 715 nm showed close correspondence with the results obtained in a similar study that estimated dredge-induced turbidity plume using the same semi-analytical algorithm at 660 nm, where the study achieved a RMSE of 3.39 with a UAV-borne MSI (Parrot Sequoia) for turbidity values ranging from 3.3 to 72.4 FNU [4]. Another study using the same semi-analytical algorithm for turbidity retrieval at 668 nm obtained an RMSE of 10.13 FNU with the MicaSense Dual Camera System [21].…”
Section: Retrieval Of Turbiditymentioning
confidence: 99%
“…The recalibration of the coefficients is required since A τ and C relate to IOPs such as absorption by particle and non-algal particles and particulate backscatter, which depends on the morphology and characteristics of the particles, as well as turbidity concentration. Recalibration of A τ and C coefficients were similarly conducted by [21] where the derived coefficients are 366.14 and 0.1956 for A τ and C at 668 nm.…”
Section: Retrieval Of Turbiditymentioning
confidence: 99%
“…Recent studies by [21,22] adopted a direct georeferencing method for high-resolution UAV RGB/multispectral snapshot imagery, in which the MosaicSeadron from [22] achieved an error rate (standard deviation) of 2.51 m at a ground sample distance (GSD) of 0.5 m/px, as well as increased the ground coverage to 33 hectares as compared to 13.45 hectares with the SfM to mosaic snapshot images of water bodies. However, the direct-georeferencing method for snapshot imagery may not always be applicable to push-broom imagery where the image output, metadata, and the temporal resolution of the captures and measurement of flight parameters are vastly different from that of snapshot imagery due to the nature of the imaging principles that differ significantly between push-broom and snapshot imaging (see Appendix A) [23,24].…”
Uncrewed-Aerial Vehicles (UAVs) and hyperspectral sensors are emerging as effective alternatives for monitoring water quality on-demand. However, image mosaicking for largely featureless coastal water surfaces or open seas has shown to be challenging. Another pertinent issue observed is the systematic image misalignment between adjacent flight lines due to the time delay between the UAV-borne sensor and the GNSS system. To overcome these challenges, this study introduces a workflow that entails a GPS-based image mosaicking method for push-broom hyperspectral images, together with a correction method to address the aforementioned systematic image misalignment. An open-source toolkit, CoastalWQL, was developed to facilitate the workflow, which includes essential pre-processing procedures for improving the image mosaic’s quality, such as radiometric correction, de-striping, sun glint correction, and object masking classification. For validation, UAV-based push-broom hyperspectral imaging surveys were conducted to monitor coastal turbidity in Singapore, and the implementation of CoastalWQL’s pre-processing workflow was evaluated at each step via turbidity retrieval. Overall, the results confirm that the image mosaicking of the push-broom hyperspectral imagery over featureless water surface using CoastalWQL with time delay correction enabled better localisation of the turbidity plume. Radiometric correction and de-striping were also found to be the most important pre-processing procedures, which improved turbidity prediction by 46.5%.
“…Variants of the algorithm involving its applicability on red to NIR wavelengths were reported in various studies, where [4,21] used 660 nm and 668 nm, and [41] chose 710 nm instead. [39] suggested 681 nm, which has the lowest error rate for suspended particle matter (SPM) retrieval.…”
Section: Assessment Of Pre-processing Methods With Turbidity Retrievalmentioning
confidence: 99%
“…Turbidity retrieval from UAV imagery with machine learning (ML) models appears to show promise. In another UAV study by [50], where various ML models were evaluated for the retrieval of suspended solids (SS), SS prediction in ranges from R 2 = 0.91 to 0.99, which performed significantly better than some studies using semi-analytical algorithms, e.g., [4,21]. With the development of CoastalWQL, its application, together with ML models, was used to extensively monitor turbidity plumes associated with land reclamation activities with an R 2 = 0.75 (see [5]).…”
Section: Retrieval Of Turbiditymentioning
confidence: 99%
“…For instance, the results obtained in this study using Nechad et al's (2010) [39] semi-analytical algorithm at 715 nm showed close correspondence with the results obtained in a similar study that estimated dredge-induced turbidity plume using the same semi-analytical algorithm at 660 nm, where the study achieved a RMSE of 3.39 with a UAV-borne MSI (Parrot Sequoia) for turbidity values ranging from 3.3 to 72.4 FNU [4]. Another study using the same semi-analytical algorithm for turbidity retrieval at 668 nm obtained an RMSE of 10.13 FNU with the MicaSense Dual Camera System [21].…”
Section: Retrieval Of Turbiditymentioning
confidence: 99%
“…The recalibration of the coefficients is required since A τ and C relate to IOPs such as absorption by particle and non-algal particles and particulate backscatter, which depends on the morphology and characteristics of the particles, as well as turbidity concentration. Recalibration of A τ and C coefficients were similarly conducted by [21] where the derived coefficients are 366.14 and 0.1956 for A τ and C at 668 nm.…”
Section: Retrieval Of Turbiditymentioning
confidence: 99%
“…Recent studies by [21,22] adopted a direct georeferencing method for high-resolution UAV RGB/multispectral snapshot imagery, in which the MosaicSeadron from [22] achieved an error rate (standard deviation) of 2.51 m at a ground sample distance (GSD) of 0.5 m/px, as well as increased the ground coverage to 33 hectares as compared to 13.45 hectares with the SfM to mosaic snapshot images of water bodies. However, the direct-georeferencing method for snapshot imagery may not always be applicable to push-broom imagery where the image output, metadata, and the temporal resolution of the captures and measurement of flight parameters are vastly different from that of snapshot imagery due to the nature of the imaging principles that differ significantly between push-broom and snapshot imaging (see Appendix A) [23,24].…”
Uncrewed-Aerial Vehicles (UAVs) and hyperspectral sensors are emerging as effective alternatives for monitoring water quality on-demand. However, image mosaicking for largely featureless coastal water surfaces or open seas has shown to be challenging. Another pertinent issue observed is the systematic image misalignment between adjacent flight lines due to the time delay between the UAV-borne sensor and the GNSS system. To overcome these challenges, this study introduces a workflow that entails a GPS-based image mosaicking method for push-broom hyperspectral images, together with a correction method to address the aforementioned systematic image misalignment. An open-source toolkit, CoastalWQL, was developed to facilitate the workflow, which includes essential pre-processing procedures for improving the image mosaic’s quality, such as radiometric correction, de-striping, sun glint correction, and object masking classification. For validation, UAV-based push-broom hyperspectral imaging surveys were conducted to monitor coastal turbidity in Singapore, and the implementation of CoastalWQL’s pre-processing workflow was evaluated at each step via turbidity retrieval. Overall, the results confirm that the image mosaicking of the push-broom hyperspectral imagery over featureless water surface using CoastalWQL with time delay correction enabled better localisation of the turbidity plume. Radiometric correction and de-striping were also found to be the most important pre-processing procedures, which improved turbidity prediction by 46.5%.
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