Satellite Derived Bathymetry (SDB) is an alternative method to obtain bathymetry information data developed by utilizing image data as data sources. This study aimed to compare the accuracy of five empiric methods: the Stumpf Method, Polynomial Method, Multilinear Regression Method (MLR), Lyzenga Method, and Van Hengel and Spitzer Methods (VHS). This research was located in Benoa, Denpasar, and Bali using SPOT 6 satellite imagery with a spatial resolution of 6 meters as the data source. The acquisition was on August 12, 2017, in situ data. The accuracy test was carried out by calculating the coefficient of determination (R2) and the RMSE value. The SPOT 6 image requires an image interpretation process, including radiometric correction and atmospheric correction using DOS and land and water masking using the NDWI equation to obtain accuracy test and bathymetric information. Stumpf method has an RMSE of 5.72 meters, R2 of 0.27. The polynomial method has an RMSE of 6.99 meters R2 of 0.01. The Multilinear Regression method has an RMSE of 5.75 meters R2 of 0.34. The Lyzenga method has an RMSE of 7.66 meters R2 of 0.09. The Van Hengel and Spitzer method has an RMSE of 6.97 meters R2 of 0.03. Based on the results of calculations from this study, the Stumpf method has the highest accuracy with an RMSE of 5.72.
Remote sensing-based research in Indonesia using satellite imagery frequently faces the challenge of cloud coverage due to the tropical country. One spatial data that can be extracted from satellite imagery is bathymetry. However, cloud-covered water bathymetric extraction still needs to be examined. This study aims to understand the ability of Landsat 7 ETM+ acquired on 29 July 2013, and Landsat 8, acquired on 24 July 2020, as the representative of non-cloudy image compared to Landsat 8, acquired on 9 August 2020, as the cloudy image. Stumpf algorithm was applied, including a statistical approach of linear regression analysis with in-situ data measurement from Single Beam Echo-Sounder (SBES) to derive the absolute bathymetric map with several classes of depth ranging from 0 – 2 m up to 10 m. To assess the accuracy, RMSE and confusion matrix was used. The result shows that Landsat 7 ETM+ yields the highest R2 with 0,52, while the lowest total RMSE (8,167 m) and highest overall accuracy of about 69% from the confusion matrix was achieved by the cloudy image of Landsat 8. Nevertheless, the highest absolute depth value yield by Landsat 8 non-cloudy image with 16,1 m. This research confirms that the highest R2 value does not always produce the best model, but it is still promised to be used. Furthermore, the quality of the imagery based on its percentage of cloud coverage is affecting the resulted model.
Processing of satellite image data for the detection of platform reef lagoons is intended as one of the geo-physical parameters of the reef landform. Panggang Island and Semakdaun Island were chosen to make the detection model because they are ideal for lagoon reef landforms and tapulang court reefs. This model is only valid in the continental shelf area and the back arc and small island tectonic type. Determination of this location is done to improve the accuracy of spectral-based data processing. Platform reefs are one of four classes of reef landforms. Sentinel-2A data with a spatial resolution of 10m, blue, green, red, and near infrared bands were selected to investigate their ability to detect lagoons. Processing of data by calculating the Optimum Index Factor (OIF) to produce a composite image and drawing transect lines to produce pixel values and spectral graphics of the lagoon. The results of data processing in the form of graphs, composite images and pixel values were built to realize a digital lagoon detection model. These results are used for lagoon growth stage analysis for the classification of three reef platform landforms, visually and digitally interpretation. This digital and visual detection system design is useful for monitoring coral reef ecosystems.
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