Deteksi Batimetri Perairan Dangkal di Pulau Menjangan, Provinsi Bali Menggunakan Citra Landsat

Gerardus David Ady Purnama Bayuaji, Seftiawan Samsu Rijal, Kuncoro Teguh Setiawan, Kholifatul Aziz
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Abstract

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.
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利用陆地卫星图像探测巴厘岛门詹甘岛的当卡尔水位计
印度尼西亚是一个热带国家,利用卫星图像进行的遥感研究经常面临云层覆盖的挑战。可以从卫星图像中提取的一个空间数据是水深测量。然而,云层覆盖水域的水深提取仍需进行检查。本研究旨在了解2013年7月29日采集的陆地卫星7 ETM+和2020年7月24日采集的土地卫星8作为非多云图像的代表与2020年8月9日采集的地面卫星8作为多云图像的能力。应用Stumpf算法,包括线性回归分析的统计方法和单波束回声测深仪(SBES)的现场数据测量,以导出深度从0–2 m到10 m的几个类别的绝对水深图。为了评估精度,使用了RMSE和混淆矩阵。结果表明,陆地卫星7号ETM+产生的R2最高,为0,52,而陆地卫星8号的多云图像获得了最低的总均方根误差(8167m)和最高的混淆矩阵总精度,约为69%。尽管如此,陆地卫星8号非云图像的绝对深度值最高,为16,1m。这项研究证实,最高的R2值并不总是产生最佳模型,但它仍然有望被使用。此外,基于云覆盖率的图像质量正在影响最终模型。
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审稿时长
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