E3S Web of Conferences (Jan 2024)

Remote sensing estimation of water transparency for Saguling Dam in the past decade (2013-2022) based on Landsat 8

  • Awfa Dion,
  • Wicaksono Aditya Nugroho,
  • Putra Raden,
  • Soewondo Prayatni

DOI
https://doi.org/10.1051/e3sconf/202448503003
Journal volume & issue
Vol. 485
p. 03003

Abstract

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Water transparency (i.e., Secchi disk depth (SDD) reflects light transmission capacity of a water body and influences growth of aquatic plants, aquatic organisms, and primary productivity. In this study, using remote sensing reflectance (Landsat 8) and SDD datasets, we predicted the water transparency in Saguling Dam for the first time. The results indicated that the models in the visible (Band 1, Band 2, and Band 3) combined with near infrared (Band 4 and Band 5) are the most robust and reliable to estimate SDD for Saguling Dam. Subsequently, multiple linear regression model was built using 125 pairs of in situ SDD results and concurrent with Landsat images during the last decade (i.e., 2013-2022). The models were validated with an independent dataset of 33 SDD measurement. The in situ remote sensing model compared well with the in situ SDD measurement where determinant coefficient (adjusted R2) and root mean square error (RMSE) ranging from 0.35 - 0.94 and 0.3 - 1.4 m, respectively. Finally, the model was applied to Landsat 8 images acquired between 2013-2022 to elucidate the spatial distribution of SDD in Saguling Dam for each year (i.e. temporal variations) with water area around 33.7 km2. The estimation results indicated that water transparency values in the inlet and outlet zone have relatively low SDD, with SDD value ranging from 0.019 – 1.01 m and 0.24 – 1.04 m, respectively. Furthermore, based on the National Water Quality Standards (i.e., Indonesia Government Regulations No. 22 year 2021/ Peraturan Pemerintah No. 22 Tahun 2021), the estimation of spatial SDD results indicated that > 99% of Saguling Dam water bodies were classified as Class IV (i.e., could be only used for irrigation). This study provides the first comprehensive remote sensing model for Saguling Dam and can provide essential information for local water quality conservation.