Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Geosfera Indonesia
Farida Ayu
Department of Geography, Faculty of Mathematics and Science, Universitas Indonesia, Depok, 16424, Indonesia
Earth & Planetary Sciences Education Environmental Science

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Decadal Monitoring of Upwelling Dynamics in Satonda Island Waters Using Landsat-8 and Machine Learning Regression Anisya Feby Efriana; Masita Dwi Mandini Manessa; Farida Ayu; Astrid Damayanti; Muhammad Haidar; Kuncoro Teguh Setiawan
Geosfera Indonesia Vol. 9 No. 2 (2024): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v9i2.47203

Abstract

Global warming and associated weather changes, notably the El Niño Southern Oscillation (ENSO), significantly impact marine ecosystems by altering water quality parameters such as chlorophyll-a (Chl-a) and sea surface temperature (SST). These changes are crucial in understanding the biogeochemical and ecological dynamics of marine environments, especially in regions affected by upwelling. This study aims to monitor upwelling events on Satonda Island, a volcanic island with unique central lake and status as a protected area using remote sensing. Utilizing Landsat-8 imagery and machine learning regression techniques—Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART)—this research evaluates the water quality in Satonda waters over a decade (2013– 2023). The RF method emerged as the most accurate in estimating Chl-a and SST, indicating its efficacy in monitoring marine ecosystems with the result (RMSE = 0.309 and 0.274). The analysis reveals seasonal upwelling patterns, characterized by decreased SST and increased Chl-a concentration, with peaks varying annually between June and November. This study highlights the crucial role of remote sensing and machine learning in monitoring the effects of climate change on marine biodiversity. It provides valuable insights into the temporal dynamics of upwelling in the shallow waters of Indonesia.
Co-Authors Anisya Feby Efriana Astrid Damayanti Kuncoro Teguh Setiawan Masita Dwi Mandini Manessa Muhammad Haidar