Deep Learning-Based Early Detection of Harmful Algal Blooms in Jakarta Bay Using High Resolution Satellite Imagery
Zahra Z. A. (a), Hanafie A. (b) , Semedi B. (c) and Chang K.T. (a*)

a) Dept. of Civil Eng. and Environmental Informatics, Minghsin Uni. of Science and Technology, Taiwan.
b) Strong Engineering Consulting Co., Shalu Township, Taichung County, Taiwan.
c) Sekolah Pascasarjana, Universitas Brawijaya, Indonesia.

Abstract

Over the past two decades, Jakarta Bay has experienced recurring harmful algal bloom events, with a noticeable increase in their frequency, intensity, and duration in recent years. These blooms have resulted in considerable ecological degradation, including widespread fish mortality, and have negatively affected local fisheries, tourism, and other coastal economic sectors. The intensification of such events is widely attributed to escalating anthropogenic pressures, including urban runoff and industrial effluents from surrounding areas. In response to this growing concern, the present study proposes an early detection framework that integrates high resolution satellite imagery with deep learning techniques. Multispectral data from Sentinel-3A and Sentinel-3B Ocean and Land Colour Instruments, acquired during the early months of 2019, particularly in March, were utilised. The analysis focused on spectral bands sensitive to chlorophyll and phytoplankton concentrations, specifically Band 4 at 490 nm, Band 6 at 560 nm, Band 8 at 665 nm, Band 10 at 681.25 nm, Band 11 at 708.75 nm, and Band 17 at 865 nm. Ground truth data from Hawkeye sensors collected during the same period were employed for validation. A convolutional neural network was developed to extract and classify spatial and spectral features associated with harmful algal bloom presence. The input data underwent rigorous pre-processing, including atmospheric correction and spatial alignment. The proposed model demonstrated robust predictive performance, achieving a classification accuracy exceeding 90%, with high precision, recall, and F1 score. These findings underscore the potential of combining artificial intelligence and satellite-based Earth observation to enable timely and accurate monitoring of harmful algal blooms. This approach offers a scalable and operationally viable tool for supporting proactive coastal management in ecologically and economically vulnerable marine environments such as Jakarta Bay.

Keywords: convolutional neural network, harmful algal bloom, chlorophyll detection, Jakarta Bay

Topic: Topic C: Emerging Technologies in Remote Sensing