POSTER Session 1

UAV Hyperspectral Image-Based Detection of Peridinium Quinquecorne Blooms Using Ensemble XGBoost Machine Learning Model: A Case Study of the Kumbalangi Backwater in Kerala, India

Harmful Algal Blooms (HABs) have implications for public health, aquaculture, and marine environment. Remote sensing has become an increasingly important tool for HAB detection and monitoring especially in inland water bodies. One of the dinoflagellates HAB species Peridinium quinquecorne (P. Quinquecorne) is known to pose significant threats to the aquatic life in the Kumbalangi backwater region in Kerala, India. This study investigates the effectiveness of combining UAV hyperspectral imagery with in-situ data to detect and monitor P. Quinquecorne blooms in the Kumbalangi backwater region. Using an ensemble XGBoost machine learning technique, the present study developed a model for accurately identifying and mapping P. Quinquecorne blooms. This model will leverage both UAV-derived hyperspectral data and in-situ measurements to enhance its accuracy and reliability. Preliminary findings indicate promising results, with the UAV based remote sensing model successfully detecting P. Quinquecorne blooms and generating spatial distribution maps. Despite the challenges in detecting the blooms at lower concentrations and assessing the species toxicity levels, the integration of UAV and in-situ data offers a cost-effective and efficient means of monitoring P. Quinquecorne blooms in the Kumbalangi backwater region. The insights from this research will be invaluable for local authorities, aquaculture industries, and environmental management, aiding in sustainable development and preservation of coastal ecosystem of Kerala.

*Karthick Murugan, Indian Institute of Technology Madras, India

Dilipkumar Jayaraj, Indian Institute of Technology Madras, India

Sandhani Chavapati Gouse, Indian Institute of Technology Madras, India

Vimalathitthan Shanmugam, Indian Institute of Technology Madras, India

Vijay Prakash Kondeti, Indian Institute of Technology Madras, India

Sai Sudha Adabala, Indian Institute of Technology Madras, India

Rajkumar Lakkimsetty, Indian Institute of Technology Madras, India

Shanmugam Palanisamy, Indian Institute of Technology Madras, India

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