Integrated UAV-Satellite Remote Sensing for High-Precision Spatial Analysis of Production and Physiological Health in Kappaphycus Aquaculture, Indonesia Nurjannah Nurdin (a,c,d*), Evangelos (b), Agus Aris (c,d), M. Akbar AS (d), Laurent Barille (b)
(a) Department of Marine Science, Marine Science and Fisheries Faculty, Hasanuddin University, Makassar, 90245. Indonesia
*nurjannahnurdin[at]unhas.ac.id
(b) Institut des Substances et Organismes de la Mer (ISOMer), Nantes Universite, UR 2160, F-44000 Nantes, France.
(c) Department of Remote Sensing and Geographic Information System, Vocational Faculty, Hasanuddin University, Makassar 90245. Indonesia
(d) Research and Development Center for Marine, Coast, and Small Islands, Hasanuddin University, Makassar 90245. Indonesia
Abstract
Climate change and seasonal fluctuations have become critical determinants of seaweed farming success in Indonesia coastal regions, particularly in South Sulawesi, Indonesia. The primary cultivated species, Kappaphycus alvarezii, is of high economic value because of its carrageenan content, which is widely used in the food industry and has other applications. Although cultivation practices are relatively simple and require low capital investment, production is frequently disrupted by shifting environmental conditions, disease outbreaks and pest infestations. Ice-ice disease, which causes tissue damage and depigmentation, is a major global threat to yield reduction. This study investigated the relationship between seasonal variability (west monsoon, east monsoon, and two transitional phases) and the growth dynamics of K. alvarezii while assessing the potential of high-resolution remote sensing for health monitoring. Environmental parameters, such as sea surface temperature, salinity, and nutrient concentration, were derived from satellite imagery. At finer scales, field observations were conducted using a multispectral UAV (DJI Phantom 4 RTK D-GPS) equipped with five spectral bands spanning the visible and near-infrared ranges. Artificial intelligence using machine learning algorithms was applied to correlate spectral reflectance with biometric traits and carrageenan content and to detect early color changes as indicators of biological stress. The findings revealed distinct seasonal patterns influencing productivity and disease vulnerability, with certain transitional periods triggering notable declines in crop quality. A predictive model that integrates geospatial and climate datasets from 2019 to 2024 successfully mapped spatial production patterns and potential stress hotspots. This approach demonstrates that combining satellite data, UAV-based monitoring, and AI-driven analysis provides an effective early warning system, optimizes harvest timing, and mitigates economic losses associated with the disease. Beyond improving operational efficiency, this strategy strengthens aquaculture resilience to climate change and supports sustainable coastal-management practices.
Keywords: UAV- Satellite Data- Precission- Seaweed Aquaculture- Physiological Health
Topic: Topic C: Emerging Technologies in Remote Sensing
