TY - GEN
T1 - Sentinel 3 OLCI and Machine Learning for Cyanobacteria Bloom Detection Over Small Inland Water Target
AU - Pacilio, Enzo
AU - Silvarrey, Alejo
AU - Pardo, Alvaro
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - High nutrient input agricultural practices and nutrient enrichment have been identified as the main factors driving cyanobacterial harmful algal blooms (cyanoHABs) formation in Uruguay. Current agricultural practices are already inflicting significant harm on aquatic ecosystems and human well-being, with future forecasts indicating a worsening of these trends. Thus, real-time detection of cyanoHABs in inland freshwater ecosystems is imperative for mitigating potential threats. Conventional cyanobacteria detection models often entail intricate procedures, necessitating the integration of biophysical, chemical, or on-site DNA sequencing measurements. Leveraging satellite imagery offers a cost-effective means to pinpoint cyanoHAB occurrences across extensive spatial and temporal scales. Within the European Space Agency's (ESA) satellite fleet, the Sentinel 3 satellites equipped with the Ocean and Land Color Instrument (OLCI) present a notable resource. In this study, we employed the cyanoHAB detection algorithm, Maximum peak-height (MPH), on OLCI data from the Laguna del Sauce lagoon to establish a baseline detection accuracy of 85%. The success of MPH's tree-like algorithm encouraged us to use machine learning classification algorithms based on decision trees to improve detection accuracy. The XGBoost classification model outperformed the other models by achieving an accuracy of 92%.
AB - High nutrient input agricultural practices and nutrient enrichment have been identified as the main factors driving cyanobacterial harmful algal blooms (cyanoHABs) formation in Uruguay. Current agricultural practices are already inflicting significant harm on aquatic ecosystems and human well-being, with future forecasts indicating a worsening of these trends. Thus, real-time detection of cyanoHABs in inland freshwater ecosystems is imperative for mitigating potential threats. Conventional cyanobacteria detection models often entail intricate procedures, necessitating the integration of biophysical, chemical, or on-site DNA sequencing measurements. Leveraging satellite imagery offers a cost-effective means to pinpoint cyanoHAB occurrences across extensive spatial and temporal scales. Within the European Space Agency's (ESA) satellite fleet, the Sentinel 3 satellites equipped with the Ocean and Land Color Instrument (OLCI) present a notable resource. In this study, we employed the cyanoHAB detection algorithm, Maximum peak-height (MPH), on OLCI data from the Laguna del Sauce lagoon to establish a baseline detection accuracy of 85%. The success of MPH's tree-like algorithm encouraged us to use machine learning classification algorithms based on decision trees to improve detection accuracy. The XGBoost classification model outperformed the other models by achieving an accuracy of 92%.
KW - Inland water
KW - Machine learning
KW - Phycocyanin
KW - Remote sensing
KW - Sentinel 3 OLCI
KW - Water quality
UR - http://www.scopus.com/inward/record.url?scp=85192237249&partnerID=8YFLogxK
U2 - 10.1109/LASCAS60203.2024.10506157
DO - 10.1109/LASCAS60203.2024.10506157
M3 - Contribución a la conferencia
AN - SCOPUS:85192237249
T3 - LASCAS 2024 - 15th IEEE Latin American Symposium on Circuits and Systems, Proceedings
BT - LASCAS 2024 - 15th IEEE Latin American Symposium on Circuits and Systems, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2024
Y2 - 27 February 2024 through 1 March 2024
ER -