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Spatiotemporal Modelling and Monitoring of Harmful Algal Blooms Using IoT in Lake Victoria Basin: Integrating IoT & Remote Sensing for Near Real-Time HAB and Water Quality Monitoring - Couverture souple
Synopsis
Harmful algal blooms (HABs) are an increasing global threat to water security, aquatic ecosystems, public health and UN's sustainable development. As freshwater ecosystems continue to face growing environmental pressure, this research presents an innovative geospatial framework that integrates satellite remote sensing with low-cost Internet of Things (IoT) technologies for near real-time monitoring of HAB dynamics in Kenya’s Lake Victoria Basin. By integrating geospatial remote sensing analytics, thermal modelling, Landsat 8 ocean colour algorithms and smart in-situ IoT sensor networks, the study successfully mapped cyanobacteria blooms, monitored chlorophyll-a concentrations and detected abnormal lake surface air temperature (LSAT) variations associated with bloom events. The framework was further validated using Sentinel-3 OLCI and NASA MODIS datasets, demonstrating strong reliability and scalability for environmental intelligence and aquatic ecosystem monitoring. The findings consequently highlight the potential of emerging technologies to transform HAB surveillance through timely reporting, early warning systems and data-driven environmental management strategies. This work therefore contributes toward advancing sustainable water resource management and supporting broader Sustainable Development Goals (SDGs) through innovative, cost-effective and scalable monitoring solutions.
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Spatiotemporal Modelling and Monitoring of Harmful Algal Blooms Using IoT in Lake Victoria Basin: Integrating IoT & Remote Sensing for Near Real-Time HAB and Water Quality Monitoring
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Spatiotemporal Modelling and Monitoring of Harmful Algal Blooms Using IoT in Lake Victoria Basin: Integrating IoT & Remote Sensing for Near Real-Time HAB and Water Quality Monitoring
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Spatiotemporal Modelling and Monitoring of Harmful Algal Blooms Using IoT in Lake Victoria Basin (Paperback)
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Paperback. Etat : new. Paperback. Harmful algal blooms (HABs) are an increasing global threat to water security, aquatic ecosystems, public health and UN's sustainable development. As freshwater ecosystems continue to face growing environmental pressure, this research presents an innovative geospatial framework that integrates satellite remote sensing with low-cost Internet of Things (IoT) technologies for near real-time monitoring of HAB dynamics in Kenya's Lake Victoria Basin. By integrating geospatial remote sensing analytics, thermal modelling, Landsat 8 ocean colour algorithms and smart in-situ IoT sensor networks, the study successfully mapped cyanobacteria blooms, monitored chlorophyll-a concentrations and detected abnormal lake surface air temperature (LSAT) variations associated with bloom events. The framework was further validated using Sentinel-3 OLCI and NASA MODIS datasets, demonstrating strong reliability and scalability for environmental intelligence and aquatic ecosystem monitoring. The findings consequently highlight the potential of emerging technologies to transform HAB surveillance through timely reporting, early warning systems and data-driven environmental management strategies. This work therefore contributes toward advancing sustainable water resource management and supporting broader Sustainable Development Goals (SDGs) through innovative, cost-effective and scalable monitoring solutions. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9789999345866
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Spatiotemporal Modelling and Monitoring of Harmful Algal Blooms Using IoT in Lake Victoria Basin (Paperback)
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Paperback. Etat : new. Paperback. Harmful algal blooms (HABs) are an increasing global threat to water security, aquatic ecosystems, public health and UN's sustainable development. As freshwater ecosystems continue to face growing environmental pressure, this research presents an innovative geospatial framework that integrates satellite remote sensing with low-cost Internet of Things (IoT) technologies for near real-time monitoring of HAB dynamics in Kenya's Lake Victoria Basin. By integrating geospatial remote sensing analytics, thermal modelling, Landsat 8 ocean colour algorithms and smart in-situ IoT sensor networks, the study successfully mapped cyanobacteria blooms, monitored chlorophyll-a concentrations and detected abnormal lake surface air temperature (LSAT) variations associated with bloom events. The framework was further validated using Sentinel-3 OLCI and NASA MODIS datasets, demonstrating strong reliability and scalability for environmental intelligence and aquatic ecosystem monitoring. The findings consequently highlight the potential of emerging technologies to transform HAB surveillance through timely reporting, early warning systems and data-driven environmental management strategies. This work therefore contributes toward advancing sustainable water resource management and supporting broader Sustainable Development Goals (SDGs) through innovative, cost-effective and scalable monitoring solutions. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9789999345866
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Spatiotemporal Modelling and Monitoring of Harmful Algal Blooms Using IoT in Lake Victoria Basin | Integrating IoT & Remote Sensing for Near Real-Time HAB and Water Quality Monitoring
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Taschenbuch. Etat : Neu. Spatiotemporal Modelling and Monitoring of Harmful Algal Blooms Using IoT in Lake Victoria Basin | Integrating IoT & Remote Sensing for Near Real-Time HAB and Water Quality Monitoring | Jacob Okello (u. a.) | Taschenbuch | Englisch | 2026 | Eliva Press | EAN 9789999345866 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 136321794
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