Researchers Use Satellite Image to Monitor Water Hyacinth Infestation in Lakes
An innovative multi-institutional project titled “Multimodal data analysis for monitoring invasive aquatic weeds in India” involving ICRISAT; University of Stirling, Scotland; University of Strathclyde, Scotland; National Institute of Plant Health Management (NIPHM), Hyderabad, India; Sanatana Dharma College (SDC), Kerala, India and CSIR-Central Scientific Instruments Organization (CSIO) Chandigarh, India, is ongoing to address water hyacinth problems. Funded under UKRI Global Challenges Research Fund through grant (FF\1920\1\37) from the Royal Academy of Engineering, United Kingdom, the project investigates emergence or resurgence of water hyacinth in lakes using satellite data.
Water hyacinth infestation in lakes poses environmental challenges and economic consequences for local community. At present, the use of satellite based data for monitoring water hyacinth poses limitations. Often these mats are present along with other floating, submerged, or semi-submerged macrophytes, making it difficult to monitor. Under this project, researchers interpret the satellite data further with increased accuracy to distinguish water hyacinth mat by collecting high-resolution data using drones equipped with multi-spectral data acquisition sensors, high-resolution satellite data, along with water quality analysis.
Co-Principal Investigator of this project, Dr Srikanth Rupavatharam, Senior Scientist (Digital Agriculture), ICRISAT says “Real-time monitoring and AI based estimation of water hyacinth biomass floating over large water bodies can significantly augment the efficiency of remediation strategies.”
Android application MPRO (ICRISAT-DAY) tool was also used to source ground truth images of water hyacinth from the banks of weed infested lakes. MPRO enables user to capture and upload images that are geo-time tagged to ICRISAT database. The multi-modal data acquisition, along with simultaneous water quality monitoring and citizen science, enables real-time accurate estimation. AI based forecasting of these mats further allows timely interventions.
This work is an attempt to make remediation less expensive through high-science driven decision making and revenue generation through efficient valorisation of the water hyacinth biomass.