Multitemporal multispectral drone imagery for water hyacinth mapping in Patancheru lake, Hyderabad, India

Citation

Akbari V, Datta A, Bhowmik D, Marino A, Kumar S, Rupavatharam S, Prabhu GN, Kleczkowski A, R. P. Sujeetha JA & Maharaj S (2022) Multitemporal multispectral drone imagery for water hyacinth mapping in Patancheru lake, Hyderabad, India. Bostater CR (Editor) & Neyt X (Editor) Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2022, Berlin, Germany, 05.09.2022-08.09.2022. https://doi.org/10.1117/12.2639004

Abstract
Pontederia crassipes, commonly known as water hyacinth (WH), is a highly invasive aquatic weed and caused significant ecological and economic impact across the world. Remediation action includes manual monitoring and removal which are often time consuming and expensive. This paper proposes the use of multi-temporal multi-spectral drone imagery for WH mapping and monitoring in Patancheru Lake, Hyderabad, India. The data collection was done in two steps: 1) multi-spectral drone imagery and 2) ground optical image capturing through an Android mobile application. Data was collected in regular interval starting from January 2021. Spectral bands were used to produce the WH detection and mapping. We compare spectral signature of clean and infested water for five different sites inside the lake. Multitemporal water quality samples of these sites were also collected together with drone data to analyse the effect of WH infestation on those parameters. The multispectral data was processed using an unsupervised machine learning classifier named expectation maximisation (EM) clustering to create a segmentation map indicating WH, water and other regions.

People

Dr Vahid Akbari

Lect in Artificial Intelligence/Data Sci, Computing Science and Mathematics - Division

Dr Savi Maharaj

Senior Lecturer, Computing Science

Dr Armando Marino

Associate Professor, Biological and Environmental Sciences