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

Akbari, V and Datta, A and Bhowmik, D and Marino, A and Kumar, Saurav and Rupavatharam, S and Prabhu, G N and Kleczkowski, A and Sujeetha, J A R P and Maharaj, S (2022) Multitemporal multispectral drone imagery for water hyacinth mapping in Patancheru lake, Hyderabad, India. In: SPIE REMOTE SENSING,Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2022, 5-8 Sep 2022, Berlin, Germany.

Full text not available from this repository. (Request a copy)


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.

Item Type: Conference or Workshop Item (Paper)
Divisions: Global Research Program - Resilient Farm and Food Systems
Uncontrolled Keywords: Aquatic vegetation, water hyacinth, multispectral drone, machine learning, water quality, spectral signature, expectation maximisation
Subjects: Others > Water Resources
Depositing User: Mr Nagaraju T
Date Deposited: 06 May 2024 08:16
Last Modified: 06 May 2024 08:16
Acknowledgement: This work was funded by the Global Challenges Research Fund through the Royal Academy of Engineering (FF/1920/1/37).
View Statistics

Actions (login required)

View Item View Item