Estimating Carbon Sequestration in Urban Water Bodies Using Remote Sensing and Modelling Techniques
The degradation of lakes poses a significant threat in the context of climate change, as lakes
are one of the biggest carbon sinks. Their deterioration not only reduces their capacity to
sequester carbon effectively, which is critical for achieving net-zero targets, but also disrupts
freshwater availability and biodiversity. It also severely impacts carbon cycles, posing
significant challenges of global warming.
Quantifying carbon sequestration capacity lost due to declining lake health is essential. Poor
water quality and reduced water levels diminish lakes’ carbon sequestration capacity.
Currently, the local governments do not have access to any technology to frequently measure
water quantity and quality of lakes, due to which they struggle to take precautionary/
corrective measures to conserve lakes and improve carbon sequestration efficiency.
To address this, there is a need for a comprehensive technological framework. By leveraging
satellite imagery, remote sensing, and machine learning models, a system can be developed
to monitor lake water quality and quantity. Machine learning models can also be used to
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predict/forecast the lake carbon sequestration capacity. This can enable local government to
make informed decision-making and proactive conservation efforts. Such tools would help
restore lake ecosystems, enhance carbon sequestration, and contribute to mitigating climate
change impacts.
