Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region

Kenneth Okechukwu Ekpetere, Mohamed Abdelkader, Ishaya Sunday, Edith Makwe, Peter Ekpetere

Hydrology 2023 article cited by 15

Source Open-access PDF More in Climate & Environment

importance 6/10 ai assisted Hydrology and Watershed Management Studies water resources stage: research

Problem

Delineating lake area extent over time in semi-arid regions and assessing how hydro-climatological factors drive lake changes.

Why it matters

Semi-arid lakes are sensitive to hydro-climatological changes affecting water depth and spatial extent.

What the AI did

A random forest supervised classification algorithm estimated lake area extent from Landsat imagery (1999-2021), combined with MODIS evapotranspiration, CHIRPS precipitation, and in situ data.

Results

not stated in source

Impact

The approach was tested on the Great Salt Lake and Lake Chad; performance figures are not stated in the abstract.

Record

Venue	Hydrology
Published	2023
Work type	article
Authors	Kenneth Okechukwu Ekpetere, Mohamed Abdelkader, Ishaya Sunday, Edith Makwe, Peter Ekpetere
Citations	15 (OpenAlex, at import time)
References	45
Model or method	Random forest supervised classification
Organization	not stated in source
Primary source	https://doi.org/10.3390/hydrology10040078
Open access	gold
Keywords	lake monitoring, random forest, Landsat, semi-arid regions, remote sensing

Importance rating

6 / 10

significant — thousands to millions affected

Semi-arid lake monitoring informs water management; abstract quantifies GSL decline exceeding 50% and Lake Chad ~30% (1999-2021), supporting decisions on threatened water resources.

Basis: stated. The source itself states the scale. Stated scale: GSL area declined >50% and LC ~30% between 1999 and 2021. Method and limits.

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