Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region
The long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learning approach, and to examine the impact of these hyd...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/10/4/78 |
| _version_ | 1797605248719650816 |
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| author | Kenneth Ekpetere Mohamed Abdelkader Sunday Ishaya Edith Makwe Peter Ekpetere |
| author_facet | Kenneth Ekpetere Mohamed Abdelkader Sunday Ishaya Edith Makwe Peter Ekpetere |
| author_sort | Kenneth Ekpetere |
| collection | DOAJ |
| description | The long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learning approach, and to examine the impact of these hydro-climatological factors on lake dynamics. In situ and remote sensing observations were employed to identify the predominant explanatory pathways for assessing the fluctuations in lake area. The Great Salt Lake (GSL) and Lake Chad (LC) were chosen as study sites due to their semi-arid regional settings, enabling the testing of the proposed approach. The random forest (RF) supervised classification algorithm was applied to estimate the lake area extent using Landsat imagery that was acquired between 1999 and 2021. The long-term lake dynamics were evaluated using remotely sensed evapotranspiration data that were derived from MODIS, precipitation data that were sourced from CHIRPS, and in situ water level measurements. The findings revealed a marked decline in the GSL area extent, exceeding 50% between 1999 and 2021, whereas LC exhibited greater fluctuations with a comparatively lower decrease in its area extent, which was approximately 30% during the same period. The framework that is presented in this study demonstrates the reliability of remote sensing data and machine learning methodologies for monitoring lacustrine dynamics. Furthermore, it provides valuable insights for decision makers and water resource managers in assessing the temporal variability of lake dynamics. |
| first_indexed | 2024-03-11T04:58:21Z |
| format | Article |
| id | doaj.art-b5ed4474785b400ebcc1f1e6b18c225a |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-11T04:58:21Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-b5ed4474785b400ebcc1f1e6b18c225a2023-11-17T19:30:15ZengMDPI AGHydrology2306-53382023-03-011047810.3390/hydrology10040078Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid RegionKenneth Ekpetere0Mohamed Abdelkader1Sunday Ishaya2Edith Makwe3Peter Ekpetere4Department of Geography and Atmospheric Science, University of Kansas, Lawrence, KS 66045, USADepartment of Civil, Environmental, and Ocean Engineering (CEOE), Stevens Institute of Technology, Hoboken, NJ 07030, USADepartment of Geography and Environmental Management, University of Abuja, Abuja PMB 117, NigeriaDepartment of Geography and Environmental Management, University of Abuja, Abuja PMB 117, NigeriaDepartment of Geography and Environmental Management, University of Abuja, Abuja PMB 117, NigeriaThe long-term variability of lacustrine dynamics is influenced by hydro-climatological factors that affect the depth and spatial extent of water bodies. The primary objective of this study is to delineate lake area extent, utilizing a machine learning approach, and to examine the impact of these hydro-climatological factors on lake dynamics. In situ and remote sensing observations were employed to identify the predominant explanatory pathways for assessing the fluctuations in lake area. The Great Salt Lake (GSL) and Lake Chad (LC) were chosen as study sites due to their semi-arid regional settings, enabling the testing of the proposed approach. The random forest (RF) supervised classification algorithm was applied to estimate the lake area extent using Landsat imagery that was acquired between 1999 and 2021. The long-term lake dynamics were evaluated using remotely sensed evapotranspiration data that were derived from MODIS, precipitation data that were sourced from CHIRPS, and in situ water level measurements. The findings revealed a marked decline in the GSL area extent, exceeding 50% between 1999 and 2021, whereas LC exhibited greater fluctuations with a comparatively lower decrease in its area extent, which was approximately 30% during the same period. The framework that is presented in this study demonstrates the reliability of remote sensing data and machine learning methodologies for monitoring lacustrine dynamics. Furthermore, it provides valuable insights for decision makers and water resource managers in assessing the temporal variability of lake dynamics.https://www.mdpi.com/2306-5338/10/4/78lake dynamicsMODISCHIRPSLandsatmachine learning |
| spellingShingle | Kenneth Ekpetere Mohamed Abdelkader Sunday Ishaya Edith Makwe Peter Ekpetere Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region Hydrology lake dynamics MODIS CHIRPS Landsat machine learning |
| title | Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region |
| title_full | Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region |
| title_fullStr | Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region |
| title_full_unstemmed | Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region |
| title_short | Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region |
| title_sort | integrating satellite imagery and ground based measurements with a machine learning model for monitoring lake dynamics over a semi arid region |
| topic | lake dynamics MODIS CHIRPS Landsat machine learning |
| url | https://www.mdpi.com/2306-5338/10/4/78 |
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