Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region
Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region
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结合卫星图像和地面测量与机器学习模型监测半干旱区湖泊动态
DOI：
10.3390/hydrology10040078
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发表时间：
2023-03
期刊：
影响因子：
3.2
通讯作者：
Kenneth Ekpetere;M. Abdelkader;S. Ishaya;E. Makwe;Peter Ekpetere
中科院分区：
文献类型：
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作者：
Kenneth Ekpetere;M. Abdelkader;S. Ishaya;E. Makwe;Peter Ekpetere
湖泊动态的长期变化受到影响水体深度和空间范围的水文气候因素的影响。本研究的主要目的是划定湖泊面积范围，利用机器学习方法，并检查这些水文气候因素对湖泊动态的影响。现场和遥感观测，以确定主要的解释途径，评估湖泊面积的波动。大盐湖（GSL）和乍得湖（LC）被选为研究地点，由于其半干旱的区域设置，使测试所提出的方法。随机森林（RF）监督分类算法被应用于估计湖泊面积的范围内使用Landsat图像，是在1999年和2021年之间获得的。长期湖泊动态进行了评估，使用遥感蒸散数据，来自中分辨率成像光谱仪，降水数据，来自CHIRPS，和原位水位测量。研究结果显示，在1999年至2021年期间，GSL面积范围显着下降，超过50%，而LC则表现出较大的波动，其面积范围的下降幅度相对较小，同期约为30%。本研究中提出的框架证明了遥感数据和机器学习方法用于监测湖泊动态的可靠性。此外，它提供了有价值的见解，决策者和水资源管理人员在评估湖泊动态的时间变化。
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.