Satellite-Based Analysis of Lake Okeechobee’s Surface Water: Exploring Machine Learning Classification for Change Detection

Abstract

Water is an essential resource for the survival of living beings. Remote-sensing data provides the best possible way to detect water bodies and monitor change over time. With a surplus amount of remote sensing data, machine learning approaches have become an effective and efficient way to detect and monitor surface water bodies. This research focused on utilizing remote sensing and machine learning approaches to monitor changes in the surface water of Lake Okeechobee, Florida, USA. This investigation used two sources of remotely sensed data, Landsat 7, and Landsat 8, for 2002 and 2022, respectively. Two machine learning algorithms, support vector machine (SVM) and random forest (RF), were adopted, considering their power and robustness, among other factors, for supervised classification. Both algorithms provided an accuracy of over 92% and a kappa statistic exceeding 0.8. Further, we used image differencing techniques to track changes across two decades. The SVM suggested an increase of 85 km2, and RF indicated an expansion of 52 km2 in the surface water area. This study explicitly demonstrates how dynamic natural resources are, especially water sources. Thus, it can provide a foundation for research that further explores environmental assessments and sustainable water resource planning in Lake Okeechobee.

Keywords

• remote sensing
• change detection
• surface water extraction
• machine learning
• Lake Okeechobee