Spatio-Temporal Land Cover Change Detection using Machine Learning and Google Earth Engine

The combination of Google Earth Engine ARIMA time-series exploration with Random Forest, a powerful machine learning technique, to offer a novel hybrid solution for spatio-temporal land cover change detection. The proposed method aims to address the challenges of identifying variations in land cover accurately and efficiently over extensive periods. To produce accurate categorization results, the Random Forest model is conditioned on past land cover data during the first step of the hybrid approach. The second section examines the temporal patterns of different land cover classes using Google Earth Engine ARIMA time-series analysis to determine trends and patterns in land cover changes. The new approach, which marries Random Forest with ARIMA time-series analysis, leverages both methods' strengths to provide a more accurate and robust spatio-temporal detection of changes. The combined approach can be employed for large regional and also global surface area monitoring programs because of its ability to be expanded and its efficacy. This approach has real-world applications in environmental management, conservation, city planning based on an intimate understanding of land cover change and their consequences. In studies of spatiotemporal LST variations, the MODIS land surface temperature (MOD11A1) component has been used widely. However, it has a limited suite of uses, partly due to the low resolution of space. To transcend this, the paper uses the proposed ARIMA-RF approach. It achieves accuracy of approximately 99.5%.