Forest ecosystems play a crucial role in regulating climate, maintaining water and soil, and balancing carbon. However, in recent years, this system has been increasingly disturbed by climate change and human activities, making precise and timely forest change monitoring urgently needed. Remote sensing technology, with its advantage of multi-temporal resolution data and automated processing capabilities, has become a key means for forest change detection. This paper focuses on multi-temporal resolution remote sensing change detection methods, systematically reviews and compares two types of technologies: bi-temporal and time series remote sensing. Bi-temporal change detection includes manual visual interpretation, traditional machine learning, and deep learning techniques; time series include research on temporal trend analysis, dynamic change monitoring, and multi-algorithm integration. By summarizing the related problems of deep learning and multi-modal time series data in practical applications, relevant solutions are proposed, providing references for improving the accuracy of forest change detection.