Self-supervised deep learning for detection of forest disturbance types in a subtropical ecosystem using transformer and Sentinel-1 and Sentinel-2 time series data

Self-supervised deep learning for detection of forest disturbance types in a subtropical ecosystem using transformer and Sentinel-1 and Sentinel-2 time series data

Accurate and timely detection of forest disturbance types is crucial for evaluating ecosystem health and global climate stability. Time series remote-sensing data offers valuable spatiotemporal information. However, frequent cloud cover in subtropical regions disrupts the temporal consistency of opt...

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Bibliographic Details
Main Authors: Ming Zhang, Guiying Li, Dengsheng Lu, Cong Xu, Haotian Zhao, Dengqiu Li
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:International Journal of Digital Earth
Subjects:
Forest disturbance types
time series
Sentinel-1/2
self-supervised deep learning
Transformer
Online Access:https://www.tandfonline.com/doi/10.1080/17538947.2025.2537325
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