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Research Progress in Timely Monitoring of Forest Harvesting

Revision:Deng Shiyu, Liang WenlanDate:2021/12/30

Recently, Zhao Feng, an Associate Professor in College of Urban and Environmental Sciences of CCNU, cooperating with scholars from Huazhong Agricultural University, Ant Group and University of Maryland in US, published a paper in top one remote sensing journal Remote Sensing of Environment, entitled Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning.

Forest harvesting is one of the major forest disturbances globally, heavily affecting ecosystem composition, structure and function. Accurate and dynamic monitoring of the extent and type of forest harvesting is critical for evaluating the impact of harvesting on the water and carbon cycles. However, most existing methods can only monitor forest harvesting at annual time step, which is insufficient for timely management intervention and lacks the temporal resolution for examining the ecological consequences of intra-annual forest dynamic.


Therefore, the research group proposed a deep learning-based (i.e., U-Net) approach using the landscape pattern from Sentinel-1 Synthetic Aperture Radar (SAR) data to produce monthly maps of forest harvesting. This approach is highly accurate and spatially transferable, which can timely monitor the forest harvesting without the influence of weather condition.

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