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This study evaluates the effectiveness of using multi-sensor remote sensing data, specifically ALOS PALSAR-2, Sentinel-1, and Sentinel-2, to predict growing stock volume in small-scale plantation forests in Tanzania. Results indicate that Sentinel-2 and its combination with Sentinel-1 provide better accuracy in predictions compared to using Sentinel-1 alone, with various factors such as extraction methods and seasonality influencing the outcomes. The findings suggest that these remote sensing techniques can

Author

leoneluefs

Language

EN