Forest growing stock volume mapping with accompanying uncertainty in heterogeneous landscapes using remote sensing data

Understanding the spatial distribution of forest properties can help improve our knowledge of carbon storage and the impacts of climate change. Despite the active use of remote sensing and machine learning (ML) methods in forest mapping, the associated uncertainty predictions are relatively uncommon. The objectives of this study were: (1) to evaluate the spatial resolution effect on growing stock volume (GSV) mapping using Sentinel-2A and Landsat 8 satellite images, (2) to identify the most key predictors, and (3) to quantify the uncertainty of GSV predictions. The study was conducted in heterogeneous landscapes, covering anthropogenic areas, logging, young plantings and mature trees. We employed an ML approach and evaluated our models by root mean squared error (RMSE) and coefficient of determination (R²) through a 10-fold cross-validation. Our results indicated that the Sentinel-2A provided the best prediction performances (RMSE = 56.6 m³/ha, R² = 0.53) in compare with Landsat 8 (RMSE = 71.2 m³/ha, R² = 0.23), where NDVI, LSWI and B08 band (near-infrared spectrum) were identified as key variables, with the highest contribution to the model. Moreover, the uncertainty of GSV predictions using the Sentinel-2A was much smaller compared with Landsat 8. The combined assessment of accuracy and uncertainty reinforces the suitability of Sentinel-2A for applications in heterogeneous landscapes. The higher accuracy and lower uncertainty observed with the Sentinel-2A underscores its effectiveness in providing more reliable and precise information for decision-makers. This research is important for further digital mapping endeavours with accompanying uncertainty, as uncertainty assessment plays a pivotal role in decision-making processes related to spatial assessment and forest management.