Spatial high-resolution modelling and uncertainty assessment of forest growing stock volume based on remote sensing and environmental covariates

Abstract

Accurate forest growing stock (GSV) mapping is crucial for informed land management decisions, climate change mitigation, and sustainable resource utilization. This study aimed to digital mapping and uncertainty assessment of GSV levels in the Bashkiriya Nature Reserve, Russia. A random forest approach was used to predict GSV at a spatial resolution of 10 m (10 ×10 m) using 8395 plots and a set of 36 environmental covariates representing remote sensing data, relief and climate variables. The results revealed that the GSV levels ranged from 1 to 439 m3/ha, with a median of 200 m3/ha. Our model was evaluated using cross-validation and three performance metrics: root mean square error (RMSE=76 m3/ha), coefficient of determination (R2 =0.55) and Nash-Sutcliffe efficiency coefficient (NSE=0.55). In addition, we assessed the uncertainty of our predictions using a 90% prediction interval. According to the assessment of covariate importance, the significance of explanatory variables decreased in the following order: remote sensing > terrain > climate. We found, that Sentinel-2A satellite data, along with spectral indices, played a pivotal role in our spatial prediction model. Among these variables, the SWIR 12 band emerged as a standout contributor, highlighting the importance of this spectral information in GSV mapping. According to the generated map, the highest GSV values were identified primarily in regions characterized by low-lying terrain with mature trees of the reserve, while the lowest levels were distinctly evident on treeless mountain peaks. While it is widely acknowledged that remote sensing constitutes a primary source for modelling forest properties, our research has underscored the significance of the SWIR spectrum and elevation in future forest investigations. An advantage of the produced map of GSV and their associated uncertainties is that the end users can incorporate these uncertainties into decision-making processes involved in forest and environmental planning.