Forest species mapping and area proportion estimation combining Sentinel-2 harmonic predictors and national forest inventory data

Abstract

European forest monitoring is a central topic nowadays due to the critical role that forests can play in combatting climate change. Crucial information on forests is the number of tree species and the area covered by each of them, as they vary concerning growth rates, wood value, value for biodiversity conservation, and susceptibility to disturbances and global warming. The primary source of forest information is national forest inventories (NFIs). However, they are updated too infrequently to accommodate climate change-related analyses, and their estimates are not based on wall-to-wall information. Remotely sensed data offer new opportunities for up-to-date and large-scale forest monitoring and for enhancing NFI estimates. However, despite the huge scientific efforts, it is still challenging to accurately map forest species through satellite imagery analysis.This study introduces a method for large-scale forest species mapping in the Netherlands using Sentinel-2 (S2) harmonic predictors and demonstrates a scientific procedure for reliably estimating area proportions from remote sensing-based species maps and comparing these estimates with NFI-based estimates.Compared to more standard predictors, harmonic predictors increased the model performance by 8% in terms of overall accuracy and the kappa coefficient by 9% while reducing omission and commission errors by as much as 18% and 13%, respectively. We estimated the area proportion of forest species for each 10-km cell covering the Netherlands first using NFI data and then using the predicted maps. Although the resulting estimates differ by source data and methods, we found an average deviation between NFI and remote sensing-based area proportion estimates of 9%, with deviations approaching 0% when increasing the number of NFI plots per cell.The outcomes of this research play an important role in understanding the relative strengths and limitations of remote sensing-based products and NFI data, as well as be a solid basis for forest species area proportion estimation when (i) no field data are available, (ii) more frequently updated estimates are required, or (iii) wall-to-wall and fine resolution spatially explicit estimates are needed.

Keywords

• Forestry
• Biodiversity
• Remote Sensing
• Modeling
• Time series