Generalized hierarchical model-based inference-based applications for Swedish NFI

Funded by: Swedish NFI Development Foundation

Duration: 2018 – 2019 years.

The originality of the topic:

In this project the methodological part presented in Saarela et al. (2016, 2018) is developed for the class of nonlinear models. As a potential application example, uncertainties due to aboveground biomass (AGB) allometrics models are propagated through the estimation procedure for AGB assessment by LiDAR data means. Swedish NFI data in combination with LiDAR data obtained from the LiDAR national survey are used in this project.



A statistical frame is developed based on the generalized hierarchical model-based estimation for the class of nonlinear models, the frame satisfies Swedish NFI needs in providing core forest parameter estimates with corresponding RMSE and relative RMSE on annual basis, based on the fusion of remotely sensed auxiliary information and field data; not only on the regional level, i.e. administrative districts, but also at the level of population elements (i.e. pixels).



Saarela, S., Wästlund, A., Holmström, E., Mensah, A.A., Holm, S., Nilsson, M., Fridman, J., & Ståhl, G. (2020). Mapping aboveground biomass and its uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors. Forest Ecosystems (accepted).