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 confidence intervals 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., Holm, S., Grafström, A., Schnell, S., Næsset, E., Gregoire, T.G., Nelson, R.F. & Ståhl, G. (2016). Hierarchical model-based inference for forest inventory utilizing three sources of information, Annals of Forest Science, 73(4), 895-910.
Saarela, S., Holm, S., Healey, S.P., Andersen, H.-E., Petersson, H., Prentius, W., Patterson, P.L., Næsset, E., Gregoire, T.G. & Ståhl, G. (2018). Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data, Remote Sensing, 10(11), 1832.