My main research interest is in the area of developing statistical methods for forest resource surveys by means of combining remotely sensed data from different sensors with field sample data.
Fagfelt
- Design-Based Inference
- Hybrid Inference
- (Hierarchical) Model-Based Inference
- Regression Analysis
- Probability Sampling
Publikasjoner
Liste med publikasjoner fra min forskning. (Cristin)
Saarela, S., Holm, S., Healey, S.P., Patterson, P.L., Yang, Z., Andersen, H.E., Dubayah, R.O., Qi, W., Duncanson, L.I., Armston, J.D., Gobakken, T., Næsset, E., Ekström, M. & Ståhl, G. (2022). Comparing frameworks for biomass prediction for the Global Ecosystem Dynamics Investigation. Remote Sensing of Environment 278, 113074. DOI
Saarela, S. , Wästlund, A., Holmström, E., Mensah, AA, 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 , 7(43), 1-17. DOI
Saarela, S. , Holm, S., Healey, SP, Andersen, H.-E., Petersson, H., Prentius, W., Patterson, PL, Næsset, E., Gregoire, TG & Ståhl, G. ( 2018). Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data. Remote Sensing , 10(11), 1832. DOI
Saarela, S. , Breidenbach, J., Raumonen, P., Grafström, A., Ståhl, G., Ducey, MJ & Astrup, R. (2017). Kriging prediction of stand level forest information using mobile laser scanning data adjusted for non-detection. Canadian Journal of Forest Research 47, 1257-1265. DOI
Saarela, S. , Andersen, H.-E., Grafström, A., Schnell, S., Gobakken, T., Næsset, E., Nelson, RF, McRoberts, RE, Gregoire, TG & Ståhl, G. ( 2017). A new prediction-based variance estimator for two-stage model-assisted surveys of forest resources. Remote Sensing of Environment 192, 1-11. DOI
Saarela, S. , Holm, S., Grafström, A., Schnell, S., Næsset, E., Gregoire, TG, Nelson, RF & Ståhl, G. (2016). Hierarchical model-based inference for forest inventory utilizing three sources of information. Annals of Forest Science, 73(4), 895-910. DOI
Saarela, S. , Schnell, S., Tuominen, S., Balazs, A., Hyyppä, J., Grafström, A. & Ståhl, G. (2016). Effects of positional errors in model-assisted and model-based estimation of growing stock volume. Remote Sensing of Environment, 172, 101-108. DOI
Saarela, S. , Schnell, S., Grafström, A., Tuominen, S., Nordkvist, K., Hyyppä, J., Kangas, A. & Ståhl, G. (2015). Effects of sample size and model form on the accuracy of model-based estimators of growing stock volume in Kuortane, Finland. Canadian Journal of Forest Research, 45, 1524–1534. DOI
Saarela, S. , Grafström, A., Ståhl, G., Kangas, A., Holopainen, M., Tuominen, S., Nordkvist, K. & Hyyppä, J. (2015). Model-assisted estimation of growing stock volume using different combinations of LiDAR and Landsat data as auxiliary information. Remote Sensing of Environment, 158, 431-440. DOI
Forskning og prosjekter
Forskningsprosjekter med nettside utenfor NMBU