2020-11-09 – 2020-12-11 PhD course “Statistics IV: Generalized Linear Models” (4 ECTS), Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden (course leader and main teacher).
2019-05-27 – 2019-05-31 NOVA PhD course “Hybrid Inference” (5 ECTS), Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway (main teacher).
2018-09-03 – 2018-09-07 NOVA PhD course “Introduction to Survey Sampling” (3 ECTS), Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden (course leader and main teacher).
2016-03-07 – 2016-03-18 AGFOREE PhD course “Use of regression models in forest resource surveys” (7 ECTS), Department of Forest Sciences, University of Helsinki, Finland (course leader and main teacher).
2015-09-24 – 2015-11-25 AGFOREE PhD course “Two general philosophies of statistical inference – design-based and model-based” (1,5 ECTS), Department of Forest Sciences, University of Helsinki, Finland (course leader and main teacher).
2015-09-07 – 2015-11-13 Master course “Regression analysis” (4 ECTS), Department of Forest Sciences, University of Helsinki, Finland (course leader and main teacher).
2015-05-25 – 2015-06-05 AGFOREE PhD course “Basic sampling with extensions” (2+4+2 ECTS), Department of Forest Sciences, University of Helsinki, Finland (course leader and main teacher).
2014-12-01 – 2014-12-12 AGFOREE PhD course “Regression analysis with examples in R” (5 ECTS), Department of Forest Sciences, University of Helsinki, Finland (course leader and main teacher).
My main research interest is in the area of developing statistical frameworks for forest inventories by means of combining RS data from different sensors and combining these auxiliary data with field sample data. I was one of the pioneers in the deeper analysis of model-based inference applications for such surveys. As a doctoral student in my third year, I independently initiated a study on hierarchical model-based estimation, where several sources of auxiliary information were combined in the framework of model-based inference. The study (Saaarela et al., 2016) has been published in 2016 and is considered as a “game changer” by a highly respected researcher in this area.
I have an intensive background in working for NFI systems. Prior to moving to Finland in 2006, I graduated from the Saint-Petersburg State Forestry Engineering Academy in August 2006 and worked for almost four years at the North-West branch “Sevzaplesproject” of ROSLESINFORG, i.e. the Federal Forestry Agency of the Russian Federation. During my employment at the “Sevzaplesproject” I was involved in the revision of forest management units located in the Karelia and Leningrad regions. During this time, I developed skills in determination and punctuation, as well as an extensive knowledge on laws and regulations. In Finland, I worked around one year for the Finnish NFI under the supervision of Professor Erkki Tomppo at METLA (current LUKE) and later applied for doctoral studies at the University of Helsinki, pursuing my goal to obtain a European higher education degree and develop my career towards research. At METLA I expanded my skills in working with large data sets, primarily in the Unix environment with ArcGIS and R software programs.
In 2011, I received a four-year grant from the Graduate School in Forest Sciences (GSForest), as the top-ranked and sole candidate who received funding for the entire duration of the doctoral studies. It was a rare chance to pursue my goals in education and research, while applying and elaborating my previous skills. Two articles by Gregoire et al. (2011) and Ståhl et al. (2011) not only shaped my doctoral dissertation, but also gave directions for my research towards developing new statistical designs for forest inventories on national scale. In November 2015, I graduated with the grade: “Pass with Distinction”, and in April 2016 I obtained the Best Doctoral Dissertation in Forest Sciences award by the Finnish Society of Forest Science.
During my university studies in the Russian Federation (1997-2006) I gained strong skills in mathematical statistics. In 2005, I won a university contest in mathematical statistics, while gaining a second Master degree in Industrial Electronics at the North-West State Technical University. During my doctoral studies (2010-2015) at the University of Helsinki in Finland, I elaborated and expanded my knowledge towards applications of mathematical statistics for surveys of natural resources, such as forests, using the model-assisted and model-based estimation frameworks. As a part of my doctoral studies, my first article was published in the highest ranked journal in my research field, i.e. Remote Sensing of Environment (5-Year Impact Factor: 7.653).
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, 7(43), 1-17. DOI
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. DOI
Saarela, S., Breidenbach, J., Raumonen, P., Grafström, A., Ståhl, G., Ducey, M.J. & 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, R.F., McRoberts, R.E., Gregoire, T.G. & 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, 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. 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