Saarela, S., Wästlund, A., Holmström, E., Mensah, A.A., Holm, S., Nilsson, M., Fridman, J. & Ståhl, G. (2020). Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors. Forest Ecosystems, 7(43), 1-17.

Saarela, S. (2020). On being a female supervisor in forest research education. Silva Fennica 54 (2), 10362.

Mensah, A.A., Petersson, H., Saarela, S., Goude, M. & Holmström, E. (2020). Using heterogeneity indices to adjust basal area – leaf area index relationship in managed coniferous stands. Forest Ecology and Management 458, 117699.


Qi, W., Saarela, S.Armston, J., Ståhl, G. & Dubayah, R. (2019). Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data. Remote Sensing of Environment 232, 111283.

Patterson, P.L., Healey, S.P., Ståhl, G., Saarela, S., Holm, S., Andersen, H.-E., Dubayah, R., Duncanson, L.I., Hancock, S., Armston, J., Kellner, J.R., Cohen, W.B. & Yang, Z. (2019). Statistical Properties of Hybrid Estimators Proposed for GEDI – NASA’s Global Ecosystem Dynamics Investigation. Environmental Research Letters 14(6), 065007.


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.

Ehlers, S., Saarela, S., Lindgren, N., Lindberg, E., Nyström, M., Persson, H.J., Olsson, H. & Ståhl, G. (2018). Assessing error correlations in remote sensing-based estimates of forest attributes for improved composite estimation. Remote Sensing 10(5), 667.

McRoberts, R.E., Næsset, E., Gobakken, T., Chirici, G., Condes, S., Hou, Z., Saarela, S., Chen, Q., Ståhl, G. & Westfall, J.A. (2018). Assessing components of the model-based variance estimator for remote sensing-assisted forest applications, Canadian Journal of Forest Research 48, 1-8.

Puliti, S., Saarela, S., Gobakken, T., Ståhl, G. & Næsset, E. (2018). Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference. Remote Sensing of Environment 204, 485-497.


Grafström, A., Schnell, S., Saarela, S., Hubbell, S.P. & Condit, R. (2017). The continuous population approach to forest inventories and use of information in the design. Environmetrics 28(8).

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.

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.


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.

McRoberts, R.E., Chen, Q., Domke, G.M., Ståhl, G., Saarela, S. & Westfall, J.A. (2016). Hybrid estimators for mean aboveground carbon per unit area. Forest Ecology and Management 378, 44-56.

Ståhl, G., Saarela, S., Schnell, S., Holm, S., Breidenbach, J., Healey, S.P., Patterson, P.L., Magnussen, S., Næsset, E., McRoberts, R.E. & Gregoire, T.G. (2016). Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation. Forest Ecosystems 3(5), 1–11.

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.


Saarela, S., Grafström, A. & Ståhl, G. (2015). Three-phase model-based estimation of growing stock volume utilizing Landsat, LiDAR and field data in large-scale surveys. PROCEEDINGS: SilviLaser 2015 – ISPRS Geospatial Week: Invited session “Estimation, inference, and uncertainty”, La Grande Motte, France, Sept. 2015.

CoverPictureSaarela, S. (2015). Use of remotely sensed auxiliary data for improving sample-based forest inventories. Dissertationes Forestales 201, 36 p.

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.

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.


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How can large-scale forest inventories be improved?” by Svetlana Saarela, more…

Grafström, A., Saarela, S. & Ene, L.T. (2014). Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space. Canadian Journal of Forest Research 44, 1156-1164.