Publications

2026

Duncanson, L.I., Montesano, P.M., Neuenschwander, A., Zarringhalam, A., Thomas, N., Minor, D., Wulder, M.A., Guenther, E., Feng, T., Leitold, V., Hancock, S., White, J.C., Armston, J., Puliti, S., Mandel, A.I., Shah, S., Silva, C., Purslow, M., Bruening, J., Breidenbach, J., Næsset, E., Saarela, S., Hunka, N., Kellner, J. R., Healey, S., Schepaschenko, D., Wallerman, J., Neigh, C., Carvalhais, N. & Dubayah, R. (in review). Global and Boreal Estimates of Woody Aboveground Biomass for 2020: Filling GEDI’s Northern Data Gap with ICESat-2 and Harmonized Landsat Sentinel. Remote Sensing of Environment. Preprint: http://dx.doi.org/10.2139/ssrn.5784282

Varvia, P., Saarela, S., Gopalakrishnan, R., Maltamo, M., Packalen, P., Popescu, S.C. & Korhonen, L. (in review). Estimating boreal forest aboveground biomass from ICESat-2 data: A validation study versus the Finnish National Forest Inventory. Remote Sensing of Environment.

Ståhl, G., Gozé, L., Papucci, E., Gobakken, T., Saarela, S., Ekström, M., Healey, S.P., Yang, Z., Kellner, J.R., Hou, Z., Xu, Q., Ørka, H.O., Nӕsset, E. & McRoberts, R.E. (in review). A closer look at uncertainties in forest ecosystem surveys using remotely sensed data and model-based inference. Remote Sensing of Environment. Preprint: http://dx.doi.org/10.2139/ssrn.5236489

Saarela, S., Gobakken, T., Ørka, H.O., Bollandsås, O.M., Næsset, E. & Ståhl, G. (in review). Handling single-year big data in forest inventory system based on remote sensing and multi-temporal data assimilation. Preprint: http://dx.doi.org/10.2139/ssrn.4838693

2025

Moan, M.Å., Bollandsås, O.M., Saarela, S., Gobakken, T., Næsset, E., Ørka, H.O. & Noordermeer, L. (2025). Site index determination using a time series of airborne laser scanning data. Forest Ecosystems 12.

Qi, W., Armston, J.D., Choi, C., Stovall, A., Saarela, S., Pardini, M., Fatoyinbo, T., Papathanasiou, K., Pascual, A. & Dubayah, R.O. (2025). Mapping Large-Scale Pantropical Forest Canopy Height by Integrating GEDI Lidar and TanDEM-X InSAR Data. Remote Sensing of Environment 318, 114534.

Saarela, S., Healey, S.P., Yang, Z., Roald, B.-E., Patterson, P.L., Gobakken, T., Næsset, E., Hou, Z., McRoberts, R.E. & Ståhl, G. (2025). A Separable Bootstrap Variance Estimation Algorithm for Hierarchical Model-Based Inference of Forest Aboveground Biomass Using Data from NASA’s GEDI and Landsat Missions. Environmetrics, 36(1).

Cosenza, D.N., Saarela, S., Strunk, J., Korhonen, L., Maltamo, M. & Packalen, P. (2025). Effects of model-overfit on model-assisted forest inventory in boreal forests with remote sensing data. Forestry: An International Journal of Forest Research, 98 (4), 507-521.

2024

Varvia, P., Saarela, S., Maltamo, M., Packalen, P., Gobakken, T., Næsset, E., Ståhl, G. & Korhonen, L. (2024). Hierarchical hybrid estimation of boreal forest biomass using ICESat-2 data. Remote Sensing of Environment, 311.

Mukhopadhyay, R., Ekström, M., Lindberg, E., Persson, H.J., Saarela, S. & Nilsson, M. (2024). Computation of prediction intervals for forest aboveground biomass predictions using generalized linear models in a large-extent boreal forest region. Forestry: An International Journal of Forest Research.

Ståhl, G., Gobakken, T., Saarela, S., Persson, H., Ekström, M., Healey, S.P., Yang, Z., Holmgren, J., Lindberg, E., Nyström, K., Papucci, E., Ulvdal, P., Ørka, H.O., Næsset, E., Hou, Z., Olsson, H., & McRoberts, R.E. (2024). Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time – And how this affects applications. Forest Ecosystems 11, 100164.

2023

Saarela, S., Varvia, P., Korhonen, L., Yang, Z., Patterson, P.L., Gobakken, T., Næsset, E., Healey, S.P. & Ståhl, G. (2023). Three-Phase Hierarchical Model-Based and Hybrid Inference. MethodsX, 11.

Bullock, E.L., Healey, S.P., Yang, Z., Acosta, R., Villalba, H., Insfrán, K.P., Melo, J.B., Wilson, S., Duncanson, L.I., Næsset,E., Armston, J.D., Saarela, S., Ståhl, G., Patterson, P.L. & Dubayah, R.O. (2023). Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory. Environmental Research Letters, 18.

Chen, F., Hou, Z., Saarela, S., McRoberts, R.E., Ståhl, G., Kangas, A., Packalen, P., Li, B. & Xu, Q. (2023). Leveraging remotely sensed non-wall-to-wall data for wall-to-wall upscaling in forest inventory. International Journal of Applied Earth Observation and Geoinformation, 119, 103314.

McRoberts, R.E., Næsset, E., Hou, Z., Ståhl, G., Saarela, S., Esteban, J., Travaglini, D., Mohammadi, J. & Chirici, G. (2023). How many bootstrap replications are necessary for estimating remote sensing-assisted, model-based standard deviations? Remote Sensing of Environment 288, 113455.

Nordermeer, L., Bielza, J.C., Saarela, S., Gobakken, T., Bollandsås, O.M. & Næsset, E. (2023). Monitoring tree occupancy and height in the Norwegian alpine treeline using a time series of airborne laser scanner data. International Journal of Applied Earth Observation and Geoinformation, 117, 103201.

2022

Lingren, N., Nyström, K., Saarela, S., Olsson H. & Ståhl, G. (2022). Importance of calibration for improving the efficiency of data assimilation for predicting forest characteristics. Remote Sensing, 14(18).

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, 113074.

Dubayah, R.O., Armston, J.D., Healey, S.P., Bruening, J.M., Patterson, P.L., Kellner, J.R., Duncanson, L.I., Saarela, S., Ståhl, G., Yang, Z., Tang, H., Blair, J.B., Fatoyinbo, L.E., Goetz, S., Hancock, S., Hansen, M., Hofton, M., Hurtt, G. & Luthcke, S. (2022). GEDI Launches a New Era of Biomass Inference from Space. Preprint submitted to Environmental Research Letters.

Dubayah, R.O., Armston, J.D., Healey, S.P., Yang, Z., Patterson, P.L., Saarela, S., Ståhl, G., Duncanson, L.I. & Kellner, J.R. (2022). GEDI L4B Gridded Aboveground Biomass Density, Version 2. ORNL DAAC, Oak Ridge, Tennessee, USA.

Varvia, P, Korhonen, L., Bruguière, A., Toivonen, J., Packalen, P., Maltamo,M., Saarela, S. & Popescu, S.C. (2022). How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass? Preprint submitted to Remote Sensing of Environment.

Duncanson, L.I., Kellner, J.R., Armston, J.D., Dubayah, R.O., Minor, D.M., Hancock, S., Healey, S.P., Patterson, P.L., Saarela, S., Marselis, S., Silva, C.E., Bruening, J., Goetz, J.S., Tang, H., Hofton, M., Blair, J.B., Luthcke, S.B., Fatoyinbo, T., et al. (2022). Aboveground Biomass Density Models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) Lidar Mission. Remote Sensing of Environment 270, 112845.

2021

Healey, S.P., Armston, J. D., Yang, Z., Dubayah, R.O., Bruening, J., Patterson, P.L., Saarela, S., Ståhl, G., Duncanson, L., Kellner, J.R. & Holm, S. (Dec. 2021). The GEDI Gridded Biomass Product: Patterns of Coverage and Precision After Two Years of Operation. PROCEEDINGS: AGU Fall Meeting 2021, AGU, Dec. 2021.

Korhonen, L., Bruguière, A., Varvia, P., Toivonen, J., Packalen, P., Maltamo, M., Saarela, S. & Popescu, S. (Sept. 2021). How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass? PROCEEDINGS: SilviLaser 2021, Vienna, Austria, Sept. 2021.

Indirabai, I., Mukhopadhyay, R., Duncanson, L.I., Armston, J.D., Ekström, M., Gobakken, T., Næsset, E. & Saarela, S. (Sept. 2021). Aboveground Biomass Assessment Using GEDI Data across Diverse Forest Ecosystems in India. PROCEEDINGS: SilviLaser 2021, Vienna, Austria, Sept. 2021.

2020

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.

2019

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.

2018

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.

2017

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.

2016

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.

2015

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.

Saarela, 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.

2014

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.