| Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference S Puliti, S Saarela, T Gobakken, G Ståhl, E Næsset Remote sensing of environment 204, 485-497, 2018 | 153 | 2018 |
| Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission L Duncanson, JR Kellner, J Armston, R Dubayah, DM Minor, S Hancock, ... Remote Sensing of Environment 270, 112845, 2022 | 152 | 2022 |
| Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation G Ståhl, S Saarela, S Schnell, S Holm, J Breidenbach, SP Healey, ... Forest Ecosystems 3, 1-11, 2016 | 148 | 2016 |
| Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data W Qi, S Saarela, J Armston, G Ståhl, R Dubayah Remote Sensing of Environment 232, 111283, 2019 | 106 | 2019 |
| Model-assisted estimation of growing stock volume using different combinations of LiDAR and Landsat data as auxiliary information S Saarela, A Grafström, G Ståhl, A Kangas, M Holopainen, S Tuominen, ... Remote Sensing of Environment 158, 431-440, 2015 | 98 | 2015 |
| GEDI launches a new era of biomass inference from space R Dubayah, J Armston, SP Healey, JM Bruening, PL Patterson, JR Kellner, ... Environmental Research Letters 17 (9), 095001, 2022 | 87 | 2022 |
| Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space A Grafström, S Saarela, LT Ene Canadian Journal of Forest Research 44 (10), 1156-1164, 2014 | 84 | 2014 |
| Hierarchical model-based inference for forest inventory utilizing three sources of information S Saarela, S Holm, A Grafström, S Schnell, E Næsset, TG Gregoire, ... Annals of Forest Science 73, 895-910, 2016 | 75 | 2016 |
| Generalized hierarchical model-based estimation for aboveground biomass assessment using GEDI and landsat data S Saarela, S Holm, SP Healey, HE Andersen, H Petersson, W Prentius, ... Remote Sensing 10 (11), 1832, 2018 | 70 | 2018 |
| Hybrid estimators for mean aboveground carbon per unit area RE McRoberts, Q Chen, GM Domke, G Ståhl, S Saarela, JA Westfall Forest Ecology and Management 378, 44-56, 2016 | 70 | 2016 |
| Statistical properties of hybrid estimators proposed for GEDI—NASA’s global ecosystem dynamics investigation PL Patterson, SP Healey, G Ståhl, S Saarela, S Holm, HE Andersen, ... Environmental Research Letters 14 (6), 065007, 2019 | 66 | 2019 |
| Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications RE McRoberts, E Næsset, T Gobakken, G Chirici, S Condés, Z Hou, ... Canadian Journal of Forest Research 48 (6), 642-649, 2018 | 52 | 2018 |
| Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors S Saarela, A Wästlund, E Holmström, AA Mensah, S Holm, M Nilsson, ... Forest Ecosystems 7, 1-17, 2020 | 49 | 2020 |
| Effects of sample size and model form on the accuracy of model-based estimators of growing stock volume S Saarela, S Schnell, A Grafström, S Tuominen, K Nordkvist, J Hyyppä, ... Canadian Journal of Forest Research 45 (11), 1524-1534, 2015 | 39 | 2015 |
| GEDI L4B gridded aboveground biomass density, version 2 RO Dubayah, J Armston, SP Healey, Z Yang, PL Patterson, S Saarela, ... ORNL DAAC, 2022 | 36 | 2022 |
| Effects of positional errors in model-assisted and model-based estimation of growing stock volume S Saarela, S Schnell, S Tuominen, A Balázs, J Hyyppä, A Grafström, ... Remote sensing of environment 172, 101-108, 2016 | 35 | 2016 |
| Comparing frameworks for biomass prediction for the Global Ecosystem Dynamics Investigation S Saarela, S Holm, SP Healey, PL Patterson, Z Yang, HE Andersen, ... Remote Sensing of Environment 278, 113074, 2022 | 19 | 2022 |
| The continuous population approach to forest inventories and use of information in the design A Grafström, S Schnell, S Saarela, SP Hubbell, R Condit Environmetrics, 2017 | 19 | 2017 |
| Assessing Error Correlations in Remote Sensing Based Predictions of Forest Attributes for Improved Composite Estimation S Ehlers, S Saarela, N Lindgren, E Lindberg, M Nyström, H Persson, ... Remote Sensing, 2018 | 17 | 2018 |
| How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass? P Varvia, L Korhonen, A Bruguière, J Toivonen, P Packalen, M Maltamo, ... Remote Sensing of Environment 280, 113174, 2022 | 8 | 2022 |
Göran StåhlSwedish University of Agricultural Sciences, SLUVerified email at slu.se
