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Svetlana Saarela

Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
2022 · Laura Duncanson, James R. Kellner, et al. · Remote Sensing of Environment · 556
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GEDI launches a new era of biomass inference from space
2022 · Ralph Dubayah, John Armston, et al. · Environmental Research Letters · 286
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Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation
2016 · Göran Ståhl, Svetlana Saarela, et al. · Forest Ecosystems · 188
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Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference
2017 · Stefano Puliti, Svetlana Saarela, et al. · Remote Sensing of Environment · 167
Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data
2019 · Wenlu Qi, Svetlana Saarela, et al. · Remote Sensing of Environment · 153
Statistical properties of hybrid estimators proposed for GEDI—NASA’s global ecosystem dynamics investigation
2019 · Paul L Patterson, Sean P Healey, et al. · Environmental Research Letters · 128
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Model-assisted estimation of growing stock volume using different combinations of LiDAR and Landsat data as auxiliary information
2014 · Svetlana Saarela, Anton Grafström, et al. · Remote Sensing of Environment · 107
Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data
2018 · Svetlana Saarela, Sören Holm, et al. · Remote Sensing · 100
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Hybrid estimators for mean aboveground carbon per unit area
2016 · Ronald E. McRoberts, Qi Chen, et al. · Forest Ecology and Management · 88
Hierarchical model-based inference for forest inventory utilizing three sources of information
2016 · Svetlana Saarela, Sören Holm, et al. · Annals of Forest Science · 85
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Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors
2020 · Svetlana Saarela, André Wästlund, et al. · Forest Ecosystems · 84
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Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space
2014 · Anton Grafström, Svetlana Saarela, et al. · Canadian Journal of Forest Research · 81
Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications
2018 · Ronald E. McRoberts, Erik Næsset, et al. · Canadian Journal of Forest Research · 60
Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory
2023 · Eric L Bullock, Sean P Healey, et al. · Environmental Research Letters · 58
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Effects of sample size and model form on the accuracy of model-based estimators of growing stock volume
2015 · Svetlana Saarela, Sebastian Schnell, et al. · Canadian Journal of Forest Research · 47
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Mapping large-scale pantropical forest canopy height by integrating GEDI lidar and TanDEM-X InSAR data
2024 · Wenlu Qi, John Armston, et al. · Remote Sensing of Environment · 44
How many bootstrap replications are necessary for estimating remote sensing-assisted, model-based standard errors?
2023 · Ronald E. McRoberts, Erik Næsset, et al. · Remote Sensing of Environment · 42
Effects of positional errors in model-assisted and model-based estimation of growing stock volume
2015 · Svetlana Saarela, Sebastian Schnell, et al. · Remote Sensing of Environment · 40
Comparing frameworks for biomass prediction for the Global Ecosystem Dynamics Investigation
2022 · Svetlana Saarela, Sören Holm, et al. · Remote Sensing of Environment · 38
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Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time – and how this affects applications
2024 · Göran Ståhl, Terje Gobakken, et al. · Forest Ecosystems · 36
Estimation of boreal forest biomass from ICESat-2 data using hierarchical hybrid inference
2024 · Petri Varvia, Svetlana Saarela, et al. · Remote Sensing of Environment · 31
Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation
2018 · Sarah Ehlers, Svetlana Saarela, et al. · Remote Sensing · 26
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How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass?
2022 · Petri Varvia, Lauri Korhonen, et al. · 25
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The continuous population approach to forest inventories and use of information in the design
2017 · A. Grafström, S. Schnell, et al. · Environmetrics · 23
How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass?
2022 · P. Varvia, L. Korhonen, et al. · Remote Sensing of Environment · 21
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Works count: 62
Citation count: 2,636
H-index: 23
I10-index: 30
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