Svetlana Saarela
Citado por
Citado por
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
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
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
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
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
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
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
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
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
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
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
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
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
GEDI L4B gridded aboveground biomass density, version 2
RO Dubayah, J Armston, SP Healey, Z Yang, PL Patterson, S Saarela, ...
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
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
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
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
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
How many bootstrap replications are necessary for estimating remote sensing-assisted, model-based standard errors?
RE McRoberts, E Næsset, Z Hou, G Ståhl, S Saarela, J Esteban, ...
Remote Sensing of Environment 288, 113455, 2023
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