Svetlana Saarela

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Göran StåhlSwedish University of Agricultural Sciences, SLUDirección de correo verificada de slu.se
Erik NaessetNorwegian University of Life SciencesDirección de correo verificada de nmbu.no
Sean P. HealeyUS Forest ServiceDirección de correo verificada de fs.fed.us
John ArmstonUniversity of MarylandDirección de correo verificada de umd.edu
Laura DuncansonUniversity of Maryland, College ParkDirección de correo verificada de umd.edu
Terje GobakkenNorwegian University of Life SciencesDirección de correo verificada de nmbu.no
Timothy G GregoireYale UniversityDirección de correo verificada de yale.edu
Juha HyyppäFinnish Geospatial Research InstituteDirección de correo verificada de nls.fi
Hans-Erik AndersenResearch Forester, USDA Forest Service Pacific Northwest Research StationDirección de correo verificada de fs.fed.us
Wenlu QiUniversity of Maryland College ParkDirección de correo verificada de umd.edu
Annika KangasResearch professor, Natural Resources Institute Finland, Tutkimusprofessori, LuonnonvarakeskusDirección de correo verificada de luke.fi
Lauri KorhonenUniversity of Eastern Finland, School of Forest SciencesDirección de correo verificada de uef.fi
Petteri PackalenNatural Resources Institute Finland (Luke), FinlandDirección de correo verificada de luke.fi
Emma HolmströmSwedish University of Agricultural ScienceDirección de correo verificada de slu.se
Stefano PulitiResearcher, Norwegian Institute for Bio-economy Research (NIBIO) National Forest InventoryDirección de correo verificada de nibio.no
Petri VarviaPostdoctoral Researcher, School of Forest Sciences, University of Eastern Finland, JoensuuDirección de correo verificada de uef.fi
Matti MaltamoProfessor of Forest Mensuration, University of Eastern FinlandDirección de correo verificada de uef.fi
Grant M. DomkeUSDA Forest ServiceDirección de correo verificada de fs.fed.us
Mark DuceyUniversity of New HampshireDirección de correo verificada de unh.edu
Rasmus AstrupNIBIODirección de correo verificada de skogoglandskap.no

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Svetlana Saarela

Norwegian University of Life Sciences

Dirección de correo verificada de nmbu.no - Página principal

Forest Biometrics Forest Inventory Forest Remote Sensing


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	271	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	168	2016
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	162	2018
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	139	2022
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	128	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	101	2015
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	87	2019
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	87	2018
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	87	2016
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	87	2014
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	77	2016
GEDI L4B gridded aboveground biomass density, version 2 RO Dubayah, J Armston, SP Healey, Z Yang, PL Patterson, S Saarela, ... ORNL DAAC, 2022	72	2022
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	70	2020
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	55	2018
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	40	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	36	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	28	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	21	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	20	2018
Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory EL Bullock, SP Healey, Z Yang, R Acosta, H Villalba, KP Insfrán, JB Melo, ... Environmental Research Letters 18 (8), 085001, 2023	18	2023

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