Forskningsartikel2025Vetenskapligt granskadÖppen tillgång

A Separable Bootstrap Variance Estimation Algorithm for Hierarchical Model-Based Inference of Forest Aboveground Biomass Using Data From NASA's GEDI and Landsat Missions

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Evaluating the performance of mainstream Swedish growth models in uneven-aged forestry systems

Grzeszkiewicz, Mateusz; Mensah, Alex Appiah; Goude, Martin et al.

Forskningsartikel2024Vetenskapligt granskadÖppen tillgång

Nexus of certain model-based estimators in remote sensing forest inventory

Zheng, Yan; Hou, Zhengyang; Stahl, Goran et al.

Forskningsartikel2024Vetenskapligt granskadÖppen tillgång

Estimation of plant density based on presence/absence data using hybrid inference

Gozé, Léna; Ekström, Magnus; Sandring, Saskia et al.

Forskningsartikel2024Vetenskapligt granskadÖppen tillgång

Vicarious calibration of GEDI biomass with Landsat age data for understanding secondary forest carbon dynamics

Jha, Nidhi; Healey, Sean P.; Yang, Zhiqiang et al.

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Model-assisted estimation of domain totals, areas, and densities in two-stage sample survey designs

Andersen, Hans-Erik; Stahl, Goran; Cook, Bruce D. et al.

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Estimation of boreal forest biomass from ICESat-2 data using hierarchical hybrid inference

Varvia, Petri; Saarela, Svetlana; Maltamo, Matti et al.

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Making decisions based on remote sensing information: good or bad?

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Is the regression to the mean in remote sensing inventories a problem for forest planning?

Ulvdal, Patrik; Sängstuvall, Lars; Ståhl, Göran et al.

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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

Stahl, Goran; Gobakken, Terje; Saarela, Svetlana et al.

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Social data: what exists in reporting schemes for different land systems?

Wood, Claire; Sandewall, Mats; Sandström, Stefan et al.

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Allard, Anna; Guerrero, Santiago; Aagaard Christensen, Andreas et al.

Forskningsartikel2023Vetenskapligt granskadÖppen tillgång

Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay's national forest inventory

Bullock, Eric L.; Healey, Sean P.; Yang, Zhiqiang et al.

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Leveraging remotely sensed non-wall-to-wall data for wall-to-wall upscaling in forest inventory

Chen, Fangting; Hou, Zhengyang; Saarela, Svetlana et al.

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How many bootstrap replications are necessary for estimating remote sensing-assisted, model-based standard errors?

McRoberts, Ronald E.; Naesset, Erik; Hou, Zhengyang et al.

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Impact of field reference errors for remote sensing predictions

Persson, Henrik; Ekström, Magnus; Ståhl, Göran.

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Conjugating remotely sensed data assimilation and model-assisted estimation for efficient multivariate forest inventory

Hou, Zhengyang; Yuan, Keyan; Stahl, Goran et al.

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Three-phase hierarchical model-based and hybrid inference

Saarela, Svetlana; Varvia, Petri; Korhonen, Lauri et al.

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Model-based estimation and mapping of plant density based on remote sensing and presence/absence data

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Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors

Lindgren, Nils; Olsson, Hakan; Nystrom, Kenneth et al.

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Multiple drivers of large-scale lichen decline in boreal forest canopies

Esseen, Per-Anders; Ekstrom, Magnus; Grafstrom, Anton et al.

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Cross-classes domain inference with network sampling for natural resource inventory

Hou, Zhengyang; McRoberts, Ronald E.; Zhang, Chunyu et al.

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Comparing frameworks for biomass prediction for the Global Ecosystem Dynamics Investigation

Saarela, Svetlana; Hol, Soren; Healey, Sean P. et al.

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GEDI launches a new era of biomass inference from space

Dubayah, Ralph; Armston, John; Healey, Sean P. et al.

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Importance of Calibration for Improving the Efficiency of Data Assimilation for Predicting Forest Characteristics

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Göran Ståhl

Publikationer

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

Evaluating the performance of mainstream Swedish growth models in uneven-aged forestry systems

2024

Nexus of certain model-based estimators in remote sensing forest inventory

Estimation of plant density based on presence/absence data using hybrid inference

Vicarious calibration of GEDI biomass with Landsat age data for understanding secondary forest carbon dynamics

Model-assisted estimation of domain totals, areas, and densities in two-stage sample survey designs

Estimation of boreal forest biomass from ICESat-2 data using hierarchical hybrid inference

Making decisions based on remote sensing information: good or bad?

Is the regression to the mean in remote sensing inventories a problem for forest planning?

Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time - and how this affects applications

2023

Social data: what exists in reporting schemes for different land systems?

Demands on monitoring

Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay's national forest inventory

Leveraging remotely sensed non-wall-to-wall data for wall-to-wall upscaling in forest inventory

How many bootstrap replications are necessary for estimating remote sensing-assisted, model-based standard errors?

Impact of field reference errors for remote sensing predictions

Conjugating remotely sensed data assimilation and model-assisted estimation for efficient multivariate forest inventory

Three-phase hierarchical model-based and hybrid inference

Model-based estimation and mapping of plant density based on remote sensing and presence/absence data

2022

Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors

Multiple drivers of large-scale lichen decline in boreal forest canopies

Cross-classes domain inference with network sampling for natural resource inventory

Comparing frameworks for biomass prediction for the Global Ecosystem Dynamics Investigation

GEDI launches a new era of biomass inference from space

Importance of Calibration for Improving the Efficiency of Data Assimilation for Predicting Forest Characteristics