Schnell, Sebastian
- Institutionen för skoglig resurshushållning, Sveriges lantbruksuniversitet
Forskningsartikel2015Vetenskapligt granskadÖppen tillgång
Saarela, Svetlana; Schnell, Sebastian; Grafström, Anton; Tuominen, Sakari; Nordkvist, Karin; Hyyppä, Juha; Kangas, Annika; Ståhl, Göran
In this study, we investigate the use of model-based inference in forest surveys in which auxiliary data are available as a probability sample. We evaluate the effects of model form and sample size on estimators of growing stock volume, based on different types of remotely sensed auxiliary data. The study was performed through Monte Carlo sampling simulation using a two-phase sampling design within a simulated study area resembling the conditions in mid-western Finland. We show that the choice of model has a minor to moderate effect on the precision of model-based estimators. Similarly, the choice of estimator of the variance-covariance matrix of model parameter estimates, which is at the core of uncertainty assessment in model-based inference, was also found to have a minor to moderate effect on the precision of model-based estimators. Regarding sample sizes, the model error contribution to the total variance remains the same regardless of the sample size of the first phase (i.e., the size of the sample of auxiliary data); to reduce the model-error contribution, there is a need to increase the sample size of the second phase (i.e., the size of the sample of field plots for developing regression models). As a baseline for comparisons, model-assisted estimators were applied and found to be about equally precise as the model-based estimators, in accordance with the theory for the case when models are estimated from the sample data.
airborne LiDAR; heteroscedasticity; Landsat; model-based estimator; sampling simulation
Canadian Journal of Forest Research
2015, Volym: 45, nummer: 11, sidor: 1524-1534 Utgivare: CANADIAN SCIENCE PUBLISHING, NRC RESEARCH PRESS
Annan geovetenskap och miljövetenskap
DOI: https://doi.org/10.1139/cjfr-2015-0077
https://res.slu.se/id/publ/69940