Model-based inference for biomass estimation in a LiDAR sample survey in Hedmark County, Norway

By Göran Ståhl, Sören Holm, Timothy G. Gregoire, Terje Gobakken, Erik Næsset, Ross Nelson. 2011

181 citations recorded by the source corpus.

In forest inventories, regression models are often applied to predict quantities such as biomass at the level of sampling units. In this paper, we propose a model-based inference framework for combining sampling and model errors in the variance estimation. It was applied to airborne laser (LiDAR) data sets from Hedmark County, Norway, where the model error proportion of the total variance was found to be large for both scanning (airborne laser scanning) and profiling LiDAR when biomass was estimated. With profiling LiDAR, the model error variance component for the entire county was as large as 71% whereas for airborne laser scanning, it was 43% of the total variance. Partly, this reflects the better accuracy of the pixel-based regression models estimated from scanner data as compared with the models estimated from profiler data. The framework proposed in our study can be applied in all types of sample surveys where model-based predictions are made at the level of individual sampling units. Especially, it should be useful in cases where model-assisted inference cannot be applied due to the lack of a probability sample from the target population or due to problems of correctly matching observations of auxiliary and target variables.

Subfields: Environmental Engineering, Insect Science, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/b62d2f27f04b2f433a3a32026762d7fb96aa43ce

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Citations in the corpus

GEDI launches a new era of biomass inference from space (2022, methodology): “The parametric models mentioned above are used to predict biomass for all footprints within a given grid cell, and the hybrid estimates of variance of the mean account for both modeling uncertainty and uncertainty related to how the cell is sampled by GEDI’s observations [18, 26].”
Adapting National Forest Inventories to changing requirements - the case of the Swedish National Forest Inventory at the turn of the 20th century (2014, methodology): “These techniques also have been evaluated as tools for making NFIs more cost-effective (e.g. Gregoire et al. 2011; Ståhl et al. 2011b; Næsset et al. 2013) but the results are inconclusive.”
Effects of uncertainty in model predictions of individual tree volume on large area volume estimates (2014, result): “The general approach is similar to the approaches reported by Cunia (1987) and Ståhl et al. (2011, 2013), but has not often been reported in the literature, perhaps because of the theoretical complexities.”
Hybrid estimators for mean aboveground carbon per unit area (2016, methodology): “For example, Ståhl et al. (2011) and Gobakken et al. (2012) obtained probability samples of strips of ALS data, used a model of the relationship between biomass and ALS metrics to predict biomass for cells that tessellated the strips, aggregated the cell predictions to obtain strip predictions,…”