Model-based inference for biomass estimation in a LiDAR sample survey in Hedmark County, NorwayThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time.

Abstract

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.

Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America

“…We used a model-based procedure for estimating AGB density from the ground-plot, PALS, and GLAS data Ståhl et al 2011). In this procedure, we do not rely on a probabilitybased forest inventory, rather we use the ground plots that were available and that were as representative as possible of our area of interest, i.e., the plots spanned a wide east to west swath across the North American boreal forest and included plots from both southern and northern portions of the biome (Table 1).…”

“…4). These maps were compiled by applying an equation for each stratum, where each stratum was a unique combination of land cover class a MB SE, standard errors for flight lines (orbits) calculated according to the model-based estimator described in Ståhl et al (2011) and in the Materials and methods section of this article. This error term includes both sampling error due to a limited number of orbits and model error due to error in the PALS-GLAS biomass model.…”

“…From these measurements, a number of lidar metrics related to forest height and canopy structure can be derived, which can then be converted into AGB (Nelson et al 1988). Both profiling and scanning lidars have been used as sampling tools (e.g., Nelson et al 2012;Gobakken et al 2012), and various statistical approaches have been developed to estimate both AGB and carbon stocks, as well as their statistical uncertainties Ståhl et al 2011). For example, in lidar-assisted model-based sampling, ground plots representative of the entire area of interest are selected.…”

Adapting National Forest Inventories to changing requirements – the case of the Swedish National Forest Inventory at the turn of the 20th century

“…These techniques also have been evaluated as tools for making NFIs more cost-effective (e.g. Gregoire et al 2011;Ståhl et al 2011b;Naesset et al 2013) but the results are inconclusive. While the techniques have a potential to considerably improve the precision of the estimates for some variables they do not improve the precision for other variables.…”

Airborne Laser Scanning to support forest resource management under alpine, temperate and Mediterranean environments in Italy

“…a ratio estimation approach like that proposed by Corona and Fattorini [2008], to a sample of the territory based on transects below the flight lines to spot samples within transects, so to adopt e.g. a multiphase/multistage estimation approach like those proposed by Gregoire et al [2011] and Ståhl et al [2011]. Maselli et al [2011] investigated the application of parametric and non parametric methods to Landsat satellite imagery in order to extend stem volume estimation from LiDAR data taken over few strips to the entire forest area.…”