Small area estimation of growing stock timber volume, basal area, mean stem diameter, and stem density for mountain forests in Austria

TL;DR

This study evaluates regression models using airborne and ground data to estimate forest attributes like volume and density in Austrian mountain forests, aiming for accurate stand-level predictions and uncertainty quantification.

Contribution

It introduces a small area estimation framework combining airborne and ground data to improve stand-level forest attribute estimation in mountain forests.

Findings

Univariate and multivariate models showed similar predictive performance.

Simpler models performed comparably to complex spatial models.

Airborne laser scanning data effectively supported stand-level estimations.

Abstract

Regression models were evaluated to estimate stand-level growing stock volume (GSV), quadratic mean diameter (QMD), basal area (BA), and stem density (N) in the Brixen im Thale forest district of Austria. Field measurements for GSV, QMD, and BA were collected on 146 inventory plots using a handheld mobile personal laser scanning system. Predictor variables were derived from airborne laser scanning (ALS)-derived normalized digital surface and terrain models. The objective was to generate stand-level estimates and associated uncertainty for GSV, QMD, BA, and N across 824 stands. A unit-level small area estimation framework was used to generate stand-level posterior predictive distributions by aggregating predictions from finer spatial scales. Both univariate and multivariate models, with and without spatially varying intercepts, were considered. Predictive performance was assessed via spatially blocked cross-validation, focusing on bias, accuracy, and precision. Despite exploratory analysis suggesting advantages of complex multivariate spatial models, simpler univariate spatial -- and in some cases, non-spatial -- models exhibited comparable predictive performance.