Combining canopy height and tree species map information for large-scale timber volume estimations under strong heterogeneity of auxiliary data and variable sample plot sizes

Abstract

A timber volume regression model applicable to the state and communal forest area of the federal German state of Rhineland-Palatinate is identified using a combination of airborne laser scanning (ALS)-derived metrics and information from a satellite-based tree species classification map available on the federal state level. As is common in many forest inventory datasets, strong heterogeneity in the ALS data due to different acquisition dates and misclassifications in the tree species classification map had noticeable effects on the regression model’s performance. This article specifically addresses techniques that improve the performance of ordinary least square regression models under such restricting conditions. We introduce a calibration technique to neutralize the effect of misclassifications in the tree species variable that originally caused a residual inflation of 0.05 in adjusted R2. Incorporating the calibrated tree species information improved the model accuracy by up to 0.07 in adjusted R2 and suggests the use of such information in forthcoming inventories. We also found that including ALS quality information as categorical variables within the regression model considerably mitigates issues with time lags between the ALS and terrestrial data acquisition and ALS quality variations (increase of 0.09 in adjusted R2). The model achieved an adjusted R2 of 0.48 and a cross-validated root-mean-square error (RMSEcv) of 46.7% under incorporation of the tree species and ALS quality information and was thus improved by 0.12 in adjusted R2 (5% in RMSEcv) compared to the simple model only containing ALS height metrics (adjusted R2=0.36, RMSEcv=51.7%).