Accuracy Assessment of Timber Volume Maps Using Forest Inventory Data and LiDAR Canopy Height Models

Abstract

Maps of standing timber volume provide valuable decision support for forest managers and have therefore been the subject of recent studies. For map production, field observations are commonly combined with area-wide remote sensing data in order to formulate prediction models, which are then applied over the entire inventory area. The accuracy of such maps has frequently been described by parameters such as the root mean square error of the prediction model. The aim of this study was to additionally address the accuracy of timber volume classes, which are used to better represent the map predictions. However, the use of constant class intervals neglects the possibility that the precision of the underlying prediction model may not be constant across the entire volume range, resulting in pronounced gradients between class accuracies. This study proposes an optimization technique that automatically identifies a classification scheme which accounts for the properties of the underlying model and the implied properties of the remote sensing support information. We demonstrate the approach in a mountainous study site in Eastern Switzerland covering a forest area of 2000 hectares using a multiple linear regression model approach. A LiDAR-based canopy height model (CHM) provided the auxiliary information; timber volume observations from the latest forest inventory were used for model calibration and map validation. classes, the optimization model successfully identified those classification schemes that achieved the highest possible accuracies for each class.

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

“…The measured volume ranged between 77.6 m 3 /ha and 807 m 3 /ha. The average volume was 421.7˘181.7 m 3 /ha, roughly representing the full range of volume values reported elsewhere for alpine areas [96,97]. The mean basal area was 42.5˘16 m 2 / ha and the canopy closure ranged between 22% and 85% with an average of 61%˘15.3%.…”

“…They obtained an R 2 value of 0.64 and a RMSE of 123 m 3 /ha (31% of the mean). Interestingly, both our simple and stepwise multiple regression model performed slightly better [96].…”

“…In the case of APEX, the availability of a larger spectral range incorporating the SWIR region might explain why our models yielded a higher accuracy. Recently, Hill et al [96] assessed the capability of LIDAR Canopy Height Models (CHM) and forest inventory data to map timber volume in a mountainous study site in Eastern Switzerland using a multiple linear regression model approach. They obtained an R 2 value of 0.64 and a RMSE of 123 m 3 /ha (31% of the mean).…”

Tree Density and Forest Productivity in a Heterogeneous Alpine Environment: Insights from Airborne Laser Scanning and Imaging Spectroscopy

“…The mean annual precipitation totals 744 mm and the average annual temperature is 0.9 • C [50]. The study area is characterized by rough topography and steep slopes [51] with an elevation range from 1800 to 2830 m above sea level (a.s.l.) [52].…”

“…TD was calculated as the number of trees per hectare by summing up all measured trees in each field plot [5]. topography and steep slopes [51] with an elevation range from 1800 to 2830 m above sea level (a.s.l.) [52].…”