Forests | |
Accuracy Assessment of Timber Volume Maps Using Forest Inventory Data and LiDAR Canopy Height Models | |
Andreas Hill1  Jochen Breschan2  | |
[1] Institute of Terrestrial Ecosystems, Swiss Federal Institute of Technology ETH Zürich, Chair of Land Use Engineering, Universitätsstrasse 16, 8092 Zürich, Switzerland; | |
关键词: forest inventory; timber volume mapping; airborne laser scanning; canopy height model; accuracy assessment; optimization techniques; | |
DOI : 10.3390/f5092253 | |
来源: mdpi | |
【 摘 要 】
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. The coefficient of determination (
【 授权许可】
CC BY
© 2014 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190021385ZK.pdf | 1587KB | download |