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Abstract
Information pertaining to forest timber volume is crucial for sustainable forest management. Remotely-sensed data have been incorporated into operational forest inventories to serve the need for ever more diverse and detailed forest statistics and to produce spatially explicit data products. In this study, data derived from airborne laser scanning and image-based point clouds were compared using three volume estimation methods to aid wall-to-wall mapping of forest timber volume. Estimates of forest height and tree density metrics derived from remotely-sensed data are used as explanatory variables, and forest timber volumes based on sample field plots are used as response variables. When compared to data derived from image-based point clouds, airborne laser scanning produced slightly more accurate estimates of timber volume, with a root mean square error (RMSE) of 26.3% using multiple linear regression. In comparison, RMSEs for volume estimates derived from image-based point clouds were 28.3% and 29.0%, respectively, using Semi-Global Matching and enhanced Automatic Terrain Extraction methods. Multiple linear regression was the best-performing parameter estimation method when compared to k-Nearest Neighbour and Support Vector Machine. In many countries, aerial imagery is acquired and updated on regular cycles of 1–5 years when compared to more costly, once-off airborne laser scanning surveys. This study demonstrates point clouds generated from such aerial imagery can be used to enhance the estimation of forest parameters at a stand and forest compartment level-scale using small area estimation methods while at the same time achieving sampling error reduction and improving accuracy at the forest enterprise-level scale.
Highlights
Accurate measurement and mapping of forest timber volume by affordable means is one of the primary objectives when designing forest inventories as an aid to forest management and operational harvesting activities
The objective of this study is to explore how these airborne laser scanning (ALS) and aerial image point cloud data can be used in conjunction with sample-based forest inventory data to provide spatially-explicit timber volume maps
The enhanced Automatic Terrain Extraction (eATE)-based point clouds were found to be the dataset which showed the lowest performance in terms of R2 and adjusted R2 among the three datasets in this comparison
Summary
Accurate measurement and mapping of forest timber volume by affordable means is one of the primary objectives when designing forest inventories as an aid to forest management and operational harvesting activities. Given the fact that in large forest estates it is not practical to measure all the trees, the traditional approach in forest mensuration is the stand- or compartment-level inventory. In this approach, depending on the characteristics of a forest stand, full assessment, sampling inventory, stand-wise expert. The concept of sample-based forest inventory for entire forest estates using statistical theory for parameter estimation was developed between 1960 and 1980 and is nowadays a widely-established system in forest management inventory practice, along with the traditional stand-wise approach which is still in use [3,4,5,6]. For forest estates that apply a sample-based forest inventory covering the entire forest area, e.g., several federal state forest enterprises in Germany, the combination of remote sensing with sample-based measurements offers a solution for wall-to-wall estimation
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