Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA

Johannes Rahlf,Johannes Breidenbach,Rasmus Astrup,Svein Solberg

doi:10.3390/f6114059

Johannes Rahlf, Johannes Breidenbach + Show 2 more

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https://doi.org/10.3390/f6114059

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Journal: Forests	Publication Date: Nov 10, 2015
Citations: 56	License type: CC BY 4.0

Affiliation: Norwegian Institute of Bioeconomy Research

Abstract

Image-based point clouds obtained using aerial photogrammetry share many characteristics with point clouds obtained by airborne laser scanning (ALS). Two approaches have been used to predict forest parameters from ALS: the area-based approach (ABA) and the individual tree crown (ITC) approach. In this article, we apply the semi-ITC approach, a variety of the ITC approach, on an image-based point cloud to predict forest parameters and compare the performance to the ABA. Norwegian National Forest Inventory sample plots on a site in southeastern Norway were used as the reference data. Tree crown objects were delineated using a watershed segmentation algorithm, and explanatory variables were calculated for each tree crown segment. A multivariate kNN model for timber volume, stem density, basal area and quadratic mean diameter with the semi-ITC approach produced RMSEs of 30%, 46%, 25%, 26%, respectively. The corresponding measures for the ABA were 30%, 51%, 26%, 35%, respectively. Univariate kNN models resulted in timber volume RMSEs of 25% for the semi-ITC approach and 22% for the ABA. A non-linear logistic regression model with the ABA produced an RMSE of 23%. Both approaches predicted timber volume with comparable precision and accuracy at the plot level. The multivariate kNN model was slightly more precise with the semi-ITC approach, while biases were larger

Highlights

Three-dimensional (3D) point clouds from remote sensing are valuable for forest inventories, because vegetation height is correlated to key forest parameters
The area-based approach (ABA) univariate kNN model was marginally better than the logistic regression model
The optimal upper asymptote was at 598 m3 ·ha−1. Both the semi-individual tree crown (ITC) approach and the ABA showed a similar level of precision and accuracy at the plot level when predicting timber volume

Summary

Introduction

Three-dimensional (3D) point clouds from remote sensing are valuable for forest inventories, because vegetation height is correlated to key forest parameters. The most prominent method of acquiring point clouds is airborne laser scanning (ALS). Remote sensing data, which increasingly attracts attention in forest inventory research, are image-based point clouds from digital aerial photogrammetry [3,4,5,6]. Algorithms and computing power allow the creation of height information over large areas with high spatial resolution from images of aerial photographic surveys. Image-based point clouds and canopy height models (CHM) provide less structural information of the canopy than ALS, but can be accurate for predicting timber volume [7,8,9]

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