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Forests 2015, 6(11), 4059-4071; doi:10.3390/f6114059

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

National Forest Inventory, Norwegian Institute of Bioeconomy Research, P.O. Box 115, NO-1431 Ås, Norway
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Academic Editor: Joanne C. White
Received: 26 June 2015 / Revised: 13 October 2015 / Accepted: 28 October 2015 / Published: 10 November 2015
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
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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 View Full-Text
Keywords: forest inventory; remote sensing; image matching; photogrammetry; kNN; tree segmentation; ALS; semi-global matching; timber volume forest inventory; remote sensing; image matching; photogrammetry; kNN; tree segmentation; ALS; semi-global matching; timber volume
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Rahlf, J.; Breidenbach, J.; Solberg, S.; Astrup, R. Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA. Forests 2015, 6, 4059-4071.

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