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