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

Forests

Johannes Rahlf¹  Johannes Breidenbach²  Svein Solberg²  Rasmus Astrup² 

[1] National Forest Inventory, Norwegian Institute of Bioeconomy Research, P.O. Box 115, NO-1431 Ås, Norway;

关键词: forest inventory;    remote sensing;    image matching;    photogrammetry;    kNN;    tree segmentation;    ALS;    semi-global matching;    timber volume;   

DOI  :  10.3390/f6114059

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.