Assessing 3D point clouds from aerial photographs for species-specific forest inventories. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Puliti, Stefano; Gobakken, Terje; Ørka, Hans Ole; Næsset, Erik
• Is Part Of: Scandinavian journal of forest research. Volume 32:Issue 1(2017)
• Page Start: 68
• Page End: 79
• Publication Date: 2017-01-02
• Subjects: Species-specific forest management inventory -- photogrammetry -- airborne laser scanning -- Dirichlet regression -- species proportions; Forests and forestry -- Scandinavia -- Periodicals; Forests and forestry -- Periodicals; Forests and forestry -- Research -- Scandinavia -- Periodicals; 634.90948
• DOI: 10.1080/02827581.2016.1186727
• Languages: English
• ISSNs: 0282-7581

Abstract

ABSTRACT: In this study we assessed the potential of using photogrammetric data for species-specific forest inventories. The method is based on a combination of Dirichlet and ordinary linear regression models. This approach was used to predict species proportions, main tree species, total, and species-specific volume. Structural and spectral variables were used as predictors. The models were validated using 63 independent validation stands. The results from airborne laser scanning (ALS) data combined with spectral data and photogrammetric data obtained using aerial imagery with different forward overlaps of 80% and 60% were compared. The best photogrammetry-based models predicted species proportions with a relative root mean square error (RMSE) of 21.4%, classified dominant species with 79% accuracy, predicted total volume with relative RMSE of 13.4%, and predicted species-specific volume with relative RMSE of 36.6%, 46.5%, and 84.9% for spruce, pine, and deciduous species, respectively. The results were similar for the three point cloud datasets obtained from aerial imagery and ALS and the accuracies of the predictions were comparable to methods used in operational FMI. The study highlights the effectiveness of forest inventories carried out using photogrammetric data, which – differently from ALS, can include species-specific information without relying on multiple data sources.