Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data
AbstractFusion of ALS and hyperspectral data can offer a powerful basis for the discrimination of tree species and enables an accurate prediction of species-specific attributes. In this study, the fused airborne laser scanning (ALS) data and hyperspectral images were used to model and predict the total and species-specific volumes based on three forest inventory approaches, namely the individual tree crown (ITC) approach, the semi-ITC approach, and the area-based approach (ABA). The performances of these inventory approaches were analyzed and compared at the plot level in a complex Alpine forest in Italy. For the ITC and semi-ITC approaches, an ITC delineation algorithm was applied. With the ITC approach, the species-specific volumes were predicted with allometric models for each crown segment and aggregated to the total volume. For the semi-ITC and ABA, a multivariate k-most similar neighbor method was applied to simultaneously predict the total and species-specific volumes using leave-one-out cross-validation at the plot level. In both methods, the ALS and hyperspectral variables were important for volume modeling. The total volume of the ITC, semi-ITC, and ABA resulted in relative root mean square errors (RMSEs) of 25.31%, 17.41%, 30.95% of the mean and systematic errors (mean differences) of 21.59%, −0.27%, and −2.69% of the mean, respectively. The ITC approach achieved high accuracies but large systematic errors for minority species. For majority species, the semi-ITC performed slightly better compared to the ABA, resulting in higher accuracies and smaller systematic errors. The results indicated that the semi-ITC outperformed the two other inventory approaches. To conclude, we suggest that the semi-ITC method is further tested and assessed with attention to its potential in operational forestry applications, especially in cases for which accurate species-specific forest biophysical attributes are needed. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Kandare, K.; Dalponte, M.; Ørka, H.O.; Frizzera, L.; Næsset, E. Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data. Remote Sens. 2017, 9, 400.
Kandare K, Dalponte M, Ørka HO, Frizzera L, Næsset E. Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data. Remote Sensing. 2017; 9(5):400.Chicago/Turabian Style
Kandare, Kaja; Dalponte, Michele; Ørka, Hans O.; Frizzera, Lorenzo; Næsset, Erik. 2017. "Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data." Remote Sens. 9, no. 5: 400.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.