Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral DataReportar como inadecuado


Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data


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1

FoxLab, Joint CNR-FEM Initiative, Fondazione E. Mach, Via E. Mach 1, 38010 San Michele all’Adige TN, Italy

2

Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway

3

Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38010 San Michele all’Adige TN, Italy





*

Author to whom correspondence should be addressed.



Academic Editors: Jixian Zhang, Jixian Zhang, Lars T. Waser and Prasad S. Thenkabail

Abstract Fusion 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

Keywords: species-specific volume; semi-individual tree crown; individual tree crown; area-based approach; k-MSN; airborne laser scanning; hyperspectral data; data fusion; forestry species-specific volume; semi-individual tree crown; individual tree crown; area-based approach; k-MSN; airborne laser scanning; hyperspectral data; data fusion; forestry





Autor: Kaja Kandare 1,2,* , Michele Dalponte 3, Hans Ole Ørka 2, Lorenzo Frizzera 3 and Erik Næsset 2

Fuente: http://mdpi.com/



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