Next Article in Journal
Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network
Next Article in Special Issue
An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells
Previous Article in Journal
Spring and Autumn Phenological Variability across Environmental Gradients of Great Smoky Mountains National Park, USA
Previous Article in Special Issue
A Flexible, Generic Photogrammetric Approach to Zoom Lens Calibration
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(5), 400; doi:10.3390/rs9050400

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

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
Received: 16 February 2017 / Revised: 19 April 2017 / Accepted: 21 April 2017 / Published: 26 April 2017
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
View Full-Text   |   Download PDF [1560 KB, uploaded 2 May 2017]   |  

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
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never 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

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top