An Examination of Diameter Density Prediction with k-NN and Airborne Lidar
AbstractWhile lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination (R2), and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km2 Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria. 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
Strunk, J.L.; Gould, P.J.; Packalen, P.; Poudel, K.P.; Andersen, H.-E.; Temesgen, H. An Examination of Diameter Density Prediction with k-NN and Airborne Lidar. Forests 2017, 8, 444.
Strunk JL, Gould PJ, Packalen P, Poudel KP, Andersen H-E, Temesgen H. An Examination of Diameter Density Prediction with k-NN and Airborne Lidar. Forests. 2017; 8(11):444.Chicago/Turabian Style
Strunk, Jacob L.; Gould, Peter J.; Packalen, Petteri; Poudel, Krishna P.; Andersen, Hans-Erik; Temesgen, Hailemariam. 2017. "An Examination of Diameter Density Prediction with k-NN and Airborne Lidar." Forests 8, no. 11: 444.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.