A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables

Author:

García-Gutiérrez J.,Martínez-Álvarez F.,Troncoso A.,Riquelme J.C.

Funder

Spanish Ministry of Science and Technology

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference36 articles.

1. L. Goncalves-Seco, D. Miranda, R. Crecente, J. Farto, Digital terrain model generation using airborne LIDAR in forested area of Galicia, Spain, Lisbon, Portugal, in: Proceedings of 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 2006, pp. 169–180.

2. A hybrid conditional random field for estimating the underlying ground surface from airborne lidar data;Lu;IEEE Trans. Geosci. Remote Sens.,2009

3. Estimating biomass carbon stocks for a Mediterranean forest in Central Spain using LiDAR height and intensity data;Garcia;Remote Sens. Environ.,2010

4. Predicting the potential habitat of oaks with data mining models and the R system;Pino-Mejias;Environ. Model. Softw.,2010

5. Mapping surface fuel models using LiDAR and multispectral data fusion for fire behavior;Mutlu;Remote Sens. Environ.,2008

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