A comparative study of machine learning regression methods on LiDAR data: A case study

Advances in Intelligent Systems and Computing

Volumn 239, Issue , 2014, Pages 249-258

  Garcia Gutierrez, Jorge ^a   Martínez Álvarez, Francisco ^b   Troncoso, Alicia ^b   Riquelme, Jose C ^a  

^a UNIVERSITY OF SEVILLE   (Spain)

^b UNIVERSIDAD PABLO DE OLAVIDE   (Spain)

Author keywords

LiDAR; Regression; Remote sensing; Soft computing

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; FEATURE EXTRACTION; LEARNING SYSTEMS; LINEAR REGRESSION; REGRESSION ANALYSIS; REMOTE SENSING; SOFT COMPUTING;

COMPARATIVE STUDIES; ENVIRONMENTAL MODEL; FIELD MEASUREMENT; LIGHT DETECTION AND RANGING; MULTIPLE LINEAR REGRESSIONS; REGRESSION; REGRESSION TECHNIQUES; SUPPORT VECTOR REGRESSION (SVR);

OPTICAL RADAR;

EID: 84927587724     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-01854-6_26     Document Type: Conference Paper

References (20)

1. Garcia, M., Riano, D., Chuvieco, E., Danson, F.M.: Estimating biomass carbon stocks for a mediterranean forest in central spain using LiDAR height and intensity data. Remote Sensing of Environment 114(4), 816-830 (2010)
2. Mutlu, M., Popescu, S.C., Stripling, C., Spencer, T.: Mapping surface fuel models using LiDAR and multispectral data fusion for fire behavior. Remote Sensing of Environment 112(1), 274-285 (2008)
3. Gonzalez-Ferreiro, E., Dieguez-Aranda, U., Gonçalves-Seco, L., Crecente, R., Miranda, D.: Estimation of biomass in eucalyptus globulus labill. forests using different LiDAR sampling densities. In: Proceedings of ForestSat (2010)
4. Muss, J.D., Mladenoff, D.J., Townsend, P.A.: A pseudo-waveform technique to assess forest structure using discrete LiDAR data. Remote Sensing of Environment 115(3), 824-835 (2010)
5. Osborne, J., Waters, E.: Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research and Evaluation 8(2) (2002)
6. Salas, C., Ene, L., Gregoire, T.G., Næsset, E., Gobakken, T.: Modelling tree diameter from airborne laser scanning derived variables: A comparison of spatial statistical models. Remote Sensing of Environment 114(6), 1277-1285 (2010)
7. Hudak, A.T., Crookston, N.L., Evans, J.S., Halls, D.E., Falkowski, M.J.: Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LIDAR data. Remote Sensing of Environment 112, 2232-2245 (2008)
8. Chen, G., Hay, G.J.: A support vector regression approach to estimate forest biophysical parameters at the object level using airborne lidar transects and quickbird data. Photogrammetric Engineering and Remote Sensing 77(7), 733-741 (2011)
9. Zhao, K., Popescu, S., Meng, X., Pang, Y., Agca, M.: Characterizing forest canopy structure with lidar composite metrics and machine learning. Remote Sensing of Environment 115(8), 1978-1996 (2011)
10. Latifi, H., Nothdurft, A., Koch, B.: Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: Application of multiple optical/LiDAR-derived predictors. Forestry 83(4), 395-407 (2010)
11. Gleason, C.J., Im, J.: Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sensing of Environment 125, 80-91 (2012)
12. Goncalves-Seco, L., Gonzalez-Ferreiro, E., Dieguez-Aranda, U., Fraga-Bugallo, B., Crecente, R., Miranda, D.: Assessing attributes of high density eucalyptus globulus stands using airborne laser scanner data. International Journal of Remote Sensing 32(24), 9821-9841 (2011)
13. Gonzalez-Ferreiro, E., Dieguez-Aranda, U., Miranda, D.: Estimation of stand variables in pinus radiata d. don plantations using different lidar pulse densities. Forestry 85(2), 281-292 (2012)
14. Dieguez-Aranda, U., et al.: Herramientas selvicolas para la gestion forestal sostenible en Galicia. Xunta de Galicia (2009)
15. McGaughey, R.: FUSION/LDV: Software for LIDAR Data Analysis and Visualization. US Department of Agriculture, Forest Service, Pacific Northwest Research Station, Seattle (2009)
16. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explorations 11(1) (2009)
17. Hughes, G.F.: On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory 14, 55-63 (1968)
18. Parejo, J.A., García, J., Ruiz-Cortés, A., Riquelme, J.C.: Statservice: Herramienta de análisis estadístico como soporte para la investigaci´on con metaheurísticas. In: Actas del VIII Congreso Expañol sobre Metaheurísticas, Algoritmos Evolutivos y Bio-inspirados (2012)
19. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1-30 (2006)
20. Luengo, J., Garcia, S., Herrera, F.: A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests. Expert Systems with Applications 36, 7798-7808 (2009)