A comparative study of regression methods to predict forest structure and canopy fuel variables from LiDAR full-waveform data

• Autores: P. Crespo Peremarch, L.A. Ruiz, A. Balaguer Beser
• Localización: Revista de teledetección: Revista de la Asociación Española de Teledetección, ISSN 1133-0953, Nº. Extra 45, 2016, págs. 27-40
• Idioma: inglés
• DOI: 10.4995/raet.2016.4066
• Títulos paralelos:
  • Estudio comparativo de métodos de regresión para la predicción de variables de estructura y combustibilidad a partir de datos LiDAR full-waveform
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• Dialnet Métricas: 2 Citas
• Resumen
  • español

    Los métodos de regresión se utilizan ampliamente en el ámbito forestal para la predicción y el cartografiado de las variables de estructura y combustibilidad. En este artículo se evalúan diferentes modelos de regresión (lineal, no lineal, árboles de regresión y ensemble). Como variables independientes se utilizaron métricas extraídas de datos LiDAR full-waveform, mientras que los valores de las variables dependientes se generaron a partir de modelos basados en datos de campo obtenidos para 78 parcelas de 16 m de radio. Se llevaron a cabo transformaciones de las variables dependientes e independientes con selección de atributos para evaluar su influencia en la predicción de la variable respuesta. Con el fin de verificar diferencias significativas y ordenar los modelos de regresión se emplearon los tests no paramétricos de Wilcoxon y Friedman, y el análisis post-hoc o los tests de comparación post-hoc por pares, como el de Nemenyi, para el test de Friedman. Las regresiones basadas en la transformación de la variable dependiente, como raíz cuadrada o logaritmo, o en la transformación de las variables independientes, obtuvieron un incremento de la R2 de hasta un 6% con respecto a la regresión lineal. Mediante el método CART (Classification and Regression Tree) se obtuvieron resultados discretos, si bien su uso puede estar indicado para la categorización o estratificación. Con el método basado en la transformación de la variable dependiente mediante raíz cuadrada se consiguieron los mejores resultados comparativos en la predicción de variables forestales, excepto para el volumen. Sin embargo, su uso no siempre implica una mejora significativa con respecto a los otros métodos de regresión usados en este trabajo.

  • English

    Regression methods are widely employed in forestry to predict and map structure and canopy fuel variables. We present a study where several regression models (linear, non-linear, regression trees and ensemble) were assessed. Independent variables were calculated using metrics extracted from full-waveform LiDAR data, while the reference data used to generate the dependent variables for the prediction models were obtained from fieldwork in 78 plots of 16 m radius. Transformations of dependent and independent variables with feature selection were carried out to assess their influence in the prediction of response variables. In order to evaluate significant differences and rank regression models we used the non-parametric tests Wilcoxon and Friedman, and post-hoc analysis or post-hoc pairwise multiple comparison tests, such as Nemenyi, for Friedman test. Regressions using transformation of the dependent variable, like square-root or logarithmic, or the independent variable, increased R2 up to 6% with respect to linear regression using unprocessed response variables. CART (Classification and Regression Tree) method provided poor results, but it may be interesting for categorisation purposes. Square-root transformation of the dependent variable is the method having the best overall results, except for stand volume. However, not always has a significant improvement with respect to other regression methods.

• Referencias bibliográficas
  • Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. In: 2nd International Symposium on Information Theory....
  • Andersen, H.E., McGaughey, R.J., Reutebuch, S.E. 2005. Estimating forest canopy fuel parameters using LiDAR data. Remote Sensing of Environment,...
  • Andersen, H.E., Breidenbach, J. 2007. Statistical properties of mean stand biomass estimators in a lidar-based double sampling forest survey...
  • Baccini, A., Laporte, N., Goetz, S.J., Sun, M., Dong, H. 2008. A first map of tropical Africa’s aboveground biomass derived from satellite...
  • Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984. Classification and regression trees. New York: Chapman and Hall.
  • Breiman, L., 2001. Random Forests. Machine Learning, 45, 5-32. http://dx.doi.org/10.1023/A:1010933404324
  • Cao, L., Coops, N.C., Hermosilla, T., Innes, J., Dai, J., She, G. 2014. Using small-footprint discrete and full-waveform airborne LiDAR metrics...
  • De’Ath, G., Fabricius, K.E. 2013. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology,...
  • Demšar, J. 2006. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Reasearch, 7, 1-30.
  • Duong, H.V. 2010. Processing and application of ICESat large footprint full waveform laser range data. Ph.D. Thesis, Delft University of Technology,...
  • Erdody, T.L., Moskal, L.M. 2010. Fusion of LiDAR and imagery for estimating forest canopy fuels. Remote Sensing of Environment, 114(4), 725-737....
  • Friedman, M. 1937. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical...
  • Friedman, M. 1940. A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics,...
  • García-Gutiérrez, J., González-Ferreiro, E., Mateos-García, D., Riquelme-Santos, J.C., Mirando, D. 2011. A comparative study between two regression...
  • García-Gutiérrez, J., González-Ferreiro, E., Riquelme-Santos, J.C., Miranda, D., Diéguez-Aranda, U., Navarro-Cerrillo, R.M. 2013. Evolutionary...
  • Guyon, I., Elisseeff, A. 2003. An introduction to variables and feature selection. Journal of Machine Learning Research, 3, 1157-1182.
  • Hannon, L., Knapp, P. 2003. Reassessing nonlinearity in the urban disadvantage/violent crime relationship: an example of methodological bias...
  • Hermosilla, T., Ruiz, L.A., Kazakova, A.N., Coops, N.C., Moskal, L.M. 2014. Estimation of forest structure and canopy fuel parameters from...
  • Hernández-Orallo, J., Ramírez, M.J., Ferri, C. 2004. Introducción a la minería de datos. Madrid: Pearson Educación S.A.
  • Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., Zhu, Y-H. 2000. Accuracy comparison of various remote sensing data sources in...
  • Lefsky, M.A., Cohen, W.B., Acker, S.A., Parker, G.G., Spies, T.A., Harding, D. 1999. Lidar remote sensing of the canopy structure and biophysical...
  • Li, L., Guo, Q., Tao, S., Kelly, M., Xu, G. 2015. Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass....
  • Luengo, J., García, S., Herrera, F. 2012. On the Choice of the best imputation methods for missing values considering three groups of classification...
  • Marabel-García, M., Álvarez-Taboada, F. 2014. Estimación de biomasa en herbáceas a partir de datos hiperespectrales, regresión PLS y la transformación...
  • Means, J.E., Acker, S.A., Fitt, B.J., Renslow, M., Emerson, L., Hendrix, C.J. 2000. Predicting forest stand characteristics with airborne...
  • Naesset, E., Bollandsas, O.M., Gobakken, T. 2005. Comparing regression methods in estimation of biophysical properties of forest stands from...
  • Nemenyi, P.B. 1963. Distribution-free multiple comparisons. Ph.D. Thesis, Princeton University, New Jersey, USA.
  • Posada, D., Buckley, T.R. 2004. Model selection and model averaging in Phylogenetics: advantages of Akaike Information Criterion and Bayesian...
  • Quinlan, J.R. 1992. Learning with continuous classes. Machine Learning, 92, 343-348.
  • Segal, M.R. 2004. Machine learning benchmarks and Random Forest regression. Technical report, Center for Bioinformatics & Molecular Biostatistics,...
  • Skowronski, N.S., Clark, K.L., Duveneck, M., Hom, J. 2011. Three-dimensional canopy fuel loading predicted using upward and downward sensing...
  • Standish, J.T., Manning, G.H., Demaershalk, J.P. 1985. Development of biomass equations for British Columbia tree species. Canadian Forestry...
  • Schwarz, G. 1978. Estimating the dimension of a model. The annals of statistics, 6(2), 461-464. http://dx.doi.org/10.1214/aos/1176344136
  • Temesgen, H., Strunk, J., Andersen, H., Flewelling, J. 2015. Evaluating different models to predict biomass increment from multi-temporal...
  • Wang, L., Zhou, X-H., 2005. A fully nonparametric diagnostic test for homogeneity of variances. The Canadian Journal of Statistics, 33(4),...
  • Wilcoxon, F., 1945. Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83. http://dx.doi.org/10.2307/3001968
  • Zar, J.H., 1999. Biostatistical analysis. Upper Saddle River, New Jersey: Prentice Hall.