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A Comparative Study between Two Regression Methods on LiDAR Data: A Case Study

Opened Access A Comparative Study between Two Regression Methods on LiDAR Data: A Case Study

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Autor: García Gutiérrez, Jorge
González Ferreiro, Eduardo
Mateos García, Daniel
Riquelme Santos, José Cristóbal
Miranda, David
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2011
Publicado en: Hybrid Artificial Intelligent Systems : 6th International Conference, HAIS 2011, Wroclaw, Poland, May 23-25, 2011, Proceedings, Part II. Lecture Notes in Computer Science, v.6679
ISBN/ISSN: 978-3-642-21222-2
Tipo de documento: Capítulo de Libro
Resumen: Airborne LiDAR (Light Detection and Ranging) has become an excellent tool for accurately assessing vegetation characteristics in forest environments. Previous studies showed empirical relationships between LiDAR and field-measured biophysical variables. Multiple linear regression (MLR) with stepwise feature selection is the most common method for building estimation models. Although this technique has provided very interesting results, many other data mining techniques may be applied. The overall goal of this study is to compare different methodologies for assessing biomass fractions at stand level using airborne Li- DAR data in forest settings. In order to choose the best methodology, a comparison between two different feature selection techniques (stepwise selection vs. genetic-based selection) is presented. In addition, classical MLR is also compared with regression trees (M5P). The results when each methodology is applied to estimate stand biomass fractions from an a...
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Tamaño: 188.1Kb
Formato: PDF

URI: http://hdl.handle.net/11441/42277

DOI: http://dx.doi.org/10.1007/978-3-642-21222-2_38

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