<p>LiDAR technology –airborne and terrestrial- is becoming more relevant in the development of forest inventories, which are crucial to better understand and manage forest ecosystems. In this study, we assessed a classification of species composition in a Mediterranean forest following the C4.5 decision tree. Different data sets from airborne laser scanner full-waveform (ALS<sub>FW</sub>), discrete (ALS<sub>D</sub>) and terrestrial laser scanner (TLS) were combined as input data for the classification. Species composition were divided into five classes: pure Quercus ilex plots (QUI); pure Pinus halepensis dense regenerated (HALr); pure P. halepensis (HAL); pure P. pinaster (PIN); and mixed P. pinaster and Q. suber (mPIN). Furthermore, the class HAL was subdivided in low and dense understory vegetation cover. As a result, combination of ALS<sub>FW</sub> and TLS reached 85.2% of overall accuracy classifying classes HAL, PIN and mPIN. Combining ALS<sub>FW</sub> and ALS<sub>D</sub>, the overall accuracy was 77.0% to discriminate among the five classes. Finally, classification of understory vegetation cover using ALS<sub>FW</sub> reached an overall accuracy of 90.9%. In general, combination of ALS<sub>FW</sub> and TLS improved the overall accuracy of classifying among HAL, PIN and mPIN by 7.4% compared to the use of the data sets separately, and by 33.3% with respect to the use of ALS<sub>D</sub> only. ALS<sub>FW</sub> metrics, in particular those specifically designed for detection of understory vegetation, increased the overall accuracy 9.1% with respect to ALS<sub>D</sub> metrics. These analyses show that classification in forest ecosystems with presence of understory vegetation and intermediate canopy strata is improved when ALS<sub>FW</sub> and/or TLS are used instead of ALS<sub>D</sub>.</p>
Evaluación del uso de LiDAR discreto, full-waveform y TLS en la clasificación por composición de especies en bosques mediterráneos
