Assessing the use of discrete, full-waveform lidar and tls to classify mediterranean forest species composition

Abstract

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 (ALSFW), discrete (ALSD) 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 ALSFW and TLS reached 85.2% of overall accuracy classifying classes HAL, PIN and mPIN. Combining ALSFW and ALSD, the overall accuracy was 77.0% to discriminate among the five classes. Finally, classification of understory vegetation cover using ALSFW reached an overall accuracy of 90.9%. In general, combination of ALSFW 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 ALSD only. ALSFW metrics, in particular those specifically designed for detection of understory vegetation, increased the overall accuracy 9.1% with respect to ALSD metrics. These analyses show that classification in forest ecosystems with presence of understory vegetation and intermediate canopy strata is improved when ALSFW and/or TLS are used instead of ALSD.