Evaluación del uso de LiDAR discreto, full-waveform y TLS en la clasificación por composición de especies en bosques mediterráneos
[EN] 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 de...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/114903 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/114903 |
| Access Level: | acceso abierto |
| Palabra clave: | Airborne laser scanning Láser escáner aerotransportado Láser escáner terrestre Clasificación Sotobosque Forestal Terrestrial laser scanning Classification Understory vegetation Forestry |
| Sumario: | [EN] 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. |
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