Assessing the potential of the dart model to discrete return lidar simulation—application to fuel type mapping

Revilla, S. ; Lamelas, M.T. (Universidad de Zaragoza) ; Domingo, D. ; de la Riva, J. (Universidad de Zaragoza) ; Montorio, R. (Universidad de Zaragoza) ; Montealegre, A.L. (Universidad de Zaragoza) ; García-Martín, A. (Universidad de Zaragoza)

Resumen: Fuel type is one of the key factors for analyzing the potential of fire ignition and propaga-tion in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction.

Idioma: Inglés

DOI: 10.3390/rs13030342

Año: 2021

Publicado en: Remote Sensing 13, 3 (2021), 342 [20 pp.]
ISSN: 2072-4292

Factor impacto JCR: 5.349 (2021)

Categ. JCR: GEOSCIENCES, MULTIDISCIPLINARY rank: 30 / 202 = 0.149 (2021) - Q1 - T1
Categ. JCR: IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY rank: 6 / 28 = 0.214 (2021) - Q1 - T1
Categ. JCR: REMOTE SENSING rank: 11 / 34 = 0.324 (2021) - Q2 - T1
Categ. JCR: ENVIRONMENTAL SCIENCES rank: 83 / 279 = 0.297 (2021) - Q2 - T1

Factor impacto CITESCORE: 7.4 - Earth and Planetary Sciences (Q1)

Factor impacto SCIMAGO: 1.283 - Earth and Planetary Sciences (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/UZ/CUD2018-HUM-01

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Análisis Geográfico Regi. (Dpto. Geograf. Ordenac.Territ.)