Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping
Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation 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...
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MDPI AG
2021-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/3/342 |
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| author | Sergio Revilla María Teresa Lamelas Darío Domingo Juan de la Riva Raquel Montorio Antonio Luis Montealegre Alberto García-Martín |
| author_facet | Sergio Revilla María Teresa Lamelas Darío Domingo Juan de la Riva Raquel Montorio Antonio Luis Montealegre Alberto García-Martín |
| author_sort | Sergio Revilla |
| collection | DOAJ |
| description | Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation 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. |
| first_indexed | 2024-03-09T04:12:53Z |
| format | Article |
| id | doaj.art-7615e7d75c964255b5ad0851612a3f6a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T04:12:53Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7615e7d75c964255b5ad0851612a3f6a2023-12-03T13:58:13ZengMDPI AGRemote Sensing2072-42922021-01-0113334210.3390/rs13030342Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type MappingSergio Revilla0María Teresa Lamelas1Darío Domingo2Juan de la Riva3Raquel Montorio4Antonio Luis Montealegre5Alberto García-Martín6Instituto Geográfico de Aragón, Pº María Agustín, 36, Edificio Pignatelli, 50071 Zaragoza, SpainGEOFOREST-IUCA Research Group, Department of Geography, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, SpainGEOFOREST-IUCA Research Group, Department of Geography, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, SpainGEOFOREST-IUCA Research Group, Department of Geography, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, SpainGEOFOREST-IUCA Research Group, Department of Geography, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, SpainGEOFOREST-IUCA Research Group, Department of Geography, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, SpainGEOFOREST-IUCA Research Group, Department of Geography, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, SpainFuel type is one of the key factors for analyzing the potential of fire ignition and propagation 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.https://www.mdpi.com/2072-4292/13/3/3423D Radiative transfer model (RTM)low-density airborne laser scanning (ALS) dataPrometheus fuel typesMediterranean forest |
| spellingShingle | Sergio Revilla María Teresa Lamelas Darío Domingo Juan de la Riva Raquel Montorio Antonio Luis Montealegre Alberto García-Martín Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping Remote Sensing 3D Radiative transfer model (RTM) low-density airborne laser scanning (ALS) data Prometheus fuel types Mediterranean forest |
| title | Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping |
| title_full | Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping |
| title_fullStr | Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping |
| title_full_unstemmed | Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping |
| title_short | Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping |
| title_sort | assessing the potential of the dart model to discrete return lidar simulation application to fuel type mapping |
| topic | 3D Radiative transfer model (RTM) low-density airborne laser scanning (ALS) data Prometheus fuel types Mediterranean forest |
| url | https://www.mdpi.com/2072-4292/13/3/342 |
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