Next Article in Journal
Analysing Urban Development Patterns in a Conflict Zone: A Case Study of Kabul
Next Article in Special Issue
Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping
Previous Article in Journal
Chlorophyll-a Variability during Upwelling Events in the South-Eastern Baltic Sea and in the Curonian Lagoon from Satellite Observations
Previous Article in Special Issue
Use of Remotely Sensed Data to Enhance Estimation of Aboveground Biomass for the Dry Afromontane Forest in South-Central Ethiopia
 
 
Article

Fuel Type Classification Using Airborne Laser Scanning and Sentinel 2 Data in Mediterranean Forest Affected by Wildfires

1
Land Change Science Research Unit, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zurcherstrasse 111, 8930 Birmensdorf, Switzerland
2
GEOFOREST-IUCA, Department of Geography, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain
3
Centro Universitario de la Defensa de Zaragoza, Academia General Militar, Ctra. de Huesca s/n, 50090 Zaragoza, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3660; https://doi.org/10.3390/rs12213660
Received: 26 September 2020 / Revised: 28 October 2020 / Accepted: 6 November 2020 / Published: 8 November 2020
(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)
Mediterranean forests are recurrently affected by fire. The recurrence of fire in such environments and the number and severity of previous fire events are directly related to fire risk. Fuel type classification is crucial for estimating ignition and fire propagation for sustainable forest management of these wildfire prone environments. The aim of this study is to classify fuel types according to Prometheus classification using low-density Airborne Laser Scanner (ALS) data, Sentinel 2 data, and 136 field plots used as ground-truth. The study encompassed three different Mediterranean forests dominated by pines (Pinus halepensis, P. pinaster y P. nigra), oaks (Quercus ilex) and quercus (Q. faginea) in areas affected by wildfires in 1994 and their surroundings. Two metric selection approaches and two non-parametric classification methods with variants were compared to classify fuel types. The best-fitted classification model was obtained using Support Vector Machine method with radial kernel. The model includes three ALS and one Sentinel-2 metrics: the 25th percentile of returns height, the percentage of all returns above mean, rumple structural diversity index and NDVI. The overall accuracy of the model after validation was 59%. The combination of data from active and passive remote sensing sensors as well as the use of adapted structural diversity indices derived from ALS data improved accuracy classification. This approach demonstrates its value for mapping fuel type spatial patterns at a regional scale under different heterogeneous and topographically complex Mediterranean forests. View Full-Text
Keywords: Prometheus fuel type; ALS; Sentinel 2; forest fires; Mediterranean forest Prometheus fuel type; ALS; Sentinel 2; forest fires; Mediterranean forest
Show Figures

Graphical abstract

MDPI and ACS Style

Domingo, D.; de la Riva, J.; Lamelas, M.T.; García-Martín, A.; Ibarra, P.; Echeverría, M.; Hoffrén, R. Fuel Type Classification Using Airborne Laser Scanning and Sentinel 2 Data in Mediterranean Forest Affected by Wildfires. Remote Sens. 2020, 12, 3660. https://doi.org/10.3390/rs12213660

AMA Style

Domingo D, de la Riva J, Lamelas MT, García-Martín A, Ibarra P, Echeverría M, Hoffrén R. Fuel Type Classification Using Airborne Laser Scanning and Sentinel 2 Data in Mediterranean Forest Affected by Wildfires. Remote Sensing. 2020; 12(21):3660. https://doi.org/10.3390/rs12213660

Chicago/Turabian Style

Domingo, Darío, Juan de la Riva, María Teresa Lamelas, Alberto García-Martín, Paloma Ibarra, Maite Echeverría, and Raúl Hoffrén. 2020. "Fuel Type Classification Using Airborne Laser Scanning and Sentinel 2 Data in Mediterranean Forest Affected by Wildfires" Remote Sensing 12, no. 21: 3660. https://doi.org/10.3390/rs12213660

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop