Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/59078
Título
Comparison of machine learning algorithms for wildland-urban interface fuelbreak planning integrating ALS and UAV-Borne LiDAR data and multispectral images
Autor
Año del Documento
2020
Editorial
MDPI
Descripción
Producción Científica
Documento Fuente
Drones, 2020, vol. 4, n. 2, 21
Zusammenfassung
Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m−2 and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m−2, ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.
Materias (normalizadas)
Gestión forestal
Algorithms
Artificial intelligence
Materias Unesco
3106 Ciencia Forestal
Palabras Clave
Artificial intelligence
UAV-LiDAR
Satellite imagery
Large-scale LiDAR
Inteligencia artificial
UAV-LiDAR
Imágenes de satélite
LiDAR a gran escala
Revisión por pares
SI
Patrocinador
Ministerio de Economía, Industria y Competitividad (DI-16-08446; DI-17-09626; PTQ-16-08411; PTQ- 16-08633)
European Commission through the project ‘MySustainableForest’ (H2020-EO-2017; 776045)
European Commission through the project ‘MySustainableForest’ (H2020-EO-2017; 776045)
Patrocinador
info:eu-repo/grantAgreement/EC/H2020/776045
Version del Editor
Propietario de los Derechos
© 2020 The Authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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