RT info:eu-repo/semantics/article
T1 Comparison of machine learning algorithms for wildland-urban interface fuelbreak planning integrating ALS and UAV-Borne LiDAR data and multispectral images
A1 Rodríguez Puerta, Francisco
A1 Alonso Ponce, Rafael
A1 Pérez Rodríguez, Fernando
A1 Águeda Hernández, Beatriz
A1 Martín García, Saray
A1 Martínez Rodrigo, Raquel
A1 Lizarralde, Iñigo
K1 Gestión forestal
K1 Algorithms
K1 Artificial intelligence
K1 Artificial intelligence
K1 UAV-LiDAR
K1 Satellite imagery
K1 Large-scale LiDAR
K1 Inteligencia artificial
K1 UAV-LiDAR
K1 Imágenes de satélite
K1 LiDAR a gran escala
K1 3106 Ciencia Forestal
AB 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.
PB MDPI
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/59078
UL https://uvadoc.uva.es/handle/10324/59078
LA eng
NO Drones, 2020, vol. 4, n. 2, 21
NO Producción Científica
DS UVaDOC
RD 24-nov-2024