Caracterización del interfaz forestal/urbano empleando LiDAR como herramienta para la estimación del riesgo de daños por incendios forestales

Abstract

Galicia is a region in NW Spain which is usually affected by a high number of forest fires, and it should meet the current regulations regarding the distance between forests and buildings. This paper aims to identify and characterize woodlands and classify buildings according to their fire risk, for a 36 km 2 area in Forcarei (Pontevedra, Spain). We used LiDAR data to generate three spatial models (DTM: Digital Terrain Model, DSM: Digital Surface Model and nDSM: Normalized Digital Surface Model) and two statistics to characterize the forest stands (density of dominant trees per hectare and their average height). The identification of forested areas was performed using an object-based classification method using the intensity image, the height model and an orthophotograph of the area, and a kappa coefficient of 0.82 was obtained in the validation. The woodlands were reclassified according to the magnitude of a possible fire, based on the density and the average height of the woodlands. The forest stands were mapped according to the magnitude of a possible fire and it was found that 1.18 km 2 would be susceptible to a low magnitude fire, 3.75 km 2 to a medium magnitude fire and 2.25 km 2 to a fire of a high magnitude. Afterwards, it was determined whether the buildings in the area complied with the legislation relating to minimum distance from the forested areas (30 meters). For those that did not meet this distance, the risk of damage in case of a wildfire was calculated. The result was that 43.01% of buildings in the area complied with the regulations, 9.95% were located in a very low risk area, 25.74% in a low risk location, 12.37% in a medium risk area and 8.93% were in a high or very high risk area.Key words: LiDAR, woodlands, buildings, forest fire, OBIA, PNOA. Caracterización del interfaz forestal/urbano empleando LiDAR como herramienta para la estimación del riesgo de daños por incendios forestalesResumen: Galicia (NO de España), afectada por alto número de incendios forestales, debería cumplir la normativa vigente en relación a la distancia entre masas forestales y edificaciones. Este trabajo tiene como objetivos identificar y caracterizar las masas forestales y clasificar las edificaciones en función del riesgo en caso de incendio para un área de 36 km 2 del municipio de Forcarei (Pontevedra). A partir de los datos LiDAR se obtuvo la imagen de intensidades, modelos espaciales (MDT: Modelo Digital del Terreno, MDS: Modelo Digital de Superficies y MANO: Modelo de Alturas Normalizadas de Objetos) y dos estadísticos para la caracterización de las masas forestales (densidad de pies dominantes por hectárea y altura promedio de dominantes). La identificación de las masas forestales se realizó con un método de clasificación orientado a objetos empleando la imagen de intensidades, el modelo de alturas y ortofotografía de la zona, obteniéndose coeficiente kappa de grado de correspondencia de 0,82 en la validación. Las masas forestales se reclasificaron en función de la magnitud de un posible incendio, ba...

Methodological Proposal for Automated Detection of the Wildland–Urban Interface: Application to the Metropolitan Regions of Madrid and Barcelona

“…As for LiDAR data, its download causes computational difficulty, both for its storage and for the Center of the National Geographic Institute of Spain (CNIG) state server, which only allows a limited number of files to be downloaded. In order to solve these LiDAR, on its side, provides accurate information on forest-fuel elements and their proximity to buildings, determining their exposure to fire risk [15]. However, this is particularly useful in zones of limited size since the spatial dataset to be processed for zones as large as those proposed in this research involves the problem of massive data management, as shown in Table 1: voluminous and complex information from the thematic (SIOSE) and geometric (LiDAR) perspectives.…”

“…There are good examples of the delimitation of WUI zones using ALS echoes in Spain, such as the analysis carried out by Badia A. and Gisbert, M. [11] at an experimental level in the metropolitan area of Barcelona, although limited to a very specific area. In Galicia, Robles et al [15] successfully obtained the exposure of settlements to wildfire risk through non-automated analysis of LiDAR data, or by using decision trees and Geographic Information Systems (GIS), as in the case of Fernández-Álvarez, M. et al [16]. Initial research in the SIOSE-INNOVA project for the delimitation of WUI zones began with the use of PNOT LiDAR in Valle del Tiétar (Segovia) and in Camp del Turia (Valencia), through active remote sensing of zones where vegetation and buildings were in close proximity to each other [17][18][19].…”

Vulnerabilidades de los elementos esenciales y evaluación integral del riesgo de desastres para la planificación urbana y la gestión del riesgo en Azogues, Ecuador

“…Este análisis ha sido aplicado y adaptado para esta investigación con la determinación de los parámetros de las variables específicos para el área de estudio, así como en la ponderación de los mismos de acuerdo a las recomendaciones del método nacional. Por otra parte, para validar el método se utilizó el coeficiente Kappa como medio de verificación espacial (Robles et al, 2016), el cual reveló un resultado de 0.63, es decir, el método utilizado posee un nivel de concordancia buena. Los parámetros utilizados para el análisis de peligrosidad física a los IF en el área de estudio se detallan en la Tabla 5.…”

Open data repositories and Geo Small Data for mapping the wildfire risk exposure in wildland urban interface (WUI) in Spain: A case study in the Valencian Region

“…Platt (2014) combined LiDAR and satellite images to determine the danger of forest fires in a home ignition zone (HIZ). In 2016 (Robles et al, 2016) The NPTO LiDAR data is downloaded from an open library in LAZ format (compressed LAS) and RGB true color or near infrared values are incorporated using the NPTO orthophotos (spatial resolution at 25 cm pixel), or with orthophotos obtained simultaneously with the LiDAR data.…”