Characterization of wildland-urban interfaces using LiDAR data to estimate the risk of wildfire damage
DOI:
https://doi.org/10.4995/raet.2016.3967Keywords:
LiDAR, woodlands, buildings, forest fire, OBIA, PNOAAbstract
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 km2 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 km2 would be susceptible to a low magnitude fire, 3.75 km2 to a medium magnitude fire and 2.25 km2 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.
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Alonso, A., Fontenla, O., Guijarro, B., Hernández, E., Paz, M.I., Jiménez, E., Legido, J.L., Carballas, T. 2003. An intelligent system for forest fire risk prediction and firefighting management in Galicia. Expert Systems with Applications, 25(4), 545-554. http://dx.doi.org/10.1016/S0957-4174(03)00095-2
Alvarez, M.F., 2006. Remote sensing and Geoinfomation Systems applied to the forest management of Eucalyptus globulus Labill. Stands damaged by Gonipterus scutellatus Gyllenhal in Galicia. Universidad de Vigo. Departamento de ingeniería de los recursos naturales y medio ambiente.
Axelsson, P. 2000. DEM generation from laser scanner data using adaptive tin models. International Archives of Photogrammetry and Remote Sensing, 33(b4), 111- 118.
Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M. 2004. Multi-resolution, Object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. of Photogrammetry and Remote Sensing, 58(3-4), 239-258. http://dx.doi.org/10.1016/j.isprsjprs.2003.10.002
Buján, S., González, E., Barreiro, L., Santé, I., Corbelle, E., Miranda, D. 2013. Clasification of rural landscapes from low-density LiDAR data: is it theoretically possible? International Journal Of Remote Sensing, 34(16), 5666-5689. http://dx.doi.org/10.1080/014311 61.2013.792230
Buján, S., González, E., Reyes, F., Barreiro, L., Crecente, R., Miranda, D. 2012. Land use classification from LiDAR data and ortho-images in a rural area. The Photogrammetric Record, 27(140), 401-422. http:// dx.doi.org/10.1111/j.1477-9730.2012.00698.x
Castedo-Dorado, F., Gómez-Vázquez, I., Fernandes, P.M., Crecente- Campo, F. 2012. Shrub fuel characteristics estimated from overstory variables in NW Spain pine stands. Forest Ecology and Management, 275, 130- 141. http://dx.doi.org/10.1016/j.foreco.2012.03.002
Chen, Y., Su, W., Li, J., Sun, Z. 2009. Hierarchical object oriented classification using very high resolution imagery and LiDAR data over urban areas. Advances In Space Research, 43(7), 1101-1110. http://dx.doi. org/10.1016/j.asr.2008.11.008
Dirección General del Catastro. Sede Electrónica Del Catastro. Available at: http://www.sedecatastro.gob. es/ Last access: May, 2014.
Evans, J.S., Hudak, A.T. 2007. A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments. IEEE Transactions on Geoscience and Remote Sensing, 45(4), 1029 - 1038. http://dx.doi.org/10.1109/TGRS.2006.890412
Fernandes, P.M. 2009. Combining forest structure data and fuel modelling to classify fire hazard in Portugal. Annals of Forest Science, 66(4), 415-415. http://dx.doi.org/10.1051/forest/2009013
Fernández-Luque, I., Aguilar, F.J., Álvarez, M.F., Aguilar, M.A., 2013. Non-Parametric Object-Based Approaches to Carry Out ISA Classification From Archival Aerial Orthoimages. Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(4), 2058-2071. http://dx.doi.org/10.1109/ JSTARS.2013.2240265
Fernández-Luque, I., Aguilar, F.J., Aguilar, M.A., Álvarez, M.F., 2014. Extraction of impervious surface areas from GeoEye-1 and WorldView-2 VHR satellite imagery using an object-based approach. Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12), 4681-4691. http://dx.doi.org/10.1109/ JSTARS.2014.2327159
García, J., González, E., Riquelme, J.C., Miranda, D., Diéguez, U., Navarro, R.M. 2014. Evolutionary feature selection to estimate forest stand variables using LiDAR. International Journal Of Applied Earth Observation And Geoinformation, 26, 119- 131. http://dx.doi.org/10.1016/j.jag.2013.06.005
Gómez-Vázquez, I., Fernandes, P.M., Arias-Rodil, M., Barrio-Anta, M., Castedo-Dorado, F. 2014. Using density management diagrams to assess crown fire potential in Pinus pinaster Ait. stands. Annals of Forest Science, 71(4), 473-484. http://dx.doi. org/10.1007/s13595-013-0350-4
Höfle, B., Pfeifer, N. 2007. Correction of laser scanning intensity data: data and model-driven approaches. ISPRS Journal of Photogrammetry And Remote Sensing, 62(6), 415-433.
http://dx.doi.org/10.1016/j. isprsjprs.2007.05.008
Hyyppa, J., Hyyppa, H., Leckie, D., Gougeon, F., Yu, X., Maltamo, M. 2008. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing, 29(5), 1339-1366. http://dx.doi.org/10.1080/01431160701736489
Infraestructura de Datos Espaciais de Galicia. Servizo Web De Fenomenos (WFS) Da Cartografia Basica De Galicia. Xunta De Galicia. Conselleria de Medio Ambiente, Territorio e Infraestruturas. Available at: http://mapas.xunta.es/directorio-de-servizos-web/ presentacion Last access: May, 2014.
Jensen, J.R. 2005. Introductory digital image processing – a remote sensing perspective. New Jersey: Pearson Prentice Hall
Kraus, K., Pfeifer, N. 1998. Determination of terrain models in wooded areas with air-borne laser scanner data. ISPRS Journal of Photogrammetry And Remote Sensing, 53(4), 193-203. http://dx.doi.org/10.1016/ S0924-2716(98)00009-4
Martínez, J., Vega, C., Chuvieco, E. 2009. Human-caused wildfire risk rating for prevention planning in Spain. Journal Of Environmental Management, 90(2), 1241-1252.
http://dx.doi.org/10.1016/j. jenvman.2008.07.005
Mcgaughey, R.J. 2014. FUSION/LDV: Software for LiDAR data analysis and visualization. United States: Department of Agriculture.
McRoberts, R., Tomppo, E. 2007. Remote sensing support for national forest inventories. Remote Sensing of Environment, 110(4), 412-419. http:// dx.doi.org/10.1016/j.rse.2006.09.034
Ministerio de Agricultura, Alimentación y Medio Ambiente. Estadísticas De Incendios Forestales. Available at: http://www.magrama.gob.es/es/ desarrollo-rural/estadisticas/INCENDIOS_ FORESTALES_2001-2010_FINAL_mod.1_tcm7- 349255.pdf Last access: May, 2014.
Sauro, J., Lewis, J.R. 2005. Estimating completion rates from small samples using binomial confidence intervals: comparisons and recommendations. Proceedings of The Human Factors And Ergonomics Society 49th Annual Meeting, 49(24), 2100-2103. http://dx.doi.org/10.1177/154193120504902407
Slocum, T.A., McMaster, R.B., Kessler, F.C., Howard, H.H. 2008. Thematic Cartography and Geovisualization. Indiana: Prentice Hall
Watt, M.S., Meredith, A., Watt, P., Gunn, A. 2014. The influence of LiDAR pulse density on the precision of inventory metrics in Young unthinned Douglas-fir stands during initial and subsequent LiDAR acquisitions. New Zealand Journal of Forestry Science, 44(18). http://dx.doi.org/10.1186/s40490- 014-0018-3
Xunta De Galicia. Ley 7/2012 de Montes de Galicia. Available at: http://www.xunta.es/dog/Publicados/2012/20120723/AnuncioC3B0-050712-0001_es.html Last access: June, 2014.
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