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Article << Previous     |     Next >>   Contents Vol 23(3)

Modelling canopy fuel variables for Pinus radiata D. Don in NW Spain with low-density LiDAR data

Eduardo González-Ferreiro A C, Ulises Diéguez-Aranda A, Felipe Crecente-Campo A, Laura Barreiro-Fernández A, David Miranda A and Fernando Castedo-Dorado B

A Department of Agroforestry Engineering, University of Santiago de Compostela, Escuela Politécnica Superior, C/ Benigno Ledo, Campus Universitario, E-27002 Lugo, Spain.
B Department of Engineering and Agricultural Sciences, University of León, Escuela Superior y Técnica de Ingeniería Agraria, Avenida de Astorga s/n, Campus de Ponferrada, E-24400 Ponferrada, Spain.
C Corresponding author. Email: edu.g.ferreiro@gmail.com

International Journal of Wildland Fire 23(3) 350-362 http://dx.doi.org/10.1071/WF13054
Submitted: 2 April 2013  Accepted: 21 September 2013   Published: 7 March 2014


 
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Abstract

Crown fire initiation and spread are key elements in gauging fire behaviour potential in conifer forests. Crown fire initiation and spread models implemented in widely used fire behaviour simulation systems such as FARSITE and FlamMap require accurate spatially explicit estimation of canopy fuel complex characteristics. In the present study, we evaluated the potential use of very low-density airborne LiDAR (light detection and ranging) data (0.5 first returns m–2) – which is freely available for most of the Spanish territory – to estimate canopy fuel characteristics in Pinus radiata D. Don stands in north-western Spain. Regression analysis indicated strong relationships (R2 = 0.82–0.98) between LiDAR-derived metrics and field-based fuel estimates for stand height, canopy fuel load, and average and effective canopy base height Average and effective canopy bulk density (R2 = 0.59–0.70) were estimated indirectly from a set of previously modelled forest variables. The LiDAR-based models developed can be used to elaborate geo-referenced raster files to describe fuel characteristics. These files can be generated periodically, whenever new freely available airborne LiDAR data are released by the Spanish National Plan of Aerial Orthophotography, and can be used as inputs in fire behaviour simulation systems.

Additional keywords: airborne laser scanning, ALS, canopy base height, canopy bulk density, canopy fuel load, forest inventory, fuel management, Galicia, Plan Nacional de Ortofotografía Aérea de España, remote sensing, Spanish PNOA project, stand height.


References

Ahokas E, Yu X, Oksanen J, Hyyppä J, Kaartinen H, Hyyppä H (2005) Optimization of the scanning angle for countrywide laser scanning. In ‘Laser Scanning 2005’. (Eds G Vosselman, C Brenner, J Hyyppä) pp. 115–119. (International Society for Photogrammetry and Remote Sensing (ISPRS): Enschede)

Albini FA, Baughman RG (1979) Estimating windspeeds for predicting wildland fire behavior. USDA Forest Service, Intermountain Forest and Range Experiment Station, Research Paper INT-221. (Odgen, UT)

Alexander ME, Cruz MG (2011) Crown fire dynamics in conifer forests. USDA Forest Service, Pacific Northwest Research Station, General Technical Report PNW-GTR-854. (Portland, OR)

Andersen HE, McGaughey RJ, Reutebuch SE (2005) Estimating forest canopy fuel parameters using LiDAR data. Remote Sensing of Environment 94, 441–449.
CrossRef |

Arroyo LA, Pascual C, Manzanera JA (2008) Fire models and methods to map fuel types: the role of remote sensing. Forest Ecology and Management 256, 1239–1252.
CrossRef |

Bater CW, Coops NC (2009) Evaluating error associated with LiDAR-derived DEM interpolation. Computers & Geosciences 35, 289–300.
CrossRef |

Belsley D (Ed.) (1991) ‘Conditioning Diagnostics: Collinearity and Weak Data in Regression.’ (Wiley: New York)

Castedo-Dorado F, Diéguez-Aranda U, Álvarez-González J (2007) A growth model for Pinus radiata D. Don stands in north-western Spain. Annals of Forest Science 64, 453–465.
CrossRef |

Castedo-Dorado F, Gómez-Vázquez I, Fernandes PM, Crecente-Campo F (2012) Shrub fuel characteristics estimated from overstory variables in NW Spain pine stands. Forest Ecology and Management 275, 130–141.
CrossRef |

Clutter JL, Forston JC, Pienaar LV, Brister GH, Bailey RL (Eds) (1983) ‘Timber Management: a Quantitative Approach.’ (Wiley: New York)

Cruz MG, Alexander ME, Wakimoto RH (2003) Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. International Journal of Wildland Fire 12, 39–50.
CrossRef |

Cruz MG, Alexander ME, Wakimoto RH (2005) Development and testing of models for predicting crown fire rate of spread in conifer forest stands. Canadian Journal of Forest Research 35, 1626–1639.
CrossRef |

Donoghue DNM, Watt PJ, Cox NJ, Wilson J (2007) Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data. Remote Sensing of Environment 110, 509–522.
CrossRef |

Draper NR, Smith H (Eds) (1998) ‘Applied Regression Analysis.’ (Wiley: New York)

Dubayah R, Drake J (2000) LiDAR remote sensing for forestry. Journal of Forestry 98, 44–46.

Erdody TL, Moskal LM (2010) Fusion of LiDAR imagery for estimating forest canopy fuels. Remote Sensing of Environment 114, 725–737.
CrossRef |

ESRI (1998) ESRI shapefile technical description. White paper. (Environmental Systems Research Institute, Inc: Redlands, CA) Available at http://www.esri.com/library/whitepapers/pdfs/shapefile.pdf [Verified 7 November 2013]

Finney MA (2003) Calculating fire spread rates across random landscapes. International Journal of Wildland Fire 12, 167–174.
CrossRef |

Finney MA (2004) FARSITE: Fire Area Simulator – model development and evaluation. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-4. (Ogden, UT)

Finney MA (2006) An overview of FlamMap modeling capabilities. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-P-41. (Fort Collins, CO)

Flewelling JW (2009) Plot size, shape, and co-registration error determine expected overlap. In ‘Extending Forest Inventory and Monitoring Over Space and Time’. pp. 1–5. (International Union of Forest Research Organizations (IUFRO), Division 4: Quebec) Available at http://blue.for.msu.edu/meeting/proc2/Flewelling.pdf [Verified 7 November 2013]

Frazer GW, Magnussen S, Wulder MA, Niemann KO (2011) Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass. Remote Sensing of Environment 115, 636–649.
CrossRef |

García M, Fiano D, Chuvieco E, Danson FM (2010) Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sensing of Environment 114, 816–830.
CrossRef |

Gatziolis D, Fried JS, Monleon VS (2010) Challenges to estimating tree height via Lidar in closed-canopy forests: a parable from western Oregon. Forest Science 56, 139–155.

Gobakken T, Næsset E (2009) Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data. Canadian Journal of Forest Research 39, 1036–1052.
CrossRef |

Gómez-Vázquez I, Crecente-Campo F, Diéguez-Aranda U, Castedo-Dorado F (2013) Modelling canopy fuel variables in Pinus pinaster Ait. and Pinus radiata D. Don stands in northwestern Spain. Annals of Forest Science 70, 161–172.
CrossRef |

Gonçalves-Seco L, Gonzalez-Ferreiro E, Diéguez-Aranda U, Fraga-Bugallo B, Crecente R, Miranda D (2011) Assessing attributes of high density Eucalyptus globulus stands using airborne laser scanner data. International Journal of Remote Sensing 32, 9821–9841.
CrossRef |

González-Ferreiro E, Diéguez-Aranda U, Miranda D (2012) Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities. Forestry 85, 281–292.
CrossRef |

González-Olabarria JR, Rodríguez F, Fernández-Landa A, Mola-Yudego B (2012) Mapping fire risk in the Model Forest of Urbión (Spain) based on airborne LiDAR measurements. Forest Ecology and Management 282, 149–156.
CrossRef |

Goodwin NR, Coops NC, Culvenor DS (2006) Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sensing of Environment 103, 140–152.
CrossRef |

Hall S, Burke I, Box D, Kaufmann M, Stoker J (2005) Estimating stand structure using discrete-return LiDAR: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management 208, 189–209.
CrossRef |

Hawkins DM (2004) The problem of overfitting. Journal of Chemical Information and Computer Sciences 44, 1–12.
CrossRef | CAS | PubMed |

Heurich M, Thoma F (2008) Estimation of forestry stand parameters using laser scanning data in temperate, structurally rich natural European beech (Fagus sylvatica) and Norway spruce (Picea abies) forests. Forestry 81, 645–661.
CrossRef |

Hodgson ME, Bresnahan P (2004) Accuracy of airborne LiDAR-derived elevation: empirical assessment and error budget. Photogrammetric Engineering and Remote Sensing 70, 331–339.
CrossRef |

Hodgson ME, Jensen J, Raber G, Tullis J, Davis BA, Thompson G, Schuckman K (2005) An evaluation of LiDAR-derived elevation and terrain slope in leaf-off conditions. Photogrammetric Engineering and Remote Sensing 71, 817–823.
CrossRef |

Höfle B, Pfeifer N (2007) Correction of laser scanning intensity data: data and model-driven approaches. ISPRS Journal of Photogrammetry and Remote Sensing 62, 415–433.
CrossRef |

Hollaus M, Wagner W, Maier B, Schadauer K (2007) Airborne laser scanning of forest stem volume in a mountainous environment. Sensors 7, 1559–1577.
CrossRef |

Hyyppä H, Yu X, Hyyppä J, Kaartinen H, Kaasalainen S, Honkavaara E, Rönnholm P (2005) Factors affecting the quality of DTM generation in forested areas. In ‘Laser Scanning 2005’. (Eds G Vosselman, C Brenner, J Hyyppä) pp. 85–90. (ISPRS: Enschede)

Hyyppä J, Hyyppä 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, 1339–1366.
CrossRef |

Jakubowski MK, Guo Q, Collins B, Stephens S, Kelly M (2013) Predicting surface fuel models and fuel metrics using LiDAR data and CIR imagery in a dense, mountainous forest. Photogrammetric Engineering and Remote Sensing 79, 37–49.
CrossRef |

Jutzi B, Gross H (2010) Investigation on surface reflection models for intensity normalization in airborne laser scanning (ALS) data. Photogrammetric Engineering and Remote Sensing 76, 1051–1060.
CrossRef |

Keane RE, Garner JL, Schmidt KM, Long DG, Menakis JP, Finney MA (1998) Development of input data layers for the FARSITE gire growth model for the Selway–Bitterroot Wilderness Complex, USA. USDA Forest Service, Rocky Mountain Research Station, General Technical Report. RMRS-GTR-3. (Ogden, UT)

Keane RE, Burgan RE, Wagtendonk JV (2001) Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire 10, 301–319.
CrossRef |

Keyes CR, O’Hara KL (2002) Quantifying stand targets for silvicultural prevention of crown fires. Western Journal of Applied Forestry 17, 101–109.

Kraus K, Mikhail EM (1972) Linear least squares interpolation. Photogrammetric Engineering 38, 1016–1029.

Kraus K, Pfeifer N (1998) Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing 53, 193–203.
CrossRef |

Lovell JL, Jupp DLB, Newnham GJ, Coops NC, Culvenor DS (2005) Simulation study for finding optimal LiDAR acquisition parameters for forest height retrieval. Forest Ecology and Management 214, 398–412.
CrossRef |

Mallows CL (1973) Some comments on Cp. Technometrics 15, 661–675.

McGaughey R (Ed.) (2012) FUSION/LDV: software for LiDAR data analysis and visualization, v. 3.0.1. USDA Forest Service, Pacific Northwest Research Station. (Seattle, WA) Available at http://forsys.cfr.washington.edu/fusion/FUSION_manual.pdf [Verified 1 April 2012]

Means J, Acker S, Fitt B, Renslow M, Emerson L, Hendrix C (2000) Predicting forest stand characteristics with airborne scanning LiDAR. Photogrammetric Engineering and Remote Sensing 66, 1367–1371.

Millie DF, Weckman GR, Young WA, Ivey JE, Carrick HJ, Fahnenstiel GL (2012) Modeling microalgal abundance with artificial neural networks: demonstration of a heuristic ‘Grey-Box’ to deconvolve and quantify environmental influences. Environmental Modelling & Software 38, 27–39.
CrossRef |

Ministerio de Fomento (2010) Plan Nacional de Ortofotografía aérea. Especificaciones Técnicas para vuelo fotogramétrico digital con vuelo LiDAR. (Ministerio de Fomento: Madrid) Available at http://www.ign.es/PNOA/ [Verified 7 November 2013]

Morsdorf F, Meier E, Kötz B, Itten KI, Dobbertin M, Allgöwer B (2004) LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sensing of Environment 92, 353–362.
CrossRef |

Mutlu M, Popescu CS, Stripling C, Spencer T (2008a) Mapping surface fuel models using LiDAR and multispectral data fusion for fire behavior. Remote Sensing of Environment 112, 274–285.
CrossRef |

Mutlu M, Popescu SC, Zhao K (2008b) Sensitivity analysis of fire behavior modeling with LiDAR-derived surface fuel maps. Forest Ecology and Management 256, 289–294.
CrossRef |

Myers RH (Ed.) (1990) ‘Classical and Modern Regression with Applications.’ (Duxbury Press: Belmont, CA)

Næsset E (2002) Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment 80, 88–99.
CrossRef |

Næsset E (2004) Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scandinavian Journal of Forest Research 19, 164–179.
CrossRef |

Næsset E, Økland T (2002) Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sensing of Environment 79, 105–115.
CrossRef |

Nakai T, Sumida A, Kodama Y, Hara T, Ohta T (2010) A comparion between various definitions of forest stand height and aerodynamic canopy height. Agricultural and Forest Meteorology 150, 1225–1233.
CrossRef |

Peña D (Ed.) (2002) ‘Regresión y diseño de experimentos.’. (Alianza Editorial, S.A: Madrid)

Pérez-Cruzado C, Mohren GMJ, Merino A, Rodríguez-Soalleiro R (2012) Carbon balance for different management practices for fast growing tree species planted on former pastureland in southern Europe: a case study using the CO2Fix model. European Journal of Forest Research 131, 1695–1716.
CrossRef |

Pesonen A, Maltamo M, Eerikäinen K, Packalèn P (2008) Airborne laser scanning-based prediction of coarse woody debris volumes in a conservation area. Forest Ecology and Management 255, 3288–3296.
CrossRef |

Popescu SC, Zhao K (2008) A voxel-based lidar method for estimating crown base height for deciduous and pine trees. Remote Sensing of Environment 112, 767–781.
CrossRef |

Reinhardt ED, Crookston NL (2003) The Fire and Fuels Extension to de Forest Vegetation Simulator. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-116. (Ogden, UT)

Reinhardt E, Scott J, Gray K, Keane R (2006) Estimating canopy fuel characteristics in five conifer stands in western United States using tree and stand measurements. Canadian Journal of Forest Research 36, 2803–2814.
CrossRef |

Reitberger J, Krzystek P, Stilla U (2008) Analysis of full waveform LiDAR data for the classification of deciduous and coniferous trees. International Journal of Remote Sensing 29, 1407–1431.
CrossRef |

Riaño D, Meier E, Allgower B, Chuvieco E, Ustin S (2003) Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling. Remote Sensing of Environment 86, 177–186.
CrossRef |

Riaño D, Chuvieco E, Ustin SL, Salas FJ, Rodríguez-Perez JR, Ribeiro LM, Viegas DX, Moreno JF, Fernández H (2007) Estimation of shrub height for fuel-type mapping combining airborne LiDAR and simultaneous color infrared ortho imaging. International Journal of Wildland Fire 16, 341–348.
CrossRef |

Rombouts J, Ferguson IS, Leech JW (2008) Variability of LiDAR volume prediction models for productivity assessment of radiata pine plantations in South Australia. In ‘Silvilaser 2008: 8th international Conference on LiDAR Applications in Forest Assessment and Inventory’, 17–19 September 2008, Edinburgh, Scotland. (Eds RA Hill, J Rosette, J Suárez) pp. 39–49. (Forest Research: Edinburgh)

Ryan TP (Ed.) (1997) ‘Modern Regression Methods.’ (Wiley: New York)

SAS Institute Inc. (2009) ‘SAS/STAT 9.1 2004. User’s guide.’ (SAS Institute Inc: Cary, NC)

Schmuck G, San-Miguel-Ayanz J, Camia A, Durrant T, Boca R, Whitmore C, Libertà G, Corti P, Schulte E (2012) Forest fires in Europe, Middle East and North Africa 2011 EUR 25483 EN. Publications Office of the European Union 2012, JRC74152

Schwarz G (1978) Estimating the dimension of a model. Annals of Statistics 6, 461–464.
CrossRef |

Scott JH, Reinhardt ED (2001) Assessing crown fire potential by linking models of surface and crown fire behaviour. USDA Forest Service Rocky Mountain Research Station, Research Paper RMRS-RP-29. (Ogden, UT)

Sithole G, Vosselman G (2004) Experimental comparison of filter algorithms for bare-earth extraction from airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing 59, 85–101.
CrossRef |

Skowronski N, Clark K, Nelson R, Hom J, Patterson M (2007) Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey. Remote Sensing of Environment 108, 123–129.
CrossRef |

Stephens PR, Watt PJ, Loubser D, Haywood A, Kimberley MO (2007) Estimation of carbon stocks in New Zealand planted Forests using airborne scanning LiDAR. International Archives of Photogrammetry. Remote Sensing and Spatial Information Sciences 36, 389–394.

Stocks BJ, Alexander ME, Wotton BM, Stefner CN, Flannigan MD, Taylor SW, Lavoie N, Mason JA, Hartley GR, Maffey ME, Dalrymple GN, Blake TW, Cruz MG, Lanoville RA (2004) Crown fire behaviour in a northern jack pine-black spruce forest. Canadian Journal of Forest Research 34, 1548–1560.
CrossRef |

Su J, Bork E (2006) Influence of vegetation, slope, and LiDAR sampling angle on DEM accuracy Photogrammetric Engineering and Remote Sensing 72, 1265–1274.
CrossRef |

Tinkham WT, Huang H, Smith AMS, Shrestha R, Falkowski MJ, Hudak AT, Link TE, Glenn NF, Marks DG (2011) A comparison of two open source LiDAR surface classification algorithms. Remote Sensing 3, 638–649.
CrossRef |

Tinkham WT, Smith AMS, Hoffman C, Hudak AT, Falkowski MJ, Swanson ME, Gessler PE (2012) Investigating the influence of LiDAR ground surface errors on the utility of derived forest inventories. Canadian Journal of Forest Research 42, 413–422.
CrossRef |

Treitz P, Kevin L, Murray W, Doug P, Nesbitt D, Etheridge D (2010) LiDAR data acquisition and processing protocols for forest resource inventories in Ontario, Canada. In ‘Silvilaser 2010: The 10th International Conference on LiDAR Applications for Assessing Forest Ecosystems’, 14–17 September 2010, Freiburg, Germany. (Eds B Koch, G Kendlar, C Teguem) pp 451–460. (Forstliche Versuchs-und Forschungsanstalt: Freiburg)

Van Wagner CE (1977) Conditions for the start and spread of a crown fire. Canadian Journal of Forest Research 7, 23–34.
CrossRef |

Wagner W, Hollaus M, Briese C, Ducic V (2008) 3D vegetation mapping using small-footprint full-waveform airborne laser scanners. International Journal of Remote Sensing 29, 1433–1452.
CrossRef |

WFDSS (2010) Wildland Fire Decision Support System. Glossary of terms. Available at https://wfdss.usgs.gov/wfdss/542.htm#o257 [Verified 7 November 2013]

Wulder MA, Bater CW, Coops NC, Hilker T, White JC (2008) The role of LiDAR in sustainable forest management. Forestry Chronicle 84, 807–826.

Zaffalon M (2005) Credible classification for environmental problems. Environmental Modelling & Software 20, 1003–1012.
CrossRef |

Zeide B (1980) Plot size optimization. Forest Science 26, 251–257.

Zhao K, Popescu S, Meng X, Pang Y, Agca M (2011) Characterizing forest canopy structure with lidar composite metrics and machine learning. Remote Sensing of Environment 115, 1978–1996.
CrossRef |


   
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