Modelling canopy fuel and forest stand variables and characterizing the influence of thinning in the stand structure using airborne LiDAR

Authors

  • A. Hevia Forest and Wood Technology Research Centre (CETEMAS)
  • J.G. Álvarez-González Universidade de Santiago de Compostela
  • E. Ruiz-Fernández Universidade de Santiago de Compostela
  • C. Prendes Forest and Wood Technology Research Centre (CETEMAS)
  • A.D. Ruiz-González Universidade de Santiago de Compostela
  • J. Majada Forest and Wood Technology Research Centre (CETEMAS)
  • E. González-Ferreiro Universidade de Santiago de Compostela; Oregon State University; USDA Forest Service-Pacific Northwest Research Station

DOI:

https://doi.org/10.4995/raet.2016.3979

Keywords:

Pinus pinaster, Airborne Laser Scanning (ALS), fuel management, canopy fuel load, canopy bulk density, canopy base height

Abstract

Forest fires are a major threat in NW Spain. The importance and frequency of these events in the area suggests the need for fuel management programs to reduce the spread and severity of forest fires. Thinning treatments can contribute for fire risk reduction, because they cut off the horizontal continuity of forest fuels. Besides, it is necessary to conduct a fire risk management based on the knowledge of fuel allocation, since fire behaviour and fire spread study is dependent on the spatial factor. Therefore, mapping fuel for different silvicultural scenarios is essential. Modelling forest variables and forest structure parameters from LiDAR technology is the starting point for developing spatially explicit maps. This is essential in the generation of fuel maps since field measurements of canopy fuel variables is not feasible. In the present study, we evaluated the potential of LiDAR technology to estimate canopy fuel variables and other stand variables, as well as to identify structural differences between silvicultural managed and unmanaged P. pinaster Ait. stands. Independent variables (LiDAR metrics) of greater explanatory significance were identified and regression analyses indicated strong relationships between those and field-derived variables (Rvaried between 0.86 and 0.97). Significant differences were found in some LiDAR metrics when compared thinned and unthinned stands. Results showed that LiDAR technology allows to model canopy fuel and stand variables with high precision in this species, and provides useful information for identifying areas with and without silvicultural management.

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Author Biographies

A. Hevia, Forest and Wood Technology Research Centre (CETEMAS)

Forest Management Area

J.G. Álvarez-González, Universidade de Santiago de Compostela

Sustainable Forest Management Unit (UXFS) - Department of Agroforestry Engineering

E. Ruiz-Fernández, Universidade de Santiago de Compostela

Sustainable Forest Management Unit (UXFS) - Department of Agroforestry Engineering

C. Prendes, Forest and Wood Technology Research Centre (CETEMAS)

Forest Management Area

A.D. Ruiz-González, Universidade de Santiago de Compostela

Sustainable Forest Management Unit (UXFS) - Department of Agroforestry Engineering

E. González-Ferreiro, Universidade de Santiago de Compostela; Oregon State University; USDA Forest Service-Pacific Northwest Research Station

Sustainable Forest Management Unit (UXFS) - Department of Agroforestry Engineering

References

Álvarez-Álvarez, P., Afif Khouri, E., Cámara-Obregón, A., Castedo-Dorado, F., Barrio-Anta, M. 2011. Effects of foliar nutrients and environmental factors on site productivity in Pinus pinaster Ait. Stands in Asturias (NW Spain). Annals of Forest Science, 68(3), 497-509. http://dx.doi.org/10.1007/s13595-011-0047-5

Andersen, H.E., McGaughey, R.J., Reutebuch, S.E. 2005. Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment, 94(4), 441-449. http://dx.doi.org/10.1016/j.rse.2004.10.013

Arias-Rodil, M. 2009. Desarrollo de una tarifa de cubicación con clasificación de productos para Pinus pinaster en Asturias. MS Thesis, Santiago de Compostela: USC.

Beukema, S.J., Greenough, J.A., Robinson, D.C.E., Kurz, W.A., Reinhardt, E.D., Crookston, N.L., Brown, J.K., Hardy, C.C., Stage, A.R. 1997. An introduction to the fire and fuels extension to FVS. In: Teck, R., Moeur, M., Adams, J.,comps. Proceedings: Forest Vegetation Simulator conference. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station, February 3-7, pp 191-195.

Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. 1984. Classification and Regression Trees. New York: Chapman & Hall.

Cruz, M.G., Alexander, M.E., Fernandes, P.A.M. 2008. Development of a model system to predict wildfire behavior in pine plantations. Australian Forestry, 71(2), 113-121. http://dx.doi.org/10.1080/00049158.2008.10676278

Dalponte, M., Martinez, C., Rodeghiero, M., Gianelle, D. 2011. The role of ground reference data collection in the prediction of stem volume with lidar data in mountain areas. ISPRS Journal of Photogrammetry and Remote Sensing, 66(6), 787-797.

http://dx.doi. org/10.1016/j.isprsjprs.2011.09.003

Fernandes, P.M., Davies, G.M., Ascoli, D., Fernández, C., Moreira, F., Rigolot, E., Stoof, C.R., Vega, J.A., Molina, D. 2013. Prescribed burning in southern Europe: developing fire management in a dynamic landscape. Frontiers in Ecology and the Environment, 11, e4-e14. http://dx.doi.org/10.1890/120298

Fernandes, P.M., Rigolot, E. 2007. The fire ecology and management of maritime pine (Pinus pinaster Ait.). Forest Ecology and Management, 241(1-3), 1-13. http://dx.doi.org/10.1016/j.foreco.2007.01.010

Finney, M.A., 2003. Calculation of fire spread rates across random Landscapes. International Journal of Wildland Fire, 12(2), 167-174. http://dx.doi. org/10.1071/WF03010

Finney, M.A. 2004. FARSITE: Fire Area Simulator- Model development and evaluation. USDA Research Paper RMRS-RP-4, 1-47.

Finney, M.A. 2006. An overview of FlamMap fire modeling capabilities. USDA Forest Service Proceedings RMRS-P-41, 213-220.

García-Gutiérrez, J., González-Ferreiro, E., Mateos- García, D., Riquelme-Santos, J.C., Miranda, D. 2011. A compartive study between two regression methods on LiDAR data: A case study. Lecture Notes in Artificial Intelligence, 6679, 311-318. http://dx.doi.org/10.1007/978-3-642-21222-2_38

García-Gutiérrez, J., González-Ferreiro, E., Riquelme- Santos, J.C., Miranda, D., Diéguez-Aranda, U., Navarro-Cerrillo, R.M. 2014. Evolutionary feature selection to estimate forest stand variables using LiDAR. International Journal of Applied Earth Observation and Geoinformation, 26, 119-131.htp://dx.doi.org/10.1016/j.jag.2013.06.005

Gleason, C.J., Im, J. 2012. Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sensing of Environment, 125, 80-91. http://dx.doi.org/10.1016/j.rse.2012.07.006

Gobakken, T., Næsset, E. 2007. Assessing effects of laser point density on biophysical stand properties derived from airborne laser scanner data in mature forest. In: ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007. Espoo, Finland, September 12- 14, pp 150-155.

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(2), 161-172. http://dx.doi.org/10.1007/s13595-012-0245-9

Gonçalves-Seco, L., González-Ferreiro, E., Diéguez- Aranda, U., Fraga-Bugallo, B., Crecente, R., Miranda, D. 2011. Assessing the attributes of high-density Eucalyptus globulus stands using airborne laser scanner data. International Journal of Remote Sensing, 32(24), 9821-9841. http://dx.doi.org/10.10 80/01431161.2011.593583

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(2), 281-292.

http://dx.doi.org/10.1093/forestry/cps002

González-Ferreiro, E., Diéguez-Aranda, U., Crecente- Campo, F., Barreiro-Fernández, L., Miranda, D., Castedo-Dorado, F. 2014. Modelling canopy fuel variables for Pinus radiata D. Don in NW Spain with low-density LiDAR data. International Journal of Wildland Fire, 23(3), 350-362. http://dx.doi. org/10.1071/WF13054

González-Olabarria, J.R., 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.

http://dx.doi. org/10.1016/j.foreco.2012.06.056

Hall, S.A., Burke, I.C., Box, D.O., Kaufmann, M.R., Stoker, J.M. 2005. Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management, 208(1-3), 189-209.

http://dx.doi. org/10.1016/j.foreco.2004.12.001

Hevia, A. 2013. Influencia de la poda en el desarrollo de masas de Pinus radiata D. Don y Pinus pinaster Aiton en Asturias. PhD Thesis, Santiago de Compostela: USC.

Hollaus, M., Wagner, W., Maier, B., Schadauer, K. 2007. Airborne laser scanning of forest stem volume in a mountainous environment. Sensors, 7, 1559- 1577. http://dx.doi.org/10.3390/s7081559

Holmgren, J., Persson, Å. 2004. Identifying species of individual trees using airborne laser scanner. Remote Sensing of Environment, 90(4), 415-423. http://dx.doi.org/10.1016/S0034-4257(03)00140-8

Jakubowksi, M.K., Guo, Q., Brandon, C., Scott, S. Maggi, K. 2013. Predicting Surface Fuel Models and Fuel Metrics Using Lidar and CIR Imagery in a Dense, Mountainous Forest. Photogrammetric Engineering & Remote Sensing, 79(1), 37-49.

http://dx.doi.org/10.14358/PERS.79.1.37

Keane, R.E., Burgan, R., van Wagtendonk, J. 2001. Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire, 10(3-4), 301-319. http://dx.doi. org/10.1071/WF01028

Keyser, T., Smith, F.W. 2010. Influence of crown biomass estimators and distribution on canopy fuel characteristics in ponderosa pine stands of the Black Hills. Forest Science, 56(2), 156-165.

Lim, K., Treitz, P., Baldwin, K., Morrison, I., Green, J. 2003. Lidar remote sensing of biophysical properties of tolerant northern hardwood forests. Canadian Journal of Remote Sensing: Journal canadien de télédétection, 29(5), 658-678. http://dx.doi. org/10.5589/m03-025

McGaughey, R. 2014. FUSION/LDV: software for LiDAR data analysis and visualization. Version 3.41. Seattle, WA: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.

Means, J.E., Acker, S.A., Fitt, B.J., Renslow, M., Emerson, L., Hendrix, C.J. 2000. Predicting forest stand characteristics with airborne scanning LiDAR. Photogrammetric Engeenering & Remote Sensing, 66(11), 1367-1371.

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(1), 88-99.

http://dx.doi. org/10.1016/S0034-4257(01)00290-5

Næsset, E. 2004. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scandinavian Journal of Forest Research, 19(2), 164-179.

http://dx.doi.org/10.1080/02827580310019257

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(1), 105-115.

http://dx.doi. org/10.1016/S0034-4257(01)00243-7

Peterson, B., Dubayah, R., Hyde, P., Hofton, M., Blair, J.B., Fites-Kaufman, J. 2007. Use of LIDAR for forest inventory and forest management application. In: McRoberts, R. E., Reams, G. A., van Deusen, P. C., McWilliams, W. H., eds. Proceedings: Seventh annual forest inventory and analysis symposium. Portland, Oregon., October 3-6, Gen. Tech. Rep. WO-77. Washington, DC: U.S. Department of Agriculture, Forest Service, pp 193-200.

R Core Team, 2014. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.

Rego, F.C. 1992. Land use changes and wildfires. In: Teller A., Mathy P., Jeffers J.N., eds. Response of Forest Ecosystems to Environmental Changes. Netherlands: Springer, pp 367-373.

http://dx.doi. org/10.1007/978-94-011-2866-7_33

Reinhardt, E., Scott, J., Gray, K., Keane, R. 2006. Estimating canopy fuel characteristics in five conifer stands in the western United States using tree and stand measurements. Canadian Journal of Forest Research, 36, 2803-2814. http://dx.doi.org/10.1139/ x06-157

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(5), 1407-1431.

http://dx.doi.org/10.1080/01431160701736448

Ruiz, L.A., Hermosilla, T., Mauro, F., Godino, M. 2014a. Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates. Forests, 5, 936-951. http://dx.doi.org/10.3390/ f5050936

Ruiz, L.A., Hermosilla, T., Kazakova, A.N., Moskal, L. M. 2014b. Comparison of forest structure estimates using discrete and full-waveform LiDAR metrics. In: ForestSat 2014. Riva del Garda, Italy, November 4-7.

Sando, R.W., Wick, C.H. 1972. A method of evaluating crown fuels in forest stands. St. Paul, MN: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station. USDA Research paper NC-84, 1-12.

Scott, J.H., Reinhardt, E.D. 2001. Assessing crown fire potential by linking models of surface and crown fire behaviour. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. USDA Research Paper RMRS-RP-29, 1-61.

Stephens, P.R., Watt, P.J., Loubser, D., Haywood, A., Kimberley, M.O. 2007. Estimation of carbon stocks in New Zealand planted Forests using airborne scanning LiDAR. Proceedings: ISPRS Workshop International archives of photogrammetry, remote sensing and spatial information sciences, 36(3), 389-394.

Sun, G., Ranson, K., Guo, Z., Zhang, Z., Montesano, P., Kimes, D. 2011. Forest biomass mapping from lidar and radar synergies. Remote Sensing of Environment, 115(11), 2906-2916.

http://dx.doi.org/10.1016/j. rse.2011.03.021

Tesfamichael, S.G., Ahmed, F.B., van Aardt, J.A.N. 2010. Investigating the impact of discrete-return lidar point density on estimations of mean and dominant plot-level tree height in Eucalyptus grandis plantations. International Journal of Remote Sensing, 31(11), 2925-2940. http://dx.doi. org/10.1080/01431160903144086

Therneau, T., Atkinson, B., Ripley, B. 2014. Rpart: Recursive Partitioning and Regression Trees. Last access June 13, 2015,

http://CRAN.R-project.org/ package=rpart.

Treitz, P., Lim, K., Woods, M., Pitt, D., Nesbitt, D., Etheridge, D. 2010. LiDAR data acquisition and processing protocols for forest resource inventories in Ontario, Canada. In: Koch, B., Kendlar, G., eds. Silvilaser 2010. The 10th International Conference on LiDAR Applications for Assessing Forest Ecosystems, Freiburg, Germany, September 14

, pp 451-460.

Van Wagner, C.E. 1977. Conditions for the start and spread of crown fire. Canadian Journal of Forest Research, 7(1), 23-34. http://dx.doi.org/10.1139/ x77-004

Vega, J.A. 2001. Efectos del fuego prescrito sobre el suelo en pinares de Pinus pinaster Ait. de Galicia (PhD Thesis). Madrid: Universidad Politécnica de Madrid.

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, Special Issue: 3D Remote Sensing in Forestry, 29(5), 1433-1452.

Yu, X., Hyyppä, J., Holopainen, M., Vastaranta, M. 2010. Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes. Remote Sensing, 2(6), 1481-1495. http://dx.doi.org/10.3390/rs2061481

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(8), 1978-1996. http://dx.doi.org/10.1016/j.rse.2011.04.001

Published

2016-02-26