Assessing post-fire regeneration in a Mediterranean mixed forest using lidar data and Artificial Neural Networks

Photogrammetric Engineering and Remote Sensing

Volumn 79, Issue 12, 2013, Pages 1121-1130

  Debouk, Haifa ^a,b,c   Riera Tatché, Ramon ^d   Vega García, Cristina ^c  

^a CENTRE TECNOLÒGIC FORESTAL DE CATALUNYA   (Spain)

^b Fruit Growing Botany and Gardening ^*  (Spain)

^c UNIVERSITY OF LLEIDA   (Spain)

^d Diputacio de Barcelona   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CANOPY HEIGHT MODELS; CLASSIFICATION MODELS; MANAGEMENT PRACTICES; POST-FIRE REGENERATIONS; PREDICTION MODEL; SITE RESTORATION; VEGETATION CONDITION; VEGETATION FRACTION COVER;

FIRES; MATHEMATICAL MODELS; NEURAL NETWORKS; OPTICAL RADAR; VEGETATION;

REFORESTATION;

ARTIFICIAL NEURAL NETWORK; CANOPY; CLASSIFICATION; DATA SET; FOREST INVENTORY; LIDAR; MIXED FOREST; REGENERATION; VEGETATION; WILDFIRE;

FIRES; MATHEMATICAL MODELS; NEURAL NETWORKS; PLANTS; RADAR; REFORESTATION;

EID: 84889661822     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.79.12.1121     Document Type: Article

References (60)

1. Abdel Malak, D., and J.G. Pausas, 2006. Fire regime and post-fire Normalized DifferenceVegetation Index changes in the eastern Iberian Peninsula (Mediterranean basin), International Journal of Wildland Fire, 15:407-413.
2. Alcázar, J., A. Palau, and C. Vega-García, 2008. A neural net model for environmental flow estimation at the Ebro River Basin, Spain, Journal of Hydrology, 349:44-55.
3. Andersen, H.E., R.J. Mcgaughey, and S.E. Reutebuch, 2005. Estimating forest canopy fuel parameters using LIDAR data, Remote Sensing of Environment, 94:441-449.
4. Blackard, J.A., and D.J. Dean, 1999. Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables, Computers and Electronics in Agriculture, 24:131-151.
5. Bridle, J.S., 1989. Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition, Neuro-computing: Algorithms, Architectures and Applications (F. Fougelman-Soulie and J. Herault, editors), Springer-Verlag, Inc., Berlin, pp. 227-236.
6. Broncano, M.J., and J. Retana, 2004. Topography and forest composition affecting the variability in fire severity and post-fire regeneration occurring after a large fire in the Mediterranean basin, International Journal of Wildland Fire, 13:209-216.
7. Broncano, M.J., J. Retana, and A. Rodrigo, 2005. Predicting the recovery of Pinus halepensis and Quercus ilex forests after a large wildfire in northeastern Spain, Plant Ecology, 180:47-56.
8. Burriel, J.A., C. Gracia, J.J. Ibàñez, T. Mata, and J. Vayreda, 2000. Inventari Ecològic i Forestal de Catalunya, Regió Forestal IV, CREAF, Bellaterra. ISBN: 84-931313-3-0.
9. Chen, K., C. Jacobson, and R. Blong, 2004. Artificial neural networks for risk decision support in natural hazards: A case study of assessing the probability of house survival from bushfires, Environmental Modelling and Assessment, 9:189-199.
10. Corne, S.A., S.J. Carver, W.E. Kunin, J.J Lennon, and W.W.S. van Hees, 2004. Predicting forest attributes in Southeast Alaska using artificial neural networks, Forest Science, 50(2):259-276.
11. Diamantopoulou, M., 2005. Artificial neural networks as an alternative tool in pine bark volume estimation, Computers and Electronics in Agriculture, 48:235-244.
12. European GNSS Supervisory Authority (GSA), 2010. What is EGNOS?, URL: http://www.esa.int/Our_Activities/Navigation/The_present_-_EGNOS/What_is_EGNOS (last date accessed: 27 August 2013).
13. Erdody, T.L., and L.M. Moskal, 2010. Fusion of LiDAR and imagery for estimating forest canopy fuels, Remote Sensing of Environment, 114:725-737.
14. Fahlman, S.E., and C. Lebiere, 1990. The Cascade-Correlation Learning Architecture, Advances in Neural Information Processing Systems, Volume 2 (D.S. Touretzky, Editor), Morgan-Kaufmann, Inc., Los Altos California, pp. 1-13.
15. Falkowski, M.J., J.S. Evans, S. Martinuzzi, P.E. Gessler, and A.T. Hudak, 2009. Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA, Remote Sensing of Environment, 113:946-956.
16. García, M., D. Riaño, E. Chuvieco, J. Salas, and F.M. Danson, 2011. Multispectral and LiDAR data fusion for fuel type zapping using Support Vector Machine and decision rules, Remote Sensing of Environment, 115:1369-1379.
17. Guan, B.T., and G. Gertner, 1991. Modeling red pine tree survival with an Artificial neural network, Forest Science, 37(5):1429-1440.
18. Hanewinkel, M., W. Zhou, and C. Schill, 2004. A neural network approach to identify forest stands susceptible to wind damage, Forest Ecology and Management, 196:227-243.
19. Hassenauer, H., D. Merkl, and M. Weingartner, 2001. Estimating tree mortality of Norway spruce stands with neural networks, Advances in Environmental Research, 5:405-414.
20. Hecht-Nielsen, R., 2004. Perceptrons, University of California San Diego Institute for Neural Computation Technical Report Series, No. INC-0403, La Jolla, California, 60 p.
21. Hilbert, D.W., and B. Ostendorf, 2001. The utility of artificial neural networks for modelling the distribution of vegetation in past, present and future climates, Ecological Modelling, 146:311-327.
22. Hill, R.A., and A.G. Thomson, 2005. Mapping woodland species composition and structure using airborne spectral and LiDAR data, International Journal of Remote Sensing, 26(17):3763-3779.
23. Hornik, K., M. Stinchcombe, and H. White, 1989. Multilayer feedforward networks are universal Approximators, Neural Net, 2:359-366.
24. Hudak, A.T., N.L. Crookston, J.S. Evans, D.E. Hall, and M.J. Falkowski, 2008. Nearest neighbour imputation of species-level, plot-scale forest structure attributes from LiDAR data, Remote Sensing of Environment, 112:2232-2245.
25. Jain, A.K., J. Mao, and K.M. Mohiuddin, 1996. Artificial neural networks: A tutorial, Computer-IEEE, March, pp. 31-44.
26. Jutras, P., S.O. Prasher, and G.R. Mehuys, 2009. Prediction of street tree morphological parameters using Artificial neural networks, Computers and Electronics in Agriculture, 67:9-17.
27. Klimasauskas, C.C., 1991. Applying neural networks - Part III: Training a neural network, PCAI, 5(3):20-24.
28. Koetz, B., F. Morsdorf, S. van der Linden, T. Curt, and B. Allgöwer, 2008. Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data, Forest Ecology and Management, 256:263-271.
29. Koza, J.R., 1993. Genetic Programming, MIT Press, Cambridge, Massachusetts.
30. Kuplich, T.M., 2006. Classifying regenerating forest stages in Amazônia using remotely sensed images and a neural network, Forest Ecology and Management, 234:1-9.
31. Lefsky, M.A., W.B. Cohen, G.G. Parker, and D.J. Harding, 2002. Lidar remote sensing for ecosystem studies, Bioscience 52(1):19-30.
32. Lek, S., M. Delacoste, P. Baran, I. Dimopoulos, J. Lauga, and S. Aulagnier, 1996. Application of neural networks to modelling nonlinear relationships in ecology, Ecological Modelling, 90:39-52.
33. Lek, S., and J.F. Guégan, 1999. Artificial neural networks as a tool in ecological modeling, An introduction, Ecological Modelling, 120:65-73.
34. Lobo, A., F. Ara, I. Martínez-Nieto, and A. Ferreruela, 2007. Impacte de l'incendi del Parc Natural de Sant Llorenç del Munt i l'Obac del 2003 i recuperació posterior: Integrant información remota i del terreny (in Catalan), VI Trobada d'Estudiosos de Sant Llorenç del Munt i l'Obac, Centre Cultural de Sant Llorenç Savall, 10 November 2005, Sant Llorenç Savall, Barcelona (VI Trobada d'Estudiosos de Sant Llorenç del Munt i l'Obac, Diputació de Barcelona, Barcelona), pp. 44-49.
35. Martínez-Sánchez, J.J., P. Ferrandis, J. De las Heras, and J.M. Herranz, 1999. Effect of burnt wood removal on the natural regeneration of Pinus halepensis after fire in a pine forest in Tus valley (SE Spain), Forest Ecology and Management, 123:1-10.
36. Martinuzzi, S., L.A. Vierling, W.A. Gould, M.J. Falkowski, J.S. Evans, A.T. Hudak, and K.T. Vierling, 2009. Mapping snags and under-story shrubs for a LiDAR-based assessment of wildlife habitat suitability, Remote Sensing of Environment, 113:2533-2546.
37. Means, J.E., S.A. Acker, B.J. Fitt, M. Renslow, L. Emerson, and C.J. Hendrix, 2000. Predicting forest stand characteristics with airborne scanning lidar, Photogrammetric Engineering & Remote Sensing, 66(11):1367-1371.
38. Mutlu, M., S.C Popescu, C. Stripling, and T. Spencer, 2008. Mapping surface fuel models using lidar and multispectral data fusion for fire behaviour, Remote Sensing of Environment, 112:274-285.
39. Næsset, E., 2004. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser, Scandinavian Journal of Forest Research, 19:164-179.
40. NEURALWARE, 2009. NeuralWorks Predict, The Complete Solution for Neural Data Modelling, User Guide, Version 3.24, NeuralWare Inc., Carnergie, Pennsylvania, 402 p.
41. Oliveira, S., and P. Fernandes, 2009. Regeneration of Pinus and Quercus after fire in Mediterranean-type ecosystems: Natural mechanisms and management practices, Silva Lusitana, 17(2):181-192.
42. Pascual, C., A. García-Abril, W.B. Cohen, and S. Martín-Fernández, 2010. Relationship between LiDAR-derived forest canopy height and Landsat images, International Journal of Remote Sensing, 31(5):1261-1280.
43. Pausas, J.G., E. Ribeiro, and R. Vallejo, 2004. Post-fire regeneration variability of Pinus halepensis in the eastern Iberian Peninsula, Forest Ecology and Management, 203:251-259.
44. Pokhriyal, P., P. Uniyal, D.S. Chauhan, and N.P. Todaria, 2010. Regeneration status of tree species in forest of Phakot and Pathri Rao watersheds in Garhwal Himalaya, Current Science, 98(2):171-175.
45. Popescu, S.C., and R.H. Wynne, 2004. Seeing the trees in the forest: using lidar and multispectral data fusion with local fi ltering and variable window size for estimating tree height, Photogrammetric Engineering & Remote Sensing, 70(5):589-604.
46. Popescu, S.C., and K. Zhao, 2008. A voxel-based lidar method for estimating crown base height for deciduous and pine trees, Remote Sensing of Environment, 112:767-781.
47. Riaño, D., E. Chuvieco, S. Condés, J. Conzález-Matesanz, and S.L. Ustin, 2007. Generation of crown bulk density for Pinus sylvestris L. from lidar, Remote Sensing of Environment, 92:345-352.
48. Ripley, B.D., 1994. Neural networks and related methods for classification, Journal of the Royal Statistical Society, 56 (3):409-456.
49. Rumelhart, D.E., G.E. Hinton, and RJ. Williams, 1986. Learning internal representations by error propagation, Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations, Volume 1 (D.E. Rumelhart and J.L. McClelland, editors), MIT Press, Cambridge, Massachusetts, pp. 318-362.
50. Salas, C, L. Ene, T.G. Gregoire, E. Næsset, and T. Gobakken, 2010. Modelling tree diameter from airborne laser scanning derived variables: A comparison of spatial statistical models, Remote Sensing of Environment, 114:1277-1285.
51. Scrinzi, G., L. Marzullo, and D. Galvagni, 2007. Development of a neural network model to update forest distribution data for managed alpine stands, Ecological Modelling, 206:331-346.
52. Solans-Vila, J.P., and P. Barbosa, 2010. Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria-Italy) using Landsat TM and ETM+ data, Ecological Modelling, 221:75-84.
53. Streutker, D.R., and N.F. Glenn, 2006. LiDAR measurement of sagebrush steppe vegetation heights, Remote Sensing of Environment, 102:135-145.
54. Vasilakos, C., K. Kalabokidis, J. Hatzopoulos, and I. Matsinos, 2009. Identifying wildland fire ignition factors through sensitivity analysis of a neural network, Natural Hazards, 50:125-143.
55. Vega-Garcia, C., B.S. Lee, P.M. Woodard, and S.J. Titus, 1996. Applying neural network technology to human-caused wildfire occurrence prediction, AI Applications, 10(3):9-18.
56. Wang, C., and N.F. Glenn, 2009. Estimation of fire-severity using pre- and post-fire LiDAR data in sagebrush steppe rangelands, International Journal of Wildland Fire, 18:848-856.
57. Werbos, P.J., 1994. The Roots of Backpropagation: from Ordered Derivatives to Neural Networks and Political Forecasting, John Wiley & Sons, New York, 319 p.
58. Wing, M.G., A. Eklund, and J. Sessions, 2010. Applying LiDAR technology for tree measurements in burned landscapes, International Journal of Wildland Fire, 19:104-114.
59. Wulder, M.A., J.C. White, F. Alvarez, T. Han, J. Rogan, and B. Hawkes, 2009. Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data, Remote Sensing of Environment, 113:1540-1555.
60. Zhang, Q.B., R.J. Hebda, Q.J. Zhang, and R.I. Alfaro, 2000. Modeling tree-ring growth responses to climatic variables using Artificial neural networks, Forest Science, 46(2):229-239.