Regional forest-fire susceptibility analysis in central Portugal using a probabilistic ratings procedure and artificial neural network weights assignment

References

AFN (2008) ‘Relatório Áreas Ardidas e Ocorrências. Estatísticas da Autoridade Florestal Nacional defesa da Floresta.’ (Autoridade Florestal Nacional: Lisbon, Portugal)

Aguado I, Chuvieco E, Martin P, Salas J (2003) Assessment of forest fire danger conditions in southern Spain from NOAA images and meteorological indices. International Journal of Remote Sensing 24, 1653–1668.
| Assessment of forest fire danger conditions in southern Spain from NOAA images and meteorological indices.Crossref | GoogleScholarGoogle Scholar |

Amatulli G, Peréz-Cabello F, de la Riva J (2007) Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty. Ecological Modelling 200, 321–333.
| Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty.Crossref | GoogleScholarGoogle Scholar |

Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-122. (Ogden, UT)

Aranha J, Alves G (2001) Criação de um Índice de Perigo de Incêndios para o Vale do Alto Tâmega. In ‘Proceedings of ESIG 2001, VI Encontro de Utilizadores de Sistemas de Informação Geográfica (USIG)’, 28–30 November 2001, Lisbon. pp. 14–15. (Instituto Geográfico Português: Lisbon, Portugal)

Aranha J, Alves G, Lopes D (2001) Burnt areas identification and analysis by means of remotely sensed images classification. A case study in northern Portugal. In ‘Proceeding of the RSPS 2001, Geomatics, Earth Observation and the Information Society’, 12–14 September 2001, London. pp. 629–641. (Remote Sensing and Photogrammetry Society: Nottingham, UK)

Atkinson PM, Tatnall AR (1997) Neural networks in remote sensing. International Journal of Remote Sensing 18, 699–709.
| Neural networks in remote sensing.Crossref | GoogleScholarGoogle Scholar |

Bachman A, Allgöwer B (2000) The need of a consistent wildfire risk terminology. In ‘Proceedings from the Joint Fire Science Conference and Workshop: Crossing the Millennium: Integrating Spatial Technologies and Ecological Principles for a New Age in Fire Management’, 15–17 June 1999, Boise, ID. (Eds LF Neuenschwander, KC Ryan, GE Gollberg, JD Greer) pp. 67–77. (University of Idaho and the International Association of Wildland Fire: Moscow, ID)

Bergonse RV, Bidarra JM (2010) Probabilidade Bayesiana e regressão logística na avaliação da susceptibilidade de ocorrência de incêndios de grande magnitude. Finisterra 89, 79–104.

Beverly JL, Herd EPK, Conner JCR (2009) Modelling fire susceptibility in west central Alberta, Canada. Forest Ecology and Management 258, 1465–1478.
| Modelling fire susceptibility in west central Alberta, Canada.Crossref | GoogleScholarGoogle Scholar |

Bishop CM (1995) ‘Neural Networks for Pattern Recognition.’ (Oxford University Press: Oxford, UK)

Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) ‘Classification and Regression Trees.’ (Wadsworth and Brooks/Cole: Monterey, CA)

Bugalho L, Pessanha L (2009) Análise dos Incêndios Florestais em Portugal e Avaliação do Índice de Risco de Incêndios Florestais (ICRIF). Territorium 16, 155–163.

Caetano M, Nunes V, Nunes A (2009) CORINE Land Cover 2006 for Continental Portugal, Relatório Técnico, Instituto Geográfico Português. (Lisbon, Portugal)

Campar de Almeida A (2007) Rural abandonment and landscape evolution in the Central Region of Portugal. In ‘International Geographical Union: Issues in Geographical Marginality – Papers presented during the Commission Meetings 2001–2004: Demographic Problems’. (Eds G Jones, W Leimgruber, É Nel) pp. 53–63. (Rhodes University: Grahamstown, South Africa)

Campar de Almeida A, Cunha L, Freiria S (2007) Massa combustível florestal – um modo expedito de a inventariar e representar. Boletim de Geógrafos 25, 5–17.

Chavez JPS (1996) Image-based atmospheric corrections – revisited and improved. Photogrammetric Engineering and Remote Sensing 62, 1025–1036.

Chuvieco E, Congalton RG (1989) Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment 29, 147–159.
| Application of remote sensing and geographic information systems to forest fire hazard mapping.Crossref | GoogleScholarGoogle Scholar |

Chuvieco E, Salas F, Vega C (1997) Remote sensing and GIS for long-term fire risk mapping. In ‘Megafires Project ENV-CT96–0256 – a review of remote sensing methods for the study of large wildland fires’. (Ed. E Chuvieco) pp. 91–108. (Universidad de Alcalá: Alcalá de Henares, Spain)

CNIG (2008) Cartografia de risco de incêndio florestal (CRIF). Nova série do Instituto Geográfico Portuguêse (s/d), Centro Nacional de Informação geográfica. (Lisbon, Portugal)

Collet D (2003) ‘Modelling Binary Data’, 2nd edn. (Chapman and Hall/CRC: Boca Raton, FL)

Cunha L, Gonçalves AB (1994) Clima e tipos de tempo enquanto características físicas condicionantes do risco de incêndio. Ensaio metodológico. Cadernos de Geografia 13, 3–13.

Dasgupta SD, Qu JJ, Hao X (2006) Design of a susceptibility index for fire risk monitoring. IEEE Geoscience and Remote Sensing Letters 3, 140–144.
| Design of a susceptibility index for fire risk monitoring.Crossref | GoogleScholarGoogle Scholar |

Daveau S, Coelho C, Costa VG, Carvalho L (1977) Répartition et rythme des précipitations au Portugal. Memórias do Centro de Estudos Geográficos 3, 1–192.

Daveau S, Ferreira A, Ferreira N, Vieira G (1997) Novas observações acerca da glaciação da Serra da Estrela. Estudos do Quaternário 1, 41–51.

DEF/ISA (2004) Carta de risco estrutural de incêndio florestal (CREIF). Departamento de Engenharia Florestal, Instituto Superior de Agronomia, Iniciativa COTEC. (Lisbon, Portugal)

Demuth H, Beale M (2001) ‘Neural Network Toolbox. For Use with MATLAB, User’s Guide, Version 4.’ (MathWorks Inc.: Natick, MA)

DGRF (2006) Incêndios florestais: relatórios. Direcção-Geral dos Recursos Florestais. (Lisbon, Portugal)

DGRF (2007) Guia técnico para elaboração do plano municipal de defesa da floresta contra incêndios. Direcção-Geral dos Recursos Florestais. (Lisbon, Portugal)

DGRF (2008) Cartografia oficial de áreas ardidas 1990–2007. Direcção de Serviços de Defesa da Floresta contra Incêndios, Programa de Gestão de Informação e Risco. Direcção-Geral dos Recursos Florestais. (Lisbon, Portugal)

Dimuccio LA, Ferreira R, Cunha L (2006) Aplicação de um modelo de redes neuronais na elaboração de mapas de susceptibilidade a movimentos de vertente. Um exemplo numa área a Sul de Coimbra (Portugal Central). In ‘II Congresso Nacional de Geomorfologia – Geomorfologia, Ciência e Sociedade’, 11–13 November 2004, Coimbra, Portugal. pp. 281–289. (Associação Portuguesa de Geomorfólogos (APGeom): Coimbra, Portugal)

Durão RM, Pereira MJ, Branquinho C, Soares A (2010) Assessing spatial uncertainty of the Portuguese fire risk through direct sequential simulation. Ecological Modelling 221, 27–33.
| Assessing spatial uncertainty of the Portuguese fire risk through direct sequential simulation.Crossref | GoogleScholarGoogle Scholar |

Ermini L, Catani F, Casagli N (2005) Artifical neural networks applied to landslide susceptibility assessment. Geomorphology 66, 327–343.
| Artifical neural networks applied to landslide susceptibility assessment.Crossref | GoogleScholarGoogle Scholar |

Fausett L (1994) ‘Fundamentals of Neural Network – Architectures, Algorithms and Applications.’ (Prentice-Hall: Englewood Cliffs, NJ)

Floreano D, Mattiussi C (2002) ‘Manuale sulle Reti Neuronali’, 2nd edn. (Il Mulino: Bologna, Italy)

Freire S, Carrão H, Caetano MR (2002) ‘Produção de Cartografia de Risco de Incêndios Florestal com Recurso a Imagens de Satélite e Dados Auxiliares.’ (Instituto Geográfico Português: Lisbon, Portugal)

Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Computation 4, 1–58.
| Neural networks and the bias/variance dilemma.Crossref | GoogleScholarGoogle Scholar |

Gonçalves AB (2006) Risco de incêndio florestal (RIF). In ‘Geografia dos Incêndios em Espaços Silvestres de Montanha: o Caso da Serra da Cabreira’. PhD dissertation, Universidade do Minho, Portugal.

Gong P (1996) Integrated analysis of spatial data for multiple sources: using evidential reasoning and artificial neural network techniques for geological mapping. Photogrammetric Engineering and Remote Sensing 62, 513–523.

Guha R, Stanton DT, Jurs PC (2005) Interpreting computational neural network quantitative structure–activity relationship models: a detailed interpretation of the weights and biases. Journal of Chemical Information and Modeling 45, 1109–1121.
| Interpreting computational neural network quantitative structure–activity relationship models: a detailed interpretation of the weights and biases.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXkvVGltLY%3D&md5=003a4f4fbf7bc23b905b788f104bf37fCAS |

Haberman S, Renshaw AE (1996) Generalized linear models and actual science. The Statistician 45, 407–436.
| Generalized linear models and actual science.Crossref | GoogleScholarGoogle Scholar |

Hagan MT, Menhaj M (1994) Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5, 989–993.
| Training feedforward networks with the Marquardt algorithm.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1c7gvFegsg%3D%3D&md5=ef5f25f1de8a1be92082b8af61cb0b33CAS |

Hagan MT, Demuth HB, Beale MH (1996) ‘Neural Network Design.’ (PWS Publishing: Boston, MA)

Hampshire JB, Pearlmutter BA (1990) Equivalence proofs for multi-layer perceptron classifiers and the Bayesian discriminant function. In ‘Proceeding of the 1990 Connectionist Models Summer School’, San Diego, CA. (Eds D Touretzky, J Elman, T Sejnowski, G Hinton) (Morgan Kaufmann: San Mateo, CA)

Haupt SE, Pasini A, Marzban C (2009) ‘Artificial Intelligence Methods in the Environmental Sciences.’ (Springer-Verlag: Berlin)

Haykin S (1999) ‘Neural Networks: a Comprehensive Foundation’, 2nd edn. (Prentice Hall: Upper Saddle River, NJ)

Hines JW (1997) ‘Fuzzy and Neural Approaches in Engineering. Matlab Supplement.’ (Wiley: New York)

IGeoE (2003) Cartas Militar de Portugal, Série M888, Escala 1 : 25 000. Instituto Geográfico do Exército. (Lisbon, Portugal)

IGP (2005) ‘Integrated Forest Fire Risk ou Carta de Risco Integrado de Incêndio Florestal (IFFR).’ (Iniciativa COTEC, Critical Software and Instituto Geográfico Português: Lisbon, Portugal)

IGP (2008). ‘Carta Administrativa Oficial de Portugal.’ (Instituto Geográfico Português: Lisbon, Portugal)

Illera P, Fernandez A, Delgrado JA (1996) Temporal evolution of the NDVI as an indicator of forest fire danger. International Journal of Remote Sensing 17, 1093–1105.
| Temporal evolution of the NDVI as an indicator of forest fire danger.Crossref | GoogleScholarGoogle Scholar |

IM (2004) ‘Cartas de risco de incêndios, situação prevista e observada (CRISPO).’ (Instituto de Meteorologia Português: Lisbon, Portugal)

INE (2008) ‘Statistical Yearbook of Centro Region (Portugal).’ (Instituto Nacional de Estatística: Lisbon, Portugal)

Jensen JR (2007) ‘Remote Sensing of the Environment. An Earth Resource Perspective’, 2nd edn. (Ed. KC Clarke) (Pearson Education, Inc.: New York)

Jordan MI (1995) Why the logistic function? A tutorial discussion on probabilities and neural networks. MIT Computational Cognitive Science Report 9503. (Massachusetts Institute of Technology) Available at https://cours.etsmtl.ca/sys843/pdf/uai.pdf [Verified 13 July 2011]

Kanungo DP, Aora MK, Sarkar S, Grupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology 85, 347–366.
| A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas.Crossref | GoogleScholarGoogle Scholar |

Keane RE, Drury SA, Karau EC, Hessburg PF, Reynolds KM (2010) A method for mapping fire hazard and risk across multiple scales and its application in fire management. Ecological Modelling 221, 2–18.
| A method for mapping fire hazard and risk across multiple scales and its application in fire management.Crossref | GoogleScholarGoogle Scholar |

Kunkel KK (2001) Surface energy budget and fuel moisture. In ‘Forest Fires: Behaviour and Ecological Effects’. (Eds EA Johnson, K Miyanishi) pp. 303–350. (Academic Press: San Diego, CA)

Kushla JD, Ripple W (1997) The role of terrain in fire mosaic of a temperate coniferous forest. Forest Ecology and Management 95, 97–107.
| The role of terrain in fire mosaic of a temperate coniferous forest.Crossref | GoogleScholarGoogle Scholar |

Lee BG (1996) Neural networks applications in the geosciences: an introduction. Computers & Geosciences 22, 955–957.
| Neural networks applications in the geosciences: an introduction.Crossref | GoogleScholarGoogle Scholar |

Lee S, Ryu J, Min K, Won J (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes and Landforms 28, 1361–1376.
| Landslide susceptibility analysis using GIS and artificial neural network.Crossref | GoogleScholarGoogle Scholar |

Lee S, Ryu J, Won J, Park H (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology 71, 289–302.
| Determination and application of the weights for landslide susceptibility mapping using an artificial neural network.Crossref | GoogleScholarGoogle Scholar |

Lee S, Ryu J, Lee MJ, Won J (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Mathematical Geology 38, 199–220.
| The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea.Crossref | GoogleScholarGoogle Scholar |

Lee S, Ryu J, Kim L-S (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslide 4, 327–338.

Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics 2, 164–168.

Li LM, Song WG, Ma J, Satoh K (2009) Artificial neural network approach for modelling the impact of population density and weather parameters on forest fire risk. International Journal of Wildland Fire 18, 640–647.
| Artificial neural network approach for modelling the impact of population density and weather parameters on forest fire risk.Crossref | GoogleScholarGoogle Scholar |

Lopez S, Gonzalez-Alonso F, Llop R (1991) An evaluation of the utility of NOAA AVHRR images for monitoring forest fire risk in Spain. International Journal of Remote Sensing 12, 1841–1851.
| An evaluation of the utility of NOAA AVHRR images for monitoring forest fire risk in Spain.Crossref | GoogleScholarGoogle Scholar |

Lourenço L (2004a) Risco Dendrocaustológico em Mapas. Núcleo de Investigação Científica de Incêndios Florestais, Faculdade de Letras, Universidade de Coimbra, Colecção Estudos 48 (Coimbra, Portugal)

Lourenço L (2004b) Manifestações do Risco Dendrocaustológico. Núcleo de Investigação Científica de Incêndios Florestais, Faculdade de Letras, Universidade de Coimbra, Colecção Estudos 50. (Coimbra, Portugal)

Lourenço L (2008) Perigos das cartas de risco. Territorium 15, 122–126.

Lozano FJ, Suárez-Seoane S, Kelly M, Luis E (2008) A multi-scale approach for modelling fire occurrence probability using satellite data and classification trees: a case study in a mountainous Mediterranean region. Remote Sensing of Environment 112, 708–719.
| A multi-scale approach for modelling fire occurrence probability using satellite data and classification trees: a case study in a mountainous Mediterranean region.Crossref | GoogleScholarGoogle Scholar |

Lyon JG, Yuan D, Lunetta RS, Eldvidge CD (1998) A change detection experiment using Vegetation Index. Photogrammetric Engineering and Remote Sensing 64, 143–150.

Maeda EE, Formaggio AR, Shimabukuro YE, Arcoverde GF, Hansen MC (2009) Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks. International Journal of Applied Earth Observation and Geoinformation 11, 265–272.
| Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks.Crossref | GoogleScholarGoogle Scholar |

Marquardt DW (1963) An algorithm for the least-squares estimation of non-linear parameters. SIAM Journal on Applied Mathematics 11, 431–441.
| An algorithm for the least-squares estimation of non-linear parameters.Crossref | GoogleScholarGoogle Scholar |

Maselli F, Romanelli S, Bottai L, Zipoli G (2003) Use of NOAA-AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas. Remote Sensing of Environment 86, 187–197.
| Use of NOAA-AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas.Crossref | GoogleScholarGoogle Scholar |

McCormick RJ, Brandner TA, Allen TFH (2000) Toward a theory of meso-scale wildfire modeling – a complex systems approach using artificial neural networks. In ‘Proceedings from the Joint Fire Science Conference and Workshop: Crossing the Millennium: Integrating Spatial Technologies and Ecological Principles for a New Age in Fire Management’, 15–17 June 1999, Boise, ID. (Eds LF Neuenschwander, KC Ryan, GE Gollberg, JD Greer) pp. 3–15. (University of Idaho and the International Association of Wildland Fire: Moscow, ID)

McCullagh P, Nelder JA (1989) ‘Generalized Linear Models’, 2nd edn. (Chapman and Hall: New York)

Moran MS, Jackson RD, Slater PN, Teillet PM (1992) Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output. Remote Sensing of Environment 41, 169–184.
| Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output.Crossref | GoogleScholarGoogle Scholar |

Moreira F, Vaz P, Catry F, Silva JS (2009) Regional variations in wildfire susceptibility of land-cover types in Portugal: implications for landscape management to minimize fire hazard. International Journal of Wildland Fire 18, 563–574.
| Regional variations in wildfire susceptibility of land-cover types in Portugal: implications for landscape management to minimize fire hazard.Crossref | GoogleScholarGoogle Scholar |

Mouillot F, Ratte J-P, Joffre R, Moreno JM, Rambal S (2003) Some determinants of the spatio-temporal fire cycle in a Mediterranean landscape (Corsica, France). Landscape Ecology 18, 665–674.
| Some determinants of the spatio-temporal fire cycle in a Mediterranean landscape (Corsica, France).Crossref | GoogleScholarGoogle Scholar |

Nogueira CDS (1990) A floresta portuguesa. DGF Informação 2, 18–28.

Nunes A (2002) Região Centro de Portugal: duas décadas de incêndios florestais. Territorium 9, 135–148.

Nunes M, Vasconcelos M, Pereira J, Dasgupta N, Alldredge R, Rego F (2005) Land cover type and fire in Portugal: do fires burn land cover selectively? Landscape Ecology 20, 661–673.
| Land cover type and fire in Portugal: do fires burn land cover selectively?Crossref | GoogleScholarGoogle Scholar |

Paola JD, Schowengerdt RA (1995) A review and analysis of backpropagation neural networks for classification of remotely sensed multi-spectral imagery. International Journal of Remote Sensing 16, 3033–3058.
| A review and analysis of backpropagation neural networks for classification of remotely sensed multi-spectral imagery.Crossref | GoogleScholarGoogle Scholar |

Pausas J (2004) Changes in fire and climate in the Eastern Iberian Peninsula (Mediterranean Basin). Climatic Change 63, 337–350.
| Changes in fire and climate in the Eastern Iberian Peninsula (Mediterranean Basin).Crossref | GoogleScholarGoogle Scholar |

Pereira JS (2006) ‘Incêndios Florestais em Portugal: Caracterização, Impactes e Prevenção.’ (Instituto Superior de Agronomia: Lisbon, Portugal)

Pereira CJM, Carreiras BJM (2005) ‘Carta de risco conjuntural de incêndio florestal (CRCIF).’ (Departamento de Engenharia Florestal, Instituto Superior de Agronomia, Iniciativa COTEC: Lisbon, Portugal)

Pereira MG, Trigo RM, Camara CC, Pereira JMC, Leite SM (2005) Synoptic patterns associated with large summer forest fires in Portugal. Agricultural and Forest Meteorology 129, 11–25.
| Synoptic patterns associated with large summer forest fires in Portugal.Crossref | GoogleScholarGoogle Scholar |

Piñol J, Terradas J, Lloret F (1998) Climate warming, wildfire hazard, and wildfire occurrence in coastal eastern Spain. Climatic Change 38, 345–357.
| Climate warming, wildfire hazard, and wildfire occurrence in coastal eastern Spain.Crossref | GoogleScholarGoogle Scholar |

Pradhan B, Lee S (2007) Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis using an artificial neural network model. Earth Science Frontiers 14, 143–151.
| Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis using an artificial neural network model.Crossref | GoogleScholarGoogle Scholar |

Prechelt L (1998) Automatic early stopping using cross validation: quantifying the criteria. Neural Networks 11, 761–767.
| Automatic early stopping using cross validation: quantifying the criteria.Crossref | GoogleScholarGoogle Scholar |

Rego FC (1992) Land use changes and wildfire. In ‘Responses of Forest Ecosystems to Environmental Changes’. (Eds A Teller, P Marthy, JNR Jeffers) pp. 367–373. (Elsevier Applied Science: London)

Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS. In ‘Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium, vol. 1’, 10–14 December 1973, Greenbelt, MD. pp. 48–62. (NASA: Washington, DC)

Siljander M (2009) Predictive fire occurrence modelling to improve burned area estimation at a regional scale: a case study in East Caprivi, Namibia. International Journal of Applied Earth Observation and Geoinformation 11, 380–393.
| Predictive fire occurrence modelling to improve burned area estimation at a regional scale: a case study in East Caprivi, Namibia.Crossref | GoogleScholarGoogle Scholar |

Silva JM (1990) La gestion forestière et la sylviculture de prévention des espaces forestiers menacés par les incendies au Portugal. Revue Forestière Français 40, 337–345.
| La gestion forestière et la sylviculture de prévention des espaces forestiers menacés par les incendies au Portugal.Crossref | GoogleScholarGoogle Scholar |

Trigo RM, Pereira JMC, Pereira MG, Mota B, Calado MT, DaCamara CC, Santo FE (2006) The exceptional fire season of summer 2003 in Portugal. International Journal of Climatology 26, 1741–1757.
| The exceptional fire season of summer 2003 in Portugal.Crossref | GoogleScholarGoogle Scholar |

USGS (2009) Global Land Survey, 2000 and 2001, Landsat ETM+, 30 m scenes p204r032_7dx2000624.ETM-EarthSat-Ortorectified and p203r032_7dx20010620.ETM-EarthSat-Ortorectified USGS. (Sioux Falls, SD)

Vadrevu KP, Eature A, Badarinath KVS (2006) Spatial distribution of forest fires and controlling factors in Andhra Pradesh, India using Spot satellite datasets. Environmental Monitoring and Assessment 123, 75–96.
| Spatial distribution of forest fires and controlling factors in Andhra Pradesh, India using Spot satellite datasets.Crossref | GoogleScholarGoogle Scholar |

Varnes DJ (1984) ‘Landslide Hazard Zonation: a Review of Principles and Practice.’ (UNESCO: Paris)

Vasconcelos MJP, Silva S, Tomé M, Alvim M, Pereira JMC (2001) Spatial prediction of fire ignition probabilities: comparing logistic regression and neural network. Photogrammetric Engineering and Remote Sensing 67, 73–81.

Vasilakos C, Kalabokidis K, Hatzopoulos J, Kallos G, Matsinos Y (2007) Integrating new methods and tools in fire danger rating. International Journal of Wildland Fire 16, 306–316.
| Integrating new methods and tools in fire danger rating.Crossref | GoogleScholarGoogle Scholar |

Verde JC (2008) Wildfire hazard assessment. MSc thesis, University of Lisbon.

Verde JC, Zêzere JL (2007) Susceptibilidade aos Incêndios Florestais (SIF). In ‘Avaliação da Perigosidade de Incêndio Florestal: Proceeding of VI Congresso da Geografia Portuguesa’, 17–20 October 2007, Lisbon, Portugal. (Eds JC Verde, LL Zezere) p. 118. (Universidade Nova de Lisboa: Lisbon, Portugal)

Verde JC, Zêzere JL (2010) Assessment and validation of wildfire susceptibility and hazard in Portugal. Natural Hazards and Earth System Sciences 10, 485–497.
| Assessment and validation of wildfire susceptibility and hazard in Portugal.Crossref | GoogleScholarGoogle Scholar |

Viegas DX (1994) Some thoughts on the wind and slope effects on fire propagation. International Journal of Wildland Fire 4, 63–64.
| Some thoughts on the wind and slope effects on fire propagation.Crossref | GoogleScholarGoogle Scholar |

Viegas DX (2006) Modelação do comportamento do fogo. In ‘Incêndios Florestais em Portugal, Caracterização, Impactes e Prevenção’. pp. 287–325. (ISAPress: Lisbon, Portugal)

Viegas DX, Bovio G, Ferreira A, Nosenzo A, Sol B (1999) Comparative study of various methods of fire danger evaluation in southern Europe. International Journal of Wildland Fire 9, 235–246.
| Comparative study of various methods of fire danger evaluation in southern Europe.Crossref | GoogleScholarGoogle Scholar |

Vieira G, Mora C (1998) General characteristics of the climate of the Serra da Estrela. In ‘Glacial and Periglacial Geomorphology of the Serra da Estrela’. (Ed. G Vieira) pp. 26–36. (CEG and Department of Geography, University of Lisbon: Lisbon, Portugal)

Vieira G, Mora C, Ramos M (2003) Ground temperature regimes and geomorphological implications in a Mediterranean mountain (Serra da Estrela, Portugal). Geomorphology 52, 57–72.
| Ground temperature regimes and geomorphological implications in a Mediterranean mountain (Serra da Estrela, Portugal).Crossref | GoogleScholarGoogle Scholar |

Yang L, Dawson CW, Brown MR, Gell M (2006) Neural network and GA approaches for dwelling fire occurrence prediction. Knowledge-Based Systems 19, 213–219.
| Neural network and GA approaches for dwelling fire occurrence prediction.Crossref | GoogleScholarGoogle Scholar |

Zhou W (1999) Verification of the non-parametric characteristics of backpropagation neural networks for image classification. IEEE Transactions on Geoscience and Remote Sensing 37, 771–779.
| Verification of the non-parametric characteristics of backpropagation neural networks for image classification.Crossref | GoogleScholarGoogle Scholar |