Fuzzy definition of Rural Urban Interface: An application based on land use change scenarios in Portugal

Environmental Modelling and Software

Volumn 104, Issue , 2018, Pages 171-187

  Amato, Federico ^a   Tonini, Marj ^b   Murgante, Beniamino ^a   Kanevski, Mikhail ^b  

^a UNIVERSITY OF BASILICATA   (Italy)

^b UNIVERSITY OF LAUSANNE   (Switzerland)

Author keywords

Fuzzy set theory; Land use land cover changes; Multilayer perceptron; Rural urban interface; Uncertainty assessment

Indexed keywords

DEFORESTATION; FIRE HAZARDS; FIRES; FUZZY SETS; LAND USE; MULTILAYERS; RURAL AREAS; UNCERTAINTY ANALYSIS;

FOREST FIRES; FUZZY-SET THEORY; LAND COVER; LAND-USE LAND-COVER CHANGES; LANDUSE CHANGE; MULTILAYERS PERCEPTRONS; PORTUGAL; RURAL-URBAN INTERFACE; SPATIOTEMPORAL EVOLUTION; UNCERTAINTY ASSESSMENT;

FUZZY SET THEORY;

ANTHROPOGENIC EFFECT; ENVIRONMENTAL POLICY; FOREST FIRE; FUZZY MATHEMATICS; LAND COVER; LAND USE CHANGE; METHODOLOGY; RURAL-URBAN COMPARISON; SPATIOTEMPORAL ANALYSIS; UNCERTAINTY ANALYSIS;

MEDITERRANEAN REGION; PORTUGAL;

EID: 85044611510     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2018.03.016     Document Type: Article

References (120)

1. Alados, C.L., Pueyo, Y., Barrantes, O., Escós, J., Giner, L., Robles, A.B., Variations in landscape patterns and vegetation cover between 1957 and 1994 in a semiarid Mediterranean ecosystem. Landsc. Ecol. 19 (2004), 545–561 https://doi.org/10.1023/B:LAND.0000036149.96664.9a.
2. Amato, F., Maimone, B.A., Martellozzo, F., Nolè, G., Murgante, B., The effects of urban policies on the development of urban areas. Sustainability, 8(297), 2016 https://doi.org/10.3390/su8040297.
3. Amato, F., Martellozzo, F., Nolè, G., Murgante, B., Preserving cultural heritage by supporting landscape planning with quantitative predictions of soil consumption. J. Cult. Herit. 23 (2017), 44–54 https://doi.org/10.1016/j.culher.2015.12.009.
4. Amato, F., Pontrandolfi, P., Murgante, B., Supporting planning activities with the assessment and the prediction of urban sprawl using spatio-temporal analysis. Ecol. Inf. 30 (2015), 365–378 https://doi.org/10.1016/j.ecoinf.2015.07.004.
5. An, P., Moon, W., Rencz, A., Application of fuzzy set theory for integration of geological, geophysical and remote sensing data. Can. J. Explor. Geophys. 27 (1991), 1–11.
6. Antrop, M., Landscape change and the urbanization process in Europe. Landsc. Urban Plann. 67 (2004), 9–26 Development of European Landscapes https://doi.org/10.1016/S0169-2046(03)00026-4.
7. Antrop, M., Changing patterns in the urbanized countryside of Western Europe. Landsc. Ecol. 15 (2000), 257–270 https://doi.org/10.1023/A:1008151109252.
8. Asadi, P., Hosseini, S.M., Ataie-Ashtiani, B., Simmons, C.T., Fuzzy vulnerability mapping of urban groundwater systems to nitrate contamination. Environ. Model. Software 96 (2017), 146–157 https://doi.org/10.1016/j.envsoft.2017.06.043.
9. Badia-Perpinyá, A., Pallares-Barbera, M., Spatial distribution of ignitions in Mediterranean periurban and rural areas: the case of Catalonia. Int. J. Wildland Fire, 15(187), 2006 https://doi.org/10.1071/WF04008.
10. Bai, Y., Wang, D., Fundamentals of Fuzzy Logic Control — Fuzzy Sets, Fuzzy Rules and Defuzzifications, in: Advanced Fuzzy Logic Technologies in Industrial Applications. 2006, Springer London, London, 17–36 https://doi.org/10.1007/978-1-84628-469-4_2.
11. Bar-Massada, A., Stewart, S.I., Hammer, R.B., Mockrin, M.H., Radeloff, V.C., Using structure locations as a basis for mapping the wildland urban interface. J. Environ. Manag. 128 (2013), 540–547 https://doi.org/10.1016/j.jenvman.2013.06.021.
12. Bar Massada, A., Radeloff, V.C., Stewart, S.I., Hawbaker, T.J., Wildfire risk in the wildland–urban interface: a simulation study in northwestern Wisconsin. For. Ecol. Manage 258 (2009), 1990–1999 https://doi.org/10.1016/J.FORECO.2009.07.051.
13. Batty, M., Cellular automata and urban form: a primer. Appl. Phys. A J. 63:2 (1997), 266–274.
14. Bonham-Carter, G.F., Bonham-Carter, G.F., CHAPTER 9 – tools for map analysis: multiple maps. Geographic Information Systems for Geoscientists, 1994, 267–337 https://doi.org/10.1016/B978-0-08-041867-4.50014-X.
15. Boutell, M.R., Luo, J., Shen, X., Brown, C.M., Learning multi-label scene classification. Pattern Recogn. 37 (2004), 1757–1771 https://doi.org/10.1016/j.patcog.2004.03.009.
16. Buckley, J.J., Uncertain probabilities III: the continuous case. Soft comput. - a fusion found. Methodol. Appl. 8 (2004), 200–206 https://doi.org/10.1007/s00500-003-0263-5.
17. Burrough, P.A., Fuzzy mathematical methods for soil survey and land evaluation. J. Soil Sci. 40 (1989), 477–492 https://doi.org/10.1111/j.1365-2389.1989.tb01290.x.
18. Bouillon, C., Fernandez Ramiro, M.M., Sirca, C., Fierro Garcıa, B., Long Fournel, M., et al. European software tools for mapping wildland urban interfaces in the mediterranean context. Modelling Fire Behaviour and Risk, 2012, Universita di Sassari DIPNET, 243–248 978.-88-904409-7-7.
19. Catry, F.X., Rego, F.C., Bação, F.L., Moreira, F., Modeling and mapping wildfire ignition risk in Portugal. Int. J. Wildland Fire, 18(921), 2009 https://doi.org/10.1071/WF07123.
20. Conedera, M., Tonini, M., Oleggini, L., Vega Orozco, C., Leuenberger, M., Pezzatti, G.B., Geospatial approach for defining the wildland-urban interface in the alpine environment. Comput. Environ. Urban Syst. 52 (2015), 10–20 https://doi.org/10.1016/j.compenvurbsys.2015.02.003.
21. Copernicus Programme, n.d. CORINE Land Cover - available at http://land.copernicus.eu/pan-european/corine-land-cover [WWW Document]. URL http://land.copernicus.eu/pan-european/corine-land-cover.
22. Derksen, S., Keselman, H.J., Backward, forward and stepwise automated subset selection algorithms: frequency of obtaining authentic and noise variables. Br. J. Math. Stat. Psychol. 45 (1992), 265–282 https://doi.org/10.1111/j.2044-8317.1992.tb00992.x.
23. d'Aquino, P., August, P., Balmann, A., Berger, T., Bousquet, F., Brondízio, E., Gotts, N.M., Agent-based models of land-use and land-cover change. Proc. of an International Workshop, 2002, 4–7.
24. Di Palma, F., Amato, F., Nolè, G., Martellozzo, F., Murgante, B., A SMAP supervised classification of landsat images for urban sprawl evaluation. Isprs int. J. Geo-Information, 5(109), 2016 https://doi.org/10.3390/ijgi5070109.
25. Diogo, V., Koomen, E., Land-use change in Portugal, 1990–2006: main processes and underlying factors. Cartogr. Int. J. Geogr. Inf. Geovisualization 47 (2012), 237–249 https://doi.org/10.3138/carto.47.4.1504.
26. Eastman, J., Van Fossen, M.E., Solarzano, L.A., Transition potential modeling for land cover change. GIS. Spat. Anal. Model, 2005, 357–386.
27. EEA, CORINE Land Cover - Part 1: Methodology. 1994.
28. EPIC WebGIS Portugal, n.d. EPIC WebGIS Portugal [WWW Document]. URL http://epic-webgis-portugal.isa.ulisboa.pt/.
29. Eslami, E., Buckley, J.J., Uncertain probabilities II: the continuous case. Soft comput. - a fusion found. Methodol. Appl. 8 (2004), 193–199 https://doi.org/10.1007/s00500-002-0262-y.
30. Foley, J.A., Ramankutty, N., Brauman, K.A., Cassidy, E.S., Gerber, J.S., Johnston, M., Mueller, N.D., O'connell, C., Ray, D.K., West, P.C., Balzer, C., Bennett, E.M., Carpenter, S.R., Hill, J., Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., Tilman, D., Zaks, D.P.M., Solutions for a cultivated planet. Nature 478 (2011), 337–342 https://doi.org/10.1038/nature10452.
31. Foley, J.A., Defries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, I.C., Ramankutty, N., Snyder, P.K., Global consequences of land use. Science 309:5734 (2005), 570–574 https://doi.org/10.1126/science.1111772.
32. Ganteaume, A., Camia, A., Jappiot, M., San-Miguel-Ayanz, J., Long-Fournel, M., Lampin, C., A review of the main driving factors of forest fire ignition over Europe. Environ. Manag. 51 (2013), 651–662 https://doi.org/10.1007/s00267-012-9961-z.
33. Gil Pontius, R. Jr., Schneider, L.C., Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agric. Ecosyst. Environ. 85 (2001), 239–248.
34. Guan, D., Gao, W., Watari, K., Fukahori, H., Land use change of Kitakyushu based on landscape ecology and Markov model. J. Geogr. Sci. 18 (2008), 455–468 https://doi.org/10.1007/s11442-008-0455-0.
35. Hanley, J.A., McNeil, B.J., The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143 (1982), 29–36 https://doi.org/10.1148/radiology.143.1.7063747.
36. Hattab, T., Ben Rais Lasram, F., Albouy, C., Sammari, C., Romdhane, M.S., Cury, P., Leprieur, F., Le Loc'h, F., The use of a predictive habitat model and a fuzzy logic approach for marine management and planning. PLoS One, 8, 2013, e76430 https://doi.org/10.1371/journal.pone.0076430.
37. Haykin, S., Neural Networks and Learning Machines. third ed., 2008.
38. Herrero-Corral, G., Jappiot, M., Bouillon, C., Long-Fournel, M., Application of a geographical assessment method for the characterization of wildland–urban interfaces in the context of wildfire prevention: a case study in western Madrid. Appl. Geogr. 35 (2012), 60–70 https://doi.org/10.1016/j.apgeog.2012.05.005.
39. Hill, J., Stellmes, M., Udelhoven, T., Röder, A., Sommer, S., Mediterranean desertification and land degradation: mapping related land use change syndromes based on satellite observations. Global Planet. Change 64 (2008), 146–157 https://doi.org/10.1016/J.GLOPLACHA.2008.10.005.
40. Holland, J.H., Goldberg, D., Genetic Algorithms in Search, Optimization and Machine Learning. 1989, Addison-Wesley, Massachusetts.
41. Instituto Português do Mar e da Atmosfera [WWW Document], 2018. URL https://www.ipma.pt/pt/index.html (accessed 3.16.18).
42. Kalnay, E., Cai, M., Impact of urbanization and land use change on climate. Nature 423 (2003), 528–531.
43. Kim, I., Arnhold, S., Ahn, S., Le, Q.B., Kim, S.J., Park, S.J., Koellner, T., Land use change and ecosystem services in mountainous watersheds: predicting the consequences of environmental policies with cellular automata and hydrological modeling. Environ. Model. Software, 2017 https://doi.org/10.1016/j.envsoft.2017.06.018.
44. Kityuttachai, K., Tripathi, N., Tipdecho, T., Shrestha, R., Ca-markov analysis of constrained coastal urban growth modeling: hua hin seaside city, Thailand. Sustainability 5 (2013), 1480–1500 https://doi.org/10.3390/su5041480.
45. Lampin-Maillet, C., Jappiot, M., Long, M., Bouillon, C., Morge, D., Ferrier, J.-P., Mapping wildland-urban interfaces at large scales integrating housing density and vegetation aggregation for fire prevention in the South of France. J. Environ. Manag. 91 (2010), 732–741 https://doi.org/10.1016/j.jenvman.2009.10.001.
46. Lasaponara, R., Lanorte, A., Pignatti, S., Lasaponara, R., Lanorte, A., Pignatti, S., Characterization and mapping of fuel types for the mediterranean ecosystems of pollino national park in southern Italy by using hyperspectral MIVIS data. Earth Interact. 10 (2006), 1–11 https://doi.org/10.1175/EI165.1.
47. Li, X., Yeh, A.G.-O., Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int. J. Geogr. Inf. Sci. 16 (2002), 323–343 https://doi.org/10.1080/13658810210137004.
48. Li, X., Yeh, A.G.-O., Calibration of cellular automata by using neural networks for the simulation of complex urban systems. Environ. Plann. 33 (2001), 1445–1462 https://doi.org/10.1068/a33210.
49. Luo, X., Dimitrakopoulos, R., Data-driven fuzzy analysis in quantitative mineral resource assessment. Comput. Geosci. 29 (2003), 3–13.
50. Malczewski, J., Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. Int. J. Appl. Earth Obs. Geoinf. 8 (2006), 270–277 https://doi.org/10.1016/J.JAG.2006.01.003.
51. Martellozzo, F., Amato, F., Murgante, B., Clarke, K.C., Modelling the impact of urban growth on agriculture and natural land in Italy to 2030. Appl. Geogr., 91, 2018 https://doi.org/10.1016/j.apgeog.2017.12.004.
52. Mas, J.-F., Kolb, M., Paegelow, M., Camacho Olmedo, M.T., Houet, T., Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ. Model. Softw. 51 (2014), 94–111 https://doi.org/10.1016/j.envsoft.2013.09.010.
53. Meyfroidt, P., Approaches and terminology for causal analysis in land systems science. J. Land Use Sci. 11 (2016), 501–522 https://doi.org/10.1080/1747423X.2015.1117530.
54. Millington, J.D.A., Perry, G.L.W., Romero-Calcerrada, R., Regression techniques for examining land use/cover change: a case study of a mediterranean landscape. Ecosystems 10 (2007), 562–578 https://doi.org/10.1007/s10021-007-9020-4.
55. Moreira, F., Rego, F.C., Ferreira, P.G., Temporal (1958–1995) pattern of change in a cultural landscape of northwestern Portugal: implications for fire occurrence. Landsc. Ecol. 16 (2001), 557–567 https://doi.org/10.1023/A:1013130528470.
56. Moreira, F., Vaz, P., Catry, F., Silva, J.S., Regional variations in wildfire susceptibility of land-cover types in Portugal: implications for landscape management to minimize fire hazard. Int. J. Wildland Fire, 18(563), 2009 https://doi.org/10.1071/WF07098.
57. Muñoz-Rojas, M., Jordán, A., Zavala, L.M., De la Rosa, D., Abd-Elmabod, S.K., Anaya-Romero, M., Impact of land use and land cover changes on organic carbon stocks in mediterranean soils (1956-2007). Land Degrad. Dev. 26 (2015), 168–179 https://doi.org/10.1002/ldr.2194.
58. Murgante, B., Las Casas, G.B., G.I.S. and Fuzzy Sets for the Land Suitability Analysis. 2004, Springer, Berlin, Heidelberg, 1036–1045 https://doi.org/10.1007/978-3-540-24709-8_109.
59. Nunes, A.N., Lourenço, L., Meira, A.C.C., Exploring spatial patterns and drivers of forest fires in Portugal (1980–2014). Sci. Total Environ. 573 (2016), 1190–1202 https://doi.org/10.1016/J.SCITOTENV.2016.03.121.
60. Nunes, M.C.S., Vasconcelos, M.J., Pereira, J.M.C., Dasgupta, N., Alldredge, R.J., Rego, F.C., Land cover type and fire in Portugal: do fires burn land cover selectively?. Landsc. Ecol. 20 (2005), 661–673 https://doi.org/10.1007/s10980-005-0070-8.
61. Nunes de Oliveira, S., Abílio de Carvalho Júnior, O., Trancoso Gomes, R.A., Fontes Guimarães, R., McManus, C.M., Deforestation analysis in protected areas and scenario simulation for structural corridors in the agricultural frontier of Western Bahia, Brazil. Land Use Pol. 61 (2017), 40–52 https://doi.org/10.1016/j.landusepol.2016.10.046.
62. Oliveira, S., Moreira, F., Boca, R., San-Miguel-Ayanz, J., Pereira, J.M.C., Assessment of fire selectivity in relation to land cover and topography: a comparison between Southern European countries. Int. J. Wildl. Fire, 23(620), 2014 https://doi.org/10.1071/WF12053.
63. Oliveira, S., Zêzere, J.L., Queirós, M., Pereira, J.M., Assessing the social context of wildfire-affected areas. The case of mainland Portugal. Appl. Geogr 88 (2017), 104–117 https://doi.org/10.1016/J.APGEOG.2017.09.004.
64. Omrani, H., Abdallah, F., Charif, O., Longford, N.T., Multi-label class assignment in land-use modelling. Int. J. Geogr. Inf. Sci. 29 (2015), 1023–1041 https://doi.org/10.1080/13658816.2015.1008004.
65. Omrani, H., Tayyebi, A., Pijanowski, B., Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework. GIScience Remote Sens. 54 (2017), 283–304 https://doi.org/10.1080/15481603.2016.1265706.
66. OpenStreetMap [WWW Document], n.d. URL https://www.openstreetmap.org/#map=5/51.500/-0.100 (accessed 6.22.17).
67. Overmars, K.P., de Groot, W.T., Huigen, M.G.A., Comparing inductive and deductive modeling of land use decisions: principles, a model and an illustration from the Philippines. Hum. Ecol. 35 (2007), 439–452 https://doi.org/10.1007/s10745-006-9101-6.
68. Overmars, K.P., Verburg, P.H., Veldkamp, T., Comparison of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model. Land Use Pol. 24 (2007), 584–599 https://doi.org/10.1016/j.landusepol.2005.09.008.
69. Parker, D.C., Manson, S.M., Janssen, M.A., Hoffmann, M.J., Deadman, P., Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann. Assoc. Am. Geogr 93 (2003), 314–337 https://doi.org/10.1111/1467-8306.9302004.
70. Pausas, J.G., Llovet, J., Rodrigo, A., Vallejo, R., Are wildfires a disaster in the Mediterranean basin? – A review. Int. J. Wildland Fire, 17(713), 2008 https://doi.org/10.1071/WF07151.
71. Pereira, M.G., Aranha, J., Amraoui, M., Land cover fire proneness in Europe. For. Syst, 23(598), 2014 https://doi.org/10.5424/fs/2014233-06115.
72. Pereira, M.G., Teresa, J.C., DaCamara, C.C., Calheiros, T., Effects of regional climate change on rural fires in Portugal. Clim. Res. Clim Res 57 (2013), 187–200 https://doi.org/10.3354/cr01176.
73. Pereira, M.G., Trigo, R.M., da Camara, C.C., Pereira, J.M.C., Leite, S.M., Synoptic patterns associated with large summer forest fires in Portugal. Agric. For. Meteorol 129 (2005), 11–25 https://doi.org/10.1016/j.agrformet.2004.12.007.
74. Platt, R., The wildland-urban interface: evaluating the definition effect. J. For. 108:1 (2010), 9–15.
75. Pontius, R.G., Boersma, W., Castella, J.C., Clarke, K., Nijs, T., Dietzel, C., Duan, Z., Fotsing, E., Goldstein, N., Kok, K., Koomen, E., Lippitt, C.D., McConnell, W., Mohd Sood, A., Pijanowski, B., Pithadia, S., Sweeney, S., Trung, T.N., Veldkamp, A.T., Verburg, P.H., Comparing the input, output, and validation maps for several models of land change. Ann. Reg. Sci. 42 (2008), 11–37 https://doi.org/10.1007/s00168-007-0138-2.
76. Pontius, R.G., Cheuk, M.L., A generalized cross-tabulation matrix to compare soft-classified maps at multiple resolutions. Int. J. Geogr. Inf. Sci. 20 (2006), 1–30 https://doi.org/10.1080/13658810500391024.
77. Pontius, R.G., Parmentier, B., Recommendations for using the relative operating characteristic (ROC). Landsc. Ecol. 29 (2014), 367–382 https://doi.org/10.1007/s10980-013-9984-8.
78. Pontius, R.G.J., Quantification error versus location error in comparison of categorical maps. Photogramm. Eng. Rem. Sens. 66 (2000), 1011–1016.
79. Pontius, R.G. Jr., Kangping, S., The total operating characteristic to measure diagnostic ability for multiple thresholds. Int. J. Geogr. Inf. Sci. 28 (2014), 570–583 https://doi.org/10.1080/13658816.2013.862623.
80. Portal do Instituto Nacional de Estatística, n.d. Portal do Instituto Nacional de Estatística [WWW Document]. URL https://www.ine.pt/xportal/xmain?xpgid=ine_main&xpid=INE.
81. Pota, M., Esposito, M., De Pietro, G., Transforming probability distributions into membership functions of fuzzy classes: a hypothesis test approach. Fuzzy Set Syst. 233 (2013), 52–73 https://doi.org/10.1016/j.fss.2013.03.013.
82. Power, C., Simms, A., White, R., Hierarchical fuzzy pattern matching for the regional comparison of land use maps. Int. J. Geogr. Inf. Sci. 15 (2001), 77–100 https://doi.org/10.1080/136588100750058715.
83. Poyatos, R., Latron, J., Llorens, P., Land use and land cover change after agricultural abandonment: the case of a mediterranean mountain area (Catalan pre-pyrenees). Mt. Res. Dev. 23 (2003), 362–368 https://doi.org/10.1659/0276-4741(2003)023[0362:LUALCC]2.0.CO;2.
84. Radeloff, V.C., Hammer, R.B., Stewart, S.I., Fried, J.S., Holcomb, S.S., McKeefry, J.F., The wildland–urban interface in the United States. Ecol. Appl. 15 (2005), 799–805 https://doi.org/10.1890/04-1413.
85. Raines, G.L., Sawatzky, D.L., Bonham-Carter, G.F., Incorporating Expert Knowledge – New Fuzzy Logic Tools in ArcGIS 10. 2010.
86. Ramankutty, N., Coomes, O.T., Land-use regime shifts: an analytical framework and agenda for future land-use research. Ecol. Soc., 21, 2016 art1 https://doi.org/10.5751/ES-08370-210201.
87. Reference Data — Copernicus Land Monitoring Service [WWW Document], n.d. URL http://land.copernicus.eu/pan-european/satellite-derived-products/view (accessed 6.22.17).
88. Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., Müller, M., pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinf., 12(77), 2011 https://doi.org/10.1186/1471-2105-12-77.
89. Robinson, D.T., Brown, D.G., Parker, D.C., Schreinemachers, P., Janssen, M.A., Huigen, M., Wittmer, H., Gotts, N., Promburom, P., Irwin, E., Berger, T., Gatzweiler, F., Barnaud, C., Comparison of empirical methods for building agent-based models in land use science. J. Land Use Sci. 2 (2007), 31–55 https://doi.org/10.1080/17474230701201349.
90. Saeidi, S., Mohammadzadeh, M., Salmanmahiny, A., Mirkarimi, S.H., Performance evaluation of multiple methods for landscape aesthetic suitability mapping: a comparative study between Multi-Criteria Evaluation, Logistic Regression and Multi-Layer Perceptron neural network. Land Use Pol. 67 (2017), 1–12 https://doi.org/10.1016/j.landusepol.2017.05.014.
91. Sheehan, T., Gough, M., A platform-independent fuzzy logic modeling framework for environmental decision support. Ecol. Inf. 34 (2016), 92–101 https://doi.org/10.1016/j.ecoinf.2016.05.001.
92. Silva, R.F.B., da Batistella, M., Moran, E.F., Drivers of land change: human-environment interactions and the Atlantic forest transition in the Paraíba Valley, Brazil. Land Use Pol. 58 (2016), 133–144 https://doi.org/10.1016/j.landusepol.2016.07.021.
93. Stewart, S.I., Radeloff, V.C., Hammer, R.B., Hawbaker, T.J., Defining the wildland–urban interface. J. For. 105 (2007), 201–207.
94. Stewart, S.I., Wilmer, B., Hammer, R.B., Aplet, G.H., Hawbaker, T.J., Miller, C., Radeloff, V.C., Wildland-urban interface maps vary with purpose and context. J. For. 107 (2009), 78–83.
95. Takada, T., Miyamoto, A., Hasegawa, S.F., Derivation of a yearly transition probability matrix for land-use dynamics and its applications. Landsc. Ecol. 25 (2010), 561–572 https://doi.org/10.1007/s10980-009-9433-x.
96. Theobald, D.M., Romme, W.H., Expansion of the US wildland–urban interface. Landsc. Urban Plann. 83 (2007), 340–354 https://doi.org/10.1016/j.landurbplan.2007.06.002.
97. Tonini, M., Parente, J., Pereira, M.G., Global spatial assessment of WUI and related land cover in Portugal. Geophys. Res. Abstr. EGU Gen. Assem. Comput. Environ. Urban Syst 19 (2017), 2017–4956.
98. Tonini, M., Pereira, M.G., Parente, J., Vega Orozco, C., Evolution of forest fires in Portugal: from spatio-temporal point events to smoothed density maps. Nat. Hazards 85 (2017), 1489–1510 https://doi.org/10.1007/s11069-016-2637-x.
99. Trigo, R.M., Sousa, P.M., Pereira, M.G., Rasilla, D., Gouveia, C.M., Modelling wildfire activity in Iberia with different atmospheric circulation weather types. Int. J. Climatol. 36 (2016), 2761–2778 https://doi.org/10.1002/joc.3749.
100. Viedma, O., The influence of topography and fire in controlling landscape composition and structure in Sierra de Gredos (Central Spain). Landsc. Ecol. 23 (2008), 657–672 https://doi.org/10.1007/s10980-008-9228-5.
101. USDI - US Department Of The Interior, USDA - US Department Of Agriculture, Urban wildland interface communities within the vicinity of federal lands that are at high risk from wildfire. Fed. Regist. 66 (2001), 751–777.
102. Uusitalo, L., Lehikoinen, A., Helle, I., Myrberg, K., An overview of methods to evaluate uncertainty of deterministic models in decision support. Environ. Model. Software 63 (2015), 24–31 https://doi.org/10.1016/j.envsoft.2014.09.017.
103. Van Doorn, A.M., Bakker, M.M., The destination of arable land in a marginal agricultural landscape in South Portugal: an exploration of land use change determinants. Landsc. Ecol. 22 (2007), 1073–1087 https://doi.org/10.1007/s10980-007-9093-7.
104. van Vliet, J., Bregt, A.K., Brown, D.G., van Delden, H., Heckbert, S., Verburg, P.H., A review of current calibration and validation practices in land-change modeling. Environ. Model. Software 82 (2016), 174–182 https://doi.org/10.1016/j.envsoft.2016.04.017.
105. Van Vliet, J., Bregt, A.K., Hagen-Zanker, A., Revisiting Kappa to account for change in the accuracy assessment of land-use change models. Ecol. Model. 222 (2011), 1367–1375 https://doi.org/10.1016/j.ecolmodel.2011.01.017.
106. van Vliet, N., Mertz, O., Heinimann, A., Langanke, T., Pascual, U., Schmook, B., Adams, C., Schmidt-Vogt, D., Messerli, P., Leisz, S., Castella, J.-C., Jørgensen, L., Birch-Thomsen, T., Hett, C., Bech-Bruun, T., Ickowitz, A., Vu, K.C., Yasuyuki, K., Fox, J., Padoch, C., Dressler, W., Ziegler, A.D., Trends, drivers and impacts of changes in swidden cultivation in tropical forest-agriculture frontiers: a global assessment. Global Environ. Change 22 (2012), 418–429 https://doi.org/10.1016/j.gloenvcha.2011.10.009.
107. Viedma, O., Moity, N., Moreno, J.M., Changes in landscape fire-hazard during the second half of the 20th century: agriculture abandonment and the changing role of driving factors. Agric. Ecosyst. Environ. 207 (2015), 126–140 https://doi.org/10.1016/j.agee.2015.04.011.
108. Wang, L.-X., Mendel, J.M., Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans. Neural Network. 3 (1992), 807–814 https://doi.org/10.1109/72.159070.
109. Wieland, R., Mirschel, W., Combining expert knowledge with machine learning on the basis of fuzzy training. Ecol. Inf. 38 (2017), 26–30 https://doi.org/10.1016/j.ecoinf.2017.01.001.
110. Yang, X., Zheng, X.-Q., Lv, L.-N., A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecol. Model. 233 (2012), 11–19 https://doi.org/10.1016/j.ecolmodel.2012.03.011.
111. Young, S., Land change monitoring, assessment, and projection (LCMAP) revolutionizes land cover and land change research (No. 172). Gen. Inf. Prod., 2017 https://doi.org/10.3133/gip172.
112. Zadeh, L.A., Is there a need for fuzzy logic?. Inf. Sci. 178 (2008), 2751–2779 https://doi.org/10.1016/j.ins.2008.02.012.
113. Zadeh, L.A., Fuzzy logic and the calculi of fuzzy rules, fuzzy graphs, and fuzzy probabilities. Comput. Math. Appl., 37(35), 1999 https://doi.org/10.1016/S0898-1221(99)00140-6.
114. Zadeh, L.A., Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man. Cybern. SMC-3, 1973, 28–44 https://doi.org/10.1109/TSMC.1973.5408575.
115. Zadeh, L.A., Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man. Cybern. SMC-3, 1973, 28–44 https://doi.org/10.1109/TSMC.1973.5408575.
116. Zadeh, L.A., Fuzzy sets. Inf. Control 8 (1965), 338–353 https://doi.org/10.1016/S0019-9958(65)90241-X.
117. Zhang, Y., He, H.S., Yang, J., The wildland–urban interface dynamics in the southeastern U.S. from 1990 to 2000. Landsc. Urban Plann. 85 (2008), 155–162 https://doi.org/10.1016/j.landurbplan.2007.11.007.
118. Zimmermann, H.-J., Fuzzy set theory. Wiley Interdiscip Rev. Comput. Stat 2 (2010), 317–332 https://doi.org/10.1002/wics.82.
119. Zimmermann, H.-J., Fuzzy set theory. Wiley Interdiscip Rev. Comput. Stat 2 (2010), 317–332 https://doi.org/10.1002/wics.82.
120. Zimmermann, H.-J., Fuzzy Set Theory—and its Applications. fourth ed., 2001, Springer, Netherlands https://doi.org/10.1007/978-94-010-0646-0.