Modelling deforestation using GIS and artificial neural networks

Environmental Modelling and Software

Volumn 19, Issue 5, 2004, Pages 461-471

  Mas, J F ^a   Puig, H ^b   Palacio, J L ^a   Sosa Lopez A ^c  

^a INSTITUTO DE MATEMÁTICAS   (Mexico)

^b CNRS   (France)

^c UNIVERSIDAD AUTÓNOMA DE CAMPECHE   (Mexico)

Author keywords

Artificial neural networks; Deforestation; Geographic information system; Land use land cover change; Spatial modelling

Indexed keywords

COMPUTER SIMULATION; DEFORESTATION; GEOGRAPHIC INFORMATION SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; RISK ASSESSMENT; SOILS;

DIGITAL DEFORESTATION MAPS; FOREST FRAGMENTATION;

FORESTRY;

ARTIFICIAL NEURAL NETWORK; DEFORESTATION; GIS; LAND USE CHANGE; MODELING; VEGETATION COVER;

EID: 1642265059     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1364-8152(03)00161-0     Document Type: Article

References (57)

1. Abdul-Wahab S.A., Al-Alawib S.M. Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environmental Modelling and Software. 17:(3):2002;219-228.
2. Almeida C.M., Batty M., Monteiro A.M.V., Câmara G., Soares-Filho B.S., Cerqueira G.C., Pennachin C.L. Stochastic cellular automata modelling of urban land use dynamics: empirical development and estimation. Computers. Environment and Urban Systems. 27:(5):2003;481-509.
3. Atkinson P.M., Tatnall A.R.L. Neural networks in remote sensing. International Journal of Remote Sensing. 18:(4):1997;699-709.
4. Benediktsson J.A., Sveinsson J.R. Feature extraction for multisource data classification with artificial neural networks. International Journal of Remote Sensing. 18:1997;727-740.
5. Bishop C.M. Neural Networks for Pattern Recognition. 1995;482 Oxford University Press, Oxford.
6. Bocco G., Mendoza M., Masera O. La dinámica de cambio del uso del suelo en Michoacán, Una propuesta metodológica para el estudio de los procesos de deforestación. Investigaciones Geográficas. 44:(18):2001;38.
7. Chelani A.B., Chalapati Rao C.V., Phadke K.M., Hasan M.Z. Prediction of sulphur dioxide concentration using artificial neural networks. Environmental Modelling and Software. 17:(2):2002;159-166.
8. Civco D.L. Artificial neural network for land cover classification and mapping. International Journal of Geographical Information Systems. 7:(2):1993;173-186.
9. Cortina Villar S., Macario Mendoza P., Ogneva-Himmelberger Y. Cambios en el uso del suelo y deforestación en el sur de los estados de Campeche y Quintana Roo, México. Investigaciones Geográficas. 38:1999;41-56.
10. De Brujin A.C. Spatial factors in urban growth: towards GIS models for cities in developing countries. ITC Journal. 4:1991;221-231.
11. Díaz Gallegos J.R., García Gíl G., Castillo Acosta O., March Mifsut I. Uso del suelo y transformación de selvas en un ejido de la Reserva de la Biosfera Calakmul, Campeche, México. Investigaciones Geográficas. 44:2001;39-53.
12. Fontan J. Changements globaux et développement. Nature - Sciences - Sociétés. 2:(2):1994;143-152.
13. Foody G.M. Land cover classification by an artificial neural network with ancillary information. International Journal of Geographical Information Systems. 9:(5):1995;527-542.
14. Foody G.M., Arora M.K. An evaluation of some factors affecting the accuracy of classification by an artificial neural network. International Journal of Remote Sensing. 18:(4):1997;799-810.
15. Geman S., Bienenstock E., Doursat R. Neural networks and the bias/variance dilemna. Neural Computation. 4:1992;1-58.
16. Geoghegan J., Villar S.C., Klepeis P., Mendoza P.M., Ogneva-Himmelberger Y., Chowdhury R.R., Turner B.L., Vance C. Modelling tropical deforestation in the southern Yucatan peninsular region: comparing survey and satellite data. Agriculture, Ecosystems and Environment. 85:2001;25-46.
17. Grainger A. Rates of deforestation in the humid tropics: estimates and measurements. The Geographical Journal. 159:(1):1993;33-44.
18. International Geosphere Biosphere Program (IGBP), 1993. Relating Land Use and Global Land Cover Change. In: Turner II, B.L., Moss, R.H., Skole, D.L. Report no. 24.
19. Intergovernmental Panel on Climate Changes (IPCC), 1996. Climate Change 1995, second assessment report. Cambridge University Press, Cambridge, UK.
20. Isaac-Márquez, R., 1993. Evaluación del cultivo de arroz como fuente de contaminación e impacto ambiental para la Laguna de Términos, Campeche, México. Tésis profesional, Escuela de Biología, Universidad Autónoma de Guadalajara, Mexico.
21. Jensen J.R., Qiu F., Ji M. Predictive modelling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data. Ecological Modelling. 20:(14):1999;2805-2822.
22. Kavzoglu T., Mather P.M. Using feature selection techniques to produce smaller neural networks with better generalisation capabilities. Proceedings of IGARSS 2000. vol. 7:2000;3069-3071.
23. Kim G., Barros A.P. Quantitative flood forecasting using multisensor data and neural networks. Journal of Hydrology. 246:(1-4):2001;45-62.
24. Lambin, E.F., 1994. Modelling deforestation processes, a review. EUR 15744 EN, TREES series B: Research Report No. 1. Joint Research Centre, Institute for Remote Sensing Applications; European Space Agency, Luxembourg, Office for Official Publications of the European Community, p128.
25. Lambin E.F. Modelling and monitoring land-cover change processes in tropical regions. Progress in Physical Geography. 21:1997;375-393.
26. Landgrebe D., Biehl L. An Introduction to MultiSpec. 2000;168 Purdue Research Foundation, West Lafayette.
27. Lek S., Guégan J.F. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling. 120:1999;65-73.
28. Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., Aulanier S. Application of neural networks to modelling non-linear relationships in ecology. Ecological Modelling. 90:1996;39-52.
29. Lek-Ang S., Deharveng L., Lek S. Predictive models of collembolan diversity and abundance in a riparian habitat. Ecological Modelling. 120:1999;247-260.
30. Li X., Yeh A.G.-O. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science. 16:(4):2002;323-343.
31. Maier H.R., Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling and Software. 15:(1):2000;101-124.
32. Manel S., Dias J.M., Ormerod S.J. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with Himalayan river bird. Ecological Modelling. 120:1999;337-347.
33. Mas J.F. Monitoring land-cover changes: a comparison of change detection techniques. International Journal of Remote Sensing. 20:(1):1999;139-152.
34. Mas J.F., Puig H. Modalités de la déforestation dans le Sud-ouest de l'Etat du Campeche, Mexique. Canadian Journal of Forest Research. 31:(7):2001;1280-1288.
35. Mas J.F., Sorani V., Alvarez R. Elaboración de un modelo de simulación del proceso de deforestación. Investigaciones Geográficas. 5:1996;43-57.
36. Mas J.F., Sosa-López A., Palacio J.L., Puig H. Modelling deforestation in the region of the Lagoon of Términos, South East Mexico. Proceedings of the ASPRS (American Society of Photogrammetry and Remote Sensing) Annual Convention, Washington, DC, May 22-26, 2000 :2000.
37. Mas J.F., Velázquez A., Díaz J.R., Mayorga R., Alcántara C., Castro R., Fernández T. Assessing land use/cover changes in Mexico. Proceedings of the 29th International Symposium on Remote Sensing of Environment (CD), Buenos Aires, Argentina, April 8-12, 2002 :2002.
38. Matheron G. La théorie des variables géné ralisées et ses applications. Les Cahiers du Centre de Morphologie Mathématiques de Fontainebleau, Fascicule. 5:1970.
39. Mausel P.W., Kramber W.J., Lee J.K. Optimum band selection for supervised classification of multispectral data. Photogrammetric Engineering and Remote Sensing. 56:(1):1990;55-60.
40. Mertens B., Lambin E.F. Spatial modelling of deforestation in southern Cameroon, spatial disaggregation of diverse deforestation processes. Applied Geography. 17:(2):1997;143-162.
41. Paruelo J.M., Tomasel F. Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models. Ecological Modelling. 98:1997;173-186.
42. Pijanowski B.C., Brown D.G., Shellito B.A., Manik G.A. Using neural networks and GIS to forescast land use changes: a land transformation model. Computers. Environment and Urban Systems. 26:(6):2002;553-575.
43. Pontius R.G., Cornell J.D., Hall C.A.S. Modelling the spatial pattern of land-use change with GEOMOD2: application and validation in Costa Rica. Agriculture, Ecosystems and Environment. 85:2001;191-203.
44. Puig H. Diversité spécifique et déforestation: l'exemple des forêts tropicales humides du Mexique. Bois et Forêts des tropiques. 268:(2):2000;41-55.
45. Rosenfield G.H., Fitzpatrick-Lins K. A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing. 52:(2):1986;223-227.
46. Rosin P.L., Fierens F. Improving neural network generalisation. Proceedings of IGARSS 1995. vol. 2:1995;1255-1257.
47. Sader A.S., Joyce A.T. Deforestation rates and trends in Costa Rica, 1940 to 1983. Biotropica. 20:(1):1988;11-19.
48. Schneider L.C., Pontius. Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agriculture Ecosystem and Environment. 85:(1-3):2001;83-94.
49. Shahshahani B.M., Landgrebe D.A. The effect of unlabelled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. IEEE Transactions on Geoscience and Remote Sensing. 32:(5):1994;1087-1095.
50. Soares-Filho B.S., Pennachin C.L., Cerqueira G. DINAMICA - a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecological Modelling. 154:(3):2002;217-235.
51. Stéphenne N., Lambin E.F. A dynamic simulation model of land-use changes in Sudano-sahelian countries of Africa (SALU). Agriculture, Ecosystems and Environment. 85:2001;154-161.
52. Tourenq C., Aulagnier S., Mesléard F., Durieux L., Johnson A., Gonzalez G., Lek S. Use of artificial neural networks for predicting rice crop damage by greater flamingos in the Camargues, France. Ecological Modelling. 120:1999;349-358.
53. Turner B.L., Villar S.C., Foster D., Geoghegan J., Keys E., Klepeis P., Lawrence D., Mendoza P.M., Manson S., Ogneva-Himmelberger Y., Plotkin A.B., Pérez Salicrup D., Chowdhury R.R., Savitsky B., Schneider L., Schmook B., Vance C. Deforestation in the southern Yucatan peninsular region: an integrative approach. Forest Ecology and Management. 154:2001;353-370.
54. Velázquez, A., Duran, E., Ramírez, I., Mas, J.-F., Ramírez, G., Bocco, G., Palacio, J.-L. Environmental conversion processes in highly biodiverse areas: the case of Oaxaca, Mexico. Global Environment Change, in press.
55. Velázquez A., Mas J.F., Palacio-Prieto J.L., Bocco G. Land cover mapping to obtain a current profile of deforestation in Mexico. Unasylva (FAO). 210:(53):2002;37-40.
56. Wang F. The use of artificial neural networks in a geographical information systems for agricultural land suitability assessment. Environment and Planning A. 26:1994;265-284.
57. Yang G., Collins M.J., Gong P. Multisource data integration with neural networks: optimum selection of net variables for lithologic. Proceedings of IGARSS 1996. vol. 4:1996;2068-2070.