Abstract
The frequent use of predictive models for analyzing of complex, natural or artificial phenomena is changing the traditional approaches to environmental and hazard problems. The continuous improvement of computer performance allows for more detailed numerical methods, based on space-time discretisation, to be developed and run for a predictive modelling of complex real systems, reproducing the way their spatial patterns evolve and pointing out the degree of simulation accuracy. In this contribution we present an application of several methods (Geomatics, Neural Networks, Land Cover Modeler and Dinamica EGO) in the tropical training area of Peten, Guatemala. During the last few decades this region, included in the Biosphere Maya reserve, has seen a fast demographic raise and a subsequent uncontrolled pressure on its own geo-resources. The test area can be divided into several sub-regions characterized by different land use dynamics. Understanding and quantifying these differences permits a better approximation of a real system; moreover we have to consider all the physical, socio-economic parameters, which will be of use for representing the complex and sometimes random human impact. Because of the absence of detailed data from our test area, nearly all the information was derived from the image processing of 11 ETM+, TM and SPOT scenes; we studied the past environmental dynamics and we built the input layers for the predictive models. The data from 1998 and 2000 were used during the calibration to simulate the land cover changes in 2003, selected as reference date for the validation. The basic statistics permit to highlight the qualities or the weaknesses for each model on the different sub-regions.
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References
Arnauld MC, Ponciano EM, Breuil-Martínez V (2000) Segunda temporada de campo en el sitio arqueológico de la Joyanca y su región, Informe 2, pp 291-315
Arrow KJ (1974) Les limites de l’organisation. Trad PUF, Paris
Atkinson PM (2004) Resolution Manipulation and Sub-Pixel Mapping. Remote Sensing Image Analysis, Springer, pp 50-70
Atkinson PM, Lewis P (2000) Geostatistical classification for remote sensing: an introduction. Computer & Geosciences 26, pp 361-371
Bonham-Carter GF (1994) Geographic Information system for GeoscientistsVolume 13: Computer Methods in the Geosciences, PERGAMON
Bruno R, Follador M, Paegelow M, Renno F, Villa N (2006) Integrating Remote Sensing, GIS and Prediction Models to Monitor the Deforestation and Erosion in Peten Reserve, Guatemala. Proceedings of IAMG’06, S09-12, Liége
Chica-Olmo M, Abarca-Hernández F (2000) Computing geostatistical image texture for remotely sensed data classification. Computer & Geosciences 26, pp 373-383
Chiles JP, Delfiner P (1999) Geostatistics. Modelling Spatial Uncertainty. Wiley, Serie in Probability and Statistics
Chung CF, Fabbri AG, Chi KH (2002) A strategy for sustainable development of nonrenewable resources using spatial prediction models. Geoenvironmental Deposit Models for Resources Explotation and Environmental Security, Dordrecht, Kluwer Academic publishers
Coquillard P, Hill DRC (1997) Modélisation et Simulation d’Ecosystemes. MASSON, Paris Milan Barcelone
De Castro FVF, Soares-Filho BS, Mendoza E (2007) Modelagem de cenarios de mudanças na região de Brasiléia aplicada ao Zoneamento Ecologico Economico do estado do Acre. Anais XIII Simposio Brasileiro de Sensoriamento Remoto, INPE, pp 5135-5142
De la Cruz JR (1982) Clasificación de zonas de vida de Guatemala a nivel de reconocimiento. Ministerio de Agricultura, Ganaderia y Alimentacíon y Instituto National Forestal. Guatemala: mimeo
Eastman JR (2001) Idrisi32 release 2 Tutorial. Clark Labs, Worcester, MA
Eastman JR (2006) Idrisi Andes Tutorial. Clark Labs, Worcester, MA
Effantin-Touyer R (2006) De la frontiére agraire á la frontiére de la nature. Thése Ecole Doctorale A.B.I.E.S, Paris
Follador M, Renno F (2006) Sustainable Planning of Non-renewable Resources using Remote Sensing and GIS Analysis. Proceeding of International Symposium Interaction Nature-Société, analyse et modéles ‘06, la Baule
Foody GM (2005) Sub-Pixel Methods in Remote Sensing. Remote Sensing Image Analysis, Springer, pp 37- 49
Geoghegan J, Villar SC, Klepeis P, Mendoza PM, Ogneva-Himmelberger Y, Chowdhury RR, Turner BL, Vance C (2001) Modeling tropical deforestation in the southern Yucatan peninsular region: comparing survey and satellite data. Agriculture Ecosystems & Environment 85, pp 25-46
Hauglustaine D, Jouzel J, Le Treut H (2005) Climat: chronique d’un bouleversement annoncé. Le Pommier, Paris
Hengl T (2006) Finding the right pixel size. Computer & Geosciences 32, pp 1283-1298
Joshi C, De Leeuw J, Skidmore AK, van Duren IC, van Oosten H (2006) Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods. International Journal of Applied Earth Observation and Geoinformation 8, pp 84-95
Kavouras M (2001) Understanding and Modeling Spatial Change. Life and Motion of socio-economic Units, Chapter 4 draft version, GISDATA series 8, Taylor & Francis, London
La Moigne JL (1994) La Theorie du Systeme General. PUF, Paris
Lee VCS, Wong HT (2007) A multivariate neuro-fuzzy system for foreign currency risk management decision making. Neurocomputing 70, pp 942-951
Matheron G (1978) Estimer et choisir. Cahiers du centre de Morphologie Mathématique de Fontainebleau, Fasc.7, Ecole de Mines de Paris
Matheron G (1989) Estimating and choosing – An Essay on Probability in Pratice. Springer Berlin
Moore ID, Turner AK, Wilson JP, Jeson SK, Band LE (1993) GIS and Land-Surface-Subsurface process Modeling. Environmental Modeling with GIS, OXFORD New York, pp 196-230
Pontius RGJ (2002) Statistical Methodc to Partition Effects of Quantity and Location during Comparison of Categorical Maps at Multiple Resolutions. Photogrammetric Engineering & Remote Sensing 10, pp 1041-1049
Pontius RGJ, Pacheco P (2004) Calibration and validation of a model of forest disturbance in the Western Ghats, India 1920–1990. GeoJournal 61, pp 325-334
Pontius RGJ, Chen H (2006) Land Use and Cover Change Modelling, Land Change Modeling with GEOMOD, Idrisi Andes Tutorial, Clark University
Paegelow M, Camacho MT (2005) Possibilities and limits of prospective GIS land cover modelling – a compared case study: Garrotex (France) and Alta Alpujarra Granadina (Spain). International Journal of Geographical Information Sciences 19, No.6, pp 697-722
Rodrigues HO, Soares-Filho BS, de Souza Costa WL (2007) Dinamica EGO, uma plataforma para modelagem de sistemas ambientais. Anais XIII Simposio Brasileiro de Sensoriamento Remoto, INPE, pp 3089-3096
Saaty TL (1977) A Scaling Method for Priorities in Hierarchical Structures. J. Math. Psychology 15, pp 234-28
Villa N, Paegelow M, Camacho MT, Cornez L, Ferraty F, Ferré L, Sarda P (2007) Various approaches for predicting land cover in mountain areas. Communications in Statistics – Simulation and Computation 36, pp 73-86
Venables WN, Ripley BD (1999) Modern Applied Statistics with S-plus, third edition. Springer New York
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Follador, M., Villa, N., Paegelow, M., Renno, F., Bruno, R. (2008). Tropical deforestation modelling: comparative analysis of different predictive approaches. The case study of Peten, Guatemala. In: Paegelow, M., Olmedo, M.T.C. (eds) Modelling Environmental Dynamics. Environmental Science and Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68498-5_3
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DOI: https://doi.org/10.1007/978-3-540-68498-5_3
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