Abstract
Land use and land cover change (LUCC) models are increasingly being used to anticipate the future of territories, particularly through the prospective scenario method. In the case of so-called trend or Business-as-Usual (BAU) scenarios, the aim is to observe the current dynamics and to extend them into the future. However, as they are implemented as baseline simulation in most current software packages, BAU scenarios are calibrated from a training period built from only two dates. We argue that this limits the quantitative estimation of future change intensity, and we illustrate it from a simple model of deforestation in Northern Ecuadorian Amazon using the Land Change Modeler (LCM) software package. This paper proposes a contribution to improve BAU scenarios calibration by mainly two enhancements: taking into account a longer calibration period for estimating change quantities and the integration of thematic data in change probabilities matrices. We thus demonstrate the need to exceed the linear construction of BAU scenarios as well as the need to integrate thematic and particularly socio-demographic data into the estimation of future quantities of change. The spatial aspects of our quantitative adjustments are discussed and tend to show that improvements in the quantitative aspects should not be dissociated from an improvement in the spatial allocation of changes, which may lead to a decrease in the predictive accuracy of the simulations.
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References
Armenteras D, Espelta JM, Rodríguez N, Retana J (2017) Deforestation dynamics and drivers in different forest types in Latin America: three decades of studies (1980–2010). Glob Environ Change 46:139–147. https://doi.org/10.1016/j.gloenvcha.2017.09.002
Baynard CW, Ellis JM, Davis H (2013) Roads, petroleum and accessibility: the case of eastern Ecuador. GeoJournal 78:675–695. https://doi.org/10.1007/s10708-012-9459-5
Bilsborrow RE, Barbieri AF, Pan W (2004) Changes in population and land use over time in the Ecuadorian Amazon. Acta Amaz 34:635–647. https://doi.org/10.1590/S0044-59672004000400015
Bromley R (1981) The colonization of humid tropical areas in Ecuador. Singap J Trop Geogr 2:15–26. https://doi.org/10.1111/j.1467-9493.1981.tb00114.x
Brown LA, Sierra R, Southgate D, Labao L (1992) Complementary perspectives as a means of understanding regional change: frontier settlement in the Ecuador Amazon. Environ Plan A 24:939–961. https://doi.org/10.1068/a240939
Camacho Olmedo MT, Mas JF (2018) Markov Chain. In: Camacho Olmedo MT, Paegelow M, Mas J-F, Escobar F (eds) Geomatic approaches for modeling land change scenarios. Springer International Publishing, Cham, pp 441–445
Camacho Olmedo MT, Paegelow M, Mas JF, Escobar F (2018) Geomatic approaches for modeling land change scenarios. An introduction. In: Camacho Olmedo MT, Paegelow M, Mas J-F, Escobar F (eds) Geomatic approaches for modeling land change scenarios. Springer International Publishing, Cham, pp 1–8
Carr DL (2003) Proximate population factors and deforestation in tropical agricultural frontiers. Popul Environ 25:585–612. https://doi.org/10.1023/B:POEN.0000039066.05666.8d
Castella J-C, Verburg PH (2007) Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam. Ecol Model 202:410–420. https://doi.org/10.1016/j.ecolmodel.2006.11.011
Chen H, Pontius RG (2010) Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable. Landsc Ecol 25:1319–1331. https://doi.org/10.1007/s10980-010-9519-5
Chhabra A, Geist H, Houghton RA, Haberl H, Braimoh AK, Vlek PLG, Patz J, Xu J, Ramankutty N, Coomes O, Lambin EF (2006) Multiple impacts of land-use/cover change. In: Lambin EF, Geist H (eds) Land-use and land-cover change. Springer, Berlin, Heidelberg, pp 71–116
Ching W-K, Huang X, Ng MK, Siu T-K (2013) Higher-order Markov chains. Markov chains. Springer US, Boston, MA, pp 141–176
Comber A, Balzter H, Cole B, Fisher P, Johnson S, Ogutu B (2016) Methods to quantify regional differences in land cover change. Remote Sens 8:176. https://doi.org/10.3390/rs8030176
De Grande P (2016) El formato Redatam/The Redatam format. Estudios Demográficos y Urbanos 31:811. https://doi.org/10.24201/edu.v31i3.15
Eastman J (2014) TerrSet geospatial monitoring and modeling system. Clark University, Worcester, MA
Eastman JR, Toledano J (2018) A short presentation of the land change modeler (LCM). In: Camacho Olmedo MT, Paegelow M, Mas J-F, Escobar F (eds) Geomatic approaches for modeling land change scenarios. Springer International Publishing, Cham, pp 499–505
Escobar F, van Delden H, Hewitt R (2018) LUCC scenarios. In: Camacho Olmedo MT, Paegelow M, Mas J-F, Escobar F (eds) Geomatic approaches for modeling land change scenarios. Springer International Publishing, Cham, pp 81–97
Floriani LD, Magillo P (2009) Triangulated irregular network. In: Liu L, Özsu TT (eds) Encyclopedia of database systems. Springer US, Boston, MA, pp 3178–3179
Garcia Alvarez D (2018) Aproximación al Estudio de la Incertidumbre en la Modelización del Cambio de Usos y Coberturas del Suelo (LUCC). Universidad de Granada
Geist H, McConnell W, Lambin EF, Moran E, Alves D, Rudel T (2006) Causes and trajectories of land-use/cover change. In: Lambin EF, Geist H (eds) Land-use and land-cover change. Springer, Berlin, Heidelberg, pp 41–70
Hiraoka M, Yamamoto S (1980) Agricultural development in the Upper Amazon of Ecuador. Geogr Rev 70:423. https://doi.org/10.2307/214077
Houet T, Aguejdad R, Doukari O, Battaia G, Clarke K (2016) Description and validation of a “non path-dependent” model for projecting contrasting urban growth futures. Cybergeo. https://doi.org/10.4000/cybergeo.27397
Houet T, Gourmelon F (2014) La géoprospective – Apport de la dimension spatiale aux démarches prospectives. Cybergeo. https://doi.org/10.4000/cybergeo.26194
Houssou L (2016) Simulation sociale à base d’agents du comportement microéconomique des ménages en Amazonie équatorienne, face aux contaminations pétrolières, aux dynamiques économiques et aux politiques publiques. MSc thesis, Université nationale du Vietnam, Institut francophone international, Hanoi, Vietnam
Jarrín-V. PS, Tapia Carrillo L, Zamora G (2017) Demografía y transformación territorial: medio siglo de cambio en la región amazónica de Ecuador/Demography and territorial transformation: half a century of change in the Amazonian Region of Ecuador. Eutopía, Revista de Desarrollo Económico Territorial 81. https://doi.org/10.17141/eutopia.12.2017.2913
Juteau-Martineau G, Becerra S, Maurice L (2014) Ambiente, petróleo y vulnerabilidad política en el Oriente Ecuatoriano: ¿hacia nuevas formas de gobernanza energética? América Latina Hoy 67:119. https://doi.org/10.14201/alh201467119137
Kitchin R (2013) Big data and human geography: opportunities, challenges and risks. Dialogues Hum Geogr 3:262–267. https://doi.org/10.1177/2043820613513388
Lambin EF, Turner BL, Geist HJ, Agbola SB, Angelsen A, Bruce JW, Coomes OT, Dirzo R, Fischer G, Folke C, George PS, Homewood K, Imbernon J, Leemans R, Li X, Moran EF, Mortimore M, Ramakrishnan PS, Richards JF, Skånes H, Steffen W, Stone GD, Svedin U, Veldkamp TA, Vogel C, Xu J (2001) The causes of land-use and land-cover change: moving beyond the myths. Glob Environ Change 11:261–269. https://doi.org/10.1016/S0959-3780(01)00007-3
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28:823–870. https://doi.org/10.1080/01431160600746456
Maestripieri N, Paegelow M (2013) Validation spatiale de deux modèles de simulation: l’exemple des plantations industrielles au Chili. Cybergeo. https://doi.org/10.4000/cybergeo.26042
Mahmood R, Pielke RA, Hubbard KG, Niyogi D, Dirmeyer PA, McAlpine C, Carleton AM, Hale R, Gameda S, Beltrán-Przekurat A, Baker B, McNider R, Legates DR, Shepherd M, Du J, Blanken PD, Frauenfeld OW, Nair US, Fall S (2014) Land cover changes and their biogeophysical effects on climate: land cover changes and their biogeophysical effects on climate. Int J Climatol 34:929–953. https://doi.org/10.1002/joc.3736
Martin D, Bracken I (1993) The integration of socioeconomic and physical resource data for applied land management information systems. Appl Geogr 13:45–53. https://doi.org/10.1016/0143-6228(93)90079-G
Mas J (2004) Modelling deforestation using GIS and artificial neural networks. Environ Model Softw 19:461–471. https://doi.org/10.1016/S1364-8152(03)00161-0
Mas J-F (1999) Monitoring land-cover changes: a comparison of change detection techniques. Int J Remote Sens 20:139–152. https://doi.org/10.1080/014311699213659
Mas J-F, Kolb M, Paegelow M, Camacho Olmedo MT, Houet T (2014) Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ Model Softw 51:94–111. https://doi.org/10.1016/j.envsoft.2013.09.010
Mas JF, Paegelow M, Camacho Olmedo MT (2018) LUCC modeling approaches to calibration. In: Camacho Olmedo MT, Paegelow M, Mas J-F, Escobar F (eds) Geomatic approaches for modeling land change scenarios. Springer International Publishing, Cham, pp 11–25
Mas J-F, Soares Filho B, Pontius R, Farfán Gutiérrez M, Rodrigues H (2013) A suite of tools for ROC analysis of spatial models. ISPRS Int J Geo-Inf 2:869–887. https://doi.org/10.3390/ijgi2030869
Matthews RB, Gilbert NG, Roach A, Polhill JG, Gotts NM (2007) Agent-based land-use models: a review of applications. Landsc Ecol 22:1447–1459
Mena CF, Bilsborrow RE, McClain ME (2006) Socioeconomic drivers of deforestation in the Northern Ecuadorian Amazon. Environ Manage 37:802–815. https://doi.org/10.1007/s00267-003-0230-z
Morin L (2015) Diagnostic agraire d’un front pionnier en Amazonie équatorienne, Paroisse de Dayuma, province d’Orellana, Equateur. MSc thesis, SupAgro Montpellier IRC
National Research Council (1998) People and pixels: linking remote sensing and social science. National Academies Press, Washington, D.C.
Oliver TH, Morecroft MD (2014) Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities: interactions between climate change and land use change. Wiley Interdiscip Rev Clim Change 5:317–335. https://doi.org/10.1002/wcc.271
Orme CDL, Davies RG, Burgess M, Eigenbrod F, Pickup N, Olson VA, Webster AJ, Ding T-S, Rasmussen PC, Ridgely RS, Stattersfield AJ, Bennett PM, Blackburn TM, Gaston KJ, Owens IPF (2005) Global hotspots of species richness are not congruent with endemism or threat. Nature 436:1016–1019. https://doi.org/10.1038/nature03850
Paegelow M (2018) Impact and integration of multiple training dates for Markov based land change modeling. In: Camacho Olmedo MT, Paegelow M, Mas J-F, Escobar F (eds) Geomatic approaches for modeling land change scenarios. Springer International Publishing, Cham, pp 121–138
Paegelow M, Camacho Olmedo MT (2010) Modelos de simulación espacio-temporal y Teledetección: el método de la segmentación para la cartografía cronológica de usos del suelo. Serie Geográfica – Universidad de Alcalá, pp 19–34
Paegelow M, Camacho Olmedo MT, Mas J-F, Houet T (2014) Benchmarking of LUCC modelling tools by various validation techniques and error analysis. Cybergeo. https://doi.org/10.4000/cybergeo.26610
Parker DC, Manson SM, Janssen MA, Hoffmann MJ, Deadman P (2003) Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann Assoc Am Geogr 93:314–337. https://doi.org/10.1111/1467-8306.9302004
Perz SG, Walker RT (2002) Household life cycles and secondary forest cover among small farm colonists in the Amazon. World Dev 30:1009–1027
Pontius GR (2000) Quantification error versus location error in comparison of categorical maps. Photogramm Eng Remote Sens 66:1011–1016
Pontius RG, Huffaker D, Denman K (2004) Useful techniques of validation for spatially explicit land-change models. Ecol Model 179:445–461. https://doi.org/10.1016/j.ecolmodel.2004.05.010
Pontius RG, Millones M (2011) Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens 32:4407–4429. https://doi.org/10.1080/01431161.2011.552923
Pontius RG, Schneider LC (2001) Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85:239–248. https://doi.org/10.1016/S0167-8809(01)00187-6
Preston SH (1996) The effect of population growth on environmental quality. Popul Res Policy Rev 15:95–108. https://doi.org/10.1007/BF00126129
Rodrigues H, Soares-Filho B (2018) A short presentation of Dinamica EGO. In: Camacho Olmedo MT, Paegelow M, Mas J-F, Escobar F (eds) Geomatic approaches for modeling land change scenarios. Springer International Publishing, Cham, pp 493–498
Soares-Filho BS, Coutinho Cerqueira G, Lopes Pennachin C (2002) Dinamica—a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecol Model 154:217–235. https://doi.org/10.1016/S0304-3800(02)00059-5
Tso B, Mather P (2009) Classification methods for remotely sensed data, 2nd edn. CRC Press
Veldkamp A, Lambin E (2001) Predicting land-use change. Agric Ecosyst Environ 85:1–6. https://doi.org/10.1016/S0167-8809(01)00199-2
Wasserstrom R, Southgate D (2013) Deforestation, Agrarian reform and oil development in Ecuador, 1964–1994. Nat Resour 04:31–44. https://doi.org/10.4236/nr.2013.41004
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Mejean, R., Paegelow, M., Saqalli, M., Kaced, D. (2020). Improving Business-as-Usual Scenarios in Land Change Modelling by Extending the Calibration Period and Integrating Demographic Data. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D., Mansourian, A. (eds) Geospatial Technologies for Local and Regional Development. AGILE 2019. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-14745-7_14
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