Publicado

2017-04-01

Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects

Aplicación de redes neuronales artificiales en la modelación de la deforestación asociada a nuevos proyectos de infraestructura vial

Palabras clave:

Artificial neural networks, prediction, deforestation, roads (en)
Redes neuronales artificiales, predicción, deforestación, vías (es)

Autores/as

Tropical deforestation is an ongoing process mainly caused by the construction of new roads, which, without proper environmental planning, contribute to biodiversity loss. Given that the artificial neural networks (ANNs) have the ability to capture nonlinear relationships, they were used to predict deforestation associated with new roads, such as the “Variante Porce” road and the “El Bagre-San Jacinto del Cauca” road in the department of Antioquia. ANN Training was carried out online using the back-propagation algorithm, part of the R software. The predictive capacity was evaluated using the area under the receiver operator characteristic curve (AUC). Also, a network that showed the best predictive capacity for the deforestation surface was generated for the baseline scenario and the simulated scenario incorporating the new roads. The comparison of scenarios suggested that new roads would increase the probability of deforestation for approximately 103.729 ha of forest.
La deforestación tropical es un proceso continuo causado principalmente por la construcción de nuevas vías, las cuales sin una planificación ambiental adecuada contribuyen a la pérdida de biodiversidad. Dado que las redes neuronales artificiales (RNAs) tienen la capacidad de capturar relaciones no lineales, se utilizaron para predecir la deforestación asociada a nuevas vías, como la Variante Porce y la vía El Bagre-San Jacinto del Cauca, en el departamento de Antioquia. El entrenamiento de las RNAs se realizó en modo on line con el algoritmo de retropropagación, en el software R. La capacidad de predicción se evaluó con el área bajo la curva ROC (AUC) y con la red que presentó mejor capacidad predictiva se generó la superficie de deforestación para el escenario base y el escenario simulado incorporando las nuevas vías. La comparación de escenarios indica que las nuevas vías incrementarían la probabilidad de deforestación de aproximadamente 103.729 ha de bosque.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Savage, A., Guillen, R., Lamilla, I. and Soto, L., Developing an effective community conservation program for cotton-top tamarins (Saguinusoedipus) in Colombia. American Journal of Primatology, 72(5), pp.379-390, 2010. DOI: 10.1002/ajp.20770

Rodríguez, N., Armenteras, D. and Alumbreros, J.R., Land use and land cover change in the Colombian Andes: Dynamics and future scenarios. Journal of Land Use Science, 8(2), pp.154-174, 2013. DOI: 10.1080/1747423X.2011.650228

Geist, H. and Lambin, E., What drives tropical deforestation? A meta-analysis of proximate and underlying causes of deforestation based on subnational case study evidence, Belgium: CIACO louvain-la-Neuve.116 P., 2001.

Mallard, F. and Francois, D., Effectiveness of the legal framework for natural areas protection relative to French road projects. Land use policy, 30(1), pp.582-591, 2012. DOI: 10.1016/j.landusepol.2012.05.006

Dávalos, L.M., Bejarano, A.C., Hall, M.A., Correa, H.L., Corthals, A. and Espejo, O.J., Forests and drugs: Coca-driven deforestation in tropical biodiversity hotspots. Environmental Science and Technology, 45, pp.1219-1227, 2011. DOI: 10.1021/es102373d

Armenteras, D., Cabrera, E., Rodríguez, N. and Retana, J., National and regional determinants of tropical deforestation in Colombia. Regional Environmental Change, 13(6), pp.1181-1193, 2013. DOI: 10.1007/s10113-013-0433-7

Soares-Filho, B., Coutinho, G. and Lopes, C., DINAMICA - A stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecological Modelling. 154(3), pp.217-235, 2002. DOI: 10.1016/S0304-3800(02)00059-5

Dalla-Nora, E.L., de Aguiar, A.P., Lapola, D.M. and Woltjer, G., Why have land use change models for the Amazon failed to capture the amount of deforestation over the last decade?. Land Use Policy, 39, pp.403-411, 2014. DOI: 10.1016/j.landusepol.2014.02.004

Brey, T., Jarre-Teichmann, A. and Borlich, O., Artificial neural network versus multiple linear regression Predicting P/B ratios from empirical data. Marine Ecology Progress Series, 140(1-3), pp. 251-256, 1996. DOI: 10.3354/meps140251

Lek-Ang, S., Deharveng, L. and Lek, S., Predictive models of collembolan diversity and abundance in a riparian habitat. Ecological Modelling, 120(2–3), pp.247-260, 1999. DOI: 10.1016/S0304-3800(99)00106-4

Khoi, D.D. and Murayama, Y., Forecasting areas vulnerable to forest conversion in the Tam Dao National Park Region, Vietnam. Remote Sensing, 2(5), pp. 1249-1272, 2010. DOI: 10.3390/rs2051249

Larsen, P., Cseke, L., Miller, R. and Collart, F., Modeling forest ecosystem responses to elevated carbon dioxide and ozone using artificial neural networks. Journal of Theoretical Biology, 359, pp.61-71, 2014. DOI: 10.1016/j.jtbi.2014.05.047

Pijanowski, B., Tayyebi, A., Doucette, J., Pekin, B., Braun, D. and Plourde J., A big data urban growth simulation at a national scale: Configuring the GIS and neural network based land transformation model to run in a High Performance Computing (HPC) environment, 51, pp. 250-258, 2014.

Gobernación de Antioquia, IDEA & IGAC., Antioquia características geográficas, Bogotá, Colombia: Imprenta Nacional de Colombia, 2007.

Secretaría de Infraestructura Física. Antioquia Mapa vial. República de Colombia, Departamento de Antioquia, 2009.

Orrego, S., Economic modeling of tropical deforestation in Antioquia (Colombia), 1980-2000: An analysis at a semi-fine scale with spatially explicit data. PhD Dissertation. Oregon State University, USA, 2009.

Geist, H. and Lambin, E., What drives tropical deforestation? A meta- analysis of proximate and underlying causes of deforestation based on subnational case study evidence, Belgium. CIACO Louvain-la-Neuve, 2001.

Thies, B., Meyer, H., Nauss, T. and Bendix, J., Projecting land-use and land-cover changes in a tropical mountain forest of Southern Ecuador. Journal of Land Use Science, 9(1), pp.1-33, 2012. DOI: 10.1080/1747423X.2012.718378

R Development Core Team. R: A language and environment for statistical computing. R foundation for statistical computing, [online]. Vienna, Austria. Available at: URL http://www.R-project.org/, 2013.

R Studio, 2013. RStudio: Integrated development environment for R (Version 0.97.551) [Computer Sofware, online]. [Accessed 2013 Jan 5]. Boston, MA. Available at: http://www.rstudio.org/.

Castejón, M., Ordieres, J.B., Vergara, E.P., Martínez-de-Pisón, F.J., Pernía, A.V. and Alba, F., AMORE: AMORE flexible neural network package. R package version 0.2-12. [online]. Available at: URL http://CRAN.R-project.org/package=AMORE, 2010.

Nguyen, G.H., Bouzerdoum, A. and Phung, S., Learning pattern classification tasks with imbalanced data sets. Pattern recognition. Vukovar, Croatia: Intech, pp. 193-208, 2009.

He, H. and Garcia, E., Learning from imbalanced data. Knowledge and data engineering, IEEE, 21(9), pp.1263-1284, 2009. DOI: 10.1109/TKDE.2008.239

Li, X. and 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), pp.323-343, 2002. DOI: 10.1080/13658810210137004

Moreira, M. and Fiesler, E., Neural networks with adaptive learning rate and momentum terms. IDIAP Technical Report, No 95-04, 1995.

Hecht-Nielsen, R., Kolmogorov’s mapping neural network existence theorem. In Proceedings of IEEE First Annual International Conference on Neural Networks. pp. III-11, 1987.

Plagianakos, V.P., Magoulas, G.D. and Vrahatis, M.N., Learning rate adaptation in stochastic gradient descent. In N. Hadjisavvas and Pardalos, P., eds. Advances in Convex analysis and global optimization. nonconvex optimization and its applications, [online]. 2001. Springer US, pp. 433-444. Available at: DOI: 10.1007/978-1-4613-0279-7_27

Bengio, Y., Neural networks for speech and sequence recognition, London: International Thomson Computer Press, 1996.

Haykin, S., Neural networks, A comprensive foundation 2a ed., Hamilton, Canada: Prentice- Hall International, Inc, 1999.

Freeman, E. and Moisen, G., A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecological Modelling, 217(1–2), pp.48-58, 2008. DOI: 10.1016/j.ecolmodel.2008.05.015

Fawcett, T., An introduction to ROC analysis. Pattern Recognition Letters. 27(8), pp.861-874, 2006. DOI: 10.1016/j.patrec.2005.10.010

Pontius Jr, P. and Batchu, K., Using the relative operating characteristic to quantify certainty in prediction of location of land cover change in India. Transactions in GIS, (4), pp. 467-484, 2003. DOI: 10.1111/1467-9671.00159

Sing, T., Sander, O., Beerenwinkel, N. and Lengauer, T., ROCR: Visualizing the performance of scoring classifiers. R package version 1.0-5, [online]. 2013. Available at: URL http://rocr.bioinf.mpi-sb.mpg.de.

Nelson, G. and Hellerstein, D., Do roads cause deforestation? Using satellite images in econometric analysis of land use. American Journal of Agricultural Economics, 79(1), pp.80-88, 1997. DOI: 10.2307/1243944

Barber, C., Cochrane, M.A, Souza Jr, C.M. and Laurance, W., Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biological Conservation, 177, pp.203-209, 2014. DOI: 10.1016/j.biocon.2014.07.004

Günther, F. and Fritsch, S., Neuralnet: Training of neural networks. The R Journal, 2(1), pp.2073-4859, 2010.

Müller, D. and Mburu, J., Forecasting hotspots of forest clearing in Kakamega Forest, western Kenya. Forest Ecology and Management, 257, pp.968-977, 2009. DOI: 10.1016/j.foreco.2008.10.032

Okwuashi, O., Isong, M., Eyo, E., Eyoh, A., Nwanekezie, O., OlayinKa, D., Udoudo, D. and Ofem, B., GIS cellular automata using artificial neural network for land use change simulation of Lagos, Nigeria. Journal of Geography and Geology, 4(2), pp.94-101, 2012. DOI: 10.5539/jgg.v4n2p94

Wilson, K., Newton, A., Echeverría, C., Wetson, C. and Burgman, M., A vulnerability analysis of the temperate forests of south central Chile. Biological Conservation, 122(1), pp.9-21, 2005. DOI: 10.1016/j.biocon.2004.06.015

Mertens, B. and Lambin, E., 1997. Spatial modelling of deforestation in southern Cameroon. Applied Geography, 17, pp.143-162, 1997. DOI: 10.1016/S0143-6228(97)00032-5

Freitas, S.R., Hawbaker, T.J. and Metzger, J.P., Effects of roads, topography, and land use on forest cover dynamics in the Brazilian Atlantic Forest. Forest Ecology and Management, 259(3), pp.410-417, 2010. DOI: 10.1016/j.foreco.2009.10.036

Huston, M., The coexistence of species on changing landscapes, Cambridge, U.K: Cambridge University Press, 1994.