Modeling susceptibility to deforestation of remaining ecosystems in North Central Mexico with logistic regression

Abstract

Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale. The susceptibility to deforestation of remaining forest ecosystems (shrubland, temperate forest and rainforest) was conducted in the state of San Luis Potosi, located in north central Mexico. Spatial analysis techniques were used to detect the deforested areas in the study area during 1993–2007. Logistic regression was used to relate explanatory variables (such as social, investment, forest production, biophysical and proximity factors) with susceptibility to deforestation to construct predictive models with two focuses: general and by biogeographical zone. In all models, deforestation has positive correlation with distance to rainfed agriculture, and negative correlation with slope, distance to roads and distance to towns. Other variables were significant in some cases, but in others they had dual relationships, which varied in each biogeographical zone. The results show that the remaining rainforest of Huasteca region is highly susceptible to deforestation. Both approaches show that more than 70% of the current rainforest area has high and very high levels of susceptibility to deforestation. The values represent a serious concern with global warming whether tree carbon is released to atmosphere. However, after some considerations, encouraging forest environmental services appears to be the best alternative to achieve sustainable forest management.

Keywords

GIS / land use change / proximity factors / statistical modeling / ROC curve / regional forest planning

References

[1] Alix-Garcia J.. A spatial analysis of common property deforestation. Journal of Environmental Economics and Management, 2007, 53: 141-157.

[2] Arcand J.L., Guillaumont P., Jeanneney S.G.. Deforestation and the real exchange rate. Journal of Development Economics, 2008, 86: 242-262.

[3] Averna-Valente R.O., Vettorazzi C.A.. Definition of priority areas for forest conservation through the ordered weighted averaging method. Forest Ecology and Management, 2008, 256: 1408-1417.

[4] Bailis R., Berrueta V., Chengappa C., Dutta K., Edwards R., Masera O., Still D., Smith K.R.. Performance testing for monitoring improved biomass stove interventions: experiences of the Household energy and health project. Energy for Sustainable Development, 2007, 11(2): 57-70.

[5] Barrett K., Kasischke E.S., McGuire A.D., Turetsky M.R., Kane E.S.. Modeling fire severity in black spruce stands in the Alaskan boreal forest using spectral and non-spectral geospatial data. Remote Sensing of Environment, 2010, 114: 1494-1503.

[6] Beguería S.. Changes in land cover and shallow landslide activity: A case study in the Spanish Pyrenees. Geomorphology, 2006, 74: 196-206.

[7] Bhattarai K., Conway D., Yousef M.. Determinants of deforestation in Nepal’s Central Development Region. Journal of Environmental Management, 2009, 91: 471-488.

[8] Boletta P.E., Ravelo A.C., Planchuelo A.M., Grilli M.. Assessing deforestation in the Argentine Chaco. Forest Ecology and Management, 2006, 228: 108-114.

[9] Braimoh A.K., Onishi T.. Geostatistical techniques for incorporating spatial correlation into land use change models. International Journal of Applied Earth Observation and Geoinformation, 2007, 9: 438-446.

[10] Calvo-Alvarado J., McLennan B., Sánchez-Azofeita A., Garvin T.. Deforestation and forest restoration in Guanacaste, Costa Rica: Putting conservation policies in context. Forest Ecology and Management, 2009, 258: 931-940.

[11] Chai S.L., Tanner E., McLaren K.. High rates of forest clearance and fragmentation pre- and post-National Park establishment: The case of a Jamaican montane rainforest. Biological Conservation, 2009, 142: 2484-2492.

[12] Chapa-Bezanilla D., Sosa-Ramírez J., de Alba-Ávila A.. Multitemporal study on forest fragmentation in sierra Fria, Aguascalientes, Mexico. Madera y Bosques, 2008, 14(1): 37-51.

[13] Chowdhury R.R.. Landscape change in the Calakmul Biosphere Reserve, Mexico: Modeling the driving forces of smallholder deforestation in land parcels. Applied Geography, 2006, 26: 129-152.

[14] Culas R.J.. Deforestation and the environmental Kuznets curve: An institutional perspective. Ecological Economics, 2007, 61: 429-437.

[15] Cronkleton P., Barton D., Medina G.. Community forest management and the emergence of multiscale-governance institutions: lessons for REDD+ development from Mexico, Brazil and Bolivia. Forests, 2011, 2: 451-473.

[16] Dendoncker N., Rounsevell M., Bogaert P.. Spatial analysis and modelling of land use distributions in Belgium. Computers, Environment and Urban Systems, 2007, 31: 188-205.

[17] Deng X., Jiang Q., Zhan J., He S., Lin Y.. Simulation on the dynamics of forest area changes in Northeast China. Journal of Geographical Sciences, 2010, 20(4): 495-509.

[18] Echeverria C., Coomes D.A., Hall M., Newton A.C.. Spatially explicit models to analyze forest loss and fragmentation between 1976 and 2020 in southern Chile. Ecological Modelling, 2008, 212: 439-449.

[19] FAO. 2010. Global Forest Resource Assessment 2010. FAO Forestry Paper 163. p.378. Available at www.fao.org.forestry/fra2010.

[20] Flamenco-Sandoval A., Ramos M.M., Masera O.M.. Assessing implications of land-use and land-cover change dynamics for conservation of a highly diverse tropical rain forest. Biological Conservation, 2007, 138: 131-145.

[21] Freitas S.R., Hawbaker T.J., Metzger J.P.. Effects of roads, topography, and land use on forest cover dynamics in the Brazilian Atlantic Forest. Forest Ecology and Management, 2010, 259: 410-417.

[22] Fuller D.O., Meijaard E.M., Christy L., Jessup T.C.. Spatial assessment of threats to biodiversity within East Kalimantan, Indonesia. Applied Geography, 2010, 30(3): 416-425.

[23] García M., Riaño D., Chuvieco E., Danson F.M.. Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sensing of Environment, 2010, 114: 816-830.

[24] Gönen M.. Analyzing receiver operating characteristic curves with SAS. 2007, Cary, NC: SAS Institute Inc, 134.

[25] Hamel L.. Model assessment with ROC curves. The Encyclopedia of Data Warehousing and Mining, 2008 2nd Edition Pennsylvania: Idea Group Publishers, 16.

[26] Hu Z., Lo C.P.. Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 2007, 31: 667-688.

[27] Legendre P.. Spatial autocorrelation: trouble or new paradigm?. Ecology, 1993, 74(6): 1659-1673.

[28] Long H., Wu X., Wang W., Dong G.. Analysis of urban-rural land-use change during 1995–2006 and its policy dimensional driving forces in Chongqing, China. Sensors, 2008, 8: 681-699.

[29] Mallinis G., Koustias N.. Spectral and spatial based classification for broad scale land cover mapping based on logistic regression. Sensors, 2008, 8: 8067-8085.

[30] Martínez M.L., Pérez-Maqueo O., Vázquez G., Castillo-Campos G., García-Franco J., Mehltreter K., Equihua M., Landgrave R.. Effects of land use change on biodiversity and ecosystem services in tropical montane cloud forests of Mexico. Forest Ecology and Management, 2009, 258: 1856-1863.

[31] Návar J.. The spatial distribution of aboveground biomass in tropical forests of Mexico. Tropical and Subtropical Agroecosystems, 2011, 13: 149-158.

[32] Pineda-Jaimes N.B., Bosque-Sendra J., Gómez-Delgado M., Franco P.R.. Exploring the driving forces behind deforestation in the state of Mexico (Mexico) using geographically weighted regression. Applied Geography, 2010, 30(4): 1-16.

[33] Pineda-Jaimes N.B., Bosque-Sendra J., Gómez-Delgado M., Plata-Rocha W.. Analysis of land use changes in the State of Mexico using regression analysis and GIS: an approach to the deforestation processes. Investigaciones Geográficas Boletín del Instituto de Geografía UNAM, 2008, 69: 33-52.

[34] Pompa-García M., Antonio-Némiga X., Carrasco-Mejorado J.A., Mendoza-Briseño M.A.. Spatial patterns of soil degradation in Mexico. African Journal of Agricultural Research, 2011, 6(5): 1109-1113.

[35] Reyes-Hernández H., Aguilar-Robledo M., Aguirre-Rivera J.R., Fortanelli-Martínez J.. Spatial configuration of land use/land cover in the Pujal Coy Project Area, Huasteca Potosina Region, Mexico. AMBIO, 2008, 37(5): 381-389.

[36] Romieu I., Riojas-Rodríguez H., Marrón-Mares A.T., Schilmann A., Pérez-Padilla R., Masera O.. Improved biomass stove intervention in rural México. Impact on the respiratory Health of Women. American Journal of Respiratory and Clinical Care Medicine, 2009, 180: 649-656.

[37] Rutherford G.N., Bebi P., Edwards P.J., Zimmermann N.E.. Assessing land-use statistics to model land cover change in a mountainous landscape in the European Alps. Ecological Modelling, 2008, 212: 460-471.

[38] Rzedowski J.. El extremo boreal del bosque tropical siempre verde en norteamerica continental. Plant Ecology, 1963, 11(4): 173-198.

[39] SAS Institute Inc. SAS/STAT 9.1 User’s Guide. 2004, Cary, NC, USA: SAS Institute Inc., 5121.

[40] Sifuentes-Amaya R., Ramírez-Valverde G.. Effects of specifying an incorrect model for logistic regression, with two independent correlated variables. Agrociencia, 2010, 44: 197-207.

[41] Velázquez A., Mas J.F., Palacio-Prieto J.L., Bocco G.. Land cover mapping to obtain a current profile of deforestation in Mexico. Unasylva, 2002, 53: 37-40.