APPENDIX S5 – Sierra San Miguelito: Land-cover classification and land use change assessment at the regional scale

We used GIS (Geographical Information Systems) to assess plant cover with the processing of spectral images. With this technique of remote sensing we generated thematic maps that allowed us to identify land use type as well as cover changes between 1979, 1989, 1999 and 2009. To generate these maps we used a combination of supervised and unsupervised Landsat satellite images from 1979, 1989, 1999 y 2009.

The Landsat images were obtained from the USGS (United States Geological Survey) [1]. To analyze the images we used GRASS-GIS (Geographic Resources Analysis Support System, v6.4.2 RC3) software; for the unsupervised classification we implemented the MLC (Maximum Likelihood Classifier) algorithm, and for the supervised classification we used SMAP (Sequential Maximum A Posteriori Classifier) algorithm [2, 3]. Steps for the unsupervised classification included: wave grouping in the Landsat image per decade, cluster formation for spatial statistics using 20 classes (pixel signatures) and cluster classification with the MLC algorithm. Steps for the supervised classification included wave grouping in the Landsat image per decade, generation of training areas, cluster formation with 8 plant cover types, and cluster classification with the SMAP algorithm.

To estimate land cover changes within the study site, the area was estimated for each land use type, for each of the four years. We used altitude isoclines (2150 m a.s.l. to the E and 1900 m a.s.l. to the S and N) to delimit the study site, using the elevation digital model for the site (USGS, 2011, figure S5.1).

Scenario - We simulated how loss of soil depth (key slow variable) (from 30 to 20 cm) observed in the Amapola landscape as a consequence of forest to grassland conversion, may have triggered regional changes in dryland hydrological function in the Sierra San Miguelito [4].

Biogeochemical model description - We parameterized the biogeochemical model BIOME-BGC [5], which is a mechanistic model to simulate pools and fluxes of C, N and water in vegetation and soils of terrestrial ecosystems [5, 6]. Values for the parameters were derived from previous studies in the area, including forest productivity [7], forest C and N pools [8, 9] and forest hydrology [10, 11]. The model was run over satellite images from Sierra San Miguelito, SLP, Mexico (figure S5.1). We ran the model for a 30-year period (1979 to 2009) to simulate water fluxes in the form of total evapotranspiration (ET) and runoff by type of plant vegetation cover. This simulation was run under current soil conditions of 30 cm average soil depth (figure S5.2). The output consisted of decadal (1979, 1989, 1999 and 2009) averages of ET and runoff by plant cover type. A second run was applied to examine scenarios of soil erosion including a 10 cm soil loss (figure S5.3) and compared to current values. Also, interannual variability of air temperature and precipitation for the 30-year simulation [12] is presented in figure S5.2. Outputs of the model were superimposed on thematic maps including the most characteristic land cover types, so estimates of hydrological responses could be made at the regional scale (figure S5.3).

Figure S5.1: Land cover/use change in the Sierra San Miguelito, San Luis Potosi, Mexico between 1979, 1989, 1999 and 2009 using false color satellite images.

Figure S5.2: Annual evapotranspiration (top) and outflow (runoff) (bottom) in relation to annual precipitation and mean annual temperature between 1979 and 2009 for three land use types in Sierra San Miguelito, San Luis Potosi, Mexico.


Figure S5.3: Outputs of Biome-BGC for total evapotranspiration (top) and surface runoff (bottom) for three land use types (grassland-yellow, chaparral-light green, forest-dark green) in the geographic region of Sierra San Miguelito, between 1979 and 2009. Simulations are presented for soil erosion scenarios with 30 cm soil depth (left side) and 20 cm soil depth (right side).

REFERENCES APPENDIX S5

[1] USGS (United States Geological Survey), 2011. U.S. Department of the Interior, U.S. Geological Survey. http://www.usgs.gov. Access 25.12.2011

[2] Neteler M. and Mitasova M., 2008. Open Source GIS: A GRASS GIS Approach. Third Edition. The International Series in Engineering and Computer Science: Volume 773. 406 pages, 80 illus., Springer, New York

[3] Hamlyn G.J., and Vaughan R.A. 2010. Remote sensing of vegetaion, Principles, Tecniques and Applications. Oxford University Press, 353p.

[4] Maestre, F. T., Salguero-Gomez, R. & Quero, J. L. It’s getting hotter in here: determining and projecting the impacts of global environmental change on drylands. Phil. Trans. R. Soc. B, this issue.

[5] Thornton, P. E., Law, B. E., Gholz, H. L., Clark, K. L., Falge, E., Ellsworth, D. S., Goldstein, A. H., Monson, R. K., Hollinger, D., Falk, M., Chen, J. & Sparks, J.P. 2002 Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agricultural and Forest Meteorology 113, 185-222

[6] Golinkoff, J. 2010 Biome BGC version 4.2:Theoretical Framework of Biome-BGC. Numerical Terradynamic Simulation Group Modeling and Monitoring Ecosystem Function at Multiple Scales. Biome-BGC. http://www.ntsg.umt.edu/project/biome-bgc

[7] Pérez-Suarez, M. Arredondo-Moreno, J. T. Huber-Sannwald, E. & Vargas-Hernandez, J. 2009 Production and quality of senesced and green litterfall in a pine-oak forest in central-northwest Mexico. Forest Ecology & Management 258, 1307-1315.

[8] Ribeiro-Palacios, M. 2007 Evaluación de la fertilidad del suelo como servicio ambiental de soporte en un sistema Humano-Ambiental, utilizando como herramienta el paradigma para el desarrollo de las zonas áridas (DDP). Caso de estudio: La Amapola, México. M.C. Thesis. Instituto Potosino de Investigación Científica y Tecnológica

[9] Pérez-Suarez, M. Arredondo J. T. & Huber-Sannwald E. 2012 Early stage of single and mixed leaf-litter decomposition in semiarid forest pine-oak: the role of rainfall and microsite. Biogeochemistry 108, 245-258.

[10] García de Alva-Verduzco, J. 2007 Aplicación del Paradigma de Desarrollo de las zonas Secas (DDP), examinando el uso del agua en un sistema humano-ambiental: estudio de caso en La Amapola, México. M.C. Thesis. Instituto Potosino de Investigación Científica y Tecnológica

[11] Pérez-Suarez, M. 2009 Understanding the role of Pinus cembroides and Quercus potosina in water and nutrient dynamics in a semi-arid forest ecosystem of central-northwest Mexico applying the functional matrix approach. PhD Thesis. Instituto Potosino de Investigación Científica y Tecnológica.

[12] D’Odorico, P. Bhattachan, A. Hydrologic variability in dryland regions: impacts on ecosystem dynamics and food security. Phil. Trans. R. Soc. B, this issue.