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Evaluation of Desertification Processes in Seridó Region (NE Brazil)

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DOI: 10.4236/ijg.2013.45B003    2,666 Downloads   3,652 Views   Citations

ABSTRACT

This paper outlines procedures to analyze the desertification processes in the semi-arid Seridó Region (NE Brazil). Using the Geosystem theory, the detection of desertification areas was based on environmental indices, digital image processing in multispectral analysis and Geographic Information System (GIS).The first step was to treat the rainfall data and NDVI satellite Modis, aiming at identifying areas which do not present vegetation cover, even during the rainy seasons.The second step was to work on a regional scale using Landsat ETM + images (2000-2005) and data collected in the field, as the evaluations of exposed surfaces, that together with MDT/SRTM-NASA and thematic maps, allowed to classify the altitude and slope of the relief, soils type, different morphologies and geology, and correlate them with the areas susceptible to desertification process. The integration of the georeferenced data, related to these indicators, allowed the identification of five different levels of susceptibility to desertification (very high, high, moderate, low and very low), and the geographic domain of each class. Based on the analysis of the dynamics of the vegetation cover, we can establish that the main results refer that there is a decrease of the biomass at the region, associated with the dense caatinga vegetation areas, but more important, with the scrub and degraded areas.

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R. Petta, L. Carvalho, S. Erasmi and C. Jones, "Evaluation of Desertification Processes in Seridó Region (NE Brazil)," International Journal of Geosciences, Vol. 4 No. 5B, 2013, pp. 12-17. doi: 10.4236/ijg.2013.45B003.

References

[1] Agenda 21, UNCED ONU., 1994. http://www.un.org/esa/dsd/agenda21/res_agenda21_12.shtml
[2] IBGE—Instituto Brasileiro de Geografia e Estatística Contagem da População—Resultados Oficiais, 2010. http://censo2010.ibge.gov.br/
[3] E. Aguado and J. E. Burt, “Understanding Weather and Climate,” 5th Edition, Prentice Hall, Upper Saddle River, New Jersey, 2010.
[4] H. A. Barbosa, A. R. Huete and W. E. Baethgen, “A 20-Year Study of NDVI Variability over the Northeast Region of Brazil,” Journal of Arid Environments, Vol. 67, No. 2, 2006, pp. 288-307.
[5] H. C. Gurgel and N. J. Ferreira, “Annual and Interannual Variability of NDVI in Brazil and Its Connections with Climate,” International Journal of Remote Sensing, Vol. 24, No. 18, 2003, pp. 3595-3609. http://dx.doi.org/10.1080/0143116021000053788
[6] Y. Julien and J. A. Sobrino, “The Yearly Land Cover Dynamics (YLCD) Method: An Analysis of Global Vegetation from NDVI and LST Parameters,” Remote Sensing of Environment, Vol. 113, 2009, pp. 329-334. http://dx.doi.org/10.1016/j.rse.2008.09.016
[7] J. P. Malingreau, C. J. Tucker and N. Laporte, “AVHRR for Monitoring Global Tropical Deforestation,” International Journal of Remote Sensing, Vol. 10, 1989, pp. 855-867. http://dx.doi.org/10.1016/j.rse.2008.09.016
[8] G. Palu-binskas, R. M. Lucas, G. M. Foody and P. J. Curran, “An Evaluation of Fuzzy and Texture-Based Classification Approaches for Mapping Regenerating Tropical Forest Classes from Landsat TM Data,” International Journal of Remote Sensing, Vol. 4, No. 16, 1995, pp. 747- 759. http://dx.doi.org/10.1080/01431169508954437
[9] B. C. Reed, J. F. Brown, D. Vanderzee, T. R. Loveland, J. W. Merchant and D. O. Ohlen, “Measuring Phenological Variability from Satellite Imagery,” Journal of Vegetation Science, Vol. 5, 1994, pp. 703-714. http://dx.doi.org/10.2307/3235884
[10] C. W. Thornthwaite, “An Approach toward a Rational Classification of Climate,” Geographical Review, Vol. 38, No. 1, 1948, pp. 55-94. http://dx.doi.org/10.2307/210739
[11] C. J. Tucker, J. E. Pinzon, M. E. Brown, D. Slayback, E. W. Pak, R. Mahoney, E. Vermote and N. El Saleous, “An Extended AVHRR 8-km NDVI Data Set Compatible with MODIS and SPOT Vegetation NDVI Data,” International Journal of Remote Sensing, Vol. 26, No. 20, 2005, pp. 4485-5598. http://dx.doi.org/10.1080/01431160500168686
[12] X. Wang, A. S. Auler, R. L. Edwards, H. Cheng, P. S. Cristalli, P. L. Smart, D. A. Richards and C. C. Shen, “Wet Periods in Northeastern Brazil over the Past 210 kyr Linked to Distant Climate Anomalies,” Nature, Vol. 432, 2004, pp. 740-743.

  
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