A classificatory approach integrating fuzzy set theory and permutation techniques for land cover analysis: a case study on a degrading area of the Rift Valley (Ethiopia)

Abstract

We suggest a classificatory approach for land cover analysis that integrates fuzzy set theory with permutation techniques. It represents a non parametric alternative and/or a complement of traditional multivariate statistics when data are scarce, missing, burdened with high degree of uncertainty and originated from different sources and/or times. According to this approach, the Operational Geographic Units (OGUs) in which landscape is subdivided and sampled are classified with hierarchical clustering methods. The clusters of a classification which are significantly sharp are used to define fuzzy sets. In this way, the original data scores are transformedbydegreesofbelonging. Weintroduce the conceptsof endogenous and exogenous fuzzy sets and we suggest to apply the Mantel test between the similarity matrices of these fuzzy sets to test the predictivity of internal variables with respect to external variables. The approach is applied to OGUs corresponding to the smallest administrative units (kebeles) of the Ethiopian Rift Valley, a degrading area with high risk of further degradation. We found that: 1) there is a high correlation between geo-physical features of the landscape (geology, rainfall and elevation) and some indicators of the human pressure such as land use/cover, land management for livestock breeding and human, household and livestock densities, 2) there is a high correlation between land degradation, measured with relative loss of Normalized Difference Vegetation Index (NDVI) and the human pressure. However, the correlation is higher when the human pressure is considered in the geo-physical context of the landscape. The approach can be easily applied to produce maps useful for planning purposes thanks to geographical information system (GIS) technology that is becoming available at low cost even to small administrative units of developing countries.

Abbreviations

CoCla:

Correspondence between Classifications

CoSiM:

Correlation between Similarity Matrices

DEM:

Digital Elevation Model

LU:

Lanscape Unit

NDVI:

Normalized Difference Vegetation Index

OGU:

Operational Geographic Unit

ShECla:

Sharpness of Exogenous classifications

USLE:

Universal Soil Loss Equation

References

  1. Anderson, M.J. and P. Legendre. 1999. An empirical comparison of permutation methods for tests of partial regression coefficient in a linear model. J. Stat. Comput. Simul. 62: 271–303.

    Article  Google Scholar 

  2. Anderson, M.J. and C. ter Braak. 2003. Permutation tests for multi-factorial analysis of variance. J. Stat. Comput. Simul. 73: 85–113.

    Article  Google Scholar 

  3. Azzali, S. and M. Menenti. 2000. Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR data. Int. J. Remote Sens. 21: 973–996.

    Article  Google Scholar 

  4. Biondini, M.E., P.W. Mielke Jr. and E.F. Redente. 1991. Permutation techniques based on Euclidean analysis spaces: a new and powerful statistic method for ecological research. In: E. Feoli and L. Orlóci (eds.), Computer Assisted Vegetation Analysis. Kluwer, Boston. pp. 221–240.

    Chapter  Google Scholar 

  5. Bonnet, E. and Y. Van de Peer. 2002. ZT: a software tool for simple and partial Mantel tests. J. Stat. Software 7(10): 1–12.

    Article  Google Scholar 

  6. Box, E.O., B.N. Holben and V. Kalb. 1989. Accuracy of the AVHRR Vegetation Index as a predictor of biomass, primary productivity and net CO2 flux. Vegetatio 80: 71–89.

    Article  Google Scholar 

  7. Burba, N., E. Feoli, M. Malaroda and V. Zuccarello. 1992. Un sistema informativo per la vegetazione. Software per l’archiviazione della vegetazione italiana e per l’elaborazione di tabelle. Manuale di utilizzo dei programmi. Quad. CETA n.12 pp. 78. Gorizia.

  8. Burba, N., E. Feoli, M. Malaroda and V. Zuccarello. 2008. MATE-DIT: a software tool supporting the application of similarity theory in community ecology. Submitted to Community Ecol.

  9. Carranza, L., E. Feoli and P. Ganis. 1998. Analysis of vegetation structural diversity by Burnaby’s similarity index. Plant Ecol. 138: 77–87.

    Article  Google Scholar 

  10. Dainelli, N., M. Benvenuti and M. Sagri. 2000. Geological Map of the Ziway-Shala Lakes Basin (Ethiopia). D.B. Map, Firenze.

    Google Scholar 

  11. Davenport, M.L. and S.E. Nicholson. 1993. On the relation between rainfall and the Normalized Difference Vegetation Index for diverse vegetation types in East Africa. Int. J. Remote Sens. 14(12): 2369–2389.

    Article  Google Scholar 

  12. De Jong, S.M. 1994. Applications of Reflective Remote Sensing for Land Degradation Studies in a Mediterranean Environment. Netherlands Geographical Studies 177, Utrecht, p. 237.

    Google Scholar 

  13. Dregne, H.E. 1983. Desertification of Arid Lands. Harwood Academic Publishers, London.

    Google Scholar 

  14. Duncan, D. 1997. Analysis and Comparison of Two AVHRR NDVI Time Series. Aerospace Engineering and Engineering Mechanics. Austin, University of Austin.

  15. Egziabher, Tewolde, E. Feoli, M. Fernetti, G. Oriolo, Zerihun Woldu. 1998. Vegetation mapping by integration of floristic analysis, GIS and remote sensing. An example from Tigray. Plant Biosyst. 132: 39–51.

    Article  Google Scholar 

  16. Estabrook, C.B. and G.F. Estabrook. 1989. Actus: a solution to the problem of small samples in the analyses of two-way contingency tables. Historical Meth. 82: 5–8.

    Article  Google Scholar 

  17. Fatovich, R. 1997. The peopling of the Tigrean Plateau of ancient and medieval times (ca 4000 B. C. - A. D. 1500): Evidence and synthesis. In: K.A. Bard (ed.), The Environmental History and Human Ecology of Northern Ethiopia in Late Holocene: Preliminary Results of Multidisciplinary Project. Istituto Universitario Orientale, Napoli, pp. 82–105.

    Google Scholar 

  18. Feoli, E. and Zerihun Woldu. 2000. Fuzzy set analysis of the Ethiopian Rift Valley vegetation. Plant Ecol. 147: 219–22.

    Article  Google Scholar 

  19. Feoli, E. and V. Zuccarello. 1986. Ordination based on classification: yet another solution? Abstr. Bot. 10: 203–219.

    Google Scholar 

  20. Feoli, E. and V. Zuccarello. 1988. Syntaxonomy, a source of useful fuzzy sets for environmental analysis? Coenoses 3: 65–70.

    Google Scholar 

  21. Feoli, E. and V. Zuccarello. 1996. Spatial pattern of ecological processes: the role of similarity in GIS applications for landscape analysis. In: M. Fisher, H. Scholten and D. Unwin (eds.), Spatial Analytical Perspectives on GIS. Taylor and Francis, London. pp. 175–185.

    Google Scholar 

  22. Feoli, E., L. Gallizia-Vuerich and Zerihun Woldu. 2002a. Processes of environmental degradation and opportunities for rehabilitation in Adwa, Northern Ethiopia. Landscape Ecol. 17: 315–325.

    Article  Google Scholar 

  23. Feoli, E., L. Gallizia-Vuerich and Zerihun Woldu. 2002b. Evaluation of the environmental degradation innorthern Ethiopia using GIS to integrate vegetation, geomorphologic, erosion and socio-economic factors. Agriculture, Environment and Ecosystem 91: 313–325.

    Article  Google Scholar 

  24. Feoli, E., G. Ferro and P. Ganis. 2006. Validation of phytosociological classifications based on a fuzzy set approach. Community Ecol. 7: 99–108.

    Article  Google Scholar 

  25. Fortin, M.J., G.M. Jacquez and B. Shipley. 2002. Computer-intensive methods. In: A.H. El-Shaarawi and W.W. Piegorsch (eds.), Encyclopedia of Environmetrics. John Wiley and Sons, Ltd, Chichester. Vol. 1, pp. 399–402.

    Google Scholar 

  26. Gamon, J.A., C.B. Field, M.L. Goulden, K.L. Griffin, A.E. Hartley, G. Joel, J. Penuelas and R. Valentini. 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Appl. 5(1): 28–41.

    Article  Google Scholar 

  27. Hill, J., and D. Peter. 1996. The Use of Remote Sensing for Land Degradation and Desertification Monitoring in the Mediterranean Basin - State of the Art and Future Research. Luxembourg, European Commission. p. 235.

  28. Hill, J., S. Sommer, W. Mehl and J. Mégier. 1996. A conceptual framework for mapping and monitoring the degradation of Mediterranean ecosystems with remote sensing. In: J. Hill and D. Peter (eds.), The Use of Remote Sensing for Land Degradation and Desertification Monitoring in the Mediterranean Basin - State of the Art and Future Research. Luxembourg, European Commission, pp. 23–44.

    Google Scholar 

  29. Hurcom, S.J. and A.R. Harrison. 1998. The NDVI and spectral decomposition for semi-arid vegetation abundance estimation. Int. J. Remote Sens. 19: 3109–3125.

    Article  Google Scholar 

  30. Hurni, H. 1985. Erosion, productivity and conservation systems in Ethiopia. Paper presented at the 4th International Conference on Soil Conservation at Maracay, Venezuela, pp. 3–9.

  31. Hurni, H. 1990. Degradation and conservation ofsoil resourcesin the Ethiopian Highlands. In: B. Messerli and H. Hurni (eds.), African Mountains and Highlands. Problems and Perspectives. African Mountains Association (AMA), Marceline, Missouri. pp. 51–63.

    Google Scholar 

  32. Kaufman, L. and P.J. Rousseeuw. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.

    Book  Google Scholar 

  33. Kong, A. and D.L. Nicolae. 2000. On a randomization procedure. Am. J. Human Genet. 7:1352–1355.

    Article  Google Scholar 

  34. Lacaze, B. 1996. Spectral characterization of vegetation communities and practical approached to vegetation cover changes monitoring. In: J. Hill and D. Peter (eds.), The Use of Remote Sensing for Land Degradation and Desertification Monitoring in the Mediterranean Basin - State of the Art and Future Research. Luxembourg, European Commission. pp. 149–166.

    Google Scholar 

  35. Legendre, P. 2000. Comparison of permutation methods for the partial correlation and partial mantel tests. J. Statist. Comput. Simul. 67: 37–73.

    Article  Google Scholar 

  36. Legendre, P. and L. Legendre. 1998. Numerical Ecology. Second English edition. Elsevier, Amsterdam.

    Google Scholar 

  37. Longley, A., Goodchild, M.F., Paul, D.J. Maguire and D.W. Rhind. 2001. Geographic Information Systems and Science. Wiley, Baffins Land, England.

    Google Scholar 

  38. Lyon, J.G., D. Yuan, R.S. Lunetta and C.D. Elvidge. 1998. A change detection experiment using vegetation indices. Photogramm. Engineer. Remote Sens. 64: 143–150.

    Google Scholar 

  39. Manly, B.F.J. 1997. Randomization, Bootstrap and Monte Carlo Methods in Biology. 2nd ed., Chapman and Hall, London.

    Google Scholar 

  40. Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. Cancer Res. 27: 209–220.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Mantel, N. and R.S. Valand. 1970. A technique of nonparametric multivariate analysis. Biometrics 26: 547–558.

    Article  CAS  Google Scholar 

  42. McDougall, I., W.H. Morton and N.A.U. Williams. 1975. Age and rate of trap series basalts at Blue Nile Gorge, Ethiopia. Nature 254: 207–208.

    Article  CAS  Google Scholar 

  43. Miklós, I., I. Somodi and J. Podani. 2005. Rearrangement of ecological data matrices via Markov chain Monte Carlo simulation. Ecology 86: 3398–3410.

    Article  Google Scholar 

  44. Myneni, R.B. and G. Asrar. 1994. Atmospheric effects and spectral vegetation indices. Remote Sens. Environ. 47: 390–402.

    Article  Google Scholar 

  45. Opdum, P., R. Foppen and C. Vos. 2002. Bridging the gap between ecology and spatial planning in landscape ecology. Landscape Ecol. 16:767–799.

    Article  Google Scholar 

  46. Pausas, J.C. and E. Feoli. 1996. Environment-vegetation relationships in the understorey of Pyrenean Pinus sylvestris forest. II. A classification approach. Coenoses 11:45–51.

    Google Scholar 

  47. Peñuelas, J.P. and I. Filella. 1998. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science 3(4): 151–156.

    Article  Google Scholar 

  48. Peres–Neto, P.R. and J.D. Olden. 2001. Assessing the robustness of randomization tests: examples from behavioural studies. Animal Behav. 61: 79–86.

    Article  Google Scholar 

  49. Pesarin, F. 1999. Permutation Testing of Multidimensional Hypotheses by nOnparametric Combination of Dependent Tests. CLEUP, Padua.

    Google Scholar 

  50. Pickup, G. and V.H. Chewings. 1996. Identifying and measuring land degradation processes using remote sensing. In: J. Hill and D. Peter (eds.), The Use of Remote Sensing for Land Degradation and Desertification Monitoring in the Mediterranean Basin - State of the Art and Future Research. Luxembourg, European Commission. pp. 135–145.

    Google Scholar 

  51. Pillar, V.D. 1996. A randomization-based solution for vegetation classification and homogeneity testing. Coenoses 11: 29–36.

    Google Scholar 

  52. Pillar, V.D. 1999. How sharp are classifications? Ecology 80: 2508–2516.

    Article  Google Scholar 

  53. Pillar, V.D. and L. Orlóci. 1996. On randomization testing in vegetation science: multifactor comparisons of relevé groups. J. Veg. Sci. 7:585–592.

    Article  Google Scholar 

  54. Podani, J. 1994. Multivariate Data Analysis in Ecology and Systematics. A Methodological Guide to the SYN-TAX 5.0 package. SPB Academic Publishing bv, The Hague.

    Google Scholar 

  55. Podani, J. 2000. Introduction to the Exploration of Multivariate Biological Data. Backhuys Publishers, Leiden.

    Google Scholar 

  56. Podani, J. and E. Feoli. 1991. A general strategy for the simultaneous classification of variables and objects in ecological data tables. J. Veg. Sci. 2:435–444.

    Article  Google Scholar 

  57. Price, J.P. 1992. Estimating vegetation amount from visible and near infrared reflectances. Remote Sens. Environ. 41: 29–34.

    Article  Google Scholar 

  58. Purevdorj, Ts., R. Tateishi, T. Ishiyama and Y. Honda. 1998. Relationships between percent vegetation cover and vegetation indices. Int. J.Remote Sens. 19: 3519–3535.

    Article  Google Scholar 

  59. Sagri, M. 1998. Land Resources inventory, environmental changes in the Abaya lake region (Ethiopia). Final report STD3 project. Contract no. TS3-CT92-0076.

  60. Thornes, J.B. 1996. Introduction. In: C.J. Brandt and J.B. Thornes, (eds.), Mediterranean Desertification and Land Use. Wiley, New York. pp. 1–11.

    Google Scholar 

  61. Tobisch, T. and T. Standovár. 2005. A comparison of vegetation patterns in the tree and herb layers of a hardwood forest. Community Ecol. 6: 29–37.

    Article  Google Scholar 

  62. Tucker, C.J., C. Vanpraet, E. Boerwinkel and A. Gastn. 1983. Satellite remote sensing of total dry matter production in the Senegalese Sahel. Remote Sens. Environ. 13: 461–474.

    Article  Google Scholar 

  63. Virgo, J.R. and R.N. Munro. 1977. Soil and erosion features of the central plateau regions of Tigray. Geoderma 20: 131–157.

    Article  Google Scholar 

  64. Wischmeier, W.H. and D.D. Smith. 1978. Predicting Rainfall Erosion Losses – A Guide to Conservation Planning. U.S. Department of Agriculture, Agriculture Handbook, n. 537.

  65. Zerihun, Woldu and Mesfin Tadesse. 1990. The status of the vegetation in the Lake regions of the Rift Valley of Ethiopia and the possibilities of its recovery. Sinet: Ethiop. J. Sci. 13(2): 97–120.

    Google Scholar 

  66. Zhao, S.X. 1986. Discussion on fuzzy clustering. 8th int. Conf. On Pattern Recognition, IEEE Press, New York. pp. 612–614.

    Google Scholar 

  67. Zimmerman, H.G. 1996. Fuzzy Set Theory and its Applications. 3rd ed. Kluwer Academic Publishers, Dordrecht.

    Book  Google Scholar 

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Feoli, E., Gallizia-Vuerich, L., Ganis, P. et al. A classificatory approach integrating fuzzy set theory and permutation techniques for land cover analysis: a case study on a degrading area of the Rift Valley (Ethiopia). COMMUNITY ECOLOGY 10, 53–64 (2009). https://doi.org/10.1556/ComEc.10.2009.1.7

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Keywords

  • Classification
  • Correlation
  • Ethiopia
  • Fuzzy sets
  • GIS
  • Land degradation
  • Permutation tests
  • Planning
  • Rift valley