Advertisement

Community Ecology

, Volume 10, Issue 1, pp 53–64 | Cite as

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)

  • E. Feoli
  • L. Gallizia-Vuerich
  • P. Ganis
  • Zerihun Woldu
Article

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.

Keywords

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

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  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.Google Scholar
  9. Carranza, L., E. Feoli and P. Ganis. 1998. Analysis of vegetation structural diversity by Burnaby’s similarity index. Plant Ecol. 138: 77–87.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle Scholar
  33. Kong, A. and D.L. Nicolae. 2000. On a randomization procedure. Am. J. Human Genet. 7:1352–1355.CrossRefGoogle 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.CrossRefGoogle 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.PubMedPubMedCentralGoogle Scholar
  41. Mantel, N. and R.S. Valand. 1970. A technique of nonparametric multivariate analysis. Biometrics 26: 547–558.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  44. Myneni, R.B. and G. Asrar. 1994. Atmospheric effects and spectral vegetation indices. Remote Sens. Environ. 47: 390–402.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  57. Price, J.P. 1992. Estimating vegetation amount from visible and near infrared reflectances. Remote Sens. Environ. 41: 29–34.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2008

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • E. Feoli
    • 1
  • L. Gallizia-Vuerich
    • 1
  • P. Ganis
    • 1
  • Zerihun Woldu
    • 2
  1. 1.Department of BiologyUniversity of TriesteItaly
  2. 2.Department of Biology, The National HerbariumAddis Ababa UniversityAddis AbabaEthiopia

Personalised recommendations