Abstract
Vegetation dynamic in semi-arid regions is widely affected by climate and hydrological regimes. These regimes create a patchy landscape which, therefore, cannot be treated as stationary in space. Overall complexity significantly increases when the temporal component is added to the analysis. However, despite the complexity involved with variation in time, most of the temporal data storage in raster data-models is done using series of snapshot layers associated with a particular time event. The reason is mainly associated with resolution problems and limitations in controlling large databases with computer resources. Recently, the combination between spatial and temporal databases has become crucial in GIS modeling for solving spatial dynamic problems. We suggest hereby the use of fuzzy theory to apply stationary rules to non-stationary environment in raster database. The method is demonstrated as an analysis of vegetation dynamics in a semi arid environment. The results display the simplicity of the combination between spatially explicit rules and raster database which allow our method to analyze ongoing processes in cell ij at the same time resolution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Saghfian, B., Julien, P.Y., Rajaie, H.: Runoff hydrograph simulation based on time variable isochrone technique. Journal of Hydrology 261, 193–203 (2002)
McBratney, A.B., Mendonsa Santos, M.L., Minasny, B.: On digital soil mapping. Geoderma 117, 3–52 (2003)
Scull, P., Franklin, J., Chadwick, O.A., McArthur, D.: Predictive soil mapping: a review. Progress in Physical Geography 27, 171–197 (2003)
Osborne, P.E., Suarez-Seoane, S.: Should data be partitioned spatially before building large scale distribution model? Ecological Modelling 157, 249–259 (2002)
Foody, G.M.: Geographical weighting as a further refinement to regression modeling: An example focused on the NDVI-rainfall relationship. Remote Sensing of Environment 88, 283–293 (2003)
Beven, K., Freer, J.: A dynamic TOPMODEL. Hydrological Processes 15, 1993–2011 (2001)
Graniero, P.A., Robinson, V.B.: A Real–time Adaptive Sampling Method for Field Mapping in Patchy, Heterogeneous Environments. Transactions in GIS 7, 31–53 (2003)
Ludwig, J.A., Wilcox, B.P., Breshears, D.D., Tongway, D.J., Imeson, A.C.: Vegetation patchesand runoff-erosion as interacting ecohydrological processes in semiarid landscape. Ecology 86, 288–297 (2005)
Huenneke, L.F., Clason, D., Muldavin, E.: Spatial heterogeneity in Chihuahuan Desert vegetation: implication for sampling methods in semi-arid ecosystem. Journal of Arid Environments 47, 257–270 (2001)
Dragicevic, S., Marceau, J.D.: An application of fuzzy logic reasoning for GIS temporal modeling of dynamic processes. Fuzzy Set and Systems 113, 69–80 (2000)
Wu, H., Li, B., Stoker, R., Li, Y.: A semi-arid grazing ecosystem simulation model with probabilistic and fuzzy parameters. Ecological Modelling 90, 147–160 (1996)
Noy-Meir, I.: Desert Ecosystems: Environment and producers. Annual review Ecology and Systematics 4, 25–51 (1973)
Bojorquez-Tapia, L.A., Juarez, L., Cruz-Bello, G.: Integrating fuzzy logic, optimization, and GIS for ecology impact assessments. Environments Management 30, 418–433 (2002)
Burrough, P.A., MaCmillan, R.A., Van Deursen, W.: Fuzzy classification methods for determining land suitability from soil profile observation and topography. Journal of Soil Science 43, 193–210 (1992)
McBratney, A.B., Odeh, I.O.A.: Application of fuzzy set in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma 77, 85–113 (1997)
Robinson, V.B.: A perspective on the fundamentals of fuzzy sets and their use in Geographic Information Systems. Transactions in GIS 7, 3–30 (2003)
Zhu, A.X., Hudson, B., Burt, J., Lubich, K., Simonson, D.: Soil mapping using GIS, expert knowledge, and Fuzzy logic. Soil Science Society America Journal 65, 1463–1472 (2001)
Baja, S., Chaphman, D.M., Dragovich, D.: A conceptual model for defining and assessing land management units using a fuzzy modeling approach in GIS environment. Environmental management 29, 647–661 (2002)
Svoray, T., Bar-Yamin, S., Henkin, Z., Gutman, M.: Assessment of herbaceous plant habitats in water-constrained environments: Predicting indirect effects with fuzzy logic. Ecological Modelling 180, 537–556 (2004)
Drgicevic, S., Maeceau, D.J.: A fuzzy set approach for modeling time in GIS. International Journal of Information Science 14, 225–245 (2000)
Brunsdon, C., Fotheringham, S., Charlton, M.: Geographically weighted regression modeling spatial non-s
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shafran-Natan, R., Svoray, T. (2006). Solving Spatio-temporal Non-stationarity in Raster Database with Fuzzy Logic. In: Min, G., Di Martino, B., Yang, L.T., Guo, M., Rünger, G. (eds) Frontiers of High Performance Computing and Networking – ISPA 2006 Workshops. ISPA 2006. Lecture Notes in Computer Science, vol 4331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11942634_62
Download citation
DOI: https://doi.org/10.1007/11942634_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49860-5
Online ISBN: 978-3-540-49862-9
eBook Packages: Computer ScienceComputer Science (R0)