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A spatial pattern analysis of the halophytic species distribution in an arid coastal environment
Badreldin, N.; Uria-Diez, J; Mateu, J; Youssef, A.; Stal, C.; El-Bana, M; Magdy, A; Goossens, R (2015). A spatial pattern analysis of the halophytic species distribution in an arid coastal environment. Environ. Monit. Assess. 187(5): -. dx.doi.org/10.1007/s10661-015-4403-z
In: Environmental Monitoring and Assessment. Kluwer: Dordrecht. ISSN 0167-6369, more
Peer reviewed article  

Available in  Authors 
    VLIZ: Open Repository 279055 [ OMA ]

Author keywords
    Arid environment; Digital image processing; Google earth; Spatialpattern analysis

Authors  Top 
  • Badreldin, N., more
  • Uria-Diez, J
  • Mateu, J
  • Youssef, A., more
  • Stal, C., more
  • El-Bana, M
  • Magdy, A
  • Goossens, R, more

Abstract
    Obtaining information about the spatial distribution of desert plants is considered as a serious challenge for ecologists and environmental modeling due to the required intensive field work and infrastructures in harsh and remote arid environments. A new method was applied for assessing the spatial distribution of the halophytic species (HS) in an arid coastal environment. This method was based on the object-based image analysis for a high-resolution Google Earth satellite image. The integration of the image processing techniques and field work provided accurate information about the spatial distribution of HS. The extracted objects were based on assumptions that explained the plant-pixel relationship. Three different types of digital image processing techniques were implemented and validated to obtain an accurate HS spatial distribution. A total of 2703 individuals of the HS community were found in the case study, and approximately 82 % were located above an elevation of 2 m. The microtopography exhibited a significant negative relationship with pH and EC (r = -0.79 and -0.81, respectively, p < 0.001). The spatial structure was modeled using stochastic point processes, in particular a hybrid family of Gibbs processes. A new model is proposed that uses a hard-core structure at very short distances, together with a cluster structure in short-to-medium distances and a Poisson structure for larger distances. This model was found to fit the data perfectly well.

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