Object-based image analysis for monitoring the spatial distribution of a halophytic species in an arid coastal environment

Speaker: Jaime Uria Diez

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 object-based image analysis for a high resolution Google Earth satellite image. The integration of image processing techniques and field work provided accurate information about the spatial distribution of HS. The extracted objects were based on assumptions that explained plant-pixel relationship. Three different types of digital image processing techniques were implemented and validated in order to obtain accurate HS spatial distribution. A total of 2703 individuals of the HS community were found in the case study, about 82% were located above 2m elevation. The microtopography showed a significant negative relationships 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 which is handled a hard-core structure at very short distances, together with a cluster structure in short-to-medium distances and a Poisson one for larger distances. This model found to fit the data perfectly well.