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A spatial pattern analysis of the halophytic species distribution in an arid coastal environment

机译：干旱沿海地区盐生植物分布的空间格局分析

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摘要

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 highresolution 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.
机译：由于在恶劣和偏远的干旱环境中需要大量的野外工作和基础设施，因此获取有关沙漠植物空间分布的信息被视为生态学家和环境建模的一项严峻挑战。一种新的方法被应用于评估干旱沿海环境中盐生植物物种（HS）的空间分布。此方法基于高分辨率Google Earth卫星图像的基于对象的图像分析。图像处理技术和现场工作的集成提供了有关HS空间分布的准确信息。提取的对象基于解释植物像素关系的假设。实施并验证了三种不同类型的数字图像处理技术，以获得准确的HS空间分布。在该案例研究中，共发现了2703名HS族个体，其中约82％位于海拔2 m以上。微观形貌与pH和EC呈显着负相关（分别为r = -0.79和-0.81，p <0.001）。使用随机点过程，尤其是吉布斯过程的混合族，对空间结构进行建模。提出了一种新模型，该模型在非常短的距离上使用硬核结构，在短至中距离上使用簇结构，而在较大距离上使用泊松结构。发现该模型非常适合数据。

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