**1. Introduction**

A fundamental fact of ecological observation is that most living organisms do not show random distributions. In fact, environmental controls and anthropogenic impacts are determinants of the spatial patterns of these organisms. This implies that it is possible to know the performance of ecosystems through the study of the spatial distribution patterns of the organisms that live in them [1,2]. This is particularly important in drylands where, as a result of water scarcity and edaphic limitations, vegetation appears to form isolated patches of one or more plant individuals [3,4]. In these ecosystems, it has been observed that the spatial pattern of these patches determines key aspects of ecosystem

functioning such as primary production [5], water and nutrient cycles [6], and biotic interactions [7,8]. Tools to produce accurate vegetation maps at the appropriate spatial scale over time could be very useful for gaining knowledge about the health and dynamics of dryland ecosystems.

Remote sensing has proven to be the most useful tool for monitoring changes in vegetation, as it is cost-e ffective, allows repeated mapping, and produces information on a large scale [9–11]. Within this technique, pixel-based methods are the most commonly used. However, these methods show several limitations for producing accurate maps of vegetation patches or plant individuals in drylands. First, pixel-based methods do not consider the spatial context in which the pixels are framed, making it di fficult to identify isolated image elements. Second, they often result in a final overlap of such elements from automatic classifications, when the analysis is based on high spatial resolution images [12]. In drylands, the land surface is characterized by scattered vegetation in a matrix of bare soil and scattered shrubs, so contextual information is very useful for image classification [13]. Both characteristics limit the possibility of identifying and classifying patches of vegetation and individual plant elements.

Several methods have emerged as an alternative to pixel-based methods for mapping individuals or vegetation patches. For example, in the case of forests, light detection and ranging (LiDAR) and very high frequency (VHF) synthetic aperture radar (SAR) images allow the characterization of various attributes of individual trees from their three-dimensional structure (e.g., [14–16]). However, this method is di fficult to use when vegetation shows reduced aerial volume such as in drylands. In these cases, object-based image analysis (OBIA) can be a good solution for mapping patches of vegetation and individual plants [17], particularly because there is currently a wide variety of freely available high spatial resolution orthoimages. OBIA can provide a more accurate and realistic identification of scattered vegetation in drylands because of the combined spectral information of each pixel with the spatial context [18,19]. This method has yielded good results in the monitoring of spatial patterns, functioning, and structure of vegetation in these environments [20,21].

OBIA may be particularly useful for assessing the dynamics of populations of long-lived plants of conservation concern. In this case, it is di fficult and costly to assess the environmental controls of population dynamics due to their high persistence and sometimes low rate of regeneration, which requires very long-term studies [22,23]. It has been proposed that the maintenance of long-lived plant populations is the result of a balance between regeneration (replacement of individuals by recruiting new recruits) and persistence (maintenance of individuals in space, physically and temporarily), or a combination of both strategies [24,25], depending on abiotic stress and biotic competition [26]. Monitoring populations of persistent individuals over time is complicated, as there are continuous disturbances in the environment that can alter their performance [24]. However, the availability of the analysis of historical aerial orthophotographs and high spatial resolution satellite images with OBIA provides a good opportunity to reconstruct the interannual dynamics of long-lived plant populations over long periods of time, thus enabling the evaluation of changes experienced by these shrub populations.

*Ziziphus lotus* (L.) Lam, a long-lived shrub from Mediterranean drylands [27], shows characteristics for a multi-temporal analysis of the spatial distribution in its populations with OBIA. This shrub species depends on groundwater [28], forms fertility islands, and is considered an engineering species [29] of an ecosystem of interest for conservation at the European level (Directive 92/43/CEE). The main European population of the shrub species is located in a flat coastal area surrounded by greenhouses in the Cabo de Gata-Níjar Natural Park, Spain. This population has been a ffected by several threats for many decades, including sand mining [30,31], reducing the amount of sand available to develop the *Z. lotus* fertility islands; urban pressure [32], which has reduced the potential distribution of *Z. lotus*; and the expansion of intensive agriculture [33,34], responsible for the decline in the level of the aquifer's water table, which may have caused the seawater intrusion [35,36]. Since 1944, several studies have evaluated this community of *Z. lotus*. Shrub patterns to identify groundwater dependence [28], the formations of shrubs in dunes [37], shrub spatial aggregation and consequences for reproductive

success [29], and mutual positive e ffects between shrubs [38] have been researched. Yet, the monitoring of the shrub population dynamics has never been studied.

Despite most of the shrub population being located within the protected area, its temporal dynamics could be a ffected by several human-induced disturbances. However, due to the slow growth of shrubs and the inertia in the extinction of individuals, it is di fficult to assess such dynamics without considering the population structure of the shrub species over the last several decades. This work proposes the use of remote sensing methods to map the spatial distributions of shrubs and to analyze their size and shape as a means of identifying anthropic disturbances. Our guiding hypothesis was that *Z. lotus*, phreatophytic shrubs, were a ffected by soil loss and seawater intrusion that decreased their cover area. On the contrary, after the legal protection of the area in 1987, the shrubs increased their cover area. Within this framework, the objectives of this work were as follows: (i) to make precision maps of scattered shrubs from historical remotely sensed images using OBIA and (ii) to extract information on changes in the shape, size, and spatial distribution of shrubs, and thus infer their relationships with human disturbances over a period of 60 years (1956–2016).

#### **2. Materials and Methods**
