1. Introduction
The global landscape is constantly being shaped by the interplay of various ecological forces, with the invasion of alien species standing out as a powerful agent of change. In this intricate ecological web, allochthonous invasive weed species have proven to be particularly resilient actors that significantly impact sensitive ecosystems.
According to the European Commission's definition of invasive weeds from Regulation (EU) No. 1143/2014, invasive plant species are those that, once introduced or spread, threaten or negatively impact biodiversity and certain ecosystem services. In recent decades, invasive weeds have severely threatened local biodiversity, ecosystem services, environmental quality and human health [
1]. The United Nations (UN) Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) estimates that about one-fifth of the Earth’s surface, including global biodiversity hotspots, is threatened by biotic invaders. This means that 60% of global species extinctions have been caused by invasive alien species alone or in combination with other causes, at an annual cost of 423 billion USD [
2].
Several hypotheses have been proposed to explain the invasion of invasive weeds into new habitats, such as the release of enemies, new weapons and empty niches. However, not every hypothesis is sufficient to explain the invasion of these species [
3]. The enemy release hypothesis states that some invasive weeds are more successful in new habitats when they are kept away from natural enemies such as pathogens and herbivores found in their original habitats, which is the case for marabou [
4]. Furthermore, the functioning of ecosystems on islands such as Cuba is more affected by invasive weeds than on the mainland [
5].
Various invasive plants introduced for the benefit of humans are known to cause ecological and economic damage as they compete for nutrients, light and water [
6] and cause significant losses in agriculture and livestock as they increase the costs of their effective management [
7]. Their impact is not only economic, as many of these weeds are known to impair ecosystem services such as aesthetics, recreation, culture and regulation [
8]. Invasive weeds also affect regulating ecosystem services such as hazard mitigation (e.g. landslides), water purification, pest control, pollination and climate change, which are inextricably linked to agriculture and forestry [
8,
9].
Eradicating an invasive weed species is a complex process that usually requires extensive resources and long-term monitoring of invasive plant dynamics. Eradication of weeds is often limited at the local level due to various constraints, such as the removal of weeds that spread over large areas. In this context, the regeneration capacity of important perennial weeds such as marabou is also a limiting factor, requiring additional and repeated treatments [
10]. Given the negative impact of many invasive weeds, there is an urgent need to prioritize and develop cost-effective and ecologically sound strategies to control them [
11].
Marabou (
Dichrostachys cinerea L. Wight & Arn.), a shrub or small tree with twisted trunks, grayish bark and smooth spines on the lateral branches, reaches a height of 4 to 5 meters, even up to 10 meters on suitable soils. It was first introduced to Cuba a century and a half ago. However, it was only in the second half of the 20th century that this seemingly harmless plant developed into a major ecological problem. Its high stress tolerance combined with its numerous thorns and resistance to cutting and burning contributed to its uncontrolled spread. Other reasons for the uncontrolled spread of the marabou plant are the dispersal of its seeds by livestock and the high persistence of these seeds in the soil. In addition, the plant reproduces by root buds and has the remarkable ability to produce shoots even when it is not completely eradicated. The collective impact of these traits is the abandonment of land where marabou occurs, as farmers struggle with the physical and economic challenges associated with eradicating the plant. As a result, this weed has spread massively throughout Cuba, particularly in areas already contaminated by overexploitation and abandoned farmland [
12].
The Valle de los Ingenios, a UNESCO World Heritage Site, is witness to the ecological consequences of the uncontrolled spread of the marabou plant.
The Valle de los Ingenios is a series of three interconnected valleys located about 12 km outside the city of Trinidad and covering an area of 270 km². These valleys — San Luis, Santa Rosa and Meyer — were a center of sugar production from the late 18th to the late 19th century. At the height of the Cuban sugar industry, more than fifty sugar cane mills were in operation in the three valleys, employing over 30,000 slaves. When the valley was declared a UNESCO World Heritage Site in 1988, it was home to dilapidated sugar mills, summer houses, slave barracks and other facilities associated with sugar cane cultivation, as well as active sugar cane plantations. However, the unstoppable advance of the invasive weed marabou has left an indelible mark on this historically and culturally significant landscape [
13].
Given the enormous challenge of field assessment, as it's almost impossible to reach the marabou thicket amidst the ubiquitous bush with its many long, hard thorns, remote sensing techniques have proven to be important tools. These techniques play a crucial role in identifying and classifying the weed in various regions of Cuba, including the Valle de los Ingenios [
14,
15].
Remote sensing, a pivotal component of our study, leverages advanced aerospace technology and geospatial data analysis to monitor vegetation cover through satellite imagery [16,17]. Image processing techniques, such as digital classification, play a critical role in extracting information from satellite images based on digital layers (DL) or reflectance. This process involves grouping pixels based on their values and associating them with specific land cover types to generate detailed maps and directories [18]. Digital image classification, a form of pattern recognition, uses surface features to recognize patterns associated with the location of each pixel [
19,
20]. Two different classification methods, supervised and unsupervised, depend on how the training statistics are obtained [
19]. In the supervised classification approach used here, the process is driven by terrain-specific prior knowledge. Users delineate representative areas for each category of interest, and the pixels within these areas are used to assign the remaining pixels to categories based on the similarity of their reflectance or DL values. The input data can come from field studies, photo interpretations, articles or maps of the area of interest [
19]. As described by Hasmadi et al [
21], the process of digital image classification involves three phases: (i) the training phase, in which the categories of interest are digitally defined; (ii) the mapping phase, in which the image pixels are grouped into categories; and (iii) the testing phase, in which the results are verified.
In these terms, remote sensing, especially when integrated across different spatial and temporal scales, has the potential to shed light on the dynamics of invasions and reveal their drivers, which can improve the efficiency of control measures [22].
These remote monitoring techniques are of particular interest for agricultural cooperation/development projects in developing countries, such as Cuba. In these countries, access to the desired areas is not always possible or granted; therefore, low-cost, regular remote monitoring would be an invaluable tool for both international cooperation agencies and local authorities [
23]. Unfortunately, neither access to regular, expensive, high-resolution satellite imagery required for such consistent analysis nor expensive GIS software licenses are always available or affordable for local authorities, thus hampering the desired technology transfer between international agencies and local institutions. As a starting point for various scientific and agronomic analysis the quantitative description of spatial patterns of plant species [
24] has improved and accelerated in recent years with the development of new geographic information technologies, such as remote sensing [
25,
26]. These technologies, using medium and high resolution satellite imagery, have already shown the ability to map global forest associations or similar detailed floristic contexts [
27,
28]. The development of an analytical tool capable of remotely quantifying the degree of marabou infestation in large areas, using free cloud-based geospatial analysis platforms and satellite imagery, would be of great interest for assessing the problem of marabou infestation development in Cuba. To achieve this goal, the Google Earth Engine (GEE) is used in this study. GEE is a cloud-based platform that serves as a repository for various data sets from different satellites and platforms. GEE offers the advantage that no satellite images need to be downloaded and the algorithms can be applied directly to the products stored on the servers. This not only increases processing speed, but also facilitates the development of applications with scalable analysis capabilities in both space and time. In addition, GEE is a free service that can be run on not very demanding computers with a medium-speed Internet connection.
The process of image analysis includes the creation of a classifier based on field data from 2014. This classifier, developed with the Google Earth Engine, serves as the basis for the assessment of marabou populations in subsequent years. The methodology involves the creation of composite images that are representative of each year and combine the mean values of each valid pixel across the year. This approach, combined with the use of Landsat imagery directly corrected for surface reflectance (USGS Level 2-A product) and Fmask to detect clouds and cloud shadows, ensures the reliability of the results. To further improve the analysis, the study introduces a temporal filter to distinguish between perennial vegetation, such as marabou, and seasonal vegetation. By isolating marabou from the broader vegetation context, the study aims to provide a nuanced understanding of its distribution patterns over the years. The classification algorithm, trained on the 2014 field data, incorporates machine learning techniques to optimize accuracy and reliability.
The analysis of images from the Valle de los Ingenios over a period from 2000 to 2018 therefore provides an excellent experimental model to test the accuracy of this new tool, as two huge human-triggered milestones related to the marabou plague in this area are well documented and can be used as benchmarks. In the future, this new tool will provide farmers, agronomists, and local authorities with a comprehensive overview of the evolution of marabou infestations throughout the valley over the years by comprehensively examining the spatial and temporal patterns of marabou distribution in Valle de los Ingenios.
The aim of this study is to provide a comprehensive, economically affordable tool to analyze the spatial and temporal distribution of the marabou in this region. Both analytical and spatial approaches will be used to understand the dynamics of its invasion and provide valuable insights to both scientists and land managers concerned with the consequences of invasive species in unique and culturally significant landscapes.
4. Discussion
The rapid spread of the marabou plant, an invasive plant species, poses a significant ecological and economic threat to the Valle de los Ingenios, a UNESCO World Heritage Site in Cuba. In our study, Google Earth Engine (GEE), a cloud-based platform for geospatial analysis, was used to comprehensively assess the spatial and temporal distribution of marabou infestation in the valley. Our results first highlight the importance of using cost-effective and user-friendly tools such as Google Earth Engine to assess marabou infestation in developing countries. GEE's ability to accurately locate and monitor marabou infestations over time, as well as its ability to detect the impact of human activities on the environment, make it a valuable tool for environmental management and decision making [
37,
38]. Furthermore, our results are consistent with previous findings that emphasize the importance of time series analysis, particularly the NDVI, for understanding the successional trajectories of both tropical forests and coastal areas [
36,
39]. The intricate relationship between forest and coastal structure and NDVI can be applied to our context and helps characterize marabou infestation stages in mixed environments such as Valle de los Ingenios.
By analysing nearly two decades of satellite imagery, our data revealed a complex pattern of marabou expansion and decline influenced by a variety of factors. Initially, the marabou population remained relatively small, but after the closure of the last sugar factory in the area in 2005, it increased dramatically. This sharp increase is attributed to the abandonment of agricultural land following the disappearance of the sugar industry in the area, which provided ideal conditions for marabou dispersal. The observed pattern of marabou dispersal indicates a complex interplay between ecological and socio-economic factors. The abandonment of agricultural land after the closure of the sugar factory created favourable conditions for marabou settlement and dispersal. Thus, the colonization dynamics of marabou followed known patterns, such as the location of initial dispersal areas in natural weed reservoirs such as hard-to-reach mountainous areas on the edge of the valley and the use of roads and railroads as dispersal routes, which are common strategies for both allochthonous and autochthonous weed dispersal [
40].
The subsequent government-led campaign against marabou in 2015, which coincided with the 500th anniversary of the city of Trinidad, led to a significant reduction in the area infested with marabou. Since then, however, the marabou population has rebounded, possibly due to the clearing (but not subsequent utilization) of previously abandoned sugarcane fields, which previously acted as a barrier to the spread of marabou. As with many other invasive weeds, allochthonous marabou is more competitive than native species and rapidly colonizes cleared areas if they are not restored or used for agriculture. This aggressive colonization dynamic has already been observed in invasive weed species [
40,
41,
42] and poses a real threat to both agriculture and biodiversity. In this respect, our results are consistent with other studies on invasive weed species which are based on phenology-based mapping, such as Labonté et al. [
43], use satellite images to determine the influence of disturbance and management practices on the spread of invasive plants [
44], or demonstrate the importance of time-series analysis in invasive species mapping [
45].
Our results also emphasize the importance of evaluating different marabou control methods, especially given the observed resurgence of marabou after clearing activities. Tailored strategies may be needed that are adapted to Cuban specificity and take into account the specific characteristics of different areas. For example, the promotion of agricultural activity and the reforestation of native forests near the Escambray Mountains and the enhancement of the tourist landscape along the transportation routes in the valley itself could be promising approaches. Our results emphasize the need for a comprehensive approach to marabou management that addresses both the ecological and socio-economic drivers of marabou expansion. This includes measures to restore abandoned agricultural land, the promotion of sustainable agricultural practices and the involvement of the local population in management measures.
It is important to acknowledge the limitations of our study, particularly in the validation process. Validation was conducted exclusively for the year 2014, which introduces potential issues related to differences between satellite platforms. The projection of the 2014 model without further validation is a limitation, and the relatively high accuracy achieved using only NDVI suggests that the use of additional complementary parameters would be beneficial for more comprehensive analyses in the future. The variation in satellite platforms can introduce discrepancies that need to be considered when interpreting the results. Addressing these limitations will help to improve the accuracy and robustness of invasive species analyses, making them more applicable for effective environmental management and decision-making. Anyway, it is worth noting that marabou, being a woody, evergreen plant, exhibits less spectral change compared to annual or deciduous woody plants. This means that NDVI may be a more reliable indicator of marabou presence than it would be for other types of invasive species.