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Article

Mapping Groundwater-Dependent Vegetation Zones: Application of GIS and Multicriteria Analysis with Field Validation

by
Osmel Abreu Rodríguez
1,2,3,*,
José Luis Arumí
1,2 and
Roberto Urrutia
2,3
1
Department of Water Resources, Universidad de Concepción, Chillán 3812120, Chile
2
Water Research Center for Agriculture and Mining (CRHIAM), ANID Fondap Center, Universidad de Concepción, Concepción 4070411, Chile
3
Centro de Ciencias Ambientales EULA, Facultad de Ciencias Ambientales, Universidad de Concepción, Concepción 4070411, Chile
*
Author to whom correspondence should be addressed.
Water 2025, 17(4), 534; https://doi.org/10.3390/w17040534
Submission received: 14 January 2025 / Revised: 7 February 2025 / Accepted: 9 February 2025 / Published: 13 February 2025
Browse Figures
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Abstract

:
Wetlands with shallow water tables allow the development of groundwater-dependent vegetation, which is fundamental to the functioning and conservation of these ecosystems. Despite their importance, only 8% of the wetland area in Chile is protected. The lack of specific research on regions such as the center-south of the country hinders the protection and effective management of these ecosystems. This study presents an economical and practical methodology for mapping phreatophytic vegetation zones in a wetland in south-central Chile, using geographic information systems (GIS) and field validation. Through a literature review, five predictor parameters of the relationship between groundwater and vegetation in wetlands were selected. Using a multi-criteria analysis based on these five selected parameters, a map was generated to identify areas of high influence of groundwater on vegetation, which allowed the definition of ten zones to identify the type of vegetation and validate the methodology. The results indicated that 100% of the selected areas presented conditions of soil moisture and phreatophytic vegetation. Fourteen species were identified, of which nine are phreatophytes, corroborating that the parameters selected for mapping are indicators of areas where the existing vegetation depends on groundwater. It was demonstrated that the applied methodology offers a solid and accessible tool to map and identify the relationship between groundwater and vegetation in wetlands, generating valuable information that provides visibility to these ecosystems for better management and conservation.

1. Introduction

Wetlands cover between 5 and 7% of the Earth’s surface [1]. They are areas of great plant productivity and rich in fauna, with high species diversity, where water is the main environmental control factor [2]. Despite their importance, wetlands are among the most threatened ecosystems on Earth, exposed to factors such as urban expansion, extensive agriculture, and climate change [3], putting their vegetation and biodiversity at risk. In general, these ecosystems are found in places where the water table is at a shallow depth, enabling the phreatophytic vegetation to develop and remain even during periods of prolonged drought [4]. This type of vegetation is defined as plants that have roots that can penetrate the top layer of soil where moisture accumulates and reach the saturated zone [5]; therefore, groundwater plays an essential role in sustaining these ecosystems and associated landscapes [6], as it provides an input flow that maintains the physicochemical conditions necessary for the flora and fauna it sustains [7], and is an essential factor for vegetation that depends mainly on this resource. This groundwater-dependent vegetation is fundamental for maintaining ecosystem functions and services within the wetland and provides a habitat for species [8].
Against this backdrop, the identification of zones in which groundwater has a high influence on vegetation is very important for the conservation of these ecosystems [9,10]. Most information available on the relationship between vegetation and groundwater comes from studies that have been carried out in places such as Australia, North America, China, and Sweden, while a small number of studies have been done in East Asia, the Middle East, Africa, and arid and semi-arid zones in South America, including Chile [11].
In Chile, only 8% of the area covered by wetlands is protected [12]. However, in recent decades the importance of these ecosystems has been recognized, resulting in legislative and judicial initiatives by the entities in charge of their protection [13]. Thus, since 2013, Chile has had a National Strategy for the Conservation and Rational Use of Wetlands [14] based on the Ramsar Convention. Nonetheless, a lack of basic and applied research on these ecosystems has been identified as one of the challenges facing the country [3]. Therefore, it is necessary to carry out studies at these sites that support the implementation of suitable policies for the conservation and sustainable use of wetlands. However, to understand the relationship between groundwater and vegetation, it is important to define their locations and boundaries using remote sensing and geographic information systems (GIS) [15,16].
Many studies have addressed the relationships between vegetation and groundwater [15,17] using various methods to identify zones where vegetation depends on groundwater, including terrestrial phreatophyte sampling [18] and isotope analysis [19]. However, these methods are costly and resource intensive, which has limited their application [10]. In addition, approaches combining vegetation characteristics and site environmental conditions such as permeability, geology, soil, and water table depth to identify areas with groundwater-dependent vegetation have been explored [20,21,22], although field validation has been little used [23].
Therefore, it is necessary to develop effective and economical methods that integrate geospatial environmental information and field validation, adopting an interdisciplinary approach with the integration of GIS technologies and collaboration between different disciplines to improve the management and protection of protected areas [24]. The objective of this study is to develop an effective methodology for identifying and mapping groundwater-dependent vegetation zones in wetlands to better understand their hydrogeological and ecological dynamics, providing useful information for their conservation and protection. A combination of a series of geospatial data on factors such as geology, land use/cover, and water table depth, along with spectral indices such as the topographic wetness index (TWI) and the normalized difference vegetation index (NDVI), integrated into a set of GIS layers, are used to map zones where there is high potential for groundwater influence on vegetation, and vegetation is sampled to assess phreatophyte species. This method can be used to identify zones where the vegetation indicates groundwater use in wetlands and is transferrable to other places. Thus, it generates information on these ecosystems that supports decision-makers and institutions to ensure sustainable management of them and protect their biodiversity.

2. Materials and Methods

2.1. Study Area

Santa Elena Lake is a body of water located in the Ñuble Region (Figure 1a), Bulnes Commune, longitude 72′38 W and latitude 36′80 S. The biodiversity and ecosystem services of this wetland are of great value to the area. It has notable freshwater avifauna biodiversity, as the lake has suitable conditions for its preservation [22]. Due to these characteristics, it was declared a protected area by the former National Environmental Commission (CONAMA) in 2002. It is currently in the Ñuble Regional Ministerial Secretary of the Environment Technical Justification File, through which it is proposed as the Santa Elena Lake Nature Sanctuary.
For the data processing and analysis of groundwater-dependent vegetation zones in this study, the area was limited to 792 ha (Figure 1b) to avoid a data overload in the software without compromising the representativeness of the results. This area was selected to incorporate zones farther from the main body of water, thus allowing a representative analysis of the changes in groundwater-dependent vegetation. The coordinates that delimit the area are (731526; 5924805); (731526; 5922523); (735276; 5922523); (735226; 5924805).
Northwest of the lake is an 8.7 ha wetland, connected to the lake, while approximately 3 km west of the lake is the Itata River after its confluence with the Diguillín River and before its confluence with the Ñuble River. To the east is Buena Vista Lake, connected to Santa Elena Lake by an irrigation canal, which acts as a natural spillway for the lake.
As it is located in the Central Valley of Chile, it has a mediterranean climate with winter rain and mild winters, with temperatures above −3 °C, and dry, warm, long summers of at least four months [25].
The main activities carried out in the area are agriculture and forestry of exotic species such as pine and eucalyptus [26].
The aquatic vegetation on the banks of the wetland (Figure 2) consists mainly of rushes and cattails. The terrestrial vegetation surrounding the wetland is made up of small groups of willows and poplars [26].

2.2. Methods

The proposed methodology for identifying and mapping groundwater-dependent vegetation zones in wetlands was designed with a practical and accessible approach, which not only ensures precision in the results, but also facilitates its application in other areas. The first step was a background analysis that allowed the selection of key parameters that have been scientifically proven to be relevant to both groundwater dynamics and vegetation.
Geospatial data on these parameters were gathered for the creation of layers in a geographic information system (GIS). Each layer was reclassified and normalized as a function of the importance of each parameter using ranges that reflect the degree of probability of interaction between groundwater and vegetation.
Subsequently, a multicriteria analysis combining the geospatial layers was carried out to generate a map of potential zones of groundwater influence on vegetation. The validation was performed via a field sampling of vegetation, in which the predominant species and terrain traits considered important for the study were identified. This approach allowed the precision of the map to be verified, confirming that the zones with the greatest probability of groundwater influence demonstrated the expected moisture conditions and the presence of groundwater-dependent vegetation. Figure 3 shows the flow diagram of the methodology adopted for this study.

2.2.1. Background Review

A literature review was conducted to identify geospatial parameters that could be predictors of the relationship between vegetation and groundwater. A total of 26 publications addressing the mapping and geospatial analysis of groundwater-dependent ecosystems were reviewed. To select predictor parameters in areas where vegetation is groundwater-dependent, those with a direct relationship to groundwater availability, soil, and vegetation were considered. In addition, it was prioritized that the information on these parameters be available in public inventories (Table 1). The review of the articles showed the relevance of these parameters to obtain precise and concrete results. Therefore, 5 geospatial parameters were selected: geology, land use/land cover, water table, NDVI, and TWI, which have sufficient evidence of their importance in similar studies (Table 2). Based on the literature analysis, the parameters of each layer were reclassified into a scale of high, medium, and low, and subsequently each of these parameters was normalized into levels of 1 to 3 (1 low, 2 medium, and 3 high).

2.2.2. GIS Data Layers

To create the layers with geospatial information in a GIS, first, the geomorphology of the zone in which the wetland is located was analyzed to understand how groundwater behaves in terms of its direction and storage. Taking into account the information available from wells, the data on static water levels were obtained, and with this information it was possible to generate a map in which the water table contour lines and groundwater flow direction can be observed.
A DEM of the area was obtained from ALOS PALSAR satellite images (12.5 m, https://search.asf.alaska.edu/#/). The model has a resolution of 12 × 12 m. The geographic coordinates of the raster were reprojected from WGS 84 to UTM 18S, then the image was cropped to focus on the study area and optimize the QGIS processes. This DEM was used to calculate topographic parameters such as slope that are necessary to process the thematic layers of the GIS.
Geology controls porosity and permeability, which are different for each substrate type, and it also affects groundwater storage [27]. The geology was acquired from the national geological map (1:1,000,000 scale) of the National Geology and Mining Service (SERNAGEOMIN, https://www.sernageomin.cl).
Land use/cover affects hydrological phenomena such as infiltration, runoff, percolation, and, therefore, groundwater [28]. It also provides information on the locations of human activities, vegetation, and wetlands [29]. The information on land use was obtained from the CONAF vegetation inventory (https://www.conaf.cl/).
With the information on measured static water levels, a map was generated with sectors or zones in the same depth range.
The topographic wetness index (TWI) is based on the idea that a terrain profile controls the distribution of water and the areas prone to water accumulation [30]. The TWI is calculated using Equation (1):
T W I = ln   α tan   β
where α is the catchment area and β is the slope. Among other uses, the TWI serves as a basic physical index that approximates the locations of surface saturation zones and the spatial distribution of surface water [31,32].
The normalized difference vegetation index (NDVI) is the ratio between the difference in the spectral reflectance of the near infrared band (NIR) and red band (R) [33,34] as shown in Equation (2):
N D V I = ( N I R − R ) ( N I R + R )
It has been demonstrated that the NDVI surpasses other indices in the quantification of vegetation that is normally scarce in arid environments [35], and it has been used in various studies to identify terrestrial ecosystems and wetlands that depend on groundwater [36,37]. Landsat 8 (https://earthexplorer.usgs.gov/) images with less than 30% cloud cover from 1 April 2023 to 31 March 2024 were used.

2.2.3. Multicriteria Analysis

For the integrated assessment, each of the geospatial parameters was normalized [38]. Then, the ranges of each layer were reclassified into levels 1 to 3 to establish their degree of importance or relationship with groundwater and vegetation, as the original variables are expressed in different units of measurement, with quite different ranges and a wide range of possibilities for interpretation in terms of representativeness [39]. Therefore, a scale of high, medium, and low was established. The scales and levels for the reclassification of the thematic layers were established based on the different bibliographic sources consulted and evaluation criteria for the different parameters that include: (1) topography of the terrain that facilitates water accumulation, (2) shallow water table, (3) slope, (4) permeability of the different types of substrates, and (5) presence of moisture in the soil. The level, scale, and reference for each parameter are shown in Table 2.
Table 2. Reclassification and normalization of geospatial parameters.
Table 2. Reclassification and normalization of geospatial parameters.
ParameterPotential ScaleClassRangeReferences
NDVILow−0.09–0.281[35,36,40,41,42]
Medium0.285–0.3452
High0.345–0.553
TWILow−0.51–41[30,43,44]
Medium4–5.52
High5.5–7.23
Land use/coverLowLake, reservoirs, ponds1[45,46,47,48,49]
Plantations
Freshwater bodies
MediumMixed forest2
Grasslands
HighNative forest3
Shrubland
Arborescent shrubland
Grassland-shrubland
Meadows
Depth rangeLow25–50 m1[50,51,52]
Medium10–25 m2
High5–10 m3
0–5 m
GeologyLowUndifferentiated tonalites and granodiorites 1[19,53]
MediumValley sediment2
HighGravel and sand3
Sandstone and siltstone

2.2.4. Mapping of Zones with High Groundwater Influence and Vegetation

To create this map, a linear superposition of the thematic layers of each of the parameters was performed, based on GIS with the help of the free software QGIS 3.34.7-Prizren. The linear combination of raster layers is a tool for analysis of geographic information and its presentation [54]. From the raster layers, different linear layer combination operations are carried out using the QGIS 3.34.7-Prizren Raster Calculator tool via Equation (3):
F 1 + F 2 + … F n / N
where Fn are the layers of each reclassified parameter and N is the number of layers.
The resulting map was classified into zones of high, medium, and low occurrence of groundwater and vegetation, respectively, allowing the linear combination performed to be visually highlighted.

2.2.5. Validation

For the field validation it was decided to perform a sampling of vegetation in zones where groundwater has a high influence on vegetation. With the map obtained from the multicriteria analysis, zones classified as having a high occurrence of groundwater and vegetation were selected, and given its compatibility with Google Earth Pro, it was analyzed to define georeferenced polygons for the sampling.
Considering tool and personnel availability, cost, and accessibility conditions, a characterization of the analyzed areas based on a visual inspection of the terrain was chosen. This technique has the advantage of quicker identification of species in a given area [55]. Sampling is proposed taking into account the objectives of the study and the characteristics of the ecosystem, guaranteeing greater precision in the results and in a more efficient manner. The aspects that were considered relevant when establishing the scope of our analysis area were to take into account a spatial gradient that includes the wetland that forms the lagoon and also the areas farther away from the main body of water in order to analyze the composition and distribution of groundwater-dependent vegetation, making sure to cover a broad spectrum of hydrological conditions and types of vegetation, since in this sense with a larger sample, more characteristics and variability of vegetation type are captured, reducing uncertainty and variability in the results.
Four field campaigns were carried out, which included an initial inspection of the areas that allowed the most suitable strategy for a survey to be developed. In addition, effective identification of species was aided by a mobile application for plant recognition, PlantNet; the Catalog of Vascular Plants of Chile; and expert and local judgement.
For the first campaign, a visit to the previously mapped areas was planned for their inspection. Aspects such as access (institutional, private, and public land) were kept in mind. This campaign allowed the best sampling strategy to be developed to save time and resources.
The remaining three campaigns were dedicated to a survey of the areas. It was planned to first survey the areas farthest from the lake to better differentiate the possible changes related to distance from the main body of water.
For the survey of these areas, the map obtained from the multicriteria analysis was used. Global positioning systems (GPS) were used to locate the dominant vegetation type in each of the polygons of the map, and a photographic camera was used to gather evidence of the plant species found, which was subsequently analyzed by a group of experts. In addition, soil characteristics and moisture conditions, information from residents of the area, and any other parameter relevant to the research were analyzed.

3. Results and Discussion

3.1. SIG Layers

3.1.1. Geomorphology

The study area is located in a geomorphological area called the Central Valley (Figure 4), which is bordered to the east by the Andes and to the west by the Coastal Range. This geomorphological unit is considered a regional sedimentary basin filled with material from the erosion of the Andes and Coastal Range that extends over a plain about 150 masl on average [56]. As seen in Figure 4, in the general area in which the wetland is located, the groundwater flow direction is E-W in the lower part, while toward the Itata River its direction is SE-NW, influenced by the Coastal Range, which acts as a hydraulic barrier [56].

3.1.2. Geology

Santa Elena Lake is located in the Itata basin, part of the Central Valley geological formation (Figure 4), where the fill thickness varies between 700 and 2000 m and consists of stratified Cenozoic volcanic and sedimentary rocks of a continental nature [24]. The Santa Elena Lake wetland lies atop the Mininco formation, which is a sequence of tuffaceous sandstones, siltstones, and conglomerates, with intercalations of claystones and tuffs [57]. In the terrain profiles carried out during the construction of wells near the lake, a surface stratum of low-permeability material is associated with the Mininco formation, and beneath it a stratum of boulders, gravel, and sand that may be associated with a paleochannel of the Itata River have been observed [56]. Therefore, two hydrogeological units can be defined: a low-permeability unit associated with consolidated deposits of the Mininco formation and a high-permeability unit associated with deposits of fluvial origin. The hydrological importance of rock type for classifying thematic layers with respect to the geological setting of the study area is considered here (Figure 5). In accordance with the rock characteristics, a high weight is assigned to sandstone and sand with silt and clay content because of their capacity to favor groundwater interactions. Moderate and low weights are assigned to the other rock complexes [44].

3.1.3. Land Use/Cover

Nine representative land uses were found in the wetland (Figure 6), which were classified taking into account the characteristics of the type of associated vegetation and its possible interaction with groundwater. Those land use categories that contain or are associated with phreatophytic vegetation species were considered, which represent groundwater-dependent species. Only the farmland and human activity areas were excluded [58]. Shrubland, arborescent shrubland and grassland-shrubland were put in the “high” category (Table 2), as they are found near riparian areas with a high potential for interaction with the groundwater of the ecosystem. Meadows were also placed in the “high” category, as they are characterized by locations in areas where the water table is at the surface or subsurface, where saturation levels may or may not occur [59]. Meanwhile, mixed and native forest were assigned to the “medium” category, as trees may be phreatophytes and use groundwater [53].

3.1.4. Static Water Level Range

Groundwater-dependent ecosystems and their associated vegetation are commonly located where the water table meets the topographic surface [60]. This type of vegetation is diverse in terms of its roots and water demands, so it has been defined that the critical depth of survival is between 9–10 m [49]. With the previously compiled information from well records [56], a map with zones in the same depth range can be obtained. Thus, areas (Figure 7) in a range of 0–10 m were assigned to the “high” category as the water table there is closer to the surface. Meanwhile, ranges of 10–25 m and 25–50 m were put in the “medium” and “low” categories, respectively, as at these depths it is difficult for the roots to access the groundwater. (Table 2).

3.1.5. TWI

The topographic wetness index TWI (Figure 8) aids in identifying the areas of greatest soil moisture due to flow saturation [44]. It was regrouped into three categories and ranged from −0.5 to 7.3 (Table 2). From −0.51 to 4 was assigned the “low” scale because these are areas with higher elevation and lower soil moisture content. The interval from 4 to 5.5 was assigned the “medium” scale and the interval from 5.5 to 7.2 was assigned the “high” scale because they are areas with topographic conditions that favor the accumulation of water, which would allow moisture during dry periods. The highest values are observed around the lake in low areas with gentle slopes.

3.1.6. NDVI

The NDVI (Figure 9) is a reliable indicator of vegetation density and vigor [36]. Values between 0 and 1 indicate the presence of vegetation, with 1 indicating the greatest possible density of green vegetation [28]. The NDVI values ranged from −0.1 to 0.5, indicating low moisture conditions; therefore, only the 0.35–0.55 range was assigned to the “high” category (Table 2) which included low areas where there is higher moisture content in the soil and denser vegetation.

3.2. Vegetation Mapping

The linear superposition of layers method was applied to obtain areas where groundwater has a high influence on vegetation, and only urban housing areas and cropland were excluded. The resulting map shows the areas with a high, medium, and low occurrence of groundwater and vegetation, represented by a false color composite, where white represents the areas excluded due to not being of interest in this study, as shown in Figure 10. The areas classified as “high” are located in riverside zones adjacent to the lakes, as well as in the wetland associated with the main lake and in valleys between Santa Elena Lake and the Itata River. The areas with a high potential for groundwater influence on vegetation are located mainly in zones with medium and low slopes, where the soil has a high potential for infiltration and moisture, with a water table depth of less than 10 m, as well as denser vegetation. The areas classified as “medium” are located in slightly higher zones, with most vegetation consisting of forestry plantations. The areas with a “low” classification are located in higher zones with steeper slopes, where the water table depth exceeds 25 m, and that due to their geological formation have low permeability.

3.3. Validation

For the field validation, 10 georeferenced polygons were defined based on map analysis with Google Earth Pro to plan the field campaigns.
In the first field validation campaign, it was confirmed that 70% of the area to be sampled was very hard to access to carry out a forest vegetation survey and possible statistical analysis due to the presence of thorny bushes and swampy zones that prevented entry into various areas. Nonetheless, it was possible to access strategic points that allowed a visual characterization of these areas. For better organization, each of the polygons selected for the sampling was numbered, as seen in Figure 11.
The characterization of the areas was carried out considering soil characteristics and moisture conditions, vegetation type and condition, information from residents of the area, and any other parameter relevant to the study.
Area 1 presents heterogenous vegetation, with phreatophyte species predominating in the dense native forest. The species found there include poplar, basket willow, boldo, rushes, and grasses. The tree species such as poplar, boldo, and willow are mature and in good health.
Areas 2 and 3 have savannah characteristics with high soil moisture conditions. Espino, an invasive species that adapts very well to different types of environments, predominates in these zones. Isolated groups of phreatophyte species such as boldo and soapbark are also present.
On the trip through areas 4 and 5, they were confirmed to have high soil moisture content, with rushes and grasses predominating.
In areas 6 and 7, as in the previous areas, phreatophytes are the predominant vegetation, with species such as white willow, basket willow, and boldo. The marshiest area is covered by different types of rushes and cattails. In addition to these species, pine specimens were also found distributed irregularly throughout the zone.
In areas 8, 9, and 10, in the vicinity of Santa Elena and Buena Vista lakes, the vegetation is composed of white willow, weeping willow, poplar, basket willow, and—closer to the lakes—rushes and cattails.
In general, the forestry plantations found in these areas presented less development and in some cases a deteriorated state of health, suggesting that these high-moisture areas do not offer optimal conditions for their proper development. In addition, the presence of invasive species interspersed with the phreatophytic vegetation was observed, a phenomenon especially notable in grassland and pasture areas. This pattern could be related to livestock activity, as the presence of cattle promotes the spread of these of invasive species.
Thus, the field validation showed that 100% of the sampling zones are dominated by phreatophytic vegetation, although it coexists with invasive species that can compete for water and soil nutrients. Through the image analysis and expert consultation, fourteen species were identified within the sampled areas (Table 3), of which nine are phreatophytes, four are invasive species, and one is part of a forestry plantation, as shown in Table 3.
Photographs were taken at several points in the visited areas (Figures S1–S8), in which significant patterns in soil moisture conditions and vegetation type were observed. Each of these points has georeferenced information (Table S1), which could be of great importance in future studies to assess the ecohydrological responses of these zones to hydroclimatic and anthropogenic changes in different periods and in turn serve as a sort of environmental monitoring network. In addition, by incorporating a map of vegetation zones dependent on groundwater, it is possible to plan and manage the territory in order to reduce risks associated with anthropogenic and environmental phenomena [61,62].
It was observed in the visited zones that there is a variation in the spatial distribution of phreatophyte species. In the highest areas, the predominant species are poplars, willows, boldo, and soapbark, while in the lower areas and those with a higher degree of soil saturation, grasslands, pastures, rushes, and cattails predominate. This variation in distribution shows a tendency of the species to follow the soil moisture gradient, which contributes to a better understanding of the hydrogeological and ecological dynamics of wetlands.

4. Conclusions

This study proposes a geospatial method with field validation, with an interdisciplinary approach that allows areas where vegetation is dependent on groundwater in wetlands to be mapped. Through a background review, five parameters predictive of the relationship between vegetation and groundwater were identified.
Based on these parameters—geology, land use/cover, water table depth, NDVI, and TWI—thematic layers were made in GIS to map groundwater-dependent vegetation zones. The resulting map allowed the study area to be classified into zones of high, medium, and low probabilities of groundwater influencing vegetation. The high-probability zones were analyzed along with satellite images, with 10 polygons within the study area for field validation obtained.
The analysis of images captured in the field and expert opinion allowed three main vegetation groups in the studied areas to be identified: forestry, phreatophyte, and invasive. A total of fourteen species were identified, nine of which are phreatophytes.
The field validation confirmed that 100% of the areas presented soil moisture conditions and phreatophytic vegetation as the dominant group, validating the method and confirming that the selected parameters are predictive of the relationship between groundwater and vegetation in these ecosystems. In addition, the spatial distribution of the species tends to follow the soil moisture gradient, which contributes to a better understanding of hydrogeological and ecological dynamics in wetlands.
The results highlight how efficient remote sensing techniques and GIS complemented by field validation can be for identifying direct relationships between groundwater and vegetation in wetlands. The methodology developed in this study can be applied to other similar wetlands, generating valuable information that will allow decision-makers to protect these vulnerable ecosystems from the natural and anthropogenic pressures to which they are subjected.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17040534/s1, Figure S1. Sampling area #1 with image taking points for vegetation identification; Figure S2. Sampling area #2 with image taking points for vegetation identification; Figure S3. Sampling area #3 with imaging points for identification; Figure S4. Sampling area #4 and #5 with image taking points for vegetation identification; Figure S5. Sampling area #6 with imaging points for vegetation identification; Figure S6. Sampling area #7 with image taking points for vegetation identification; Figure S7. Sampling area #8 with image taking points for vegetation identification; Figure S8. Sampling area #9 and #10 with image taking points for vegetation identification. Table S1. Geospatial locations of points where images were captured.

Author Contributions

Conceptualization, O.A.R. and J.L.A.; methodology, O.A.R. and J.L.A.; software, O.A.R.; validation, O.A.R.; formal analysis, O.A.R.; investigation, O.A.R.; resources, J.L.A.; data curation, O.A.R., J.L.A., and R.U.; writing—original draft preparation, O.A.R.; writing—review and editing, O.A.R., J.L.A., and R.U.; visualization, O.A.R.; supervision, J.L.A. and R.U.; project administration, O.A.R. and J.L.A.; funding acquisition, J.L.A. and R.U. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Beca de Doctorado Nacional de la Agencia de Investigación y Desarrollo ANID and the APC was funded by CRHIAM Water Research Center: ANID/FONDAP/1523A0001.

Data Availability Statement

All data generated by this study are available in this manuscript.

Acknowledgments

We want to thank CRHIAM Water Research Center: ANID/FONDAP/1523A0001 and acknowledge Carlos Andrés Cea Eriza and Felipe Andrés Recabal Navarrete for supporting field campaigns. We also acknowledge Andres Acuna, Agrícola Las Lagunas Limitada for providing us with access to the study area.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Junk, W.J.; An, S.; Finlayson, C.M.; Gopal, B.; Květ, J.; Mitchell, S.A.; Mitsch, W.J.; Robarts, R.D. EFFECTS OF CLIMATE CHANGE ON WETLANDS Current state of knowledge regarding the world’s wetlands and their future under global climate change: A synthesis. Aquat. Sci. 2013, 75, 151–167. [Google Scholar] [CrossRef]
  2. Ramsar, S.D. Manual de la Convención de Ramsar: Guía a la Convención sobre los Humedales; Secretaría de la Convención de Ramsar: Gland, Switzerland, 2006; 96p. [Google Scholar]
  3. Ramsar. 12a Reunión de la Conferencia de las Partes. Available online: https://www.ramsar.org/es/evento/12a-reunion-de-la-conferencia-de-las-partes (accessed on 27 April 2024).
  4. De la Hera, À.; Gurrieri, J.; Puri, S.; Custodio, E.; Manzano, M. Ecohydrology and hydrogeological processes: Groundwater–ecosystem interactions with special emphasis on abiotic processes. Ecohydrol. Hydrobiol. 2016, 16, 99–105. [Google Scholar] [CrossRef]
  5. Naumburg, E.; Mata-Gonzalez, R.; Hunter, R.G.; Mclendon, T.; Martin, D.W. Phreatophytic Vegetation and Groundwater Fluctuations: A Review of Current Research and Application of Ecosystem Response Modeling with an Emphasis on Great Basin Vegetation. Environ. Manag. 2005, 35, 726–740. [Google Scholar] [CrossRef] [PubMed]
  6. Aslam, R.A.; Shrestha, S.; Pandey, V.P. Groundwater vulnerability to climate change: A review of the assessment methodology. Sci. Total Environ. 2018, 612, 853–875. [Google Scholar] [CrossRef]
  7. Howard, J.; Merrifield, M. Mapping Groundwater Dependent Ecosystems in California. PLoS ONE 2010, 5, e11249. [Google Scholar] [CrossRef]
  8. El-Hokayem, L.; De Vita, P.; Usman, M.; Link, A.; Conrad, C. Mapping potentially groundwater-dependent vegetation in the Mediterranean biome using global geodata targeting site conditions and vegetation characteristics. Sci. Total. Environ. 2023, 898, 166397. [Google Scholar] [CrossRef]
  9. Kløve, B.; Ala-Aho, P.; Bertrand, G.; Gurdak, J.J.; Kupfersberger, H.; Kværner, J.; Muotka, T.; Mykrä, H.; Preda, E.; Rossi, P.; et al. Climate change impacts on groundwater and dependent ecosystems. J. Hydrol. 2014, 518, 250–266. [Google Scholar] [CrossRef]
  10. Hoyos, C.P.; Krakauer, N.Y.; Khanbilvardi, R.; Armstrong, R.A. A review of advances in the identification and characterization of groundwater dependent ecosystems using geospatial technologies. Geosciences 2016, 6, 17. [Google Scholar] [CrossRef]
  11. Hernandez, J.O. Ecophysiological effects of groundwater drawdown on phreatophytes: Research trends during the last three decades. Land 2022, 11, 2061. [Google Scholar] [CrossRef]
  12. Ministry of the Environment of Chile, United Nations Development Program (PNUD) and Global Environment Facility (GEF). National Biodiversity Strategy (2017–2030). Available online: https://estrategia-aves.mma.gob.cl/wp-content/uploads/2023/03/MMA_2017_Estrategia_Nacional_Biodiversidad_2017-2030.pdf (accessed on 3 May 2024).
  13. Bergamini, K.; Martínez, C.; Mollenhauer, K.; Pérez, C.; Hernández, J.; Gutiérrez, P. Toolkit para la gestión y conservación de humedales. Una propuesta de fortalecimiento a su actual institucionalidad. In Propuestas para Chile; Concurso de Políticas Públicas UC: Santiago, Chile, 2019; pp. 255–288. [Google Scholar]
  14. Ministerio del Medio Ambiente. Ministerio del Medio Ambiente. Plan Nacional de Protección de Humedales 2018–2022. 2018. Available online: https://mma.gob.cl/wp-content/uploads/2018/11/Plan_humedales_Baja_confrase_VERSION-DEFINITIVA.pdf (accessed on 26 September 2024).
  15. Orellana, F.; Verma, P.; Loheide, S.P.; Daly, E. Monitoring and modeling water-vegetation interactions in groundwater-dependent ecosystems. Rev. Geophys. 2012, 50, 3003. [Google Scholar] [CrossRef]
  16. Coruhlu, Y.E.; Baser, V.; Yildiz, O. Object-based geographical data model for determination of the cemetery sites using SWOT and AHP integration. Surv. Rev. 2021, 53, 108–121. [Google Scholar] [CrossRef]
  17. Erostate, M.; Huneau, F.; Garel, E.; Ghiotti, S.; Vystavna, Y.; Garrido, M.; Pasqualini, V. Groundwater dependent ecosystems in coastal Mediterranean regions: Characterization, challenges and management for their protection. Water Res. 2020, 172, 115461. [Google Scholar] [CrossRef] [PubMed]
  18. Meinzer, E. Plants as Indicators of Ground Water; US Government Printing Office: Washington, DC, USA, 1927.
  19. Jones, C.; Stanton, D.; Hamer, N.; Denner, S.; Singh, K.; Flook, S.; Dyring, M. Field investigation of potential terrestrial groundwater-dependent ecosystems within Australia’s Great Artesian Basin. Hydrogeol. J. 2020, 28, 237–261. [Google Scholar] [CrossRef]
  20. Naghibi, S.A.; Dashtpagerdi, M.M. Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features. Hydrogeol. J. 2017, 25, 169–189. [Google Scholar] [CrossRef]
  21. Ozdemir, A. GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. J. Hydrol. 2011, 411, 290–308. [Google Scholar] [CrossRef]
  22. Sener, E.; Davraz, A. Assessment of groundwater vulnerability based on a modified DRASTIC model, GIS and an analytic hierarchy process (AHP) method: The case of Egirdir Lake basin (Isparta, Turkey). Hydrogeol. J. 2013, 21, 701–714. [Google Scholar] [CrossRef]
  23. Duran-Llacer, I.; Arumí, J.L.; Arriagada, L.; Aguayo, M.; Rojas, O.; González-Rodríguez, L.; Rodríguez-López, L.; Martínez-Retureta, R.; Oyarzún, R.; Singh, S.K. A new method to map groundwater-dependent ecosystem zones in semi-arid environments: A case study in Chile. Sci. Total Environ. 2022, 816, 151528. [Google Scholar] [CrossRef]
  24. Elgueta, S.; Rubio, X. Estratigrafia del Terciario del Sector Occidental de la Depression Central Entre los 37° y 38°S; Servicio Nacional Geol. Mineria: Santiago, Chile, 1991; pp. 723–727. [Google Scholar]
  25. Santibáñez, Q.; Fernando. Atlas Agroclimático de Chile. Estado Actual y Tendencias del Clima. Tomo VI: Regiones de Aysén y Magallanes, Santiago. 2017. Available online: http://www.agrimed.cl/atlas/tomo4.html (accessed on 17 June 2024).
  26. González-Acuña, D.; Benavente, C.; Figueroa, R. Avifauna de la Laguna Santa Elena, Región de Biobío. Boletín Chil. De Ornitol. 2004, 10, 13–18. [Google Scholar]
  27. Miraki, S.; Zanganeh, S.H.; Chapi, K.; Singh, V.P.; Shirzadi, A.; Shahabi, H.; Pham, B.T. Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach. Water Resour. Manag. 2019, 33, 281–302. [Google Scholar] [CrossRef]
  28. Pal, S.; Kundu, S.; Mahato, S. Groundwater potential zones for sustainable management plans in a river basin of India and Bangladesh. J. Clean. Prod. 2020, 257, 120311. [Google Scholar] [CrossRef]
  29. Mallick, J.; Khan, R.A.; Ahmed, M.; Alqadhi, S.D.; Alsubih, M.; Falqi, I.; Hasan, M.A. Modeling Groundwater Potential Zone in a Semi-Arid Region of Aseer Using Fuzzy-AHP and Geoinformation Techniques. Water 2019, 11, 2656. [Google Scholar] [CrossRef]
  30. Qadir, J.; Bhat, M.S.; Alam, A.; Rashid, I. Mapping groundwater potential zones using remote sensing and GIS approach in Jammu Himalaya, Jammu and Kashmir. GeoJournal 2020, 85, 487–504. [Google Scholar] [CrossRef]
  31. Beven, K.J.; Kirkby, M.J. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol. Sci. J. 1979, 24, 43–69. [Google Scholar] [CrossRef]
  32. O’Loughlin, E.M. Prediction of Surface Saturation Zones in Natural Catchments by Topographic Analysis. Water Resour. Res. 1986, 22, 794–804. [Google Scholar] [CrossRef]
  33. Rodríguez-López, L.; Duran-Llacer, I.; González-Rodríguez, L.; Abarca-Del-Rio, R.; Cárdenas, R.; Parra, O.; Martínez-Retureta, R.; Urrutia, R. Spectral analysis using LANDSAT images to monitor the chlorophyll-a concentration in Lake Laja in Chile. Ecol. Inform. 2020, 60, 101183. [Google Scholar] [CrossRef]
  34. Szabó, S.; Elemér, L.; Kovács, Z.; Püspöki, Z.; Kertész, A.; Singh, S.K.; Balázs, B. NDVI dynamics as reflected in climatic variables: Spatial and temporal trends–a case study of Hungary. GIsci. Remote Sens. 2019, 56, 624–644. [Google Scholar] [CrossRef]
  35. Huntington, J.; McGwire, K.; Morton, C.; Snyder, K.; Peterson, S.; Erickson, T.; Niswonger, R.; Carroll, R.; Smith, G.; Allen, R. Assessing the role of climate and resource management on groundwater dependent ecosystem changes in arid environments with the Landsat archive. Remote. Sens. Environ. 2016, 185, 186–197. [Google Scholar] [CrossRef]
  36. Barron, O.V.; Emelyanova, I.; Van Niel, T.G.; Pollock, D.; Hodgson, G. Mapping groundwater-dependent ecosystems using remote sensing measures of vegetation and moisture dynamics. Hydrol. Process. 2014, 28, 372–385. [Google Scholar] [CrossRef]
  37. Doody, T.M.; Barron, O.V.; Dowsley, K.; Emelyanova, I.; Fawcett, J.; Overton, I.C.; Pritchard, J.L.; Van Dijk, A.I.; Warren, G. Continental mapping of groundwater dependent ecosystems: A methodological framework to integrate diverse data and expert opinion. J. Hydrol. Reg. Stud. 2017, 10, 61–81. [Google Scholar] [CrossRef]
  38. Molero, E.; Grindla, A.L.; Asensio, J.J. Escenarios de aptitud y modelización cartográfica del crecimiento urbano mediante técnicas de evaluación multicriterio. GeoFocus Int. Rev. Geogr. Inf. Sci. Technol. 2007, 7, 120–147. [Google Scholar]
  39. de Cos Guerra, O.; Martín Latorre, E. Evaluación Multicriterio y Delimitación de Espacios Funcionales: Aplicación SIG para la Definición de Mapas Comarcales. 2007. Available online: https://repositorio.unican.es/xmlui/bitstream/handle/10902/4380/SIG%20y%20evaluaci%c3%b3n%20multicriterio%20propuesta%20metodol%c3%b3gica.pdf?sequence=6&isAllowed=y (accessed on 18 August 2024).
  40. Ortiz, M.; Morales, L.; Silva, P.; Acevedo, E. Estimación del nivel freático a partir del NDVI Landsat en la Pampa del Tamarugal (Chile). Rev. De Teledetección 2012, 37, 42–50. [Google Scholar]
  41. Eamus, D.; Fu, B.; Springer, A.E.; Stevens, L.E. Groundwater dependent ecosystems: Classification, identification techniques and threats. In Integrated Groundwater Management: Concepts, Approaches and Challenges; Springer: Berlin/Heidelberg, Germany, 2016; pp. 313–346. [Google Scholar]
  42. Gou, S.; Gonzales, S.; Miller, G.R. Mapping Potential Groundwater-Dependent Ecosystems for Sustainable Management. Groundwater 2014, 53, 99–110. [Google Scholar] [CrossRef] [PubMed]
  43. Muñoz, N.A.C. Método Para la Caracterización de las Formas del Terreno en Zonas de Montaña Utilizando Modelos Digitales de Elevación. Caso: Departamento del Cauca. Universidad Nacional de Colombia. [Online]. 2013. Available online: https://repositorio.unal.edu.co/handle/unal/21209 (accessed on 18 August 2024).
  44. PArulbalaji; Padmalal, D.; Sreelash, K. GIS and AHP Techniques Based Delineation of Groundwater Potential Zones: A case study from Southern Western Ghats, India. Sci. Rep. 2019, 9, 2082. [Google Scholar] [CrossRef]
  45. Nosetto, M.D.; Jobbágy, E.G.; Paruelo, J.M. Land-use change and water losses: The case of grassland afforestation across a soil textural gradient in central Argentina. Glob. Change Biol. 2005, 11, 1101–1117. [Google Scholar] [CrossRef]
  46. Le Maitre, D.C.; Scott, D.F.; Colvin, C. Review of information on interactions between vegetation and groundwater. Water 1999, 25, 137–152. [Google Scholar]
  47. Jobbágy, E.G.; Jackson, R.B. Groundwater use and salinization with grassland afforestation. Glob. Change Biol. 2004, 10, 1299–1312. [Google Scholar] [CrossRef]
  48. Scanlon, B.R.; Jolly, I.; Sophocleous, M.; Zhang, L. Global impacts of conversions from natural to agricultural ecosystems on water resources: Quantity versus quality. Water Resour. Res. 2007, 43, 2–12. [Google Scholar] [CrossRef]
  49. Inzunza, E.H. Cambios en los Niveles de Aguas Subterráneas y su Efecto Sobre la Zona no Saturada y la Vegetación. Enfoque conceptual y numérico. 2014 Henseleit Inzunza, E. Cambios en los Niveles de Aguas Subterráneas y su Efecto Sobre la zona no Saturada y la Vegetación. Enfoque Conceptual y Numérico. 2014. Available online: https://repositorio.uchile.cl/handle/2250/115577 (accessed on 3 February 2025).
  50. Mould, D.J.; Frahm, E.; Salzmann, T.; Miegel, K.; Acreman, M.C. Acreman. Evaluating the use of diurnal groundwater fluctuations for estimating evapotranspiration in wetland environments: Case studies in southeast England and northeast Germany. Ecohydrology 2010, 3, 294–305. [Google Scholar] [CrossRef]
  51. Martinet, M.C.; Vivoni, E.R.; Cleverly, J.R.; Thibault, J.R.; Schuetz, J.F.; Dahm, C.N. On groundwater fluctuations, evapotranspiration, and understory removal in riparian corridors. Water Resour. Res. 2009, 45, 6–9. [Google Scholar] [CrossRef]
  52. Butler, J.J.; Kluitenberg, G.J.; Whittemore, D.O.; Loheide, S.P.; Jin, W.; Billinger, M.A.; Zhan, X. A field investigation of phreatophyte-induced fluctuations in the water table. Water Resour. Res. 2007, 43, 6–12. [Google Scholar] [CrossRef]
  53. Dresel, P.E.; White, M.; Zydor, H.; Griffioen, P.; Clark, R.; Reid, M. Development of a Method to Map Sensitivity of Groundwater Dependent Ecosystems in Victoria; Department of Primary Industries: Melbourne, Australia, 2010. [Google Scholar]
  54. Vera, P.G. Combinación lineal de capas ráster con QGIS. 2016. Available online: https://www.researchgate.net/profile/Pedro-Galindo-Vera/publication/301220759_Combinacion_lineal_de_capas_rater_con_QGIS/links/570d1f2d08ae3199889bb36d/Combinacion-lineal-de-capas-raster-con-QGIS.pdf (accessed on 30 August 2024).
  55. Sutherland, W.J. Ecological Census Techniques: A Handbook; Cambridge University Press: Cambridge, UK, 2006. [Google Scholar]
  56. Aquaterra. Estudio Hidrogeológico Cuencas Bío Bío e Itata. Cuenca Itata. Tomo 1: Informe Final. 1 December 2011. Available online: https://repositoriodirplan.mop.gob.cl/biblioteca/handle/20.500.12140/25823 (accessed on 28 September 2024).
  57. Anavalón Cabrera, A. Evolución tectonosedimentaria de los depósitos plio-pleistocenos del valle central de Chile, entre los 36° 50’ S y 38° 50’ S. 2018. Available online: https://repositorio.sernageomin.cl/handle/0104/25674 (accessed on 2 September 2024).
  58. Klausmeyer, K.; Howard, J.; Keeler-Wolf, T.; Davis-Fadtke, K.; Hull, R.; Lyons, A. Mapping Indicators of Groundwater Dependent Ecosystems in California: Methods Report; The Nature Conservancy: San Francisco, CA, USA, 2018. [Google Scholar]
  59. Ahumada, M.; Faúndez, L. Guía Descriptiva de los Sistemas Vegetacionales Azonales Hídricos Terrestres de la Ecorregión Altiplánica; Servicio Agrícola y Ganadero: Santiago, Chile, 2009. [Google Scholar]
  60. Martínez-Santos, P.; Díaz-Alcaide, S.; De la Hera-Portillo, A.; Gómez-Escalonilla, V. Mapping groundwater-dependent ecosystems by means of multi-layer supervised classification. J. Hydrol. 2021, 603, 126873. [Google Scholar] [CrossRef]
  61. El Concepto-Herramienta de la Seguridad Territorial y la Gestión de Humedales | Biodiversidad en la Práctica. Available online: https://revistas.humboldt.org.co/index.php/BEP/article/view/453 (accessed on 29 January 2025).
  62. Cámara Artigas, R.; Rodrigues de Viana, E.; Díaz del Olmo, F. Cartografía del Manglar de la Desembocadura del río Paraiba (Estado de Paraiba, Brasil) Como Instrumento Para la Gestión de Humedales Tropicales: Aplicación de Técnicas de Teledetección en Imágenes QuickBird y Landsat. In Biogeografía: Una Ciencia para la Conservación del Medio: (VI Congreso Español de Biogeografía, Alicante, 2010); Dialnet: La Rioja, Spain, 2010; pp. 425–433. ISBN 978-84-933457-1-6. Available online: https://dialnet.unirioja.es/servlet/articulo?codigo=3333673 (accessed on 29 January 2025).
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Figure 1. Location of study area, Santa Elena Lake wetland. (a) Geographic context with respect to Chile; (b) groundwater-dependent vegetation analysis zone.
Figure 1. Location of study area, Santa Elena Lake wetland. (a) Geographic context with respect to Chile; (b) groundwater-dependent vegetation analysis zone.
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Figure 2. Characteristic vegetation of study area.
Figure 2. Characteristic vegetation of study area.
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Figure 3. Flow diagram of methodology adopted for mapping of groundwater-dependent vegetation zones.
Figure 3. Flow diagram of methodology adopted for mapping of groundwater-dependent vegetation zones.
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Figure 4. Geomorphology, water table contour lines, and groundwater flow direction.
Figure 4. Geomorphology, water table contour lines, and groundwater flow direction.
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Figure 5. Reclassified geological map.
Figure 5. Reclassified geological map.
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Figure 6. Reclassified land use/cover.
Figure 6. Reclassified land use/cover.
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Figure 7. Reclassified static water level map.
Figure 7. Reclassified static water level map.
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Figure 8. Reclassified TWI map.
Figure 8. Reclassified TWI map.
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Figure 9. Reclassified NDVI map.
Figure 9. Reclassified NDVI map.
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Figure 10. Map of groundwater-dependent vegetation zones: (1) geology, (2) land use/cover, (3) static water level, (4) TWI, (5) NDVI.
Figure 10. Map of groundwater-dependent vegetation zones: (1) geology, (2) land use/cover, (3) static water level, (4) TWI, (5) NDVI.
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Figure 11. Areas selected for field validation.
Figure 11. Areas selected for field validation.
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Table 1. Sources and types of information used in this study.
Table 1. Sources and types of information used in this study.
NameSourceInformation
Alaska Satellite Facility (ASF)https://search.asf.alaska.edu/#/Digital elevation model (DEM)
National Geology and Mining Service (SERNAGEOMIN)https://www.sernageomin.clGeology
National Forestry Corporation (CONAF)https://www.conaf.cl/Land use/cover
General Water Directorate (DGA)https://dga.mop.gob.cl/Static water level data
United States Geological Survey (USGS)https://earthexplorer.usgs.gov/Satellite images for the calculation of spectral indices
Ministry of the Environmentwww.mma.gob.clEnvironmental legislation and regulations for wetland protection
The Convention on Wetlandswww.ramsar.orgInformation on wetlands in Chile
Scopushttps://www.scopus.comReview of scientific articles on mapping of potential groundwater zones and conceptual aspects of vegetation in wetlands. Selection of geospatial parameters predictive of the presence of groundwater and vegetation
Web of Sciencehttps://www.webofscience.com
Google Scholarhttps://scholar.google.com
Table 3. Plant species identified in the field.
Table 3. Plant species identified in the field.
Scientific NameCommon NameClassification
Salix viminalis L.Basket willowPhreatophyte
Salix alba L.White willow
Salix babylonica L.Weeping willow
Populus nigra L.Black poplar
Peumus boldus MolinaBoldo
Quillaja saponaria MolinaSoapbark
Typha domingensisCattail
PoaceaeGrasses
Juncus articulatusRush
Acacia dealbataSilver wattle Invasive
Rubus ulmifoliusElmleaf blackberry
Rosa eglanteriaSweetbriar
Acacia cavenEspino
Pinus radiataPineForestry
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Rodríguez, O.A.; Arumí, J.L.; Urrutia, R. Mapping Groundwater-Dependent Vegetation Zones: Application of GIS and Multicriteria Analysis with Field Validation. Water 2025, 17, 534. https://doi.org/10.3390/w17040534

AMA Style

Rodríguez OA, Arumí JL, Urrutia R. Mapping Groundwater-Dependent Vegetation Zones: Application of GIS and Multicriteria Analysis with Field Validation. Water. 2025; 17(4):534. https://doi.org/10.3390/w17040534

Chicago/Turabian Style

Rodríguez, Osmel Abreu, José Luis Arumí, and Roberto Urrutia. 2025. "Mapping Groundwater-Dependent Vegetation Zones: Application of GIS and Multicriteria Analysis with Field Validation" Water 17, no. 4: 534. https://doi.org/10.3390/w17040534

APA Style

Rodríguez, O. A., Arumí, J. L., & Urrutia, R. (2025). Mapping Groundwater-Dependent Vegetation Zones: Application of GIS and Multicriteria Analysis with Field Validation. Water, 17(4), 534. https://doi.org/10.3390/w17040534

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