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Article

Effects of Wind Erosion Control Measures on Vegetation Dynamics and Soil-Surface Materials through Field Observations and Vegetation Indices in Arid Areas, Southeastern Tunisia

by
Ameni Khatteli
1,2,*,
Abderrazak Tlili
3,
Mohamed Chaieb
2 and
Mohamed Ouessar
1
1
Laboratory of Eremology and Combating Desertification (LR16IRA01), Institut des Régions Arides (IRA), University of Gabes, Medenine 4119, Tunisia
2
Laboratory of Biology & Ecophysiology of Plants in Arid Environments, Faculty of Sciences of Sfax, University of Sfax, Sfax 3038, Tunisia
3
Laboratory of Pastoral Ecosystems and Valorization of Spontaneous Plants and Associated Microorganisms (LR16IRA03), Institut des Régions Arides (IRA), University of Gabes, Medenine 4119, Tunisia
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(19), 14256; https://doi.org/10.3390/su151914256
Submission received: 11 August 2023 / Revised: 8 September 2023 / Accepted: 12 September 2023 / Published: 27 September 2023
(This article belongs to the Section Environmental Sustainability and Applications)
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Versions Notes

Abstract

:
Effective land management in the Djeffara plain, southeastern Tunisia, is being constrained by increasing land degradation issues due to arid climate conditions and soil erosion. Thus, this study aims to assess the impact of the integrated control measures, namely windbreaks and controlled grazing, on the restoration of land cover dynamics in six managed rangeland areas. Land cover changes were monitored using satellite data and the derived vegetation indices (the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI)) from Landsat 8 (OLI), both within and outside the protected areas. The findings reveal that the implemented protection measures lead to an increase in vegetation cover, diversity, and plant density. They play an important role in stabilizing the upper soil layer. The oldest protected areas, particularly those that are well-maintained with controlled seasonal grazing, experienced a reduction in sand movement. The reintroduction of grazing should, however, be controlled to prevent degradation risks. The results show strong correlations between vegetation cover and both calculated vegetation indices, (0.73 < R2 < 0.91), with more accurate estimating for the SAVI. The findings of this research can guide decision-makers for restoring degraded rangelands and planning effective control measures for wind erosion.

1. Introduction

Progressive land degradation and continuous biodiversity loss define huge challenges for healthy ecosystem functioning in many countries all over the world [1]. Climate change, drought periods, frequent extreme events, the overexploitation of natural resources, and inappropriate management measures have induced short- and long-term adverse impacts on different environmental components, namely soil fertility and plant diversity [2,3,4,5]. These impacts have commonly been observed and they have induced irreversible effects inhibiting the rehabilitation of the degraded resources and the restoration of pristine conditions. Effective management approaches rely, correspondingly, on an accurate evaluation of the amplitude of the processes in terms of spatial extent and disturbance exposition duration. Thus, a large spectrum of functional indices and structural attributes have been developed to assess land degradation issues, namely plant richness and vegetation cover, that have gained widespread recognition [6,7,8].
Direct in-field measurements are time-consuming, labor-intensive, and often limited to small monitored plots [9]. Therefore, an approach combining ecological indicators with remote sensing defines a powerful tool for environmental studies [10]. The normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) are frequently employed to characterize vegetation cover [11].
The NDVI is an index measuring levels of almost low vegetation cover, and is commonly used for reliable information, gained through the vegetation’s differential absorption, on the visible incident solar radiation and the reflection NIR ratio. To adjust to the variations in the soil moisture and account for the brightness of soil covered with lower sparse vegetation, the SAVI is used, relying on the correction to the L-factor [3,12,13]. It refers to a constant adjustment factor between −1 and 1, reducing the sensitivity of the index to soil brightness. Different vegetation densities intersect the soil line at the same location. The L-factor is defined as 0.5 and it is useful for applications in sparse vegetative cover [12,14].
Tunisian arid ecosystems provide a suitable model for studying land degradation due to their geomorphologic features, hydro-climatic conditions, and anthropogenic activities. These vast areas are predominantly occupied by Chamaephytes and Hemicryptophytes, which represent the resilient life forms in the desert ecosystems of SE Tunisia and play a crucial ecological role [15,16,17]. The natural vegetation in these regions is threatened by abiotic factors such as low rainfall and high temperatures, as well as anthropogenic pressures like overgrazing and wood harvesting. The cumulative impacts of these factors compromise the quality and the stability of soil resources, resulting in a progressive soil degradation [18]. They contribute to high erosion rates, which is a prevalent issue throughout Tunisia. Overgrazing in particular leads to the decline or disappearance of highly palatable plant species, the proliferation of unpalatable species, and ultimately soil degradation [19].
For decades, these steppe zones were primarily used for grazing and have faced significant pressure from the growing population [20]. Various rangeland management measures in the short and long term have been implemented to enhance pastoral production and mitigate the risks of soil degradation and erosion, such as rehabilitation and restoration activities as well as grazing enclosures. Protected areas, for example, play an indispensable role in preventing biodiversity loss and safeguarding threatened ecosystems [21]. Soil and water conservation techniques present, furthermore, practical and rational solutions to erosion-related challenges.
The previous studies of land degradation assessment in the Tunisian rangelands have used (1) localized field measurements with restraint application and a limited analysis of ecosystem dynamics or (2) remote sensing data as a non-destructive solution for these limits. In the context of these shortcomings, this study aims to evaluate the wind erosion impact control measures on the degraded ecosystems of the study area (SE Tunisia) via an integrated approach that combines field measurements and remote sensing data over an extended period (2020–2023). Different ecological indicators related to vegetation and soil surface are evaluated to investigate the correlation between vegetation cover and the commonly employed radiometric indices (the NDVI and SAVI), and to estimate the effectiveness of the remote sensing techniques.

2. Materials and Methods

2.1. Site Description

This study was carried out in the Djeffara natural rangelands, located in the Medenine governorate of southeastern Tunisia (Figure 1).
The region is characterized by an arid Mediterranean climate with limited, unpredictable annual rainfall and high summer temperatures. The southern sector is characterized by a rainy season during the winter months (December–February) and dry periods during the rest of the year. The annual average temperature varies between 12.5 °C in January and 30.4 °C in August, while the average annual precipitation ranges between 150 and 200 mm [22]. The wind velocity is as high as 35 km/h and the potential evapotranspiration ranges from 1400 to 1700 mm/year [23,24]. During the study monitoring period (2020/2021 to 2022/2023), the mean annual rainfall in the Medenine region did not exceed 125 mm. The first two years were particularly dry, with only 95.9 mm and 39.3 mm of rainfall, respectively, while the last year experienced higher rainfall with 141.5 mm until July 2023 (Table 1).
The primary land uses in the study area are grazing (livestock breeding) and dry agriculture [25,26]. The management of windbreaks in the study sites involves a combination of mechanical and biological measures, including the use of palisades and reforestation. The natural vegetation cover in these sites is primarily composed of forbs, grasses, and shrubs (Figure 2a), such as Rhanterium suaveolens Desf., Gymnocarpos decander Forssk., Thymelaea microphylla Coss. & Durieu ex Meisn., Retama reatam (Forssk.) Webb, and Stipagrostis pungens (Desf.) de Winter. The soils in the study area are predominantly calcareous, characterized by the presence of gypsum crusts and/or sand accumulation (Figure 2b).

2.2. Methodology

The present study was conducted within twelve plots (SE Tunisia), of which six were located inside a protected area, while the remaining six served as the control, outside the protected areas. These sites covered an area of 6680 hectares (Table 2).
To assess the dynamics of the land cover in the study area, a diachronic analysis approach was used, which enabled the evaluation of vegetation cover changes in the managed sites over an extensive time period of nearly half a century. The monitored vegetation indices examine the evolution of the vegetation cover throughout the entire study area (Djeffara plain–SE Tunisia), while the field measurements focus on the vegetation and soil surface structure. The objective was to understand the impacts of various factors (such as protection measures, exploitation regimes, and climatic conditions) on the effectiveness of wind erosion control management strategies. The data obtained from both the remote sensing and the field measurements can be utilized to examine the relationships between vegetation indices and vegetation cover.

2.2.1. Vegetation Monitoring

To monitor the vegetation cover and soil surface, a field survey was conducted using the quadrat point method [27] in the designated study sites during the spring season over the three consecutive monitoring years: 2021 (Y1), 2022 (Y2), and 2023 (Y3). In each site, a total of five randomly placed 20 m lines were established both inside and outside (control) the sites. Along each line transect, a fine pin was vertically lowered at 20 cm intervals, and the interceptions between the vegetation and ground surface components (such as litter, aeolian sand, soil crust, stones, etc.) were recorded. For unknown species, samples were collected and identified using flora books and specific databases. In addition to the line transects, three plots were established at each site, including one large plot (20 m2) for measuring the perennial plant density, in which two small plots (1 m2 each) were used for assessing annual species. To compare the plant diversity between the sites and their respective controls, Jaccard’s similarity index (Sj) was computed using the formula described by [28].
Sj (x, y) = c/(a + b − c)
where a represents the number of species in line x, b represents the number of species in line y, and c denotes the number of shared species between x and y.
Vegetation cover (VC) was calculated as follows [23].
Vc = (n/N) × 100
where n represents the number of points with vegetation presence and N represents the total number of points.

2.2.2. Satellite Data Processing

Imagery scenes from Landsat 8 (OLI) at 30 m resolution, on 21 April 2021, 8 April 2022, and 3 April 2023, representing the field measurements period, were freely acquired from the US Geological Survey (USGS) website, georeferenced, and accompanied by complete metadata files (MTL) describing their characteristics (Table 3).
The vegetation indices were calculated following the guidelines outlined in Table 4. The studied sites and the sampled transects were geolocalized on the satellite images. For each line transect, a 1-pixel value was extracted from the corresponding vegetation index raster image (NDVI and SAVI). As a result, ten pixels referring to the 10 line transects were used to compare and derive correlations between the vegetation indices and vegetation cover in each site. The satellite imagery choice was made based on the length of the transect line that should be covered by the image pixel (30 m2).

2.2.3. Data Analysis

Following the field measurements conducted in 2021, 2022, and 2023, the collected data were subjected to a normality test. A three-way analysis of variance (ANOVA) was employed to compare the variations in the selected ecological indicators, considering the effects of the sites, protection measures, and years. The Tukey post hoc test was applied for further analysis. To examine the variance in annual plant density, the nonparametric Kruskall–Wallis test was used. Plant diversity was assessed using the Jaccard similarity test. The relationship between the vegetation cover (VC) and vegetation indices (NDVI and SAVI) was examined using a simple linear regression. All statistical analyses and image processing were performed using the OriginPro 2022 and QGIS 3.10.3 software. Principal component analysis (PCA) was carried out aiming to show the relation between the different types of soil, the sites, and the vegetation cover.

3. Results

3.1. Plant Density

The mean densities of perennial and annual plants, both inside and outside the studied sites during the monitored period (2021 2022 and 2023), are shown in Figure 3 and Figure 4, respectively. The greatest density of the perennial plants was observed in the HZM site during spring 2023 with 20.54 plants/m2, while the lowest mean values recorded in the FJE site during spring 2022 was 1.17 plants/m2. The three-way analysis of variance indicated a highly significant variation (p ≤ 0.001) in the average perennial densities across the different sites and years, but no significant difference in values was found between the inside and outside locations. In terms of yearly variations, the highest averages were observed in the first year (2021), followed by a notable decline in the subsequent two suitable years for SMK, HDM, and HMA within the protected areas. However, the HZM site displayed a remarkable regeneration, especially with the dominant species Helianthemum kahiricum (Delile), within the protected area.
Regarding annual plants, there were notable variations observed across the studied years and between the species both inside and outside of the protected areas. However, no significant difference was detected between the sites throughout the study. The highest densities were observed in the initial year (spring 2021), with mean values of 115.6, 66.4, 49, and 65 plants m−2 inside the FJE, SMK, HMA, and TAG sites, respectively. Conversely, the lowest values were recorded during the second year of 2022, which marked the second consecutive year of drought with only 33.5 mm of annual rainfall. When comparing the annual density means, a highly significant difference (p ≤ 0.001) was found between the year 2022 and the other two years (2021 with 33 mm and 2023 with 124.5 mm until June), while no significant difference was observed between the latter two years. Within the same site and year, the protection had a significant positive impact on the density of the annual plants inside the FJE, SMK, HMA, and TAG sites in 2021.
Similarly, a significant difference (p ≤ 0.05; p ≤ 0.01) was observed for the HMA and HZM sites in 2022. In the third year, the densities increased compared to 2022 across all sites, but there was no significant difference between the inside and the outside, except for the HMA site. The HMA site exhibited, however, a higher annual density inside the protected area compared to the overexploited outside area.

3.2. Vegetation Cover

Changes in vegetation cover (VC) inside and outside the studied sites from 2021 to 2023 are presented in Table 5. In spring 2021, the highest average VC values were observed within the HDM and SMK sites, measuring 76 ± 6.16 and 73.3 ± 4.5%, respectively. Conversely, the lowest averages were recorded outside the HZM site in 2022 and 2023, measuring 10.6 ± 4.33 and 15.6 ± 9.44%, respectively. The VC means decreased during the driest year (2022) inside and outside all surveyed sites. The analysis of variance showed a highly significant increase (p ≤ 0.001) under the protection effect at the HZM site in spring 2022, and the FJE and HDM sites in spring 2023, whereas no difference was detected for the other studied sites.

3.3. Diversity

The highest similarity index recorded was 0.68, observed between both inside and outside the TAG site, while the lowest index was −0.003, observed between both inside and outside the HDM and FJE sites (Figure 5). Within the sites, the similarity index exceeded or equaled 50% for the inventoried species inside and outside TAG (0.68), HMA (0.65), and HZM (0.49). In contrast, the similarity index was relatively low for the SMK site (0.42), HDM site (0.35), and FJE site (0.28), indicating significant differences (p ≤ 0.05) between the species counts inside and outside these areas. These findings demonstrate the positive impact of protection on species diversity and allow for the classification of sites into two distinct groups.
The first group includes the HZM, HMA, and TAG sites, which exhibit significant similarities between the protected areas and their respective controls. The second group consists of the SMK, FJE, and HDM sites, which show considerable dissimilarities.
This classification can be attributed, in part, to key climatic factors such as precipitation and its distribution, as well as wind intensity, in relation to the surrounding topography and the obstacles present in the studied sites. Additionally, the initial state of ecosystem resilience, the exploitation regime (complete enclosure, controlled grazing, continuous grazing), and the management practices of protection sites (such as maintaining palisades and implementing reforestation bands) contribute to these distinctions.

3.4. Soil-Surface Materials

The analysis of the line transect data conducted at the studied sites revealed the presence of six types of soil-surface materials. These materials primarily included soil crust, bare soil, litter, sand movement, gypsum crust, stones, and vegetation canopies. Among these materials, soil crust has a higher percentage within the HMA, TAG, SMK, and HZM sites. In contrast, the FJE and HDM sites showed a higher percentage of soil crust outside the protected areas. Moving sand exhibited strong dominance inside and outside the TAG site, as well as outside the HMA site, with percentages of 74%, 45%, and 53%, respectively. Significant increases in litter percentages were observed inside the FJE, HDM, HMA, and HZM sites as a result of the protection survey.
In order to examine the impact of protection on vegetation dynamics in relation to the state of the soil surface, a principal component analysis (PCA) was conducted (Figure 6). This analysis involved variables related to soil-surface materials and vegetation cover (VC). The PCA generated two principal components, PC1 and PC2, with eigenvalues greater than two, collectively explaining 61% of the total variation. PC1 accounted for 32.3% of the variation, while PC2 explained 28.7%. PC1 exhibited high positive loadings for bare soil, litter, and gypsum crust, while it had high negative loadings for moving sand, stones, and soil crust. This suggests that PC1 primarily reflects the difference between the soil-surface particles (bare soil vs. moving sand) and the presence of deposited litter, with a limited contribution from the VC. PC2 was characterized by significant loadings of soil crust, VC, litter, and stones on the positive side, and moving sand and gypsum crust on the negative side. PC2 indicated a grouping of sites that exhibited positive regulation, as well as sites that were more sensitive to wind erosion (as indicated by the deposition of sand or the formation of hard gypsum crusts that hindered plant establishment). As noted in Figure 6, the score plot revealed three main clusters based on the observations inside and outside the protected areas. The first cluster included HDM, SMK, FJE, HZM, and HMA observations inside the protected areas, as well as HDM and FJE observations outside the protected areas.
Higher VC, litter, and soil crust characterized this group of sites, predominantly composed of protected plots. The second cluster consisted of TAG, HMA, and HZM observations outside the protected areas, along with TAG observations inside the protected area. This cluster was distinguished by high percentages of moving sand in these sites. The third cluster comprised only the SMK site outside the protected area, which was characterized by a high percentage of gypsum crust dominating the soil surface. There is a negative correlation between moving sand and vegetation cover (VC), soil crust, and litter. This distribution clearly demonstrates the positive impact of protection on both vegetation cover and soil-surface materials, with the exception of the TAG site, which appears to be predominantly characterized by the presence of sands.
In fact, this site seems to be the least maintained, as evidenced by the presence of destroyed palisades and weak grazing control. Regarding the outside plots at the HDM and FJE sites, their distribution can be attributed to the prevalence of non-palatable or rarely palatable plants. This suggests an abundance of such plants, which in turn contributes to the stability of the upper soil layer.

3.5. Vegetation Indices

The field measurements define numerous challenges in terms of cost, time, and practicality. To overcome these challenges, the correlation between the vegetation cover and vegetation indices derived from the satellite imagery has proven to be consistently reliable. However, in arid regions, additional corrections and adjustments are often necessary. Therefore, in this study, two commonly used vegetation indices, the NDVI (normalized difference vegetation index) and SAVI (soil-adjusted vegetation index), were employed. During the driest year (2022), the lowest values of both the NDVI and SAVI were observed, particularly in the TAG and HZM sites. Conversely, the highest values were recorded inside the SMK and HDM sites for both calculated indices. These findings confirm the variations in vegetation cover obtained through the field measurements, aligning with the results derived from the vegetation indices (as presented in Table 6).
Figure 7 reveals the positive correlations between the vegetation cover (VC) percentages and the values of the two calculated indices, the NDVI and SAVI. These correlations were moderate for the NDVI (0.46 < R2 < 0.72) and strong for the SAVI (0.73 < R2 < 0.91), measured at the same locations and on the same dates as the survey field. The HZM site exhibited the highest correlations for both indices, with R2 values of 0.82 for the NDVI and 0.91 for the SAVI. On the other hand, the TAG and HMA sites demonstrated the lowest R2 values, with 0.46 for the NDVI and 0.73 for the SAVI.
Based on the results presented in Table 6 and Figure 7, it is evident that the vegetation indices effectively indicate the same variations as those deduced from the data collected during the field surveys. Additionally, the SAVI index holds a distinct advantage over the NDVI in arid zones, allowing for a more accurate estimation of vegetation cover. The data are the means obtained for the same sites (location of line transects inside and outside protected areas) and the same dates during the three years of study (April 2021 to April 2023).

4. Discussion

4.1. Plant Density

The findings of the current study indicate an increase in the perennial herbaceous species coverage of various biological types inside and outside the sites during the monitoring period. In spring 2022, outside the HDM site, this difference can be attributed to the abundance of Thymelaea mycrophylla Coss. & Durieu, with a density of 4.2 plants per square meter, explained by the species’ ability to withstand harsh conditions and their low quality and palatability index [31]. Additionally, Helianthemum kahiricum is the most abundant and dominant species within the HZM site (fully enclosed), accounting for over 80% of the total density. It is important to note that both sites are well-protected, and the two seasons mentioned (spring 2021 and 2023) received more than 90 mm of rainfall. It appears that the combined effects of the climate and the protection measures enabled plants to complete their phenological cycles, and increase their aboveground physiology [32]. In protected sites with limited or no grazing, annual plants are typically more abundant [33]. The annual species, however, reveal a relative resilience to some adverse conditions such as drought and grazing due to their short life cycle and seed dormancy [34]. In fact, the decrease in the number of annuals, or their local absence for some species, is related mainly to the low annual rainfall of 40 mm in 2022. Grazing duration and intensity also affect the abundance, distribution, and response of plant communities, depending on their palatability. They define the variable integrative change in plant diversity with respect to their different dynamic environmental controlling factors. The significant difference observed in SMK (seasonal grazing) during 2021 highlights, for example, the impact of grazing on annuals, influenced by their palatability. Measurements taken outside HZM in the spring seasons of 2021 and 2023 indicate that a complete exclusion from grazing reduces the overall number of annual plants. The other sites present, however, positive effects of grazing on the taxonomic diversity. These findings highlight that the grazed systems have simultaneous complex effects on the vegetation cover and they may reduce the functional species diversity related to some physiological modifications [35].

4.2. Soil Surface–Vegetation Cover

Evaluating the grazing impacts on soil proprieties is essential for effective land management, especially in the vulnerable dry ecosystems of arid lands. Indeed, the moving sand percentage is the most measured indicator for the influence of grazing on soil erosion, notably in the non-controlled sites (HMA and TAG). Furthermore, non-controlled grazing accelerates the soil’s chemical and physical degradation, soil instability, and fertility loss [36,37,38]. The higher percentage of soil crusts within the SMK and HZM sites can be attributed to the protective effect, particularly in HZM, where grazing is completely restricted. In areas with continuous or seasonal grazing, soil crusts are broken via trampling, allowing for seed germination, root penetration, and improved water infiltration [39].
Several authors have examined the dynamics of arid rangelands in southern Tunisia [40,41,42]. The obtained results, in agreement with previously published data, suggest that vegetation cover appears to be influenced by the combined effects of grazing and drought [43]. Correspondingly, the vegetation cover was higher within the sites during spring 2021 and spring 2023 compared to spring 2022, which can be explained by the absence of annuals in the second season (the driest year). Precipitation plays a crucial role in seed germination, vegetation growth, and subsequently, alters species richness and density [44,45]. The low cover and species richness outside the sites may be attributed to intensive grazing, particularly in spring 2022. The vegetation cover values in HDM and SMK (seasonal grazing) are higher than in HZM (grazing exclusion). These findings align with previous studies demonstrating that fenced rangelands exhibit greater productivity compared to open grazing areas [46,47].

4.3. Erosion-Controlling Management

The reciprocal effects between soil, vegetation structures, and physiognomy can be influenced by various factors, such as climate and human activities. This ecological interdependence can lead to a disturbance of the composition and structure of the soil surface. Consequently, a cascade of degradation processes may occur, including soil loss through erosion and the disruption of nutrient cycles and availability [48]. These processes contribute to a gradual decline of ecosystem functions and services, which is particularly accelerated in arid and desert environments. Depending on local conditions, recommended management approaches typically involve mechanical interventions such as windbreaks, followed by reforestation [49]. Certainly, this specific procedural sequence should be carefully observed, particularly in arid ecosystems with a high sensitivity to erosion, enhancing the effectiveness of restoration and rehabilitation approaches [23]. The initial step involves the installation of mechanical windbreaks, such as palisades, to stabilize the moving sand. Subsequently, the focus shifts to the establishment of biological windbreaks through reforestation, followed by their protection and maintenance after implementation [50,51].
In our case study, the positive effects of wind erosion control on plant cover dynamics appear to align with the findings reported by [52], demonstrating a clear regeneration of spontaneous plant cover. However, it is important to note that the ecological and morphological impacts of these measures can vary from site to site, contingent upon natural, anthropogenic, and technical factors.
Meanwhile, Sterk [53] underscored that mulching with crop residues emerges as the superior wind control method in the Sahel. Nevertheless, due to limited biomass yields, this technique may be deemed inadequate. Consequently, he concluded that the regeneration of natural vegetation holds promise as a viable control strategy. In South Africa, long-term livestock exclusion and reforestation initiatives have been put into action in degraded grasslands [54]. Moreover, Cheng [55] reported that vegetation restoration is the preferred and widely utilized practice for combating wind erosion, primarily employing the two key mechanisms. It is evident that vegetation cover serves as a protective shield for the soil surface, decomposing wind forces and facilitating the deposition and fixation of sand [56].
In addition to mechanical interventions, such as palisades and others, afforestation has been shown to yield ecological and economic benefits, as confirmed by several authors [57,58,59]. This approach positively influences vegetation growth and promotes the ecological restoration of degraded ecosystems.
The success of micro-scale ecological restoration is heavily dependent on climatic conditions, ongoing maintenance, and the extent of exploitation (e.g., grazing), which, when optimized, can stimulate the restoration process [47]. The gradual vegetation restoration (richness and cover) in highly degraded ecosystems typically initiates with the establishment of resilient species. As environmental conditions improve, less tolerant plant species gradually populate the area, nutrient cycles resume, the surface layer of the soil becomes more stable, and the presence of litter plays a significant role in fixing sand particles, trapping seeds, and retaining moisture [60]. Our research findings support this theory of possible succession and provide evidence of the severe impact of drought, particularly exacerbated by overexploitation and the inadequate maintenance of windbreaks.

4.4. Vegetation Indices–Vegetation Cover

The obtained results illustrate a strong correlation between vegetation indices (such as the NDVI and SAVI) and vegetation cover, as widely reported in previous studies [61,62]. In agreement with [63,64] the NDVI is more relevant with regard to forest vegetation cover and gives a false reflection of green biomass when the green canopy cover is lower than 30%. For arid and semi-arid areas, the SAVI outperforms the NDVI, particularly with Landsat 30 m resolution data [65]. The SAVI, designed to enhance the sensitivity of the NDVI to soil background [66], emerges as the most suitable index for arid zones after incorporating soil adjustments in its formula. Our findings support many studies highlighting this among slope-based indices. The SAVI demonstrates high ability in minimizing the effects of soil background [62,67,68]. This study presented an opportunity to enhance monitoring approaches and expand the study area by incorporating remote sensing techniques, specifically by calculating vegetation indices to estimate plant cover. In fact, the variable and complex environmental characteristics define the suitability of some vegetation indices for the assessment of vegetation cover. Thus, each indicator of the commonly used criteria has a specific significance that limits its sensitivity. The NDVI has, for example, limited accuracy for the evaluation of the vegetation cover of rangeland, while the SAVI is more affective for arid ecosystems. The results depend furthermore on the resolution of the satellite imagery.

5. Conclusions

This study aimed to monitor changes in land cover in six specific arid sites within the Djeffara plain in southeastern Tunisia, where wind erosion control measures were implemented to protect roads in El Fje, croplands in El Hezma and Henchir Mayouf, rangelands in Taguelmit and Sidi Makhlouf, and urban areas in Henchir Dghim. The results showed that the control measures improved vegetation richness and structure and reduced sand movement in the protected areas. Seasonal grazing was found to be more beneficial compared to full enclosure, although grazing intensity should be carefully managed. Continuous enclosure reduced wind erosion but hindered soil hydration and seed trapping, increasing the risk of degradation during dry periods. This study utilized remote sensing tools to enhance monitoring and analysis, finding significant correlations between vegetation indices and vegetation cover. The soil-adjusted vegetation index (SAVI) showed notable improvements compared to the NDVI, confirming its recommendation for estimating vegetation cover in arid rangelands. Protecting arid zones through effective management, including careful site selection, promotes ecosystem structure and functions. Further research may focus on interactions between rehabilitation trees and naturally regenerated vegetation, helping in species selection for better management.

Author Contributions

Conceptualization: A.K. and A.T.; methodology: A.K., A.T., M.O., and M.C.; software: A.K. and A.T.; validation: A.K. and A.T.; formal analysis: M.C. and M.O.; investigation: A.K.; resources: A.K.; data curation: A.K.; writing: A.K.; writing—review and editing: A.T., M.O., and M.C.; visualization: A.K.; supervision: M.C., M.O., and A.T.; project administration: M.O.; funding acquisition: M.O. All authors have read and agreed to the published version of the manuscript.

Funding

This study was jointly funded by the Arid Regions Institute of Medenine (IRA, Tunisia) (Labs LR16IRA01 and LR16IRA03), the Faculty of Sciences of Sfax (Lab UR/11/ES-71), and the PRIMA a program funded by the EC under the H2020 framework project ISFERALDA (1363/5745).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available through reasonable request from the corresponding author.

Acknowledgments

Our thanks are extended to Kamel Dadi, technician at IRA Medenine, for his valuable assistance. We would like to appreciate the support of the Forest Division at the Regional Department of Agriculture (Medenine) by providing the basic information on wind erosion control works in the governorate of Medenine.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study area and sites localization.
Figure 1. Study area and sites localization.
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Figure 2. Rangeland of El Fje site (a), soil profile (b).
Figure 2. Rangeland of El Fje site (a), soil profile (b).
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Figure 3. Perennial plant density (plant m−2) inside and outside protected areas in the six studied sites (FJE, SMK, HDM, HMA, HZM, and TAG) during the spring of 2021 (Y1), 2022 (Y2), and 2023 (Y3). a, b indicates significant differences according to Tukey’s test. Values are means ± SD (n = 5).
Figure 3. Perennial plant density (plant m−2) inside and outside protected areas in the six studied sites (FJE, SMK, HDM, HMA, HZM, and TAG) during the spring of 2021 (Y1), 2022 (Y2), and 2023 (Y3). a, b indicates significant differences according to Tukey’s test. Values are means ± SD (n = 5).
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Figure 4. Annual plant density (plant m−2) inside and outside protected areas in the six studied sites (FJE, SMK, HDM, HMA, HZM, and TAG) during the spring of 2021 (a), 2022 (b), and 2023 (c). (*) indicate significant differences between sides in the same site and same year, according to Kruskall–Wallis test. Values are means ± SD (n = 5).
Figure 4. Annual plant density (plant m−2) inside and outside protected areas in the six studied sites (FJE, SMK, HDM, HMA, HZM, and TAG) during the spring of 2021 (a), 2022 (b), and 2023 (c). (*) indicate significant differences between sides in the same site and same year, according to Kruskall–Wallis test. Values are means ± SD (n = 5).
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Figure 5. Jaccard’s similarity index between and within studied sites. Labels “I” and “O”: inside and outside, respectively. Index values between the inside and outside regions within the same location are indicated by the green−colored ellipse. * Correlation is significant at the 0.05 level.
Figure 5. Jaccard’s similarity index between and within studied sites. Labels “I” and “O”: inside and outside, respectively. Index values between the inside and outside regions within the same location are indicated by the green−colored ellipse. * Correlation is significant at the 0.05 level.
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Figure 6. PCA−biplot (score plots and loadings) of 12 locations, inside and outside the studied sites (FJE, SMK, HDM, HMA, HZM, and TAG). Arrows in blue indicate the loadings of soil-surface materials and vegetation cover as variables. Green circles with dashed lines indicate the groups obtained via group average cluster method.
Figure 6. PCA−biplot (score plots and loadings) of 12 locations, inside and outside the studied sites (FJE, SMK, HDM, HMA, HZM, and TAG). Arrows in blue indicate the loadings of soil-surface materials and vegetation cover as variables. Green circles with dashed lines indicate the groups obtained via group average cluster method.
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Figure 7. Correlation (R2) between vegetation cover percentage (VC), and normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI). One asterisk (*) indicates p < 0.05; two asterisks (**) indicate p < 0.01.
Figure 7. Correlation (R2) between vegetation cover percentage (VC), and normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI). One asterisk (*) indicates p < 0.05; two asterisks (**) indicate p < 0.01.
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Table 1. Monthly temperature and rainfall data of Medenine weather station, 2020–2023.
Table 1. Monthly temperature and rainfall data of Medenine weather station, 2020–2023.
Year SepOctNovDecJanFebMarAprMayJunJulAugTotal
2020–2021P (mm)025.631.829.204.054.750.5000095.9
T (C°)28.622.518.514.315.516.115.924.526.129.931.533.2-
2021–2022P (mm)13.42000.7612.40.538.383.81000039.3
T (C°)30.523.219.113.711.614.716.420.233.631.231.832.0-
2022–2023P (mm)13.50017.0050.3019.041.700-141.5
T (C°)32.027.625.612.511.315.123.119.622.427.535.5--
Table 2. Description of the studied sites with their respective codes and coordinates.
Table 2. Description of the studied sites with their respective codes and coordinates.
SiteCodeArea (ha)Protection PeriodManagement Type Exploitation RegimeCoordinates
(WGS 84)
El FjeFJE114Since 1962Palisades of dry palm/reforestationSeasonal grazing33°29′16.08″ N, 10°37′54.80″ E
Sidi MakhloufSMK100Since 1965Palisades of dry palm/reforestationSeasonal grazing33°29′39.30″ N, 10°28′54.85″ E
Henchir DghimHDM98Since 1984Palisades of dry palm/reforestationSeasonal grazing33°29′38.22″ N, 10°25′53.43″ E
El HezmaHZM50Since 2000Wire mesh/reforestationFenced33°15′3.34″ N, 10°27′48.11″ E
Henchir
Mayouf		HMA86Since 1999Palisades of dry palm/reforestationSeasonal grazing33°16′14.45″ N, 10°51′11.57″ E
TaguelmitTAG100Since 1991Palisades of dry palm/reforestationSeasonal grazing32°55′8.39″ N, 11°19′42.06″ E
Table 3. Metadata of acquired Landsat imagery.
Table 3. Metadata of acquired Landsat imagery.
Landsat Product IDSensor IDAcquisition Date Spatial ResolutionCloud CoverPath/Row
LC08_L2SP_190037_20210421_20210430_02_T1OLI_TIRS21 April 202130 m1.56190/37
LC08_L2SP_190037_20220408_20220412_02_T1OLI_TIRS8 April 202230 m0.04190/37
LC08_L2SP_190037_20230403_20230405_02_T1OLI_TIRS3 April 202330 m7.43190/37
https://glovis.usgs.gov/app (accessed on 20 April 2023) [29].
Table 4. Vegetation indices and their corresponding formulas.
Table 4. Vegetation indices and their corresponding formulas.
Vegetation IndexNormalized Vegetation IndexSoil-Adjusted Vegetation Index
AcronymNDVISAVI
AuthorRouse et al. (1974) [30]Huete (1988) [12]
Formula(NIR − Red)/(NIR + Red) [30]((NIR − Red)/(NIR + Red + 0.5)) × (1.5) [12]
Landsat 8 (OLI) bands (30 m)(5 − 4)/(5 + 34)((5 − 4)/(5 + 4 + 0.5)) × 1.5
Table 5. Vegetation cover (%) in the studied sites during spring of 2021, 2022, and 2023. I: inside, O: outside protected area. Letters indicate the differences between means using Tukey test.
Table 5. Vegetation cover (%) in the studied sites during spring of 2021, 2022, and 2023. I: inside, O: outside protected area. Letters indicate the differences between means using Tukey test.
SitePositionSpring 2021Spring 2022Spring 2023
FJEI61.4 ± 10.01abcd31.8 ± 9.98fghijk57.2 ± 16.97 ***abcde
FJEO54.2 ± 15.03abcdef28.6 ± 5.81ghijk23.2 ± 7.98 ***ijk
SMKI73.3 ± 4.5ab49.5 ± 10.4bcdefgh47.8 ± 14.19cdefgh
SMKO61.4 ± 10.01abcde38 ± 8.63efghij26.8 ± 6.37hijk
HDMI76 ± 6.16 a48.8 ± 17.19defgh50.6 ± 10.35 ***bcdefg
HDMO61.4 ± 10.01abcd40.4 ± 3.43defghi15.8 ± 2.28 ***jk
HMAI58.5 ± 16.78abcde25.75 ± 1.5hijk48 ± 17.36defgh
HMAO41 ± 6.44defghi19.75 ± 2.5ijk33.6 ± 7.89fghij
TAGI71.4 ± 14.7abc35.2 ± 8.1efghij40.6 ± 13.35defghi
TAGO51.2 ± 14.46bcdef21.6 ± 10.64ijk27.4 ± 3.78hijk
HZMI42 ± 3.91defghi39.6 ± 11.84 ***defghi37.8 ± 4.6efghij
HZMO31.8 ± 5.35fijk10.6 ± 4.33 ***k15.6 ± 9.44jk
asterisk indicates the significant difference between inside and outside position in the same site and same year (*** p ≤ 0.001). Values are means ± SD (n = 5).
Table 6. Values of normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) based on Landsat 8 (OLI) images in the studied sites for the two positions (inside and outside) during three successive years (2021 to 2023).
Table 6. Values of normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) based on Landsat 8 (OLI) images in the studied sites for the two positions (inside and outside) during three successive years (2021 to 2023).
INSIDEOUTSIDE
202120222023202120222023
FJENDVI0.170.110.160.140.090.09
SAVI0.190.130.190.160.110.10
SMKNDVI0.230.160.120.140.100.08
SAVI0.260.180.150.190.130.13
HDMNDVI0.270.180.150.140.120.08
SAVI0.290.180.230.200.160.09
HMANDVI0.180.090.140.120.130.08
SAVI0.210.110.200.150.100.11
TAGNDVI0.220.100.090.110.090.07
SAVI0.250.120.140.130.090.09
HZMNDVI0.130.110.110.100.080.08
SAVI0.150.130.130.120.090.09
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Khatteli, A.; Tlili, A.; Chaieb, M.; Ouessar, M. Effects of Wind Erosion Control Measures on Vegetation Dynamics and Soil-Surface Materials through Field Observations and Vegetation Indices in Arid Areas, Southeastern Tunisia. Sustainability 2023, 15, 14256. https://doi.org/10.3390/su151914256

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Khatteli A, Tlili A, Chaieb M, Ouessar M. Effects of Wind Erosion Control Measures on Vegetation Dynamics and Soil-Surface Materials through Field Observations and Vegetation Indices in Arid Areas, Southeastern Tunisia. Sustainability. 2023; 15(19):14256. https://doi.org/10.3390/su151914256

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Khatteli, Ameni, Abderrazak Tlili, Mohamed Chaieb, and Mohamed Ouessar. 2023. "Effects of Wind Erosion Control Measures on Vegetation Dynamics and Soil-Surface Materials through Field Observations and Vegetation Indices in Arid Areas, Southeastern Tunisia" Sustainability 15, no. 19: 14256. https://doi.org/10.3390/su151914256

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