Nothing Special   »   [go: up one dir, main page]
Next Article in Journal
Quantitative Simulation and Planning for the Heat Island Mitigation Effect in Sponge City Planning: A Case Study of Chengdu, China
Previous Article in Journal
Investigation of Diverse Urban Carbon Emission Reduction Pathways in China: Based on the Technology–Organization–Environment Framework for Promoting Socio-Environmental Sustainability
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 14
Issue 2
10.3390/land14020262
land-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Six Decades of Rural Landscape Transformation in Five Lebanese Villages

by
Abed Al Kareem Yehya
1,
Thanh Thi Nguyen
1,
Martin Wiehle
1,2,
Rami Zurayk
3 and
Andreas Buerkert
1,*
1
Organic Plant Production and Agroecosystems Research in the Tropics and Subtropics (OPATS), Faculty of Organic Agricultural Sciences, University of Kassel, Steinstr. 19, 37213 Witzenhausen, Germany
2
Centre for International Rural Development—Tropenzentrum, Faculty of Organic Agricultural Sciences, University of Kassel, Steinstr. 19, 37213 Witzenhausen, Germany
3
Landscape Design and Ecosystem Management, Maroon Semaan Faculty of Engineering and Architecture, American University of Beirut, Bliss St., Beirut 1107-2020, Lebanon
*
Author to whom correspondence should be addressed.
Land 2025, 14(2), 262; https://doi.org/10.3390/land14020262
Submission received: 23 December 2024 / Revised: 20 January 2025 / Accepted: 22 January 2025 / Published: 26 January 2025
Browse Figures
Figure 1
<p>Methodological approach to combine primary and secondary data collection for this study.</p> "> Figure 2
<p>Map of selected villages from five agroecological zones in Lebanon. (<b>a</b>) Tal Abbass (El Gharbi) in the northern zone; (<b>b</b>) El Abde in the coastal zone; (<b>c</b>) Mikrak in the Bekaa zone; (<b>d</b>) Batloun in the Mount Lebanon zone; (<b>e</b>) Sinay in the southern zone. Sources: Global Administrative Areas Database (GADM), Environmental Systems Research Institute (ESRI), and United States Geological Survey (USGS) assessed in August 2024 using ArcGIS Pro 3.2.0.</p> "> Figure 3
<p>LULC class variations in the five villages from 1962 to 2023 (percentage out of 100) in five agroecological zones of Lebanon.</p> "> Figure 4
<p>Spatial LULC change in Sinay village (Lebanon) based on high-resolution landscape maps (1962 and 2014) and 2023 Sentinel-2A satellite data: (<b>a</b>) 1962; (<b>b</b>) 2014; and (<b>c</b>) 2023.</p> "> Figure 5
<p>Urbanization rate in the five Lebanese villages studied.</p> "> Figure 6
<p>Sanky diagram showing the change in the number of patches in Tal Abbass, Lebanon (manual input via <a href="http://sankeymatic.com" target="_blank">sankeymatic.com</a>).</p> "> Figure 7
<p>LULC change in Mikrak village (Lebanon) derived from high-resolution landscape maps (1962 and 2014) and 2023 Sentinel-2A satellite data: (<b>a</b>) 1962; (<b>b</b>) 2014; and (<b>c</b>) 2023.</p> "> Figure 8
<p>Variation in farmers’ perceptions of the availability and accessibility of water resources across villages in Lebanon The box plot shows vertical lines (whiskers) representing the data range, horizontal lines for the median, and “x” marks for the mean of farmer perceptions.</p> "> Figure 9
<p>Predicted variation in land use change versus education in five villages of Lebanon. The blue line indicates the fitted regression line and the gray distances from the regression line show the respective confidence intervals.</p> "> Figure 10
<p>The variation in the logistic regression coefficients (n = 151) in five villages of Lebanon. The blue points represent the coefficient estimates for variables, while the horizontal lines indicate the confidence intervals around these estimates.</p> "> Figure 11
<p>Trends in the export value of fruits from Lebanon to global markets (source: Ministry of Economy and Trade (Lebanon) and the International Trade Center (ITC)).</p> "> Figure A1
<p>Spatial LULC change in Tal Abbass (Lebanon): (<b>a</b>) 1962; (<b>b</b>) 2014; and (<b>c</b>) 2023.</p> "> Figure A2
<p>Spatial LULC change in El Abde (Lebanon): (<b>a</b>) 1962; (<b>b</b>) 2014; and (<b>c</b>) 2023.</p> "> Figure A3
<p>Spatial LULC change in Batloun (Lebanon): (<b>a</b>) 1962; (<b>b</b>) 2014; and (<b>c</b>) 2023.</p> "> Figure A4
<p>Variation in the landscape metrics of five villages in Lebanon using Fragstat 4.0 (1962–2023): (<b>a</b>) number of patches (NP); (<b>b</b>) mean patch size (MPS).</p> ">
Versions Notes

Abstract

:
During the last six decades, Lebanon’s landscapes have undergone significant regime shifts whose causes are under-investigated. Using land cover maps from 1962 and satellite imagery from 2014 and 2023 in five randomly selected villages across Lebanon’s major agroecological zones (AEZs), we identified salient trends in the urbanization-driven transformation of land use and land cover (LULC). Household socio-economic characteristics and environmental pressures were analyzed as independent variables influencing land use decisions. Logistic regression (LR) was employed to assess the significance of these variables in shaping farmers’ choices to transition toward “perennialization”—namely fruit tree monocropping or protected agriculture. The LR results indicate that education reduced the likelihood of “perennialization” by 45% (p < 0.001). Farm size positively influenced “perennialization” (p < 0.01), suggesting that land availability encourages this agricultural practice. In contrast, water availability negatively affects “perennialization” (p < 0.01), though farmers may still opt to irrigate by purchasing water during shortages. Our findings underline the complex interplay of socio-economic and environmental dynamics and historical events in shaping Lebanon’s rural landscapes and they offer insights into similar transformations across the Middle East and North Africa (MENA) region.

1. Introduction

The evolution of rural landscapes worldwide is an outcome of complex social-ecological processes over time [1,2,3]. Natural landscapes are increasingly dominated by settlements, agricultural systems, and woodlands [4,5]. This often entails a shift from small-scale traditional subsistence farming to larger-scale systems that exhibit increasing intensities of resource use [6,7]. The conversion of about 50% of the Earth’s land surface by cropping and livestock production has major implications for ecosystem services important for human well-being [8,9].
Drivers of rural landscape transformations have socio-economic, political, and environmental components. Major regime shifts have been triggered by rural–urban transformation whereby currently around 55% of the global population lives in urban areas with a projection to rise to 68% by 2050 [10]. The unprecedented pace of urbanization, combined with a global population expected to reach nine billion by 2050, puts significant pressure on the environment and food security [11,12,13,14]. In many countries of the Global South, farmers escape low agricultural incomes and resulting poverty by out-migration from rural areas [15]. In the Middle East and North Africa (MENA) region, foreign aid and remittances from oil-rich Arab states have accelerated this transition by enabling governments to expand public employment, thereby enhancing the urbanization-driven transformation of land use and land cover (LULC) [16,17]. This reflects an increasing prioritization of urban consumers over rural producers, often at the expense of rural sustainability and equality [17]. Several recent studies have described the socio-economic changes that accompany agricultural transformation and LULC changes at the national level [18,19] or across the MENA and the Mediterranean [17,20] region. However, studies that link the transformation of local rural landscapes to the underlying socio-economic causes and environmental pressures and analyze trajectories of change in land use are rare.
Remote sensing data and geographic information systems (GISs) allow researchers to detect, measure, and monitor land use and land cover (LULC) changes and facilitate predictive modeling. Many LULC changes are the result of specific landscape processes such as fragmentation, attrition, aggregation, creation, and dissection—whose patterns evolve over time and require systematic analysis [21,22,23]. Many studies have computed landscape metrics to quantify and understand changing landscape processes [24,25]. Such metrics are essential to capture landscape complexity but are insufficient for more effective management and planning [26]. Knowledge of spatio-temporal LULC change alone risks becoming a merely retrospective academic exercise and, as such, does not contribute to rural development and sustainable land use [26,27]. To foster effective land use policies, research must integrate analyses of socio-economic characteristics and environmental pressures with traditional spatial–temporal assessments of the changing landscapes [28,29].
Farmers are at the intersection of LULC change processes and land use policies, and it is important to understand their decision-making to predict landscape transformation. Their land use choices reflect both individual motivations and broader societal change [30,31]. Furthermore, farmers’ financial and physical capital (e.g., access to roads and water supply) influence the extent to which they can decide upon their land use practices [32,33]. External factors, such as access to new local and global export markets, are also strong motivators for altering land use [34]. Farmers’ land use decisions may cascade to influence land use systems across borders with regional or even global consequences [34,35].
This study aims at filling existing knowledge gaps on understanding drivers for LULC change for Lebanon as part of the MENA region, characterized by weak governance and high political instability [36,37]. To this end, the following research questions guided our study:
  • How have LULC patterns in rural Lebanon evolved in response to major events such as the Lebanese Civil War (1975), the Syrian conflict (2011), and the economic collapse (2019)? How have urbanization and agricultural trends in rural villages shifted over time? What happened to the natural land cover during that time?
  • What are the dominant landscape change processes in rural Lebanon’s natural landscape, and what changes can landscape metrics reflect?
  • How did local socio-economic and environmental pressures affect agricultural transformation in Lebanese villages?

2. Materials and Methods

For this study, a mixed-method approach was chosen to integrate qualitative and quantitative primary data collection, including expert interviews and household questionnaires, and data from secondary sources, such as satellite imagery and official ministry records, to address the research questions (Figure 1).

2.1. Study Sites and Temporal Frame

To analyze processes of LULC transformation in Lebanon over the last 60 years, we compared satellite data of 1962 with those of 2014 and 2023, whereby the latest dataset was combined with ground truthing of one randomly selected village each from the country’s five different agroecological zones (AEZs; Figure 2). The random selection process was performed using Microsoft Excel (version 1808) using the RAND function to assign random values and sort to select an agricultural village from each zone. The villages had the following characteristics:
  • Tal Abbass (El Gharbi) is part of Central Akkar that expands along the Al-Estwan River. It covers an area of 400 hectares (ha), has a semi-arid climate at an altitude range of 30 to 60 m, an average total annual precipitation of 784 mm, and an average monthly temperature ranging from 15 to 32 °C. The main grown crops are potato (Solanum tuberosum L.), wheat (Triticum aestivum L.), vegetables, and fruits like olive (Olea europaea L.) and citrus (Citrus medica L.). In 2011, Syrian refugees settled in the region, adding to its socio-economic changes and environmental pressure.
  • El Abde is in the northern coastal zone near the ancient Phoenician port of Ibirta. It hosts the Nahr el Bared Palestinian refugee camp. The village covers an administrative area of 400 ha at an elevation range of 2 to 51 m. The village receives an average annual precipitation of 750 mm and has an average monthly temperature from 16 to 33 °C. The village is known for its diverse agricultural production. Wheat is the major crop, but the village is also known for widespread greenhouses, where a variety of vegetables are grown. In addition, the village has a range of fruit orchards including citrus.
  • Mikrak is a village located in the arid Bekaa zone. It covers an area of 1450 ha at an elevation of 961 to 1032 m. It has an average annual precipitation of 580 mm and an average monthly temperature range of 10 to 34 °C. Mikrak is renowned for hosting large sheep and goat flocks, as its vast rangelands and grasslands make it an ideal location for pastoral activities. The village has experienced urbanization along its main roads and has seen recent industrial growth.
  • Batloun is located in the Chouf District of Mount Lebanon near Al Barouk River. The village spans 350 ha at an elevation of 950 to 1080 m and receives over 900 mm of precipitation annually, though it faces water shortages due to unsustainable management practices. The average monthly temperature ranges from 10 to 30 °C. It is known for its ancient agricultural terraces and historical significance. The area is particularly suitable for growing non-irrigated cereals, vine (Vitis vinifera L.), and apple (Malus domestica Borkh.).
  • Sinay is located in the southern zone of the country at an elevation of 185 to 285 m. It covers an administrative area of 424 ha, has an average annual precipitation of 750 mm, and an average monthly temperature of 14 to 32 °C. The village has experienced the destructive consequences of several wars. It is known for its agricultural production and fertile lands suitable for wheat, barley (Hordeum vulgare L.), tobacco (Nicotiana tabacum L.), and olive.

2.2. LULC Mapping and Landscape Analysis

To analyze spatial transformation in the selected rural landscapes, we adopted a mixed-methods approach by integrating geographical information with primary field data. Primary data collection included household surveys on local livelihoods and land use decisions on processes that might have influenced transformations.

2.2.1. Data Sources

For our mapping of changes in LULC, we used (i) a historical land cover map of 1962, (ii) land cover data of the National Council for Scientific Research in Lebanon between 2012 and 2017, (iii) freely available Airbus imagery from Google Earth, and (iv) satellite images from Sentinel-2A (Table 1). LULC maps for each year were created by delineating homogeneous patches and assigning attributes to each pattern following the CORINE Land Cover (CLC) nomenclature [35].
We categorized the LULC in the study area into 11 classes based on the classification scheme in the CLC guidelines (Table 2; [38]). This classification also considered areas of mining and water extraction, roads, rivers, and village boundaries using data from the Global Administrative Areas Database (GADM) 4.1.

2.2.2. Digitization of High-Resolution Landscape Maps of 1962 and 2014

Before analysis, the land cover map of 1962 and Airbus satellite imagery from 2014 were georeferenced using the Universal Transverse Mercator (UTM) projection at Zone 36 North and the WGS1984 ellipsoid. This was achieved by selecting 10 to 20 ground control points (GCPs) for each sheet/image. The GCPs were compared with datasets of the ArcGIS Pro 3.2.0. base map at pre-identifying matching points. After completing the georeferencing using an affine transformation function, the root mean square error (RMSE) for both datasets was below 30 meters. Following the approach in [39], we drew the land cover polygons of the land cover map of 1962 and the Airbus satellite imagery. The final classified datasets were then transformed into raster format and overlaid with roads, rivers, boundaries, and mineral extraction sites using ArcGIS pro 3.2.0.

2.2.3. Classification of 2023 Sentinel-2A Satellite Images

Sentinel-2A images with minimal cloud cover were acquired from Google Earth Engine (GEE) in late April 2022 [40]. These images were already radiometrically and geometrically corrected and orthorectified in GEE and clipped to the study areas. A total of 70 to 80 training points was selected for ground truthing through field visits. A supervised classification was performed using the random forest (RF) algorithm, known for its robustness and high accuracy [40,41]. The collected 220 training samples per class and study village were used to train the RF classifier employed for the definition of the above mentioned LULC classes. Post-classification processing included majority filtering to remove isolated pixels and manual correction of obvious misclassifications on ArcGIS pro 3.2.0.

2.2.4. Accuracy Assessment

Given the lack of ground truth or reference data for the 1962 map of the Lebanese Ministry of Defense, we validated it by inspecting and matching the legend of the scanned map to the digitalized map. For the 2014 dataset, accuracy was assessed using a confusion matrix of vector data from the National Council for Scientific Research—Lebanon (CNRS-L; [42]). The accuracy of the LULC classes of 2023 were evaluated using a confusion matrix with 375 random reference sample points generated by an equalized stratified sampling strategy in ArcGIS Pro 3.2.0. Each class was assigned equal weight (70–80 sample points per class on average) to ensure a balanced representation. The actual LULC features were visually identified using Sentinel-2A images cross-checked with Airbus imagery 2023 and ground truth data, allowing for an accurate determination of the overall accuracies and the kappa coefficient.

2.2.5. Detection of Changes in and Transformation of LULC

To analyze the change and transformation in LULC over the six decades, the distribution of LULC classes (in ha) was calculated for each dataset. LULC change analysis was conducted for 1962–2014 and 2014–2023. A change matrix was generated for each period by cross-tabulating land cover classifications for gains, losses, and persistence of each land cover type [40,43].

2.2.6. Landscape Metrics

To identify landscape change processes, we selected the number of patches (NP) and the mean patch size (MPS; [25,44]). These metrics were calculated using Fragstat v4. at the LULC class and the whole landscape levels to comprehensively assess possible occurring landscape processes with a focus on natural landscapes (shrublands and grassland; [45]). The input was a classified raster dataset of the different LULC classes. The raster dataset was defined within the specified extent of the village landscape (village’s borders using GADM 4.1 data). The configuration was based on an 8-cell neighborhood rule, where each cell is considered adjacent to its eight surrounding cells (both orthogonal and diagonal neighbors). To determine the changing landscape process, we calculated the changes in the landscape metric for 1962–2014 and 2014–2023. The potential processes of landscape change based on temporal metric variation are attrition, aggregation, creation, dissection, and fragmentation [22,24].

2.3. Primary Data

Primary data were collected from May to October 2023 in the five selected villages using a combination of key informant interviews (KIIs) and household surveys. In the first stage, we conducted an exploratory field survey to identify land use practices within the landscape. KIIs were carried out with two experts in agriculture or natural resource management per village. For the household survey, we determined sample size using Cochran’s equation [46].
n = z 2 . p ( 1 − p ) e 2
where n is the desired sample size, z is the value of the standard deviation (1.96), e is the acceptable error (0.05), and p is the proportion of the target population (10%). This led to a total sample size of 151 respondents which was distributed across the five villages as follows: 23 farmers in Tal Abbass, 30 in El Abde, 58 in Mikrak, 17 in Batloun, and 23 in Sinay. Sample size was proportional to the size of the population and agricultural activities in each village. The questionnaire was pre-tested with ten randomly selected households. Interviews were semi-structured, with data being recorded with the interviewees’ consent and transcribed. In addition to in-person interviews, follow-up surveys were conducted via phone calls or WhatsApp wherever needed. All interviews were conducted in Arabic and lasted for 30 min. Whenever possible, triangulation was performed by cross-verification of the information from household surveys and key informant interviews. The collected data were organized in Excel for further analysis [47].

2.4. Additional Secondary Data

To investigate the role of markets as a driver of farmers’ land use decisions, we utilized calculations of fruit export value based on statistics from the Ministry of Economy and Trade and derived from the website of the International Trade Center (ITC) (trademap.org). In our study, we focused on fruits and vegetables cultivated in the villages, as these products reflect the likelihood of market-driven production. The export data value, reported in thousands of United States (US) dollars per year, serves as a proxy for market value, with higher values indicating higher market demand [48]. We extracted and analyzed data from the period of 2004–2021 to discuss long-term trends in market export dynamics.

2.5. Data Analysis

We used the households’ socio-economic characteristics and environmental pressures as independent variables influencing land use decisions [29,33]. Herein, we assumed that they determined the likelihood of shifting land use toward protected agriculture (greenhouses) or fruit tree monocropping referred to as “perennialization” (olive trees, vineyards, and field crops were excluded).
We employed logistic regression (LR) to assess the relationship between the significance (Z-value) of the household’s decision to shift to tree monocropping or protected agriculture “perennialization” over the last decade [49]. Data analysis was performed using R software version 4.4.1, where a Z-value > ±1.96 indicated significance. The results are presented in an LR table with Z-values for coefficients and visualized using a regression plot. LR was evaluated for multicollinearity using the variance inflation factor (VIF; Appendix C), with all explanatory variables showing VIF values below 4, indicating no multicollinearity [50]. The model’s goodness of fit was assessed using the Hosmer–Lemeshow test, which produced a non-significant result (p value > 0.05), suggesting a good fit to the data [51].

3. Results

3.1. Accuracy Assessment of LULC Mapping

Classifications in Tal Abbass and El Abde showed an overall accuracy of 94% in 2014 and 92% in 2023. Mikrak had a slightly lower accuracy, with 92% in 2014 and 90% in 2023. With 90% in 2014 and 88% in 2023, Batloun had the lowest accuracy, which may be due to slope effects. A confusion matrix of LULC classification of each village using the training dataset was constructed (Supplementary Data S1). The digitalized classified map of the 1962 “Carte du Liban” closely matched the original map’s legend after visual interpretation.

3.2. LULC Mapping Results and Landscape Analysis

During the post-civil war period, agricultural activities have transformed in most villages (Figure 3). Field crops expanded by over 300 ha at the expense of 30.8% of grasslands in Mikrak, while in Sinay and Tal Abbass, field crops expanded and relocated at the expense of grasslands and shrublands (Figure 4; Appendix A). Vineyards expanded by 20%, particularly at the expense of shrubland in Tal Abbass. Protected agriculture took over shrublands by 23.6% in El Abde and 7.3% Tal Abbass (Appendix A). Traditional mountain terraces (field crops and vineyards) have nearly disappeared due to historical shifts, as exemplified in Batloun.
The post-civil war period was characterized by the expansion of urban landscapes in all villages (Figure 5). LULC mapping revealed that urban areas encroached on agricultural land in Sinay and Batloun, whereas in Mikrak, Tal Abbass, and El Abde, urban growth primarily displaced natural vegetation. The urban expansion rate exceeded 100% in all villages (Figure 5), aligning with findings from other LULC studies in Lebanon [52,53].
The period spanning the Syrian war and the subsequent economic collapse (2014–2023) led to significant transformations in agricultural practices across villages. In Sinay, fruit tree monocropping expanded by around sevenfold from 16 to 139.6 ha (Figure 4 and Table A5)—a shift toward high-value crops like avocado (Persea americana Mill.) [54]. Tal Abbass and El Abde witnessed the consolidation of fruit tree cultivation alongside the emergence of protected agriculture, which replaced some traditional orchards (Figure A1 and Figure A2). During the same time period, while urbanization continued to expand, industrial and commercial LULC patterns also grew in El Abde, Mikrak, and Sinay, encroaching on natural landscapes (Figure 5).

3.3. Landscape Metrics and Process Identification

Across villages, the NP of the rural landscape increased in the post-civil war period (except for El Abde and Tal Abbass) and continued to rise from 2014 to 2023 (Appendix B). This “patchiness” often correlates with the fragmentation processes of landscapes [22,55]. During the six decades of study, the MPS metric consistently decreased in all villages, indicating that the landscape became increasingly fragmented and that some dominant patches may have been replaced by other LULC classes [47].
Fragmentation was the dominant process of urbanization-induced LULC change, particularly in Tal Abbass, where mainly shrublands were affected (Figure 6; Appendix B). In El Abde and Batloun, only isolated natural patches remained of previous shrublands, likely reducing ecological connectivity, while the decline of traditional agricultural terraces in Batloun’s vineyards and Sinay’s olive groves exemplified typical attrition processes. In Tal Abbass and El Abde, classical aggregation was reflected in the consolidation of citrus orchards (Appendix A). In Mikrak, dissection transformed grassland LULC during 1962–2014 (Figure 7) with a significant decrease in MPS.

3.4. Results of the Household Survey on Land Use in the Last Decade

3.4.1. Socio-Economic Characteristics of Households

Across five study villages, 75% of the household heads were >50 years old, while only a few younger farmers joined the sector. Most farmers had limited formal education, averaging around five years of schooling. Unskilled seasonal labor, primarily of Syrian and Palestinian origin, was commonly employed, particularly on farms where agriculture was not the primary source of household income. Across villages, 49% of the farmers depended entirely on agriculture, while the rest supplemented their income with off-farm work. City resident farmers generally had higher incomes and savings compared to local farmers. Over time, income disparities across households have grown, particularly since the depreciation of the Lebanese currency in 2019, further destabilizing farming incomes. The contribution of agricultural subsidies varied widely, and cooperatives are ineffective, limiting the development of the farms.
Land tenure was split evenly between ownership and lease/share agreements, with city residents owning more land. Farms were predominantly small, averaging 3.5 ha (standard error = 0.44). Mikrak had the largest average farm size, with two farms above 50 ha. Mikrak’s land is mostly classified as agriculture (KII-1) and showed the largest land size in 2010 that remains well above the Lebanese national average of 1.4 hectares despite the dissection process of its grassland seen in 2023 (Section 3.2). Two landowners interviewed in Mikrak have merged their family land ownership and turned grassland for field crops and fruit tree cultivation for exports (KII-1). Similarly, a farmer in Sinay cultivated over 60 hectares of avocado trees in 2020. Farmers of Tal Abbass and Batloun (KII-2) generally maintained farm sizes ranging between 1 and 20 hectares. In Tal Abbass, political intervention has made it difficult to divide agricultural land further, as building on parcels smaller than 1000 square meters is prohibited and the village has no urban classification. The village municipality only allows for 5% construction on agricultural land. According to the Mayor of Tal Abbass (KII-3), “most of the land is designated for agriculture, and construction requires a permit from the municipality, with a maximum building area of 100 square meters”. Only about 10% of the total land is classified as urban, further divided into subcategories A and B. However, the municipality is currently working to change the legal classification to make it more urbanized.

3.4.2. Water Access and Land Use

The availability and accessibility of water resources were key factors for differentiating the intensity of landscape transformations across the five villages. According to farmers’ input in El Abde and Batloun, reliable water supplies from rain, wells, and proximity to rivers enabled them to cultivate larger plots and expand their agricultural operations (above 50% average; Figure 8). Reliable water access has been essential for sustaining more intensive agricultural patterns in these villages, such as the aggregation of fruit tree orchards and the proliferation of high-value cash crops. In contrast, farmers in Tal Abbass and Sinay faced significant water shortages (average below 50%) and poor infrastructure, with some also constrained by their small farm sizes. The results of the survey and LULC analysis provide evidence that farmers in Tal Abbass have eventually turned to water-intensive greenhouse cultivation and fruit tree monocropping, driven by the economic incentives of growing high-demand cash crops (vegetables and avocado).

3.4.3. Results of the Logistic Regression Analysis

The results of the LR analysis show that education has a significant negative effect on the likelihood of “perennialization” (Z-value of −3.41 > ±1.96; Table 3 and Figure 9). For each year of education, the likelihood effect of “perennialization” decreases by 45% (p < 0.001) (Figure 9). Farming area increases “perennialization” by 5% for each additional unit of land (p < 0.01). Water availability was a significant negative factor (p < 0.01), whereby an increase in water availability was associated with a 3% reduction in the likelihood of “perennialization”. Water availability is obviously a critical input for the agricultural systems in the study region, whereby in case of water shortages, the interviewed farmers may still irrigate by purchasing water (76% of farmers reported irrigation activities).
Off-farm income and land ownership variables did not significantly affect the likelihood of “perennialization”, and the farmer coefficients varied widely among villages (Figure 10), whereas variables like age, labor count, and irrigation were not significant but the most consistent among farmers (low coefficient variation; Figure 10). Permanent residents may be more inclined to “perennialization” (p = 0.06).

4. Discussion

4.1. Spatio-Temporal Transformation Across Lebanon’s Villages and AEZs

The spatio-temporal transformations reflected the local conditions of the villages and AEZs. In the northern, southern, and coastal zones (Tal Abbass, Sinay, and El Abde), the expansion of urban landscapes, fruit tree monocropping, and greenhouses represented a shift toward more intensive and controlled farming practices for maximizing yields and ensuring year-round production for a growing urban demand. This mirrors the expansion of greenhouses in the arid coastal regions in the Global North countries [56]. A general LULC study on Lebanon reported a major urban expansion over the years associated with banking conditions and weak governance [57], while another study focused on the coastal zone found that LULC might be changing based on farmers’ response to climate change conditions [58]. Under climate change scenarios for the MENA region, agricultural land is expected to experience increasing aridity, prompting farmers to adopt responses that may compromise sustainability at local and regional levels [58,59].
In the mountain zone (and some parts of other zones), traditional agricultural activities including terraces and mulberry trees were reduced, while clear wooded land was planted, marking a shift in the ancient composition of the mountain landscape due to historical changes ([60,61]; Appendix A). Other villages in the mountain zone of Lebanon underwent similar spatio-temporal changes, with urban expansion as the dominant trend overtaking agriculture and natural areas [19]. This expansion reflects a trend in the MENA region and globally, wherein urban transformation strongly alters the traditional rural landscape [62,63].
In the arid Bekaa zone (Mikrak), the landscape metrics provide another geospatial lens to monitor change in the natural landscape. The reduced availability of grazing land (natural grasslands) and the surge of industrial livestock farms had major effects on traditional livestock production, as reflected by the MPS and the isolation of niche ecosystems in arid zones [64]. Similar patterns of increased land fragmentation and ecological degradation, such as those observed on the Qinghai–Tibetan Plateau of China, were associated with grassland degradation [65]. Such land fragmentation and ecological degradation have been widely observed in the MENA and other regions, where land use policies have contributed to resource fragmentation and have exacerbated grassland degradation [66,67].

4.2. Socio-Economic and Environmental Drivers and Farmers’ Land Use Decisions in Rural Villages

The socio-economic conditions of households across all villages were shaped by inefficient and inconsistent local land management policies and marketing channels, compelling farmers to adapt to market trends to sustain their livelihoods. Despite land and water issues in these villages, agricultural production was largely dictated by regulations of the Ministry of Economy and Trade in Lebanon. The latest figures highlighted the continued promotion of cash crops (primarily fruit exports) by the ministry [68], as evidenced by a surge in export value during the period of the Syrian war (2011) and the subsequent economic collapse (Figure 10). This aligns with Lebanon-wide research findings that describe Lebanon as an export-oriented agricultural market characterized by national procurement channels, where large retail outlets trade with dedicated or contracted specialized wholesalers and food producers [48,69].
In addition to that, the combined findings from our LULC analysis and household surveys revealed how socio-economic and environmental pressures shape agricultural transformation and environmental sustainability in the villages in the last decade. The LULC change (2014–2023) and LR results highlight a clear shift toward the monocropping of fruit trees and protected agriculture practices, aligning with market trends favoring short-term economic return. Despite small farm sizes and land tenure limitations, LR results suggest that households with lower education levels are more likely to prioritize immediate financial benefits, making this transition particularly distinct among less educated farmers, regardless of water scarcity challenges and the long-term sustainability risks associated with “perennialization”. While aware of water availability issues, farmers may undervalue sustainability considerations, favoring short-term productivity in line with market demands (Figure 11). This pattern aligns with broader trends in the MENA region, where climate change exacerbates risks to fruit cultivation, yet immediate profitability often overshadows environmental and sustainability concerns [70,71]. Educated farmers, on the other hand, are more likely to adopt efficient and sustainable agricultural systems. Their increased access to information on technological advancements, market dynamics, and innovative practices—such as modern irrigation techniques—enables them to optimize both economic and environmental outcomes [72].

4.3. Political Instability and Weak Governance as Drivers of LULC Change in Lebanon

The landscape pattern variations can be reflected by the mosaic of political conflicts in Lebanon that have played a significant role in shaping these changes across villages. The intertwining of socio-political dynamics, legal frameworks, and historical events, such as the Lebanese Civil War, has profoundly influenced LULC change in the rural landscape, as found in our study and others [73]. For example, in the south AEZ, Sinay’s long history of land fragmentation, driven by diaspora investments and integration into global markets, has led to accelerated real estate development, transforming natural landscapes into urban areas. This pattern has also been documented in other studies on Lebanon [42,74] and the Mediterranean [75], highlighting how rural areas have been transformed into multifunctional landscapes driven by foreign direct and indirect land ownership, investment, and seasonal residence. It can contribute to macro-level agriculture and food security challenges, as export earnings and net remittances are often spent on food imports [76].
In contrast, in El Abde and Tal Abbass, the effects of intense urbanization of the plains, which accelerated following the migration of Palestinian and Syrian refugees, highlighted the consequences of regional conflict [42,74]. The refugees constituted the labor that is mostly essential for agricultural expansion and urban development. These transformations align with broader trends observed in the MENA region, characterized by weak governance and political instability, similar to patterns documented in post-independence in Algeria [77], the post-Arab Spring period around Greater Cairo [78,79], and throughout Southeast Asia [80] and globally [7]. A recent regional study sums up how LULC changes in major Arab cities were shaped by critical political events such as international treaties, recent migration waves, and population growth [81]. These findings complement our study to underscore the urgent need for improved governance and policy reforms to ensure balanced and sustainable agricultural landscapes in Lebanon and beyond.

5. Conclusions

The transformation of Lebanon’s rural landscapes is characterized by urbanization, the “perennialization” of agricultural areas, and the fragmentation of natural landscapes. These changes are strongly influenced by critical historical events, including the Lebanese Civil War, the Syrian conflict, and the economic collapse of 2019. As urbanization continues, agricultural landscape shifts differ across the investigated villages, with rural landscapes becoming increasingly fragmented and characterized by “patchiness.” Logistic regression analysis further highlights the role of household socio-economic characteristics and environmental pressures in shaping farmers’ land use decisions over the past decade. Farmers with limited education are often guided by short-term market trends and financial incentives regardless of the consequences on long-term sustainability. To address these challenges, integrated water governance strategies and land management policies should be developed to enhance resource sustainability and irrigation efficiency, reduce landscape fragmentation, and support long-term agricultural resilience in rural areas.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land14020262/s1, Table S1: Confusion Matrices for the LULC classification 2014 and 2023; S1.1: Tal Abbass 2014; S1.2: El Abde 2014; S1.3: Mikrak 2014; S1.4: Batloun 2014; S1.5: Sinay 2014; S1.6: Tal Abbass 2023; S1.7: El Abde 2023; S1.8: Mikrak 2023; S1.9: Batloun 2023; S1.10: Sinay 2023.

Author Contributions

Conceptualization, A.A.K.Y.; methodology, A.A.K.Y. and T.T.N.; software, A.A.K.Y.; validation, A.A.K.Y., R.Z. and A.B.; formal analysis, A.A.K.Y., R.Z. and A.B.; investigation, A.A.K.Y.; resources, A.A.K.Y. and R.Z.; data curation, A.A.K.Y.; writing—original draft preparation, A.A.K.Y.; writing—review and editing, A.A.K.Y., T.T.N., M.W., R.Z. and A.B.; visualization, A.A.K.Y.; supervision, T.T.N., M.W. and A.B.; project administration, A.A.K.Y. and A.B.; funding acquisition, A.A.K.Y. and A.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Katholischer Akademischer Ausländer-Dienst (KAAD)-Catholic Academic Exchange Service, Germany.

Data Availability Statement

Data are contained within the article and supplementary materials.

Acknowledgments

We would like to extend our special thanks to KAAD—Katholischer Akademischer Ausländer-Dienst (KAAD)-Catholic Academic Exchange Service, Germany for providing a scholarship to the first author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Spatial LULC change in the study villages of Lebanon based on high-resolution landscape maps (1962 and 2014) and 2023 Sentinel-2A satellite data.
Land 14 00262 g0a1
Figure A1. Spatial LULC change in Tal Abbass (Lebanon): (a) 1962; (b) 2014; and (c) 2023.
Figure A1. Spatial LULC change in Tal Abbass (Lebanon): (a) 1962; (b) 2014; and (c) 2023.
Land 14 00262 g0a1
Table A1. Change in land class area in Tal Abbass (Lebanon) from 1962 to 2023 (in ha).
Table A1. Change in land class area in Tal Abbass (Lebanon) from 1962 to 2023 (in ha).
Land Cover Class196220142023% Δ Area (1962–2014)% Δ Area (2014–2023)
Fruit Trees13398.040.1−26.3−59.1
Protected Agriculture043.356.0+7.3+29.3
Olives050.035.0-−30.0
Vineyards018.018.0+20-
Field Crops51290.5327.2+469.6+12.6
Discontinuous Urban Fabric1135.357.0+220.9+61.5
Continuous Urban Fabric00.22.2->100
Industrial00.20.2--
Shrubland2020.10.1−99.9-
Grassland000--
Land 14 00262 g0a2
Figure A2. Spatial LULC change in El Abde (Lebanon): (a) 1962; (b) 2014; and (c) 2023.
Figure A2. Spatial LULC change in El Abde (Lebanon): (a) 1962; (b) 2014; and (c) 2023.
Land 14 00262 g0a2
Table A2. Change in land class area in El Abde (Lebanon) from 1962 to 2023 (in ha).
Table A2. Change in land class area in El Abde (Lebanon) from 1962 to 2023 (in ha).
Land Cover Class196220142023% Δ Area (1962–2014)% Δ Area (2014–2023)
Fruit Trees85.055.732.8−7.3−5.7
Protected Agriculture094.7125.823.67.7
Olives06.26.21.5-
Field Crops034.818.58.6−4.0
Discontinuous Urban Fabric17.9126.9129.927.20.7
Continuous Urban Fabric20.267.370.411.70.7
Industrial09.111.32.20.5
Shrubland220.75.65.6−53.6-
Grassland56.40.040.04−14.0-
Table A3. Change in land class area in Mikrak (Lebanon) between 1962 and 2023 (in ha).
Table A3. Change in land class area in Mikrak (Lebanon) between 1962 and 2023 (in ha).
Land Cover Class196220142023% Δ Area (1962–2014)% Δ Area (2014–2023)
Vineyards-1.031.030.000.00
Fruit Trees2.950.869.3>100036.4
Olives-20.220.2--
Field Crops-302.8321.8-6.2
Discontinuous Urban Fabric1.98.9612.9366.644.5
Continuous Urban Fabric3.6561.761.71591.70.0
Industrial005.50.00New
Shrubland80.463.154.7−21.5−13.2
Grassland1361942.1903−30.7−4.1
Land 14 00262 g0a3
Figure A3. Spatial LULC change in Batloun (Lebanon): (a) 1962; (b) 2014; and (c) 2023.
Figure A3. Spatial LULC change in Batloun (Lebanon): (a) 1962; (b) 2014; and (c) 2023.
Land 14 00262 g0a3
Table A4. Change in land class area in Batloun (Lebanon) between 1962 and 2023 (in ha).
Table A4. Change in land class area in Batloun (Lebanon) between 1962 and 2023 (in ha).
Land Cover Class196220142023% Δ Area (1962–2014)% Δ Area (2014–2023)
Vineyards16.700−3.0-
Fruit Trees33.646.635.62.3−2.0
Field Crops33.021.321.3−2.1−0.01
Discontinuous Urban Fabric9.3139.4141.223.40.2
Continuous Urban Fabric000.7-0.1
Clear Wooded Land34.4151.6151.621.1−0.1
Shrubland424.8170.8181.4−46.11.7
Grassland024.424.44.4−0.02
Table A5. LULC change in Sinay (Lebanon) from 1962 to 2023 (in ha).
Table A5. LULC change in Sinay (Lebanon) from 1962 to 2023 (in ha).
Land Cover Class196220142023% Δ Area (1962–2014)% Δ Area (2014–2023)
Fruit Trees1.3316139.7+11.91+772.81
Protected Agriculture-0.60.6+0.140
Olives3.77.37.4+0.84+0.04
Field Crops194.6195.992.2+0.73−52.93
Discontinuous Urban Fabric-0.21.0+0.05+1.35
Continuous Urban Fabric0.735.336.5+8.15+0.23
Industrial02.93.3+6.68+0.10
Shrubland36.129.413.9−1.64−52.72
Grassland186.2138.3130.3−11.6−1.67

Appendix B

Landscape metrics calculated in the five studied villages of Lebanon.
Table A6. Landscape metrics calculated in the study of five Lebanese villages [40,41].
Table A6. Landscape metrics calculated in the study of five Lebanese villages [40,41].
IndexDescription
Number of patches (NP)
NP = 1 when the landscape contains only
1 patch.		Quantifies the number of patches in the landscape. NP is a crucial tool for analyzing the landscape processes in Table A7 [18].
Mean patch size (MPS)
= T o t a l   A r e a   ( h a ) N u m b e r   o f   P a t c h e s   ( N P ) in ha		Quantifies the mean volume of patches within the landscape. Variations in MPS reflect changes in land fragmentation or aggregation. This is crucial for gauging the level of landscape fragmentation or connectivity [40].
Table A7. Landscape change processes and their respective description for five Lebanese villages according to [18].
Table A7. Landscape change processes and their respective description for five Lebanese villages according to [18].
Landscape Change ProcessPatch AreaNumber of Patches
AttritionDecreasedDecreased
AggregationStays constant or increased
CreationIncreasedIncreased
DissectionDecreased
FragmentationStrongly decreased
Land 14 00262 g0a4
Figure A4. Variation in the landscape metrics of five villages in Lebanon using Fragstat 4.0 (1962–2023): (a) number of patches (NP); (b) mean patch size (MPS).
Figure A4. Variation in the landscape metrics of five villages in Lebanon using Fragstat 4.0 (1962–2023): (a) number of patches (NP); (b) mean patch size (MPS).
Land 14 00262 g0a4

Appendix C

Statistical data for the logistic regression analysis.
Table A8. Variance inflation factor (VIF) values for predictor variables in logistic regression analysis of five villages in Lebanon.
Table A8. Variance inflation factor (VIF) values for predictor variables in logistic regression analysis of five villages in Lebanon.
PredictorVIF Value
Education1.08
Age1.10
Farming_Area3.22
Land_Ownership1.03
Off_Farm_Income1.04
Labor_Count1.05
Resident_Status1.08
Water_Availability3.20
Irrigation1.09

References

  1. Meeus, J.H.A.; Wijermans, M.P.; Vroom, M.J. Agricultural landscapes in Europe and their transformation. Landsc. Urban Plan. 1990, 18, 289–352. [Google Scholar] [CrossRef]
  2. Firmino, A. Agriculture and landscape in Portugal. Landsc. Urban Plan. 1999, 46, 83–91. [Google Scholar] [CrossRef]
  3. Agergaard, J.; Kirkegaard, S.; Birch-Thomsen, T. Between village and town: Small-town urbanism in Sub-Saharan Africa. Sustainability 2021, 13, 1417. [Google Scholar] [CrossRef]
  4. Ellis, E.C.; Gauthier, N.; Goldewijk, K.K.; Bird, R.B.; Boivin, N.; Díaz, S.; Fuller, D.Q.; Gill, J.L.; Kaplan, J.O.; Kingston, N.N.; et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl. Acad. Sci. USA 2021, 118, e2023483118. [Google Scholar] [CrossRef]
  5. Bürgi, M.; Celio, E.; Diogo, V.; Hersperger, A.M.; Kizos, T.; Lieskovsky, J.; Pazur, R.; Plieninger, T.; Prishchepov, A.V.; Verburg, P.H. Advancing the study of driving forces of landscape change. J. Land Use Sci. 2022, 17, 540–555. [Google Scholar] [CrossRef]
  6. Maher, L.A.; Richter, T.; Stock, J.T. The Pre-Natufian Epipaleolithic: Long-term behavioral trends in the Levant. Evol. Anthropol. Issues News Rev. 2012, 21, 69–81. [Google Scholar] [CrossRef] [PubMed]
  7. Rodríguez-Caballero, E.; Cantón, Y.; Moussa, M.; Solé-Benet, A. Irrigated land expansion since 1985 in Southern Tunisia. J. Afr. Earth Sci. 2017, 129, 146–152. [Google Scholar] [CrossRef]
  8. Ellis, E.C. Ecology in an anthropogenic biosphere. Ecol. Monogr. 2015, 85, 287–331. [Google Scholar] [CrossRef]
  9. IPBES. Chapter 2.2 Status and trends—Nature. In Assessment Report on Biodiversity and Ecosystem Services; Dirzo, R., Demissew, S.W., Eds.; IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services): Bonn, Germany, 2019. [Google Scholar] [CrossRef]
  10. UNDESA. World Urbanization Prospects; United Nations Department of Economic and Social Affairs: New York, NY, USA, 2018; Available online: https://population.un.org/wup/assets/WUP2018-Report.pdf (accessed on 20 April 2022).
  11. Foley, J.A.; DeFries, R.; Asner, G.P.; Barford, C.; Bonan, G.; Carpenter, S.R.; Chapin, F.S.; Coe, M.T.; Daily, G.C.; Gibbs, H.K.; et al. Global consequences of land use. Science 2005, 309, 570–574. [Google Scholar] [CrossRef] [PubMed]
  12. Ellis, E.C.; Ramankutty, N. Putting people in the map: Anthropogenic biomes of the world. Front. Ecol. Environ. 2008, 6, 439–447. [Google Scholar] [CrossRef]
  13. Rockström, J.; Steffen, W.; Noone, K.; Persson, Å.; Chapin, F.S.; Lambin, E.F.; Lenton, T.M.; Scheffer, M.; Folke, C.; Schellnhuber, H.J.; et al. A safe operating space for humanity. Nature 2009, 461, 472–475. [Google Scholar] [CrossRef]
  14. FAO. The State of Food and Agriculture 2023; FAO (Food and Agriculture Organization of the United Nations): Rome, Italy, 2023; Available online: https://openknowledge.fao.org/handle/20.500.14283/cc7724en (accessed on 9 July 2024).
  15. Akram-Lodhi, A.H.; Kay, C. Surveying the agrarian question (part 1): Unearthing foundations, exploring diversity. J. Peasant Stud. 2010, 37, 177–202. [Google Scholar] [CrossRef]
  16. Jaad, A.; Abdelghany, K. The Story of five mena cities: Urban growth prediction modeling using remote sensing and video analytics. Cities 2021, 118, 103393. [Google Scholar] [CrossRef]
  17. Bahn, R.; Tell, T.; Zurayk, R. Social Life, from rural to urban. In The societies of the Middle East and North Africa; Routledge: London, UK, 2021; Available online: https://www.taylorfrancis.com/chapters/edit/10.4324/9780429507311-2/social-life-rural-urban-rachel-bahn-tariq-tell-rami-zurayk (accessed on 20 February 2024).
  18. Mohamed, M.A.; Anders, J.; Schneider, C. Monitoring of changes in land use/land cover in Syria from 2010 to 2018 using multitemporal landsat imagery and GIS. Land 2020, 9, 226. [Google Scholar] [CrossRef]
  19. Thomson, W.; Gharios, C.; Zurayk, R. From silk to concrete: Exploring the socio-spatial aspects of the agrarian question(s) in Mount Lebanon. J. Peasant Stud. 2022, 50, 1613–1635. [Google Scholar] [CrossRef]
  20. Lasanta, T.; Nadal-Romero, E.; Errea, M.P. The footprint of marginal agriculture in the Mediterranean mountain landscape: An analysis of the Central Spanish Pyrenees. Sci. Total Environ. 2017, 599–600, 1823–1836. [Google Scholar] [CrossRef] [PubMed]
  21. Shi, P.; Zhang, Y.; Li, Z.; Li, P.; Xu, G. Influence of land use and land cover patterns on seasonal water quality at multi-spatial scales. Catena 2017, 151, 182–190. [Google Scholar] [CrossRef]
  22. Baldini, C.; Marasas, M.E.; Drozd, A.A. Three decades of landscape change across the largest peri-urban horticultural region of Argentina: Urban growth, productive intensification and the need for resilient landscape management. J. Environ. Plan. Manag. 2022, 65, 1781–1820. [Google Scholar] [CrossRef]
  23. Li, Y.; Mi, W.; Ji, L.; He, Q.; Yang, P.; Xie, S.; Bi, Y. Urbanization and agriculture intensification jointly enlarge the spatial inequality of river water quality. Sci. Total Environ. 2023, 878, 162559. [Google Scholar] [CrossRef]
  24. Kaminski, A.; Bauer, D.M.; Bell, K.P.; Loftin, C.S.; Nelson, E.J. Using Landscape metrics to characterize towns along an urban-rural gradient. Landsc. Ecol. 2021, 36, 2937–2956. [Google Scholar] [CrossRef]
  25. Wan, H.; Yoon, J.; Srikrishnan, V.; Daniel, B.; Judi, D. Landscape metrics regularly outperform other traditionally-used ancillary datasets in dasymetric mapping of population. Comput. Environ. Urban Syst. 2023, 99, 101899. [Google Scholar] [CrossRef]
  26. Dadashpoor, H.; Azizi, P.; Moghadasi, M. Land use change, urbanization, and change in landscape pattern in a metropolitan area. Sci. Total Environ. 2019, 655, 707–719. [Google Scholar] [CrossRef]
  27. Bürgi, M.; Bieling, C.; von Hackwitz, K.; Kizos, T.; Lieskovský, J.; Martín, M.G.; McCarthy, S.; Müller, M.; Palang, H.; Plieninger, T.; et al. Processes and driving forces in changing cultural landscapes across Europe. Landsc. Ecol. 2017, 32, 2097–2112. [Google Scholar] [CrossRef]
  28. Epanchin-Niell, R.S.; Jackson-Smith, D.B.; Wilson, R.S.; Ashenfarb, M.; Dayer, A.A.; Hillis, V.; Iacona, G.D.; Markowitz, E.M.; Marquart-Pyatt, S.T.; Treakle, T. Private land conservation decision-making: An integrative social science model. J. Environ. Manag. 2022, 302, 113961. [Google Scholar] [CrossRef] [PubMed]
  29. Eshetu, S.B.; Sieber, S.; Lana, M.; Löhr, K. Decision-making factors influencing land use transformation and its implication on forest landscape restoration in Ethiopia. J. Land Use Sci. 2024, 19, 211–229. [Google Scholar] [CrossRef]
  30. Lambin, E.F.; Turner, B.L.; Geist, H.J.; Agbola, S.B.; Angelsen, A.; Bruce, J.W.; Coomes, O.T.; Dirzo, R.; Fischer, G.; Folke, C.; et al. The causes of land-use and land-cover change: Moving beyond the myths. Glob. Environ. Change 2001, 11, 261–269. [Google Scholar] [CrossRef]
  31. Müller-Hansen, F.; Schlüter, M.; Mäs, M.; Donges, J.F.; Kolb, J.J.; Thonicke, K.; Heitzig, J. Towards representing human behavior and decision making in earth system models—An overview of techniques and approaches. Earth Syst. Dyn. 2017, 8, 977–1007. [Google Scholar] [CrossRef]
  32. Starfinger, M. Financing smallholder tree planting: Tree collateral & thai ‘tree banks’—Collateral 2.0? Land Use Policy 2021, 111, 105765. [Google Scholar] [CrossRef]
  33. Mulu, S.; Asfaw, Z.; Alemu, A.; Teketay, D. Determinants of decision making by smallholder farmers on land allocation for small-scale forest management in Northwestern Ethiopian highlands. Land 2022, 11, 838. [Google Scholar] [CrossRef]
  34. Githinji, M.; van Noordwijk, M.; Muthuri, C.; Speelman, E.N.; Jan Hofstede, G. Farmer land-use decision-making from an instrumental and relational perspective. Curr. Opin. Environ. Sustain. 2023, 63, 101–303. [Google Scholar] [CrossRef]
  35. Karner, K.; Mitter, H.; Schönhart, M. A conceptualized land use system and data to support integrated landscape assessments in Austria. In Alpine Landgesellschaften Zwischen Urbanisierung und Globalisierung; Larcher, M., Schmid, E., Eds.; Springer Fachmedien: Wiesbaden, Germany, 2022; pp. 211–227. [Google Scholar] [CrossRef]
  36. Kaufmann, K.; Kraay. Mastruzzi The Worldwide Governance Indicators: Methodology and Analytical Issues; World Bank: Washington, DC, USA, 2010; Available online: https://documents.worldbank.org/en/publication/documents-reports/documentdetail/630421468336563314/The-worldwide-governance-indicators-methodology-and-analytical-issues (accessed on 10 August 2024).
  37. World Bank. Weak Governance in MENA Region Worsens Deepening Land Crisis. 2023. Available online: https://worldbank.org/en/news/press-release/2023/01/18/weak-governance-in-mena-region-worsens-deepening-land-crisis (accessed on 10 August 2024).
  38. EEA. CORINE Land Cover Nomenclature (CLC); European Environment Agency (EEA): Vienna, Austria, 2019; Available online: https://land.copernicus.eu/content/corine-land-cover-nomenclature-guidelines/docs/pdf/CLC2018_Nomenclature_illustrated_guide_20190510.pdf#page=7.22 (accessed on 21 June 2024).
  39. Davenport, A.E.; Davis, J.D.; Woo, I.; Grossman, E.E.; Barham, J.; Ellings, C.S.; Takekawa, J.Y. Comparing automated classification and digitization approaches to detect change in Eelgrass bed extent during restoration of a large river delta. Northwest Sci. 2017, 91, 272–282. [Google Scholar] [CrossRef]
  40. Tun, P.T.; Nguyen, T.T.; Buerkert, A. Transformation of agricultural landscapes and its consequences for natural forests in Southern Myanmar within the last 40 years. Remote Sens. 2023, 15, 1537. [Google Scholar] [CrossRef]
  41. Xie, S.; Liu, L.; Zhang, X.; Yang, J.; Chen, X.; Gao, Y. Automatic land-cover mapping using landsat time-series data based on Google Earth Engine. Remote Sens. 2019, 11, 3023. [Google Scholar] [CrossRef]
  42. Gharios, C.; El Nour, S.; Mundy, M.; Zurayk, R. Transformation rurale, paysage et conflit dans un village du Liban Sud, Sinay. Économie Rural. Agric. Aliment. Territ. 2016, 353-354, 9–26. [Google Scholar] [CrossRef]
  43. Cetin, M. A satellite based assessment of the impact of urban expansion around a Lagoon. Int. J. Environ. Sci. Technol. 2009, 6, 579–590. [Google Scholar] [CrossRef]
  44. Schlesinger, J. Agriculture Along the Urban-Rural Continuum: A GIS-Based Analysis of Spatio-Temporal Dynamics in Two Medium-Sized African Cities. Ph.D. Thesis, University of Freiburg, Freiburg im Breisgau, Germany, 2013. Available online: https://researchgate.net/profile/Johannes-Schlesinger-2/publication/283648245_Agriculture_along_the_urban-rural_continuum_a_GIS-based_analysis_of_spatio-temporal_dynamics_in_two_medium-sized_African_cities/links/56a9de5908ae7f592f0ded02/Agriculture-along-the-urban-rural-continuum-a-GIS-based-analysis-of-spatio-temporal-dynamics-in-two-medium-sized-African-cities.pdf (accessed on 10 March 2023).
  45. McGarigal, K.; Cushman, S.E.E. Fragstat v4: Spatial Pattern Analysis Program for Categorical Maps. Computer Software Program. Available online: https://fragstats.org/index.php/license (accessed on 21 July 2024).
  46. Cochran, W.G. Sampling techniques. In Wiley Series in Probability and Mathematical Statistics, 3rd ed.; Wiley: New York, NY, USA, 1977; Available online: https://scribd.com/document/444123201/William-G-Cochran-Sampling-Techniques-Third-Edition-pdf (accessed on 10 May 2022).
  47. Noble, H.; Heale, R. Triangulation in research, with examples. Evid. Based Nurs. 2019, 22, 67–68. [Google Scholar] [CrossRef]
  48. Hamade, K. Lebanon’s Agriculture: Dynamics of Contraction in the Absence of Public. Vision and Policies. In Arab NGO Network for Development, Arab Watch on Economic and Social Rights, Right to Food; The Arab NGO Network for Development: Beirut, Lebanon, 2019; pp. 256–271. Available online: https://annd.org/arabwatch2019/righttofood/en/index.pdf (accessed on 17 July 2022).
  49. Malek, Ž.; Verburg, P.H. Mapping global patterns of land use decision-making. Glob. Environ. Change 2020, 65, 102170. [Google Scholar] [CrossRef]
  50. Kleinbaum, D.G.; Kupper, L.L.; Nizam, A.; Rosenberg, E.S. Applied Regression Analysis and Other Multivariable Methods; Cengage Learning: Boston, MA, USA, 2013; Available online: https://amazon.com/Applied-Regression-Analysis-Multivariable-Methods/dp/0495384968 (accessed on 20 June 2024).
  51. Surjanovic, N.; Lockhart, R.A.; Loughin, T.M. A Generalized Hosmer–Lemeshow Goodness-of-Fit Test for a family of generalized linear models. TEST 2024, 33, 589–608. [Google Scholar] [CrossRef]
  52. Hassan, H.E.H.; Ardillier-Carras, F.; Charbel, L. Les changements d’occupation des sols dans la Béqaa Ouest (Liban): Le rôle des actions anthropiques. Cah. Agric. 2019, 28, 10. [Google Scholar] [CrossRef]
  53. Al-Sayah, M.J.; Sarkissian, R.D.; Abdallah, C. Review of Recent Events in Lebanon (2011–2020) and Their Effect on Land Degradation. 2022. Available online: https://arablandinitiative.gltn.net/sites/default/files/2023-03/review-of-recent-events-in-lebanon-2011-20-and-their-effect-on-land-degradation_30nov2022.pdf (accessed on 15 June 2024).
  54. Cárceles Rodríguez, B.; Durán Zuazo, V.H.; Franco Tarifa, D.; Cuadros Tavira, S.; Sacristan, P.C.; García-Tejero, I.F. Irrigation alternatives for Avocado (Persea Americana Mill.) in the Mediterranean Subtropical region in the context of climate change: A Review. Agriculture 2023, 13, 1049. [Google Scholar] [CrossRef]
  55. Bogaert, J.; Farina, A.; Ceulemans, R. Entropy increase of fragmented habitats: A sign of human impact? Ecol. Indic. 2005, 5, 207–212. [Google Scholar] [CrossRef]
  56. Tong, X.; Zhang, X.; Fensholt, R.; Jensen, P.R.D.; Li, S.; Larsen, M.N.; Reiner, F.; Tian, F.; Brandt, M. Global area boom for greenhouse cultivation revealed by satellite mapping. Nat. Food 2024, 5, 513–523. [Google Scholar] [CrossRef]
  57. Al-Shaar, W.; Bonin, O. Factors behind the dynamics of land use evolution: Case of Lebanon. SN Appl. Sci. 2021, 3, 677. [Google Scholar] [CrossRef]
  58. Harik, G.; Alameddine, I.; Zurayk, R.; El-Fadel, M. An integrated socio-economic agent-based modeling framework towards assessing farmers’ decision making under water scarcity and varying utility functions. J. Environ. Manag. 2023, 329, 117055. [Google Scholar] [CrossRef] [PubMed]
  59. Hamed, M.M.; Sobh, M.T.; Ali, Z.; Nashwan, M.S.; Shahid, S. Aridity shifts in the MENA Region under the Paris agreement climate change scenarios. Glob. Planet. Change 2024, 238, 104483. [Google Scholar] [CrossRef]
  60. Rachid, G. Evolution of Natural Resource Management in Mount Lebanon: The Case of Batloun 1935–2005. Master’s Thesis, American University of Beirut, Beirut, Lebanon, 2007. Available online: https://scholarworks.aub.edu.lb/handle/10938/7560 (accessed on 19 June 2022).
  61. Zurayk, R. The writing on the walls: Adonis, Ishtar and the terraces of Mount Lebanon. Eflux. 2021. Available online: https://e-flux.com/architecture/exhausted/379297/the-writing-on-the-walls-adonis-ishtar-and-the-terraces-of-mount-lebanon/ (accessed on 19 September 2023).
  62. Ghodieh, A. Urban built-up area estimation and change detection of the occupied West Bank, Palestine, using multi-temporal aerial photographs and satellite images. J. Indian Soc. Remote Sens. 2020, 48, 235–247. [Google Scholar] [CrossRef]
  63. Güneralp, B.; Reba, M.; Hales, B.U.; Wentz, E.A.; Seto, K.C. Trends in urban land expansion, density, and land transitions from 1970 to 2010: A global synthesis. Environ. Res. Lett. 2020, 15, 044015. [Google Scholar] [CrossRef]
  64. Hobbs, N.T.; Galvin, K.A.; Stokes, C.J.; Lackett, J.M.; Ash, A.J.; Boone, R.B.; Reid, R.S.; Thornton, P.K. Fragmentation of rangelands: Implications for humans, animals, and landscapes. Glob. Environ. Change 2008, 18, 776–785. [Google Scholar] [CrossRef]
  65. Jiao, J.; Li, S.; Wang, W.; Qi, L.; Degen, A.A.; Bai, Y.; Jiao, T.; Shang, Z. Land parcellation reduced spatial redundancy resulting in grassland degradation. People Nat. 2024, 6, 2528–2542. [Google Scholar] [CrossRef]
  66. Li, W.; Huntsinger, L. China’s grassland contract policy and its impacts on herder ability to benefit in inner Mongolia: Tragic feedbacks. Ecol. Soc. 2011, 16. [Google Scholar] [CrossRef]
  67. UNESCWA. Arab Horizon 2030: Prospects for Enhancing Food Security in the Arab Region; UNESCWA (United Nations Economic and Social Commission for Western Asia): Beirut, Lebanon, 2017; Available online: https://www.unescwa.org/publications/arab-horizon-2030-prospects-enhancing-food-security-arab-region (accessed on 5 December 2024).
  68. MoET (Ministry of Economy and Trade) Lebanon’s Fresh Fruit and Vegetable Exporters Shine at Fruit Attraction. 2023. Available online: https://lebfresh.org/lebanons-fresh-fruit-and-vegetable-exporters-shine-at-fruit-attraction-2023/ (accessed on 10 November 2024).
  69. Seyfert, K.; Chaaban, J.; Ghattas, H. Food security and the supermarket transition in the Middle East: Two case studies. In Food Security in the Middle East; Babar, Z., Mirgani, S., Eds.; C Hurst & Co Publishers Ltd.: London, UK, 2014; pp. 185–219. [Google Scholar] [CrossRef]
  70. El Yaacoubi, A.; Malagi, G.; Oukabli, A.; Hafidi, M.; Legave, J.-M. Global warming impact on floral phenology of fruit trees species in Mediterranean region. Sci. Hortic. 2014, 180, 243–253. [Google Scholar] [CrossRef]
  71. Fernandez, E.; Mojahid, H.; Fadón, E.; Rodrigo, J.; Ruiz, D.; Egea, J.A.; Ben Mimoun, M.; Kodad, O.; El Yaacoubi, A.; Ghrab, M.; et al. Climate change impacts on winter chill in Mediterranean temperate fruit orchards. Reg. Environ. Change 2022, 23, 7. [Google Scholar] [CrossRef]
  72. Jaafar, H.; Kharroubi, S.A. Views, practices and knowledge of farmers regarding smart irrigation apps: A national cross-sectional study in Lebanon. Agric. Water Manag. 2021, 248, 106759. [Google Scholar] [CrossRef]
  73. Gérard, J.; Nehmé, C. Lebanon: A geography of contrasts. J. Mediterr. Geogr. 2020, 131. [Google Scholar] [CrossRef]
  74. Traboulsi, F. A History of Modern Lebanon, 2nd ed.; Pluto Press: London, UK, 2007; Available online: https://plutobooks.com/9780745332741/a-history-of-modern-lebanon/ (accessed on 19 June 2024).
  75. Blázquez Salom, M. More villas and more barriers: Gentrification and the enclosure of rural land on Majorca. Méditerranée Rev. Géographique Pays Méditerranéens J. Mediterr. Geogr. 2013, 120, 25–36. [Google Scholar] [CrossRef]
  76. Woertz, E. Agriculture and development in the wake of the Arab spring. Int. Dev. Policy Rev. Int. Polit. Dév. Online 2017, 7, 144–169. [Google Scholar] [CrossRef]
  77. Yeddiou, M.; Guessoum, D.-E. Constantine—Algeria- informal expansion, private lands value at stake. towards a preventive approach to informal land use. Land Use Policy 2023, 134, 106930. [Google Scholar] [CrossRef]
  78. Salem, M.; Tsurusaki, N. Impacts of rapid urban expansion on peri-urban landscapes in the Global South: Insights from landscape metrics in Greater Cairo. Sustainability 2024, 16, 2316. [Google Scholar] [CrossRef]
  79. Yehya, A.A.K.; Nguyen, T.T.; Wiehle, M.; Buerkert, A. Drivers of urbanization effects in five countries of the MENA region—A review. J. Land Use Sci. 2024, 19, 304–320. [Google Scholar] [CrossRef]
  80. Burra, D.D.; Parker, L.; Than, N.T.; Phengsavanh, P.; Long, C.T.M.; Ritzema, R.S.; Sagemueller, F.; Douxchamps, S. Drivers of land use complexity along an agricultural transition gradient in Southeast Asia. Ecol. Indic. 2021, 124, 107402. [Google Scholar] [CrossRef]
  81. Riad, P.; Graefe, S.; Hussein, H.; Buerkert, A. Landscape Transformation processes in two large and two small cities in Egypt and Jordan over the last five decades using remote sensing data. Landsc. Urban Plan. 2020, 197, 103766. [Google Scholar] [CrossRef]
Land 14 00262 g001
Figure 1. Methodological approach to combine primary and secondary data collection for this study.
Figure 1. Methodological approach to combine primary and secondary data collection for this study.
Land 14 00262 g001
Land 14 00262 g002
Figure 2. Map of selected villages from five agroecological zones in Lebanon. (a) Tal Abbass (El Gharbi) in the northern zone; (b) El Abde in the coastal zone; (c) Mikrak in the Bekaa zone; (d) Batloun in the Mount Lebanon zone; (e) Sinay in the southern zone. Sources: Global Administrative Areas Database (GADM), Environmental Systems Research Institute (ESRI), and United States Geological Survey (USGS) assessed in August 2024 using ArcGIS Pro 3.2.0.
Figure 2. Map of selected villages from five agroecological zones in Lebanon. (a) Tal Abbass (El Gharbi) in the northern zone; (b) El Abde in the coastal zone; (c) Mikrak in the Bekaa zone; (d) Batloun in the Mount Lebanon zone; (e) Sinay in the southern zone. Sources: Global Administrative Areas Database (GADM), Environmental Systems Research Institute (ESRI), and United States Geological Survey (USGS) assessed in August 2024 using ArcGIS Pro 3.2.0.
Land 14 00262 g002
Land 14 00262 g003
Figure 3. LULC class variations in the five villages from 1962 to 2023 (percentage out of 100) in five agroecological zones of Lebanon.
Figure 3. LULC class variations in the five villages from 1962 to 2023 (percentage out of 100) in five agroecological zones of Lebanon.
Land 14 00262 g003
Land 14 00262 g004
Figure 4. Spatial LULC change in Sinay village (Lebanon) based on high-resolution landscape maps (1962 and 2014) and 2023 Sentinel-2A satellite data: (a) 1962; (b) 2014; and (c) 2023.
Figure 4. Spatial LULC change in Sinay village (Lebanon) based on high-resolution landscape maps (1962 and 2014) and 2023 Sentinel-2A satellite data: (a) 1962; (b) 2014; and (c) 2023.
Land 14 00262 g004
Land 14 00262 g005
Figure 5. Urbanization rate in the five Lebanese villages studied.
Figure 5. Urbanization rate in the five Lebanese villages studied.
Land 14 00262 g005
Land 14 00262 g006
Figure 6. Sanky diagram showing the change in the number of patches in Tal Abbass, Lebanon (manual input via sankeymatic.com).
Figure 6. Sanky diagram showing the change in the number of patches in Tal Abbass, Lebanon (manual input via sankeymatic.com).
Land 14 00262 g006
Land 14 00262 g007
Figure 7. LULC change in Mikrak village (Lebanon) derived from high-resolution landscape maps (1962 and 2014) and 2023 Sentinel-2A satellite data: (a) 1962; (b) 2014; and (c) 2023.
Figure 7. LULC change in Mikrak village (Lebanon) derived from high-resolution landscape maps (1962 and 2014) and 2023 Sentinel-2A satellite data: (a) 1962; (b) 2014; and (c) 2023.
Land 14 00262 g007
Land 14 00262 g008
Figure 8. Variation in farmers’ perceptions of the availability and accessibility of water resources across villages in Lebanon The box plot shows vertical lines (whiskers) representing the data range, horizontal lines for the median, and “x” marks for the mean of farmer perceptions.
Figure 8. Variation in farmers’ perceptions of the availability and accessibility of water resources across villages in Lebanon The box plot shows vertical lines (whiskers) representing the data range, horizontal lines for the median, and “x” marks for the mean of farmer perceptions.
Land 14 00262 g008
Land 14 00262 g009
Figure 9. Predicted variation in land use change versus education in five villages of Lebanon. The blue line indicates the fitted regression line and the gray distances from the regression line show the respective confidence intervals.
Figure 9. Predicted variation in land use change versus education in five villages of Lebanon. The blue line indicates the fitted regression line and the gray distances from the regression line show the respective confidence intervals.
Land 14 00262 g009
Land 14 00262 g010
Figure 10. The variation in the logistic regression coefficients (n = 151) in five villages of Lebanon. The blue points represent the coefficient estimates for variables, while the horizontal lines indicate the confidence intervals around these estimates.
Figure 10. The variation in the logistic regression coefficients (n = 151) in five villages of Lebanon. The blue points represent the coefficient estimates for variables, while the horizontal lines indicate the confidence intervals around these estimates.
Land 14 00262 g010
Land 14 00262 g011
Figure 11. Trends in the export value of fruits from Lebanon to global markets (source: Ministry of Economy and Trade (Lebanon) and the International Trade Center (ITC)).
Figure 11. Trends in the export value of fruits from Lebanon to global markets (source: Ministry of Economy and Trade (Lebanon) and the International Trade Center (ITC)).
Land 14 00262 g011
Table 1. Data sources and their description for land use and land cover (LULC) analysis in Lebanon.
Table 1. Data sources and their description for land use and land cover (LULC) analysis in Lebanon.
Data SourceDescriptionScaleYearData Source Link
Carte Du Liban (1962)LULC map as soft copy for digitalization1:20,0001962https://geolyon.mom.fr/seriesCartes/7a46704a-9527-4cc3-a34a-7de3605eb206
(Accessed on 22 January 2023)
Google Earth (Airbus Imagery)High-resolution imagery from various data providers, used for detailed visual interpretation≤0.65 m2014, 2023https://airbus.com/en/products-services/space/earth-observation/satellite-imagery
(Accessed on 12 March 2023)
National Council for Scientific Research—Lebanon (CNRS-L)LULC maps available at CNRS-L, used to support and validate the classification processVector files2013–2017https://cnrs.edu.lb/site/SubPage.aspx?pageid=111
(Accessed on 2 October 2023)
Sentinel-2A ImagesSatellite imagery, imported into ArcGIS Pro 3.2.0 for mapping and analysis10 m2023https://sentiwiki.copernicus.eu/web/sentinel-2
(Accessed on 12 Febraury 2024 via
Google Earth Engine)
Table 2. Examples of the LULC class identification in Lebanon according to [38]. Image source: Airbus Satellite Imagery 2023, downloaded from Google Earth Pro 7.3.6.9796 (scale 100 ft).
Table 2. Examples of the LULC class identification in Lebanon according to [38]. Image source: Airbus Satellite Imagery 2023, downloaded from Google Earth Pro 7.3.6.9796 (scale 100 ft).
LULC ClassDescriptionExample Image in April 2023
Urban landscapesContinuous
urban fabric		Densely built-up areas, such as cities and urban centersLand 14 00262 i001
Discontinuous
urban fabric		Less densely built areas, often including suburban regions with some green spacesLand 14 00262 i002
Industrial or commercialPublic and private service facilities or factoriesLand 14 00262 i003
Agricultural landscapesField cropsGeneral agricultural purposes like grains, tubers, and vegetable farmingLand 14 00262 i004
OlivesCultivation of olive treesLand 14 00262 i005
Fruit treesOrchard of growing various fruit trees other than olives such as citrus, peach, and avocadoLand 14 00262 i006
VineyardsVineLand 14 00262 i007
Protected agricultureGreenhousesLand 14 00262 i008
Natural landscapesGrasslandSemi-natural vegetation with very low shrubs (mostly used for pasture)Land 14 00262 i009
ShrublandDense and tall shrubland (with rocks)Land 14 00262 i010
Clear wooded landVegetation pattern of native or exotic coniferous and/or broad-leaved treesLand 14 00262 i011
Table 3. Logistic regression results with Z-values for coefficients of land use decision-making in five villages of Lebanon.
Table 3. Logistic regression results with Z-values for coefficients of land use decision-making in five villages of Lebanon.
CoefficientsEstimateStandard ErrorZ-Valuep Value
Intercept0.721.110.650.52
Education−0.450.13−3.410.001 ***
Age−0.010.01−0.530.59
Farming Area0.050.022.610.01 **
Land Ownership0.410.381.080.28
Off-Farm Income0.270.920.290.77
Labor Count0.040.070.640.52
Resident Status0.420.231.880.06.
Water Availability−0.030.01−2.540.01 **
Irrigation0.040.480.090.93
Note: significant variables affecting farmer land use decisions toward “perennialization” at 0.001 (***) and 0.01 (**) levels of significance.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yehya, A.A.K.; Nguyen, T.T.; Wiehle, M.; Zurayk, R.; Buerkert, A. Six Decades of Rural Landscape Transformation in Five Lebanese Villages. Land 2025, 14, 262. https://doi.org/10.3390/land14020262

AMA Style

Yehya AAK, Nguyen TT, Wiehle M, Zurayk R, Buerkert A. Six Decades of Rural Landscape Transformation in Five Lebanese Villages. Land. 2025; 14(2):262. https://doi.org/10.3390/land14020262

Chicago/Turabian Style

Yehya, Abed Al Kareem, Thanh Thi Nguyen, Martin Wiehle, Rami Zurayk, and Andreas Buerkert. 2025. "Six Decades of Rural Landscape Transformation in Five Lebanese Villages" Land 14, no. 2: 262. https://doi.org/10.3390/land14020262

APA Style

Yehya, A. A. K., Nguyen, T. T., Wiehle, M., Zurayk, R., & Buerkert, A. (2025). Six Decades of Rural Landscape Transformation in Five Lebanese Villages. Land, 14(2), 262. https://doi.org/10.3390/land14020262

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop