Geographies, Volume 5, Issue 4 (December 2025) – 15 articles

The Tafrata Irrigated Perimeter (TIP) in Taourirt province, located in a semi-arid environment, faces pressures from intensive agriculture and unsustainable resource use, leading to soil degradation, low organic matter, salinity risks, and nutrient imbalances. Despite the need for effective management, limited studies have used spatial and geostatistical tools to assess soil quality in the region. This study aims to evaluate the physico-chemical quality of TIP soils and to identify management priorities for sustainable agricultural development. To achieve this, 84 soil samples analyzed for particle size, density, electrical conductivity, pH, organic matter, total carbonate content, potassium, and phosphorus. GIS was used to generate thematic maps. Findings show that 55% of the area consists of balanced sandy loam soils, with 76% of samples having slightly alkaline pH. Phosphorus and potassium concentrations average 35.23 (mg∙kg⁻¹) and 166.06 (mg∙kg⁻¹), respectively. While 76% of soils are non-saline, 87% have moderate carbonate content. Organic matter is critically low at 1.46%, raising concerns about soil fertility and water retention. The study emphasizes the need for sustainable agricultural practices to manage soil variability and improve fertility, offering actionable insights to support long-term soil health and resource sustainability in the TIP. Full article

This article examines the expansion of short-term rentals in Valparaíso, Chile, through the Airbnb platform. The study addresses the broader context of digital platforms transforming housing markets, with a focus on Latin American cities, where the implications of short-term rental growth remain understudied. The main objective is to understand how Airbnb is reshaping the spatial, economic, and social dimensions of rental housing in Valparaíso. Methodologically, the article employs quantitative methods, combining spatial analysis techniques (using ArcGIS) and descriptive statistical analysis. The results reveal that entire homes cluster in heritage-tourism hills (Concepción and Alegre) and coastal zones with panoramic views, where nightly rates can exceed the citywide average threefold, while shared rooms are dispersed in lower-income hills. Likewise, the study identifies a heterogeneous host profile; half of the hosts are owners who have another residence to live in, while the other half offers rooms within their own homes, indicating that platform usage is a complementary income strategy. These dynamics reflect asset-based welfare logics, repositioning housing as a hybrid asset for income generation rather than solely a domestic space. Even in the absence of large-scale corporate landlords, this fragmented market contributes to housing commodification and intensifies spatial inequalities. The study highlights the need for regulatory frameworks tailored to the socio-territorial specificities of heritage Latin American cities, which face both housing deficits and tourism pressures. Full article

Weeds continue to pose a serious challenge to agriculture, reducing both the productivity and quality of crops. In this paper, we explore how modern deep learning, specifically Vision Transformers (ViTs), can help address this issue through fast and accurate weed classification. We developed a transformer-based model trained on the DeepWeeds dataset, which contains images of nine different weed species collected under various environmental conditions, such as changes in lighting and weather. By leveraging the ViT architecture, the model is able to capture complex patterns and spatial details in high-resolution images, leading to improved prediction accuracy. We also examined the effects of model optimization techniques, including fine-tuning and the use of pre-trained weights, along with different strategies for handling class imbalance. While traditional oversampling actually hurt performance, dropping accuracy to 94%, using class weights alongside strong data augmentation boosted accuracy to 96.9%. Overall, our ViT model outperformed standard Convolutional Neural Networks, achieving 96.9% accuracy on the held-out test set. Attention-based saliency maps were inspected to confirm that predictions were driven by weed regions, and model consistency under location shift and capture perturbations was assessed using the diverse acquisition sites in DeepWeeds. These findings show that with the right combination of model architecture and training strategies, Vision Transformers can offer a powerful solution for smarter weed detection and more efficient farming practices. Full article

This study employs an integrated framework that combines field-based measurements, remote sensing, and Geographic Information Systems (GISs) to monitor vegetation dynamics and assess the suitability of a steppe range reserve for livestock grazing. Forty-three surface and subsurface soil samples were collected in April and November 2021 to capture seasonal variations. Above-ground biomass (AGB) measurements were recorded at five sampling locations across the reserve. Six Sentinel-2 satellite imageries, acquired around mid-March 2016–2021, were processed to derive time-series Normalized Difference Vegetation Index (NDVI) data, capturing temporal shifts in vegetation cover and density. The GIS-based Multi-Criteria Decision Analysis (MCDA) was employed to model the suitability of the reserve for livestock grazing. The results showed higher salinity, total dissolved solids (TDSs), and nitrate (NO₃) values in April. However, the percentage of organic matter increased from approximately 7% in April to over 15% in November. The dry forage productivity ranged from 111 to 964 kg/ha/year. On average, the reserve’s dry yield was 395 kg/ha/year, suggesting moderate productivity typical of steppe rangelands in this region. The time-series NDVI analyses showed significant fluctuations in vegetation cover, with lower NDVI values prevailing in 2016 and 2018, and higher values estimated in 2019 and 2020. The grazing suitability analysis showed that 13.8% of the range reserve was highly suitable, while 24.4% was moderately suitable. These findings underscore the importance of tailoring grazing practices to enhance forage availability and ecological resilience in steppe rangelands. By integrating satellite-derived metrics with in situ vegetation and soil measurements, this study provides a replicable methodological framework for assessing and monitoring rangelands in semi-arid regions. Full article

Land use change strongly influences ecosystem carbon services. This study evaluates long-term variations in carbon storage resulting from land use transitions in the Southeast region of Vietnam during 1990–2020. The analysis uses ALOS (JAXA) land use data in combination with QGIS-based spatial analysis to estimate carbon stocks. Land use trajectories were classified according to their dominant driving processes (urbanization, restoration, succession, reclamation, and reverse succession) to assess how each process affects carbon storage. The results indicate that total carbon stock increased from 475 million tons in 1990 to 502 million tons in 2010, before declining to 462 million tons in 2020. Carbon loss was mainly caused by urban expansion and ecological degradation, while ecological succession and forest restoration only partially compensated for these losses. This study develops a spatial framework for analyzing land use trajectories based on natural and anthropogenic dynamics, reflecting the region’s current administrative boundaries to improve management relevance. These findings underscore the need for sustainable land management, controlled urbanization, and ecosystem restoration to maintain carbon sequestration capacity and support Vietnam’s net-zero emission goals. Full article

The paper examines the current landscape, as well as the promises and pitfalls, of the digital transition in agricultural production and rural areas in Greece. It questions whether digitalisation is a viable option given the demographic dynamics, gaps in digital infrastructure, and heavy reliance on migrant labour in rural Greece. The methodological approach employs a mixed-methods design, integrating statistical and cartographic analyses of available census data with qualitative methods (semi-structured interviews, ethnographic observations, and a focus group). The main research question is grounded in a brief theoretical framework that addresses critiques of the inevitability of technological innovation and highlights the need to understand the complex dynamics of digital change. The paper analyses the dynamics and challenges of digital change in rural Greece, examining how demographic change and ageing, the structure and size of farms, and dependence on migrant labour relate to gaps and inequalities in digital infrastructure and skills. A critique of the prevailing discourse on digital transformation is supported by a discussion of the recently collected qualitative empirical material. The concluding section highlights the key findings and provides policy recommendations. Full article

Despite growing international interest in seasonal effects on wetland vegetation mapping, there is a notable lack of research focused on South Africa’s unique fynbos wetlands, leaving a critical gap in understanding the spatiotemporal dynamics of fynbos ecosystems. This study aimed to assess the ability of Parrot Sequoia and MicaSense RedEdge-M UAV data collected during six seasonal periods between 2018 and 2020 to discriminate between fynbos wetland vegetation species. It also identifies the most suitable time of year for accurate species-level classification. The highest classification accuracy (OA = 98.0%) was achieved in late winter and early summer (OA = 90.1%), while the lowest (OA = 57.2%) occurred in mid-autumn. Most species attained high user and producer accuracies, though Erica serrata and Tetraria thermalis were more inconsistently classified. A Kruskal–Wallis test revealed a significant effect of seasonality on user and producer accuracy as well as kappa (p < 0.05). A Wilcoxon rank-sum test indicated that the accuracy metrics were not significantly different (p > 0.05) when different sensors were used within the same season. The results suggest that conservation agencies and researchers should collect remote sensing data at the end of winter to take advantage of phenological differences between plant species. Full article

Reliable and representative ground data is fundamental for accurate crop classification using satellite imagery. This study demonstrates a structured approach to ground truth planning in the Bareilly district, Uttar Pradesh, where wheat is the dominant crop. Pre-season spectral clustering of Sentinel-2 Level-2A NDVI time-series data (November–March) was applied to identify ten spectrally distinct zones across the district, capturing phenological and land cover variability. These clusters were used at the village level to guide spatially stratified and optimized field sampling, ensuring coverage of heterogeneous and agriculturally significant areas. A total of 197 ground truth points were collected using the iCrops mobile application, enabling standardized and photo-validated data collection with offline functionality. The collected ground observations formed the basis for random forest supervised classification, enabling clear differentiation between major land use and land cover (LULC) classes with an overall accuracy of 91.6% and a Kappa coefficient of 0.886. The findings highlight that systematic ground data collection significantly enhances the reliability of remote sensing-based crop mapping. The outputs serve as a valuable resource for agricultural planners, policymakers, and local stakeholders by supporting crop monitoring, land use planning, and informed decision-making in the context of sustainable agricultural development. Full article

Rural poverty in Indonesia remains a complex issue involving various aspects. West Manggarai, East Nusa Tenggara, is a national tourist destination and a significant focus of national development, yet poverty rates remain very high. Therefore, this study developed a Composite Poverty Index (CPI) using the Sustainable Rural Livelihoods Approach (SRLA) to illustrate the complexity of rural deprivation in West Manggarai Regency. The CPI was developed by normalizing eighteen validated indicators across five livelihood capitals—human, social, natural, physical, and financial. These indicators were then classified using a Likert-type scale, and their weights were determined through the Analytic Hierarchy Process (AHP) to produce village-level CIP scores. The results show that most villages fall into the “Moderate” category (CIP: 0.40–0.60), reflecting chronic but not extreme deprivation. Spatial inequalities are evident, particularly in access to education, infrastructure, clean water, financial services, and ecological resources. Remote villages recorded higher CIP scores. Natural and economic capital were weakest, while human and social capital performed relatively well. Therefore, poverty alleviation in West Manggarai requires an integrated strategy tailored to local spatial conditions and livelihood capital. Full article

This study integrates the price-to-income ratio (PIR) with Pareto distribution characteristics to provide a novel approach for evaluating home affordability across U.S. counties. The methodology offers a new lens for the analysis of home affordability by capturing both the extreme values and central tendencies of PIR. The study normalizes the resulting Pareto parameters to a common scale and integrates data from the Zillow Home Value Index and the U.S. Department of Commerce’s SAIPE program to create a single affordability index. The findings point to significant regional differences: coastal and urban regions, such as California and New York, face significant affordability challenges, whereas the Midwest, especially Kansas, has higher affordability. The results highlight the significance of targeted policy interventions and are consistent with the body of research on systemic risk and housing market dynamics. This study also opens new avenues for future research, including the impact of economic factors on affordability and cross-regional comparative studies. The suggested approach encourages more equitable access to housing by providing policymakers with a useful tool to track and manage challenges related to housing affordability. Full article

Vulnerability is often analysed as a static condition of residents at a location, exposed to disaster and other risks. Studies on individual aspects of mobility and vulnerability exist, but comprehensive studies or guiding frameworks are lacking. The paper’s unique contribution compared to existing vulnerability models lies in emphasising vulnerability not only at fixed places, but also during transit, movement, and temporary phases. This paper highlights the current state of research on mobility vulnerability within disaster risk contexts. Through a systematic literature review, the study discovers a lack of research analysing specific vulnerabilities during mobility. Additionally, existing vulnerability frameworks are improved by incorporating (i) disaster risk and impact scenarios, (ii) different types of movements and mobilities linked to disaster risk situations, (iii) multiple localities, modalities, and temporalities, as well as multiple risks during sequences of movement and stationary phases, (iv) daily and occasional hazards, and (v) emic and etic perspectives on vulnerability. The findings of this study aim to inform future research on risk and vulnerability, supporting more effective responses amidst the changing dynamics of disaster situations. Full article

This study evaluates the impact of different territorial contexts on academic mobility within the framework of the Erasmus Programme, using data on Key Action 1 exchanges between 2015 and 2023. Using official EU datasets and a gravity model framework, the research investigates how economic performance, geographical distance, EU membership, AUF (Agence Universitaire de la Francophonie) regional affiliation, and state contiguity shape international academic flows. The research developed two gravity models: one aimed to measure the potential barriers to academic flows through a residuals analysis, and the second integrated territorial delineations as predictors. In both models, the core of the explanatory variable is formed by indicators describing the economic performance of states and the distance between countries. When applied, the models converge in emphasizing that the inclusion of states in different territorial configurations has a strong effect on the structuring of academic flows. This suggests that the Erasmus Programme exhibits trends of overconcentration of flows in a limited number of countries, questioning the need for a more polycentric strategy and a reshaping of the funding mechanisms. Even if the gravity models behave well, given the limited number of predictors, further studies may need to incorporate qualitative indicators for a more comprehensive evaluation of the interactions. Full article

Europe has experienced a growing divergence in trends of population change across the urban hierarchy. A key driver of this divergence is internal migration, which underpins the efficient functioning of the economy by enhancing labor market flexibility and allowing people to choose the most desired locations. Internal migration in Latvia is of increasing importance, as the propensity to change residence within national borders has become the primary mechanism of demographic change, shaping population redistribution across regions and the urban hierarchy. We used Latvia as a case study, exemplified by the monocentric urban system with Riga City at its center, as well as a relatively dense network of small towns spread across all regions. Small towns in Latvia, although not characterized by high levels of internal migration, exhibit notable changes in their demographic and socioeconomic composition. Our analysis uses administrative data on registered migration for each year from 2011 to 2021 to characterize migration patterns, as well as data from the 2011 and 2021 census rounds on 1-year migration to analyze the composition of the migrant population. The results showed sociodemographic variations in the characteristics of individuals migrating to small towns. Understanding the temporal and spatial dynamics of internal migration patterns and compositional effects is vital for effective local and regional development policies to plan essential services and infrastructure. Full article

The Sohodol Gorges has become a location of interest for tourists seeking ecological experiences and outdoor activities. The main purpose of the present study is to evaluate the attitudes of Romanian tourists toward the development of geotourism in this region following the COVID-19 pandemic. In conjunction with the research questions, hypotheses, variables, and research methodology, the following research objectives were emphasized in this study of the Oltenia region: (1) investigate how certain socio-demographic variables, such as age, gender, level of education, and occupation, influence tourists’ perceptions of the various aspects of geotourism development in the Sohodol Gorges; (2) analyze the different dimensions of geotourism, including its economic, ecological, and socio-cultural impacts, thus contributing to a deeper understanding of how geotourism is perceived in the study area in the post-pandemic context. For a qualitative evaluation of the information presented in this study, the authors used a qualitative survey with open questions and closed questions as a data collection method. For data processing and analysis, the EViews version 12.0 software package was used, enabling complex statistical analyses such as multiple regressions and correlation coefficient determination. These techniques were essential for identifying and interpreting the relationships between demographic variables and tourist perceptions. The research results provide a detailed picture of the influence that demographic and behavioral factors have on tourists’ perceptions in the context of post-COVID-19 geotourism development in the Sohodol Gorges of Romania. Education level and age play a significant role in shaping economic and environmental perceptions, indicating that tourists with higher education levels are more aware of the economic and ecological impact of tourism. Full article

Climate change poses significant risks to both natural and urban systems, and fostering climate literacy among younger generations is increasingly recognized as a key component of resilience strategies. This paper presents the outcomes of a transnational climate education project involving high school students from Cinisello Balsamo (Italy) and Edremit (Turkey), developed under the EU-funded Town Twinning program. The project combined scientific seminars, experiential learning, and digital tools (including carbon footprint calculators and immersive virtual glacier tours) to enhance climate knowledge and civic engagement. Youth Climate Councils were established to co-develop local sustainability proposals and engage with municipal authorities. Quantitative tests and qualitative evaluations confirmed significant learning gains and high satisfaction among participants. A comparative analysis with international initiatives highlights the project’s unique integration of scientific rigor, participatory methods, and cross-border cooperation. The proposed model offers a replicable framework for embedding place-based climate education into urban governance and youth policy. Full article