This study presents a comprehensive assessment of groundwater quality changes in the Harran Plain, one of Turkey’s largest agricultural regions and a core zone of the Southeastern Anatolia Project (GAP), encompassing approximately 1,500 km². The research primarily aims to evaluate the impacts of intensive agricultural activity, evolving land use, and irrigation practices on groundwater pollution. Electrical conductivity (EC) and nitrate (NO₃⁻) concentrations were selected as key indicators of water quality. Spatial distribution maps based on seasonal averages were generated for the years 2005 and 2015 using data collected from 24 observation wells across the plain and evaluated against international standards (the WHO and the EPA) for drinking and irrigation water. In 2005, several wells exhibited critically high contamination levels, with EC values reaching up to 8,235 µS/cm and NO₃⁻ concentrations exceeding 720 mg/L. By 2015, these values had significantly declined in most areas—down to 2,510 µS/cm for EC and 327 mg/L for NO₃⁻—except in W11 (Uğurlu) and W14 (Kızıldoruk), where elevated levels persisted. These improvements are attributed to the implementation of closed drainage systems, adoption of pressurized irrigation methods, improved fertilizer management, and the introduction of more balanced and sustainable cropping patterns. Concurrently, a major transformation in land use was observed, including a shift from traditional cotton and grain farming to high-value, low-water-demand crops. However, the expansion of residential and industrial zones in certain areas introduced new environmental pressures, with some wells recording increased NO₃⁻ levels. Uncontrolled land development and irregular irrigation practices were identified as contributing factors. Moreover, the conversion of pasture and fallow lands into cultivated areas appears to have altered the groundwater recharge regime, impacting overall water quality. These findings highlight the critical role of integrated water–land policy approaches for sustainable groundwater management in arid agroecosystems.

Soil carbon sequestration is a vital strategy for mitigating climate change, particularly in ecologically sensitive regions like the Himalayas. This study evaluates the spatio-temporal dynamics of carbon stock potential in Sirmaur district of Himachal Pradesh over a period of 30 years (1993–2023). To achieve this, multi-temporal Landsat satellite imagery (30 m resolution) for the years 1993, 2003, 2013, and 2023 was used to generate Land Use Land Cover (LULC) maps through random forest classification in a GIS environment. The resulting LULC maps served as key inputs for the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Carbon Storage and Sequestration model. This model quantifies total carbon stock by aggregating above-ground biomass, below-ground biomass, soil organic carbon, and dead organic matter across various land use categories. The results showed a steady increase in higher carbon density zones over the study period. Notably, the area under the “Very High” carbon density class (>15.6852 Mg/ha) expanded from 1,276.86 sq.km in 1993 to 1,379.66 sq.km in 2023. Conversely, the “Low” carbon density class (0–7.826 Mg/ha) reduced from 836.04 to 679.24 sq.km. Forests and agroforestry systems emerged as the dominant contributors to total carbon stock. This research highlights the importance of remote sensing and modeling frameworks in understanding carbon dynamics. It also provides scientific evidence to support climate-resilient land management and policy planning for carbon sequestration, particularly in fragile mountainous ecosystems.

The Makazhoy Hollow, a region of ecological and cultural significance in southeastern Chechnya within the Vedensky Zakaznik and Argun Museum-Reserve, exemplifies the interplay between traditional practices and evolving institutional contexts. This research investigates land use transformation stages in the Hollow, influenced by shifts in land ownership, economic strategies, and socio-cultural factors. Its contribution lies in informing sustainable master planning for mountainous areas, integrating heritage conservation with local economic growth.

Based on historical geography and landscape ecology methods, the study analyzed archival materials, cartographic sources, multi-temporal satellite imagery, and field data, including geobotanical descriptions and local interviews. Historical periodization identified key land use stages: traditional (19th–early 20th centuries), characterized by extensive animal husbandry and agriculture; Soviet (1920s–1980s), driven by ethnic resettlements and collectivization; post-Soviet (1990s–early 21st century), marked by regional conflict and land use decline; and modern (2000s onwards), associated with tourism and partial recovery. Institutional factors significantly shaped land use across these phases.

The study concludes that land use change in the Makazhoy Hollow is a complex process shaped by interacting environmental, economic, social, and institutional factors. Sustainable development requires integrating historical knowledge, preserving traditional livelihoods, promoting tourism, and creating effective land governance to protect natural and cultural heritage. The results inform sustainable development strategies and land-use planning in similar mountainous areas.

Urbanization continues to place immense pressure on land use systems, resulting in habitat fragmentation, biodiversity loss, climate vulnerability, and a growing disconnect between people and nature. In response, nature-based solutions (NbSs) have gained prominence as an integrated approach to reintroduce natural systems into cities and support the emergence of biophilic urbanism. This paper explores the role of NbSs in shaping biophilic cities by conducting a systematic literature review and critically evaluating their strengths, weaknesses, opportunities, and threats (SWOT) in urban planning and design.

Using a systematic search of peer-reviewed journal articles, policy reports, and case studies published between 2000 and 2024, the review identifies key themes, implementation strategies, and outcomes associated with NbSs in urban contexts. The SWOT framework is applied to categorize the findings and analyze the strategic potential and limitations of NbSs within biophilic design practices. Strengths identified include ecosystem service enhancement, climate resilience, mental and physical health benefits, and aesthetic and cultural value. However, weaknesses such as fragmented governance, lack of measurable indicators, maintenance burdens, and potential for socio-spatial inequalities (e.g., green gentrification) are also evident.

Opportunities are highlighted in emerging policy mandates, climate adaptation funding, regenerative design movements, and community-led green infrastructure. At the same time, threats include land use conflicts, political inertia, uneven distribution of green benefits, and commodification of urban nature. The paper concludes by recommending pathways to better integrate NbSs into urban policy, land management, and planning systems, ensuring they support equitable, inclusive, and ecologically regenerative cities.

By situating NbSs within the biophilic city agenda, this critical review contributes to a deeper understanding of how land use futures can be shaped through design strategies that restore human–nature relationships and enhance urban sustainability.

The Papagayo Protective Forest, the second largest tropical dry forest in Guayaquil, is an ecosystem of high ecological and social value, threatened by urban expansion and the absence of effective conservation policies. Although declared a protected area in 2012, the lack of governmental intervention has allowed its progressive degradation. Currently, the forest faces severe pressures such as informal urbanization, recurrent forest fires, human invasions, and the expansion of agricultural activities, all of which have intensified its fragmentation and compromised ecological connectivity. To analyze these changes, land cover data from the MapBiomas Ecuador platform for the years 2007 and 2023 were used. The information was processed in QGIS for land use classification, and landscape metrics were applied using Fragstats 4.2 software, including patch area (AREA), number of patches (NP), mean shape (SHAPE_MN), mean fractal dimension (FRAC_MN), diversity index (SHDI), and evenness index (SHEI). The results indicate a significant loss of 471 hectares of open forest, a reduction in natural forest, an increase in agricultural area from 837 to 1,272 hectares, and the appearance of 28 hectares of urban infrastructure. Furthermore, the number of patches increased significantly, along with a greater diversity of land use classes, indicating accelerated fragmentation. This loss of spatial continuity hinders wildlife mobility, reduces the forest’s ability to regenerate, and compromises the provision of ecosystem services such as water regulation and temperature control. In conclusion, the conservation of the Papagayo Forest demands urgent attention through effective public policies, reforestation, sustainable territorial planning, and community participation to prevent its progressive disappearance.

Illegal mining has rapidly expanded in the Ecuadorian Amazon, intensifying deforestation, ecosystem degradation, and socio-territorial conflicts—particularly within hydrologically and culturally sensitive areas. This study assesses the environmental and social pressures of mining in the Jatunyacu River basin by developing a Mining Pressure Index (MPI) based on a multi-criteria spatial framework. The index incorporates six key variables: type of mineral exploitation, proximity to rivers, and presence of biosphere reserves, indigenous territories, and conservation units. Each variable was normalized and weighted using the Analytic Hierarchy Process (AHP), ensuring consistency (CR = 0.095) in the prioritization of influencing factors. Spatial datasets were sourced from governmental geoportals, complemented by land cover classifications from MapBiomas Ecuador (2023), and structured within a 1 km × 1 km grid to standardize spatial resolution. The results show that mining pressure is disproportionately concentrated in the middle and lower zones of the basin, primarily driven by illegal alluvial metallic mining in areas with alluvial deposits near major rivers. These high-pressure zones coincide with protected and indigenous territories within the Sumaco Biosphere Reserve, revealing complex overlaps between extractive activities and social–environmental vulnerabilities. In contrast, upper basin areas—characterized by coarse conglomerates, granite lithology, and stricter conservation regimes—exhibit significantly lower MPI values. The study also identifies four distinct mining pressure hotspots with implications for ecosystem integrity and indigenous livelihoods. By integrating geospatial and socio-environmental data through a transparent and reproducible methodology, the MPI provides a robust decision support tool for environmental authorities, land planners, and policy-makers. It enables zoning of high-risk areas, supports targeted mitigation and restoration strategies, and informs governance frameworks aimed at reducing conflicts and promoting sustainable land use. The methodological approach can be adapted to other extractive frontiers across the Amazon basin, contributing to a more nuanced understanding of mining impacts at the intersection of ecology, land tenure, and indigenous rights.

Land use and land cover classification (LULC) is a method used for sustainable land management and studying land use change over time, particularly in expanding areas such as the city of Guayaquil, Ecuador. This study compares three supervised classification algorithms—Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—applied to the mosaic created from multispectral Landsat-9 images of 2023, obtained through Google Earth Engine (GEE). Validation was carried out by comparing the results with reference data from the MapBiomas Ecuador project, which has shown high reliability in South American contexts. The models were trained using samples obtained through visual interpretation of the Sentinel image mosaic, categorizing four classes (forest, crops, non-vegetated areas, and water). Standard metrics such as the kappa coefficient, overall accuracy, and confusion matrices were used to assess the performance of the algorithms. The results showed that the SVM algorithm performed the best (kappa = 0.91, accuracy = 93%), surpassing RF (kappa = 0.88) and ANN (kappa = 0.86). SVM demonstrated a stronger ability to manage nonlinearly separable classes, and its robustness against band dimensionality explains its superior performance, which aligns with previous findings in remote sensing. SVM showed greater spatial similarity with the patterns identified by MapBiomas, particularly in defining urban areas and water bodies. This research supports using SVM as an effective tool that requires minimal computational resources in equatorial regions, where obtaining images with low cloud covers presents an additional challenge. Furthermore, the importance of validating machine learning models using open and reliable sources is emphasized. Its application is recommended for urban planning studies, coverage change monitoring, and environmental impact assessments.

Introduction:
Rapid urbanisation intensifies land entropy—manifested as spatial disorder, escalating resource dissipation, and ecological fragmentation—especially in cities that continue to sprawl horizontally. This paper advances Parametric Timber Urbanism, a design paradigm in which algorithmically generated cross-laminated-timber (CLT) and glulam megastructures stack mixed-use neighbourhoods vertically, shrinking urban footprints while lowering material and energy entropy and maintaining architectural flexibility.

Methods:
A three-tier workflow was adopted:

1. Generative Modelling—Rhino-Grasshopper scripts evolved mass-timber superstructures from a 12 m × 12 m grid, incorporating variable core positions and modular facade panels.

2. Multi-Objective Optimisation—A genetic algorithm balanced floor-area ratio, daylight autonomy, carbon sequestration, and structural efficiency, producing hundreds of candidate morphologies.

3. Entropy-Based Assessment—Integrated lifecycle inventory and exergy accounting quantified embodied energy, cumulative entropy generation, and circular-material loops. Benchmark scenarios compared timber towers to conventional steel–concrete high-rise typologies across three global climate zones.

Results:
Optimised timber megastructures achieved a 65% reduction in embodied carbon and a 48% drop in cumulative exergy loss relative to steel–concrete towers of equivalent composition. Land-use efficiency rose from 3.5 to 9.2 in floor-area ratio, enabling a 72% decrease in ground coverage while preserving gross floor area. Passive-environmental performance improved: daylight and natural-ventilation indices increased by 28%, and thermal-mass modulation cut annual operational energy by 21%. Modular plug-in units allow 30-year lifecycle reconfigurations with <5 % additional material, demonstrating adaptive capacity without intensifying entropy.

Conclusions:
Algorithmic mass-timber systems can deliver high-density, low-entropy urban morphologies that outperform conventional high-rise construction across environmental, structural, and socio-economic metrics. By pairing parametric optimisation with entropy-based evaluation, Parametric Timber Urbanism offers a scalable template for sustainable vertical expansion, aligning urban growth with carbon neutrality and circular resource flows.

Abstract:

Achieving sustainable farming in dry and semi-dry areas depends on accurately identifying optimal land zones by evaluating environmental factors. This research presents a combined approach merging object-oriented image processing with pixel-level modeling to evaluate farming land suitability in Iran's Anjir Plain. By employing Landsat-9 satellite data, 525 spatial units were created via multi-scale segmentation, ensuring boundaries matched natural terrain characteristics. Eleven environmental criteria related to water, climate, and terrain were normalized and combined using the Ordered Weighted Averaging (OWA) method to generate a suitability score (0–1) for each unit.

Findings revealed that altitude (r = 0.8), soil composition and bedrock (r = 0.7), and nearness to groundwater sources (r = 0.7) had the strongest positive correlation with farming suitability, whereas local climate conditions and temperature variables exhibited adverse effects. Merely 7 spatial units, spanning about 8,670 hectares, surpassed the high-suitability benchmark of 0.9, marking them as prime areas for agricultural expansion. Validation using 40 randomly sampled field data points yielded 90% accuracy and an 80% Kappa score. The combined method improved both classification precision and spatial consistency, ensuring better conformity with actual land divisions.

These outcomes highlight the efficacy of blending object-based analysis and OWA for pinpointing feasible agricultural lands, especially in water-limited settings. The framework serves as a practical planning aid for optimizing land use and managing farming resources in arid regions.

Drinking water is a fundamental resource for the sustainability of life on Earth, so the preservation and efficient management of this resource are essential. However, due to the constant increase in population and urban expansion together with climate change, the consumption of drinking water has constantly increased; on the other hand, its availability is lower. It is in this context that the use of rainwater emerges, standing out due to being a sustainable, simple, and effective option, where it becomes possible to use water harvested from precipitation, using it for purposes that do not require the level of filtration and treatment of drinking water, such as the irrigation of green spaces, thus contributing to the reduction in the consumption of this resource. Therefore, the main objective of this work was to study and develop a pilot rainwater harvesting system, using as the study area Building 1 of the University of Algarve, on the Gambelas Campus. It was also decided to carry out a comparative analysis, thus comparing the results obtained in the analysis of this work with those of national and international example cases, in order to verify the efficiency of this system. It was thus verified through the analysis of the results that Gambelas has a typical Mediterranean climate, noting a scarcity of precipitation between June and September. It was also verified that with the amount of rainwater possibly stored, reaching 623 658 l or 623.658 m3, it would be sufficient to meet the estimated demand, reaching 441.36 m3. It is therefore concluded that the installation of the proposed rainwater harvesting system would effectively and sustainably meet the water demand for irrigation of the green spaces in the courtyard of Building 1 and its surroundings, thus making use of rainwater and reducing the consumption of drinking water.