University of Tehran PressDesert2008-087530220251201The Persian Gulf: from Ancient Maps to Satellite Images.22824810595110.22059/jdesert.2025.105951ENMohammadMansourmoghaddamCenter for Remote Sensing and GIS Research, Shahid Beheshti University, Tehran 1983969411, Iran.Seyed KazemAlavipanahDepartment of Remote Sensing and GIS, Faculty of Geography, The University of Tehran, Tehran 1417935840, Iran.Journal Article20260209The Persian Gulf, as a semi-enclosed marginal sea of the Indian Ocean, has long been regarded by historical geographers and cartographers as a distinct geographic phenomenon. This study aims to integrate historical cartographical sources with satellite imagery and geographical information systems (GIS) to investigate the spatial continuity and geographical stability of the Gulf from antiquity to the present.. Historical maps, obtained from the National Cartography Center of Iran (NCC) and the Circle of Ancient Iranian Studies (CAIS) and spanning from classical antiquity to the early modern period, were analyzed to document patterns of geographic depiction and labeling of the Persian Gulf across different cartographic sources. Contemporary geospatial analyses were conducted using 17 Landsat-8 and Landsat-9 scenes to generate a satellite-based mosaic, from which key spatial characteristics including area, maximum length, average width, and coastline distribution in Iran, Iraq, Kuwait, Saudi Arabia, Bahrain, Qatar, the United Arab Emirates, and Oman were derived. In addition, the geometric centroid and Standard Deviational Ellipse (SDE) were calculated to assess the overall spatial configuration and directional distribution of the Persian Gulf. The results indicate a high degree of consistency in the location, extent, and geometric characteristics of the region over time, with a pronounced spatial association along the northern coastline. The close correspondence between historical cartographic representations and satellite-derived spatial metrics highlights the continuity of the geographic feature commonly identified as the Persian Gulf. By linking ancient maps with contemporary remote sensing data, this study provides a spatially explicit perspective on the long-term cartographic representation of the Persian Gulf.https://jdesert.ut.ac.ir/article_105951_a1480e9457304874454b582d82a8704f.pdfUniversity of Tehran PressDesert2008-087530220251201Release of Potassium, Calcium, and Magnesium from Vermiculite Clay Soil of the Jiroft Mine.24927010521910.22059/jdesert.2025.105219ENSoheila SadatHashemiDepartment of Soil Science, Faculty of Agriculture, Malayer University, Malayer, Hamedan, IranYeganehChahardoliDepartment of Soil Science, Faculty of Agriculture, Malayer University, Malayer, Hamedan, IranZahraVarasteh KhanlariDepartment of Soil Science, Faculty of Agriculture, Malayer University, Malayer, Hamedan, IranJournal Article20251223The purpose of this research was to investigate the release of non-exchangeable potassium (K), magnesium (Mg), and calcium (Ca) from vermiculite clay soil using different extractants as a natural source to achieve sustainable agriculture. The treatments included four extractants: hydrochloric acid, calcium chloride, citric acid, and oxalic acid, each at a concentration of 0.01 M, applied in ten half-hour intervals. The cumulative release of K, Mg, and Ca from the vermiculite clay soil was measured, and the data were fitted to five kinetic equations. The results of the analysis of variance indicated that the effect of the extractant on the release of K, Mg, and Ca was significant (p < 0.01). The maximum cumulative releases for HCl, calcium chloride, citric acid, and oxalic acid solutions were found to be 160.7, 62.1, 140.1, and 95.2 mg kg<sup>-1</sup>, respectively, for K element and 192, 1872, 1776 and 528 mg kg<sup>-1</sup>, respectively, for Mg release and cumulative release maximums for HCl, citric acid, and oxalic acid solutions were estimated to be 1600, 3040, and 1120 mg kg<sup>-1 </sup>respectively, for Ca release. The highest and lowest release amounts for K were related to HCl and calcium chloride, respectively, as the extractant. The power function (r = 0.95 to 0.99, p<0.01), parabolic diffusion (r = 0.76 to 0.99, p<0.01), and Elovich models (r = 0.85 to 0.89, p<0.01) were identified as the most effective equations for predicting the release of three elements from vermiculite clay soil. Mineralogical results indicated that the component of the vermiculite was transformed to smectite clay in two HCl and citric acid extractants. In conclusion, vermiculite clay soil can serve as a natural source to meet the K and Mg requirements of plants.https://jdesert.ut.ac.ir/article_105219_058b0cd78396e53738af1b9aa638a3e2.pdfUniversity of Tehran PressDesert2008-087530220251201Land Use and Land Cover Change Assessment Using Support Vector Machine and Random Forest.27128610607810.22059/jdesert.2025.106078ENAminMousaviDepartment of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, 1417853933, Iran.Sayyed MahmoudEnjavinezhadDepartment of Soil Sciences, School of Agriculture, Shiraz University, Shiraz, Iran.Seyed KazemAlavipanahDepartment of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, 1417853933, Iran.María FernándezRaga3 Department of Chemistry Applied Physics, Industrial Engineering School, University of León, León, Spain.SedighehMalekiDepartment of Soil Sciences, School of Agriculture, Shiraz University, Shiraz, Iran.Seyed JavadNaghibiDepartment of Soil Sciences, School of Agriculture, Shiraz University, Shiraz, Iran and Head of Research & Extension Office, Landscape & Green Spaces Organization of Shiraz Municipality, Shiraz, 45366-78, Iran.Journal Article20260217This study evaluates land use and land cover (LULC) changes in Fars Province, Iran, using machine learning algorithms, specifically comparing the performance of two non-parametric support vector machine (SVM) and random forest models. With rapid urbanization and agricultural expansion, accurate LULC classification is critical for environmental monitoring and land management. Applying the Google Earth Engine platform, multi-temporal Landsat 8 imagery was assessed. The findings demonstrated all classification methods presented high accuracy metrics and kappa coefficient values. The SVM algorithm attaining a mean overall accuracy of 91.42% for Landsat 8 imagery to show best performance among all evaluated methods. According to LULC change detection performed by the most accurate classification algorithm, the results indicated an increase in urban parks, gardens, and mountainous rangelands, while barren lands experienced a decline. The evaluation of LULC changes impacts on land surface temperature (LST) shows that enhanced vegetation cover played a key role in reducing LST. A remarkable decrease in both maximum and minimum LST values was observed, declining 37.31°C and 22.47°C in 2019 to 34.45°C and 19.98°C in 2023, respectively. Furthermore, the findings highlight integrating high-resolution satellite imagery with the SVM algorithm leads to achieve a highly accurate and efficient approach for LULC mapping. Consequently, this method proves to be a valuable tool for decision-making in natural resource management and urban planning in similar regions.https://jdesert.ut.ac.ir/article_106078_92cd2cd47d954e4f7bc710e98a87ad6b.pdfUniversity of Tehran PressDesert2008-087530220251201Forecasting NDVI Variability Using SPI-Driven Hybrid Deep Learning in a Semi-Arid Environment.28730910549610.22059/jdesert.2025.105496ENSaeedErfaniFaculty of Natural Resources, Semnan University, Semnan, IranJournal Article20260112Effective land management in semi-arid regions is contingent upon the accurate forecasting of vegetative alterations in response to climatic variations. This research utilize the CNN-LSTM model as a hybrid deep learning framework to predict fluctuations in Normalized Difference Vegetation Index (NDVI) using lagged Standardized Precipitation Index (SPI) and NDVI inputs. The objective of the model is to capture the enduring memory effects of vegetation that impact plant growth, as well as to account for short-term variations in precipitation. A dataset comprising MODIS NDVI and monthly SPI data from 2001–2022 was developed for the region of Semnan, Iran, which is characterized by its extreme aridity. After extensive preprocessing, various configurations of NDVI and SPI lags were systematically assessed. The optimal performance was obtained utilizing one-month SPI values with both 1- and 2-month time lags, in conjunction with a 1-month NDVI lag, resulting in notable accuracy (RMSE = 0.0038; r = 0.968).<br />The application of explainable artificial intelligence methodologies—including SHAP, LIME, and Random Forest feature importance—validated that NDVI lag-1 consistently emerged as the most significant predictor across all analytical approaches. Additionally, SPI lags made substantial contributions, with SPI-1 generally demonstrating a more pronounced impact than lags associated with longer precipitation durations. These results underscore the pivotal influence of short-term vegetative memory and recent precipitation anomalies in determining the dynamics of NDVI within dryland ecosystems.https://jdesert.ut.ac.ir/article_105496_6f6ec2d48fcb63453465ca8902ecbf13.pdfUniversity of Tehran PressDesert2008-087530220251201Desert Dust Deposition: Impacts on Physiological Responses, Chlorophyll Pigments, and Stomatal Conductance in Wheat and Cowpea.31033210542210.22059/jdesert.2025.105422ENAlirezaRashkiDepartment of desert and arid Zones management, Faculty of
Natural Resource and Environment, Ferdowsi University of
Mashhad, Mashhad, IranZahraHatamiDepartment of Agrotechnology, Ferdowsi University of Mashhad, Mashhad, IranParvizRezvani MoghaddamDepartment of Agrotechnology, Ferdowsi University of Mashhad, Mashhad, Iran, Mashhad, IranMehdiNasiri MahallatiDepartment of Agrotechnology, Ferdowsi University of Mashhad, Mashhad, Iran, Mashhad, IranJournal Article20260106Deposition of dust particles on plant leaves reduces light interception. Additionally, dust accumulation in stomata decreases gas exchange in leaves. The effects of dust deposition following sand and dust storms (SDS) are critical, and the physiological responses of plants to dust deposition as an abiotic stress factor are of primary importance. We hypothesized that dust storm occurrence negatively affects leaf traits in wheat (<em>Triticum aestivum L</em>.) and cowpea (<em>Vigna unguiculata L</em>.). The effects of desert dust on photosynthetic pigment contents and stomatal conductance were studied in both species. Wheat and cowpea plants were subjected to dust treatments in a factorial layout based on a randomized complete block design in Dezful and Mashhad. Experimental treatments included desert dust concentration (0, 500, and 1500 µg m<sup>−3</sup>), number of dust applications (once, twice, thrice), and dust type (samples collected during dust storms in Dezful and Zabol, two of the most dust-prone regions of Iran). Dust application reduced stomatal conductance in both plants at both locations. Increasing dust concentration reduced chlorophyll a+b, a, and b in wheat leaves, while only chlorophyll b in cowpea leaves was significantly affected. Overall, this study provides new insights into how desert dust affects photosynthetic pigments and stomatal conductance in wheat and cowpea through shading and stomatal occlusion during dust storms.https://jdesert.ut.ac.ir/article_105422_ea42804e174ed5ef0f9d25ccc11783fe.pdfUniversity of Tehran PressDesert2008-087530220251201Optimization of Biodiesel Production Process from Borage (Borago officinalis L.) Oil Using Response Surface Method33334810541910.22059/jdesert.2025.105419ENFarideSalariDepartment of Plant Production and Genetics, Faculty of Agriculture, University of Kurdistan, Sanandaj, IranShivaKhalesroAgronomy and Plant Breeding Department
University of Kurdistan
Kurdistan
IranSamiraZareeiDepartment of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, IranGholamrezaHeidariDepartment of Plant Production and Genetics, Faculty of Agriculture, University of Kurdistan, Sanandaj, IranJournal Article20260106A substantial share of the world’s total energy production comes from fossil fuels. In addition to the declining availability of fossil fuel resources, the use of diesel fuel poses numerous environmental threats. Therefore, identifying diverse, renewable, and clean energy sources is essential to ensure the continued advancement of human society. In this study, the effect of four factors, including the molar ratio of alcohol to oil, the weight percentage of catalyst, temperature, and reaction time on the percentage of methyl ester conversion and biodiesel production yield from borage oil seed was investigated at three levels. Design Expert software, Response Surface Methodology (RSM), and Central Composite Design (CCD) were used for statistical analysis and process optimization. The highest desirable process yield was obtained under optimal conditions for biodiesel production, with a molar ratio of 1:8, a weight percentage of catalyst of 0.5, a reaction temperature of 60 °C, and a reaction time of 90 minutes. The biodiesel produced from borage oil through transesterification reaction had satisfactory results in terms of various parameters, including methyl ester content (99.94%), kinematic viscosity at 40 °C (4.1 cSt), density (0.8884 g/ml), flash point (163 °C), pour point (-3 °C), cloud point (-1.3 °C), acid number (0.08 mg KOH/g), and freezing point (-13 °C), in accordance with ASTM 6751-08 and EN 14214-08 standards.https://jdesert.ut.ac.ir/article_105419_51ac2e76d3298f5666a610a7758a5c2b.pdfUniversity of Tehran PressDesert2008-087530220251201Assessment of the Climate Variability Effects on Planted Forests (Case study: Dry lands, Semnan Province).34937010608010.22059/jdesert.2025.106080ENMohsenAbdolhoseiniDepartment of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran.HassanKhosraviDepartment of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran.AliTaviliDepartment of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran.GholamrezaZehtabianDepartment of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran.HamidrezaKeshtkarDepartment of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran.EsmaeilHeydari AlamdarlouDepartment of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran.Journal Article20260217Afforestation of wind erosion hotspots using drought-tolerant species such as <em>Haloxylon</em> spp. remains a central strategy in mitigating desertification and stabilizing mobile dunes in Iran's arid regions. Despite these efforts, the long-term sustainability of such ecosystems is becoming increasingly uncertain due to the combined pressures of climate variability and anthropogenic disturbances. This study evaluated the ecological resilience of planted desertland forests in Semnan Province by quantifying the relative contributions of climatic and human influences. A time series of Landsat 8 imagery from 2013 to 2022 was used to derive the Forest Canopy Density (FCD) model. Vegetation dynamics were statistically correlated with drought patterns, as measured by the Standardized Precipitation–Evapotranspiration Index (SPEI), using Pearson correlation analysis. The findings revealed that the FCD maintained a consistent range between 44.9% and 55.8%, indicating a moderate and stable vegetation cover characteristic of established Haloxylon stands in arid environments. The correlation analysis revealed a weak and statistically non-significant association (R = 0.21, p > 0.05, n = 10) between FCD and SPEI at a 9-month lag. This lack of significant climatic coupling highlights that precipitation variability alone explains a negligible portion of vegetation dynamics, strongly pointing to the dominance of non-climatic drivers such as anthropogenic disturbances and groundwater dependency. Furthermore, based on the Residual Trend Analysis (RESTREND), the Mann-Kendall trend test on residuals revealed no statistically significant anthropogenic degradation or restoration trends across the study area (Z-values ranging from -1.40 to +1.40). This indicates that human pressures likely remained constant rather than intensified during the 2013–2022 period, resulting in a fragile status quo. The compounded effects of prolonged droughts and unsustainable water resources management thus shape the sustainability of these afforested systems. The adoption of integrated, climate-informed, and human-responsive land management approaches is strongly recommended to safeguard these critical desert ecosystems.https://jdesert.ut.ac.ir/article_106080_9408eec823863197d56d5530376168e2.pdfUniversity of Tehran PressDesert2008-087530220251201Comparative Experimental and FLOW-3D Numerical Study of Hydraulic Behavior in SMBF Portable Flumes under Free and Submerged Flow Conditions.37138710617810.22059/jdesert.2025.106178ENBahareRastipisheDepartment of Arid and Mountainous Region Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran.AliSalajeghehDepartment of Arid and Mountainous Region Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran.YounesAminpourHydraulic and Aquatic Environment Engineering Research Institute, Water Research Institute, Ministry of Energy, Tehran, Iran.AmirhosseinParsamehrDepartment of Rangeland and Watershed Management (Nature Engineering), Faculty of Agriculture, Fasa University, Fasa, Shiraz, Iran.Journal Article20260225In an era of water scarcity, accurate flow discharge measurement in open channels forms the cornerstone of hydraulic design and management. Portable sidewall-mounted semicircular (SMBF) flumes offer an ideal solution for laboratory and field applications due to their simplicity, portability, and high precision. However, the transition from free to submerged flow regimes complicates hydraulic performance, necessitating comprehensive investigation. This study employed a combined experimental and numerical approach to evaluate submergence effects on flow structures in SMBF flumes, using contraction ratios of 0.342 and 0.561 across diverse discharges (0.006 to 0.041 m³/s). Experiments were conducted at the Central Water Research Laboratory, University of Tehran, while numerical simulations utilized FLOW-3D software based on RANS equations and the RNG k-ε turbulence model, validated with R² = 0.95 and mean relative error below 8%. It was found that submergence reduced drawdown by up to 50%, lowers the Froude number from 1.2 to 0.8 (33% reduction), and decreases peak velocity by 40%, while extending the flow recovery length by 1 m. Turbulence intensity and vorticity were found to increase by approximately 100%, mean static pressure rises by 30%, and total head loss escalates from 0.1 to 0.3 (200%). These alterations diminish hydraulic efficiency but enhance flow stability. For precise discharge measurement, submergence ratios below 0.5 were recommended to minimize turbulence amplification and flow asymmetry. The findings yield improved calibration equations and provide a foundation for optimizing flume designs in irrigation and drainage networks.University of Tehran PressDesert2008-087530220251201A Comparative Analysis of Feed-Forward and Long Short-Term Memory Networks for Solar Radiation Estimation.38841610618210.22059/jdesert.2025.106182ENSoheilaMohtashamiDepartment of Irrigation & Reclamation Engineering, University of Tehran, Karaj, Alborz Iran.ZahraAghashariatmadariIrrigation &amp; Reclamation Engrg. Dept.
University of Tehran
Karaj, Iran.Journal Article20260225Accurate estimation of solar radiation is essential for numerous industrial applications, energy management, and agricultural planning. This study investigates the effectiveness of advanced machine learning models for solar radiation prediction in Kerman Province, Iran, utilizing a comprehensive set of meteorological variables. Following rigorous quality control procedures and correlation-based feature selection, the dataset was divided into training (80%) and testing (20%) subsets. Two Neural Networks, namely Long Short-Term Memory (LSTM) with the Adam optimizer and Feed-Forward Neural Network (FFNN), were developed and trained under six input scenarios, employing various learning algorithms including Levenberg–Marquardt (LM), Bayesian Regularization (BR), Gradient Descent (GD), and Resilient Propagation (RP) at both daily and monthly timescales. The results indicate that the FFNN-BR model under scenario 6, incorporating a wide range of meteorological inputs, yielded the highest accuracy for monthly radiation estimation (R<sup>2</sup> = 0.92, ARE = 4.5%). For daily radiation prediction, the LSTM model under scenario 4 provided superior performance (R<sup>2</sup> = 0.91, ARE = 1.35%). These findings underscore the importance of model selection and input configuration in enhancing solar radiation estimation accuracy, offering valuable insights for renewable energy resource assessment in arid regions.University of Tehran PressDesert2008-087530220251201The Structural Limits of Cooling: (Fragmentation and Shape Inefficiency Constrain Green Infrastructure in an Arid Urban Heat Island).41743310618710.22059/jdesert.2025.106187ENImanSaeediDepartment of Environmental Science and Engineering, Faculty of Natural Resources and Environment, Malayer University, Malayer, Hamedan, Iran.Journal Article20260225The mitigation of the Urban Heat Island (UHI) effect necessitates a comprehensive understanding of how the spatial configuration of Green Infrastructure (GI) influences its thermal performance. Traditional metrics often fail to capture this functional relationship. This study addresses this gap by integrating the Green Space Thermal Effectiveness Index (GSTEI) with landscape ecology metrics to analyze GI in the rapidly urbanizing City of Qom, Iran. Using Landsat 9 Land Surface Temperature (LST) and Sentinel-2 NDVI data from the summer of 2023, the continuous GSTEI (net cooling in ∘C) was calculated and reclassified into four discrete thermal performance classes (Low: 0 − 2∘C to Very High: > 10∘C). The FRAGSTATS analysis revealed that the overall urban GI landscape is highly fragmented, characterized by a high Edge Density (ED = 78.51 m/ha) and low patch dominance (LPI = 2.51%). Critically, thermal performance was found to be strongly configuration-dependent: the least effective GI (Low cooling class) exhibited the highest fragmentation (Patch Density = 243.21 per ha) and the lowest aggregation (Clumpiness = 0.5664). Conversely, higher-performing classes were significantly more clumped, demonstrating the thermal benefits of spatial consolidation. Paradoxically, the Very High cooling class (> 10 °C) was characterized by the smallest mean patch size (0.084 ha), suggesting that intensive, high-density vegetation can occasionally bypass the structural constraints of small area. Adjacency analysis further confirmed a strict thermal flow gradient, where low-performing patches had the highest interface with the heat-emitting built-up matrix. These findings confirm that fragmentation significantly compromises the cooling function of urban green space. The study provides a quantifiable framework for planners, emphasizing that while consolidation of fragmented patches is generally essential for UHI mitigation, small patches can achieve exceptional cooling through high-density design, offering a dual strategy for arid urban environments.