Search Results (221)

This study presents a terrain-integrated Soil Management Unit (SMU) framework for precision agriculture in semi-arid tropical basaltic soils. Using high resolution (10-ha grid) sampling across 4627 geo-referenced locations and machine learning-enhanced integration of terrain attributes with legacy soil maps, and (3) quantitative validation of intra-SMU homogeneity, 15 SMUs were delineated based on landform, soil depth, texture, and slope. Principal Component Analysis (PCA) revealed SMU11 as the most heterogeneous (68.8%). Geo-statistical analysis revealed structured variability in soil pH (range = 1173 m) and nutrients availability with micronutrient sufficiency following Mn > Fe > Cu > Zn, (Zn deficient in SMU13). Organic carbon strongly correlated with key nutrients (AvK, r = 0.83 and Zn, r = 0.86). This represents the first systematic implementation of terrain-integrated SMU delineation in India’s basaltic landscapes, demonstrating a potential for 20–25% input savings. The spatially explicit fertility-integrated SMU framework provides a robust basis for developing decision support systems aimed at optimizing location-specific nutrient and land management strategies. Full article

Wildfire and drought are key drivers of shrubland expansion in southwestern US landscapes. Stand-replacing fires in conifer forests induce shrub-dominated stages, and changing climatic patterns may cause a long-term shift to deciduous shrubland. We assessed change in deciduous fractional shrub cover (DFSC) in the eastern Jemez Mountains from 2019 to 2023 using topographic and Sentinel-2 satellite data and evaluated the impact of spatial scale on model performance. First, we built a 10 m and a 20 m random forest model. The 20 m model outperformed the 10 m model, achieving an R-squared value of 0.82 and an RMSE of 7.85, compared to the 10 m model (0.76 and 9.99, respectively). We projected the 20 m model to the other years of the study using imagery from the respective years, yielding yearly DFSC predictions. DFSC decreased from 2019 to 2022, coinciding with severe drought and a 2022 fire, followed by an increase in 2023, particularly within the 2022 fire footprint. Overall, DFSC trends showed an increase, with elevation being a key variable influencing these trends. This framework revealed vegetation dynamics in a semi-arid system and provided a close look at post-fire regeneration in deciduous resprouting shrubs and could be applied to similar systems. Full article

Rangelands form the ecological and economic backbone of Namibia, yet the woody plant dynamics that sustain these landscapes remain sporadically quantified across the semi-arid interior. We investigated the characteristics (stand structure, regeneration, richness, diversity, composition, ecological importance, and indicator species) of woody communities along a pronounced south-to-north rainfall gradient (85–346 mm yr⁻¹) at five representative sites: Warmbad, Gibeon, Otjimbingwe, Ovitoto, and Sesfontein. Field sampling combined point-centered quarter surveys (10 points site⁻¹) and belt transects (15 plots site⁻¹). The basal area increased almost ten-fold along the gradient (0.4–3.4 m² ha⁻¹). Principal Coordinates Analysis (PCoA) arranged plots in near-perfect rainfall order, and Permutational Multivariate Analysis of Variance (PERMANOVA) confirmed significant site differences (F_3,56 = 9.1, p < 0.001). Nanophanerophytes dominated hyper-arid zones, while microphanerophytes appeared progressively with increasing rainfall. Mean annual precipitation explained 45% of the variance in mean height and 34% of Shannon diversity but only 5% of stem density. Indicator value analysis highlighted Montinia caryophyllacea for Warmbad (IndVal = 100), Rhigozum trichotomum (75.8) for Gibeon, Senegalia senegal (72.6) for Otjimbingwe, and Senegalia mellifera (97.3) for Ovitoto. Rainfall significantly influences woody structure and diversity; however, other factors also modulate density and regeneration dynamics. This quantitative baseline can serve as a practical toolkit for designing site-specific management strategies across Namibia’s aridity gradient. Full article

Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article

The Caucasus region, characterized by its complex topography and diverse climatic regimes, exhibits pronounced spatial variability in temperature and precipitation patterns. This study investigates the seasonal behavior of air temperature, precipitation, vertical temperature gradients, and inversion phenomena across distinct landscape types using observational data from 63 meteorological stations for 1950–2022. Temperature trends were analyzed using linear regression, while vertical lapse rates and inversion layers were assessed based on seasonal temperature–elevation relationships. Precipitation regimes were evaluated through Mann-Kendall trend tests and Sen’s slope estimators. Results reveal that temperature regimes are strongly modulated by landscape type and elevation, with higher thermal variability in montane and subalpine zones. Seasonal temperature inversions are most frequent in spring and winter, especially in western lowlands and enclosed valleys. Precipitation patterns vary markedly across landscapes: humid lowlands show autumn–winter maxima, while arid and semi-arid zones peak in spring or late autumn. Some landscapes exhibit secondary maxima and minima, influenced by Mediterranean cyclones and regional atmospheric stability. Statistically significant trends include increasing cool-season precipitation in humid regions and decreasing spring rainfall in arid areas. These findings highlight the critical role of topography and landscape structure in shaping regional climate patterns and provide a foundation for improved climate modeling, ecological planning, and adaptation strategies in the Caucasus. Full article

Rivers in tropical semi-arid regions face increasing anthropogenic pressures yet remain critically understudied despite their global importance. This study evaluated the aquatic macroinvertebrate community structure in the Ranchería River, Colombia, across three land use conditions: conserved zones (CZs), urban/agricultural zones (UAZs), and mining influence zones (MZs). Ten sampling stations were established, and macroinvertebrate communities were assessed alongside physical, chemical, and hydromorphological variables during the dry season (January–March 2021). A total of 9288 individuals from 84 genera across 16 orders were collected. Generalized Linear Models revealed significant differences among zones for 67 genera (79.8%), indicating strong community responses to land use gradients. Conserved zones exhibited the highest diversity according to the Hill numbers and were dominated by sensitive taxa, including Simulium, Smicridea, and Leptohyphes. Urban/agricultural zones showed the lowest richness (35 genera) and were characterized by disturbance-tolerant species, particularly Melanoides. Mining zones displayed intermediate diversity but exhibited severe habitat alterations. A redundancy analysis with variance partitioning revealed that land use types constituted the primary driver of community structure (a 24.1% pure effect), exceeding the physical and chemical variables (19.5%) and land cover characteristics (19.2%). The integrated model explained 63.5% of the total compositional variation, demonstrating that landscape-scale anthropogenic disturbances exert a greater influence on aquatic communities than local environmental conditions alone. Different anthropogenic activities create distinct environmental filters affecting macroinvertebrate assemblages, emphasizing the importance of land use planning for maintaining aquatic ecosystem integrity in semi-arid watersheds. Full article

This study explores the water harvesting systems of mgouds in southern Tunisia and boqueras in southeastern Spain to understand their adaptation to semi-arid conditions and geomorphic contexts. These systems use ephemeral water through medieval-origin infrastructures to increase the water supply to rainfed crops. The hypothesis is that the diversity of these systems stems from environmental rather than cultural factors. By employing a qualitative–analytical approach, this study compares concentrated runoff diversion systems to investigate the use of boqueras/mgouds in terraces and glacis in the arid and semi-arid areas of Tunisia and the southeastern Iberian Peninsula. The research involved performing detailed geomorphological and climatological analyses and comparing structural complexities and water management strategies across different regions. The results indicate significant variability in system size and complexity. Tunisian mgouds are typically simpler and more individualised, while Spanish boqueras are larger and more complex due to more frequent and intense torrential rainfall. No common patterns were identified between the two regions. This study reveals that both types of systems reflect sophisticated adaptations to manage water scarcity and mitigate the impacts of intense rainfall, with geomorphic and climatic factors playing a decisive role. The primary conclusion is that the design and functionality of these water systems are predominantly influenced by environmental conditions rather than cultural factors. This research provides insights for developing sustainable water management strategies in other semi-arid regions. Full article

The assessment of the hydrological functions of landscapes and the landscape–hydrological background is an important instrument for minimizing damage from rivers and preventing water conflicts under conditions of data scarcity for hydrological modeling. To assess the climate–hydrological background of the East Kazakhstan region, the Selyaninov Hydro-thermal Coefficient and the Vysotsky–Ivanov Moisture Coefficient were used. The East Kazakhstan region is a typical continental arid and semi-arid region. The presence of mountain ranges, such as the Altai, makes the climate and environment in the region highly varied. A dataset from 30 weather stations for the period 1961–2023 was used for calculations. Three interpolation methods and landscape extrapolation were used to construct maps of the coefficients. Over the observation period, the values of the moisture indices at the weather stations in the region fluctuated within a wide range. Both coefficients are in the range from extra arid to extra humid climates. Full article

A clear understanding of drought perceptions and adaptation behaviors adopted by farmers is an important way to cope with climate change and achieve sustainable agricultural development. Karst is a type of landscape where the dissolving of the bedrock has created sinkholes, sinking streams, caves, springs, and other characteristic features. The study took the Huajiang karst dry-hot river valley area located in the southwestern part of Guizhou as the study area and used questionnaire survey method, the index of perception and the diversity index of adaptation strategy to explore the risk perception, adaptation perception and adaptation behavior of farmers to non-climatic droughts in the subtropical karst dry-hot valleys. A total of 530 questionnaires were distributed and 520 were returned. The results show that (1) the farmers’ risk perception of drought is stronger than adaptation perception, which shows that although farmers are well aware of the possible risks posed by drought, their subjective initiative and motivation to adapt to drought are weaker; (2) in the face of drought, farmers prioritize selected non-farm measures for adaptation, followed by crop management and finally water resource management; and (3) compared to farmers in arid and semi-arid regions, those in karst hot-dry river valleys exhibit distinct adaptive behaviors in response to drought, particularly in water resource management. Full article

Soil salinization poses a significant threat to agricultural productivity, food security, and ecological sustainability in arid and semi-arid regions. Effectively and timely mapping of different degrees of salinized soils is essential for sustainable land management and ecological restoration. Although deep learning (DL) methods have been widely employed for soil salinization extraction from remote sensing (RS) data, the integration of multi-source RS data with DL methods remains challenging due to issues such as limited data availability, speckle noise, geometric distortions, and suboptimal data fusion strategies. This study focuses on the Keriya Oasis, Xinjiang, China, utilizing RS data, including Sentinel-2 multispectral and GF-3 full-polarimetric SAR (PolSAR) images, to conduct soil salinization classification. We propose a Dual-Modal deep learning network for Soil Salinization named DMSSNet, which aims to improve the mapping accuracy of salinization soils by effectively fusing spectral and polarimetric features. DMSSNet incorporates self-attention mechanisms and a Convolutional Block Attention Module (CBAM) within a hierarchical fusion framework, enabling the model to capture both intra-modal and cross-modal dependencies and to improve spatial feature representation. Polarimetric decomposition features and spectral indices are jointly exploited to characterize diverse land surface conditions. Comprehensive field surveys and expert interpretation were employed to construct a high-quality training and validation dataset. Experimental results indicate that DMSSNet achieves an overall accuracy of 92.94%, a Kappa coefficient of 79.12%, and a macro F1-score of 86.52%, positively outperforming conventional DL models (ResUNet, SegNet, DeepLabv3+). The results confirm the superiority of attention-guided dual-branch fusion networks for distinguishing varying degrees of soil salinization across heterogeneous landscapes and highlight the value of integrating Sentinel-2 optical and GF-3 PolSAR data for complex land surface classification tasks. Full article

Groundwater serves as a vital resource for sustainable water supply, particularly in semi-arid regions where surface water availability is limited. This study explores groundwater potential zones in the East Desert, Qift–Qena, Egypt, using a multidisciplinary approach that integrates remote sensing (RS), geographic information systems (GIS), geostatistics, and field validation with water wells to develop a comprehensive groundwater potential mapping framework. Sentinel-2 imagery, ALOS PALSAR DEM, and SMAP datasets were utilized to derive critical thematic layers, including land use/land cover, vegetation indices, soil moisture, drainage density, slope, and elevation. The results of the groundwater potentiality map of the study area from RS reveal four distinct zones: low, moderate, high, and very high. The analysis indicates a notable spatial variability in groundwater potential, with “high” (34.1%) and “low” (33.8%) potential zones dominating the landscape, while “very high” potential areas (4.8%) are relatively scarce. The limited extent of “very high” potential zones, predominantly concentrated along the Nile River valley, underscores the river’s critical role as the primary source of groundwater recharge. Moderate potential zones include places where infiltration is possible but limited, such as gently sloping terrain or regions with slightly broken rock structures, and they account for 27.3%. These layers were combined with geostatistical analysis of data from 310 groundwater wells, which provided information on static water level (SWL) and total dissolved solids (TDS). GIS was employed to assign weights to the thematic layers based on their influence on groundwater recharge and facilitated the spatial integration and visualization of the results. Geostatistical interpolation methods ensured the reliable mapping of subsurface parameters. The assessment utilizing pre-existing well data revealed a significant concordance between the delineated potential zones and the actual availability of groundwater resources. The findings of this study could significantly improve groundwater management in semi-arid/arid zones, offering a strategic response to water scarcity challenges. Full article

The conservation of local breeds plays a strategic role in maintaining genetic variability, ensuring adaptive responses to environmental challenges, and preserving the cultural and socioeconomic structures of traditional communities. In this context, this study explores the potential disappearance of traditional knowledge about local breeds from an ethnozootechnical perspective. The objectives were (I) to establish the breeding history of goat and sheep breeds/ecotypes in the semi-arid region of Paraíba; (II) to estimate the diversity index; and (III) to evaluate the selection criteria used by local communities in four territories: Coletivo, Borborema, Folia, and Casaco. The study aims to support genetic conservation and improvement programs. Data collection was participatory, involving breeders from all territories. To recover the breeds’ history, questionnaires were applied to the oldest breeders, called the “guardians.” Two workshops were held to assess the diversity of breeds in the past landscape (PP) and current landscape (PA), using the Recall technique. Responses were recorded in spreadsheets for analysis. Descriptive statistics and multiple correspondence analysis (MCA) were used to assess animal distribution. The Shannon index indicated a drop in goat breed diversity, from 1.3 (PP) to 0.87 (PA). For sheep breeds, it decreased slightly from 0.7 to 0.66. Breeders reported valuing traits such as adaptability, disease resistance, fertility, and conformation. Their strong emotional connection with the animals highlights the breeds’ cultural relevance. A strong connection was found between the loss of genetic material in the studied territories and the extinction of local communities’ knowledge about local breeds. Full article

Agroforestry systems (AFSs) have been recognized for their ecological potential, yet quantitative assessments of their hydrological functions in semi-arid regions remain limited. This study evaluates soil moisture retention and potential aquifer recharge in two agroforestry systems compared to a traditional rainfed maize system in the semi-desert region of Celaya, Mexico, where aquifer depletion is a growing concern. Field measurements during the 2022 rainy season included precipitation, soil moisture at multiple depths, and soil physical properties across seven vegetation covers. The results show significantly higher moisture content, improved uniformity, and enhanced recharge potential under tree species such as Bursera graveolens and Lysiloma divaricatum. These effects are attributed to vegetation cover, organic matter input, and reduced evaporation. This study provides empirical evidence supporting the integration of AFSs into regional water management strategies, offering a nature-based solution for aquifer recovery and climate adaptation in arid landscapes. Full article

Microtopography plays a crucial role in regulating soil moisture in arid and semi-arid regions, thereby significantly influencing vegetation growth and distribution. The Loess Plateau, characterized by a deeply incised and fragmented landscape, necessitates an in-depth understanding of the microtopograph–soil moisture–vegetation relationship to guide effective vegetation restoration. This study, based on field investigations and laboratory analyses in the hilly-gully region of the Loess Plateau, employed one-way ANOVA, Duncan’s multiple range test, and structural equation modeling to examine the effects of microtopography on vegetation community characteristics. The results revealed that microtopography significantly affects vegetation diversity and stability. Vegetation diversity and stability were higher on shady slopes than on sunny slopes, with diversity indices increasing by approximately 38% in certain regions. Additionally, downslope positions exhibited greater vegetation diversity than upslopes, with richness indices increasing by approximately 33% and the M. Godron index decreasing by 8.49, indicating enhanced stability. However, the effects of gullies varied significantly across different regions. Soil moisture content was higher on shaded slopes than on sunny slopes and greater at downslope positions than at upslopes, reaching up to 12.89% in gullies. Slope position exerted a direct and significant positive effect on soil moisture, which, in turn, indirectly influenced vegetation diversity and stability. This study reveals the dominant regulatory role of slope position in soil moisture, vegetation diversity, and stability, providing new perspectives and evidence for developing vegetation restoration strategies on the Loess Plateau and promoting the sustainable growth of regional vegetation. Full article

Understanding the spatial and seasonal dynamics of surface water bodies is imperative for addressing water security challenges in water-scarce regions. This study aimed to evaluate the efficacy of multi-date Sentinel-2-derived spectral indices, specifically the normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and Sentinel 2 Water Index (SWI), in conjunction with landscape metrics for mapping spatial and seasonal fluctuations in surface water bodies. Google Earth Engine (GEE) was employed for this assessment. The research achieved impressive overall accuracies, ranging from 96 to 100% for both dry and wet seasons, highlighting the robustness of the methodology. The study revealed significant differences in water bodies in terms of size and coverage between the dry and wet seasons. Surprisingly, the dry season exhibited a higher prevalence of water bodies when compared to the wet season, indicating unexpected patterns of water availability in the region and the substantial heterogeneity of water bodies. Meanwhile, the wet season was characterized by extensive coverage. These findings challenge conventional assumptions about water resource availability during different seasons. Based on the findings, the study recommends that water resource management strategies in semi-arid regions consider the observed seasonal variability in water bodies. Policymakers and stakeholders should adopt adaptive management approaches to address the unique challenges posed by differing water body dynamics in dry and wet seasons. Future research endeavors should explore the underlying factors driving these seasonal fluctuations and assess the potential long-term impacts on water availability. This can help to develop more resilient and sustainable water security strategies to cope with changing climate conditions in semi-arid tropical environments. Full article