Search Results (1,146)

An indicator-based approach was implemented to assess the contributions of soils in supplying ecosystem services, providing a scalable tool for modeling the spatial heterogeneity of soil functions at regional and local scales. The method consisted of (i) the definition of soil-based ecosystem services (SESs), using available point data and thematic maps; (ii) the definition of appropriate SES indicators; (iii) the assessment and mapping of potential SESs provision for the Emilia-Romagna region (22.510 km²) in NE Italy. Depending on data availability and on the role played by terrain features and soil geography and its complexity, maps of basic soil characteristics (textural fractions, organic C content, and pH) covering the entire regional territory were produced at a 1 ha resolution using digital soil mapping techniques and geostatistical simulations to explicitly consider spatial variability. Soil physical properties such as bulk density, porosity, and hydraulic conductivity at saturation were derived using pedotransfer functions calibrated using local data and integrated with supplementary information such as land capability and remote sensing indices to derive the inputs for SES assessment. Eight SESs were mapped at 1:50,000 reference scale: buffering capacity, carbon sequestration, erosion control, food provision, biomass provision, water regulation, water storage, and habitat for soil biodiversity. The results are discussed and compared for the different pedolandscapes, identifying clear spatial patterns of soil functions and potential SES supply. Full article

This paper presents a conceptual and methodological framework for using Geographical Information Systems (GIS) to “deep map” cultural heritage sites along Ireland’s Wild Atlantic Way, with a focus on the 1588 Spanish Armada wrecks in County Kerry and archaeological landscapes in County Sligo’s “Yeats Country.” Drawing on interdisciplinary dialogues from the humanities, social sciences, and geospatial sciences, it illustrates how digital spatial technologies can excavate, preserve, and sustain intangible cultural knowledge embedded within such palimpsestic landscapes. Using MAXQDA 24 software to mine and code historical, literary, folkloric, and environmental texts, the study constructed bespoke GIS attribute tables and visualizations integrated with elevation models and open-source archaeological data. The result is a richly layered cartographic method that reveals the spectral and affective dimensions of heritage landscapes through climate, memory, literature, and spatial storytelling. By engaging with “deep mapping” and theories such as “Spectral Geography,” the research offers new avenues for sustainable heritage conservation, cultural tourism, and public education that are sensitive to both ecological and cultural resilience in the West of Ireland. Full article

The present paper deals with an inhabited, creeping mountainous landmass with profound surface deformation that affects the local community. The scope of the paper is to gather surficial and subsurface information in order to understand the parameters of this creeping mass, which is usually affected by several parameters, such as its geometry, subsurface water, and shear zone. Therefore, a combined aerial and surface investigation has been conducted. The aerial investigation involves UAV’s LiDAR acquisition for the terrain model and a comparison of historical aerial photographs for land use changes. The multi-technique surface investigation included resistivity (ERT) and seismic (SRT, MASW) measurements and density determination of geological formations. This combination of methods proved to be fruitful since several aspects of the landslide were clarified, such as water flow paths, the internal geological structure of the creeping mass, and its geometrical extent. The depth of the shear zone of the creeping mass is delineated at the first five to ten meters from the surface, especially from the difference in diachronic resistivity change. Full article

Fractional Vegetation Cover (FVC) is a crucial indicator describing vegetation conditions and provides essential data for ecosystem health assessments. However, due to the low and sparse vegetation in alpine meadows, it is challenging to obtain pure vegetation pixels from Sentinel-2 imagery, resulting in errors in the FVC estimation using traditional pixel dichotomy models. This study integrated Sentinel-2 imagery with unmanned aerial vehicle (UAV) data and utilized the pixel dichotomy model together with four machine learning algorithms, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Deep Neural Network (DNN), to estimate FVC in an alpine meadow region. First, FVC was preliminarily estimated using the pixel dichotomy model combined with nine vegetation indices applied to Sentinel-2 imagery. The performance of these estimates was evaluated against reference FVC values derived from centimeter-level UAV data. Subsequently, four machine learning models were employed for an accurate FVC inversion, using the estimated FVC values and UAV-derived reference FVC as inputs, following feature importance ranking and model parameter optimization. The results showed that: (1) Machine learning algorithms based on Sentinel-2 and UAV imagery effectively improved the accuracy of FVC estimation in alpine meadows. The DNN-based FVC estimation performed best, with a coefficient of determination of 0.82 and a root mean square error (RMSE) of 0.09. (2) In vegetation coverage estimation based on the pixel dichotomy model, different vegetation indices demonstrated varying performances across areas with different FVC levels. The GNDVI-based FVC achieved a higher accuracy (RMSE = 0.08) in high-vegetation coverage areas (FVC > 0.7), while the NIRv-based FVC and the SR-based FVC performed better (RMSE = 0.10) in low-vegetation coverage areas (FVC < 0.4). The method provided in this study can significantly enhance FVC estimation accuracy with limited fieldwork, contributing to alpine meadow monitoring on the Qinghai–Tibet Plateau. Full article

Understanding the impacts of the spatial non-stationarity of environmental factors on surface soil moisture (SSM) in different seasons is crucial for effective environmental management. Yet, our knowledge of this phenomenon remains limited. This study introduces an interpretable machine learning framework that combines the SHapley Additive exPlanations (SHAP) method with two-step clustering to unravel the spatial drivers of SSM across Iran. Due to the limited availability of in situ SSM data, the performance of three global SSM datasets—SMAP, MERRA-2, and CFSv2—from 2015 to 2023 was evaluated using agrometeorological stations. SMAP outperformed the others, showing the highest median correlation and the lowest Root Mean Square Error (RMSE). Using SMAP, we estimated SSM across 609 catchments employing the Random Forest (RF) algorithm. The RF model yielded R² values of 0.89, 0.83, 0.70, and 0.75 for winter, spring, summer, and autumn, respectively, with corresponding RMSE values of 0.076, 0.081, 0.098, and 0.061 m³/m³. SHAP analysis revealed that climatic factors primarily drive SSM in winter and autumn, while vegetation and soil characteristics are more influential in spring and summer. The clustering results showed that Iran’s catchments can be grouped into five categories based on the SHAP method coefficients, highlighting regional differences in SSM controls. Full article

This study, conducted within the SteamBioAfrica project, assessed the potential of Digital Soil Mapping (DSM) to estimate Soil Organic Carbon (SOC) across key regions of southern Africa: Otjozondjupa and Omusati (Namibia), Chobe (Botswana), and KwaZulu-Natal (South Africa). Random Forest (RF) models were implemented in the Google Earth Engine (GEE) environment, integrating multi-source datasets including real-time Sentinel-2 imagery, topographic variables, climatic data, and regional soil samples. Three model configurations were evaluated: (A) climatic, topographic, and spectral data; (B) topographic and spectral data; and (C) spectral data only. Model A achieved the highest overall accuracy (R² up to 0.78), particularly in Otjozondjupa, whereas Model B resulted in the lowest RMSE and MAE. Model C exhibited poorer performance, underscoring the importance of multi-source data integration. SOC variability was primarily influenced by elevation, precipitation, temperature, and Sentinel-2 bands B11 and B8. However, data scarcity and inconsistent sampling, especially in Chobe, reduced model reliability (R²: 0.62). The originality of this study lay in the scalable integration of real-time Sentinel-2 data with regional datasets in an open-access framework. The resulting SOC maps provided actionable insights for land-use planning and climate adaptation in savanna ecosystems. Full article

In recent years, under the influence of climate change and human activities, droughts and floods have occurred frequently in the Yangtze River Basin (YRB), seriously threatening socioeconomic development and ecological security. The topography and climate of the YRB are complex, so it is crucial to develop appropriate drought and flood policies based on the drought and flood characteristics of different sub-basins. This study calculated the water storage deficit index (WSDI) based on the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GFO) mascon model, extended WSDI to the bidirectional monitoring of droughts and floods in the YRB, and verified the reliability of WSDI in monitoring hydrological events through historical documented events. Combined with the wavelet method, it revealed the heterogeneity of climate responses in the three sub-basins of the upper, middle, and lower reaches. The results showed the following. (1) Compared and verified with the Standardized Precipitation Evapotranspiration Index (SPEI), self-calibrating Palmer Drought Severity Index (scPDSI), and documented events, WSDI overcame the limitations of traditional indices and had higher reliability. A total of 21 drought events and 18 flood events were identified in the three sub-basins, with the lowest frequency of drought and flood events in the upper reaches. (2) Most areas of the YRB showed different degrees of wetting on the monthly and seasonal scales, and the slowest trend of wetting was in the lower reaches of the YRB. (3) The degree of influence of teleconnection factors in the upper, middle, and lower reaches of the YRB had gradually increased over time, and, in particular, El Niño Southern Oscillation (ENSO) had a significant impact on the droughts and floods. This study provided a new basis for the early warning of droughts and floods in different sub-basins of the YRB. Full article

The evolutionary patterns and influencing factors of the coupling coordination among multiple functions of cultivated land serve as an important basis for emphasizing the value of cultivated land utilization and promoting coordinated regional development. The entropy weight TOPSIS model, coupling coordination degree (CCD) model, spatial autocorrelation analysis, and Geodetector were employed in this study along with panel data from 125 cities in the Yangtze River Economic Belt (YREB) for 2010, 2015, 2020, and 2022. Three key aspects in the region were investigated: the spatiotemporal evolution of cultivated land functions, characteristics of coupling coordination, and their underlying influencing factors. The results show the following: (1) The functions of cultivated land for food production, social support, and ecological maintenance are within the ranges of [0.023, 0.460], [0.071, 0.451], and [0.134, 0.836], respectively. The grain production function (GPF) shows a continuous increase, the social carrying function (SCF) first decreases and then increases, and the ecological maintenance function (EMF) first increases and then decreases. Spatially, these functions exhibit non-equilibrium characteristics: the grain production function is higher in the central and eastern regions and lower in the western region; the social support function is higher in the eastern and western regions and lower in the central region; and the ecological maintenance function is higher in the central and eastern regions and lower in the western region. (2) The coupling coordination degree of multiple functions of cultivated land is within the range of [0.158, 0.907], forming a spatial pattern where the eastern region takes the lead, the central region is rising, and the western region is catching up. (3) Moran’s I index increased from 0.376 in 2010 to 0.437 in 2022, indicating that the spatial agglomeration of the cultivated land multifunctionality coupling coordination degree has been continuously strengthening over time. (4) The spatial evolution of the coupling coordination of cultivated land multifunctionality is mainly influenced by the average elevation and average slope. However, the explanatory power of socioeconomic factors is continuously increasing. Interaction detection reveals characteristics of nonlinear enhancement or double-factor enhancement. The research results enrich the study of cultivated land multifunctionality and provide a decision-making basis for implementing the differentiated management of cultivated land resources and promoting mutual enhancement among different functions of cultivated land. Full article

Urban environments are increasingly affected by extreme weather conditions, posing significant risks to vulnerable populations, such as the homeless. This research applies geospatial analysis to identify areas of extreme heat and moisture within the Athens metropolitan area in Greece. The analysis utilizes satellite-derived land surface temperature (LST), vegetation density index (NDVI), build-up density index (NDBI), Topographic Wetness Index (TWI), and other terrain-based factors to develop high-fidelity risk zones. These zones are critical for informing targeted interventions and policy measures aimed at protecting vulnerable groups from heat waves and extreme moisture. This research integrates a geospatial analysis approach for mapping and evaluating heat and moisture vulnerability zones. This approach integrates remote sensing data, GIS-based modeling, and terrain analysis. The findings can provide local authorities and social services with the necessary information to design adaptive strategies for climate change resilience. Full article

Plant diversity, as a critical indicator of wetland ecosystem health and functionality, has garnered extensive research attention. However, systematic and quantitative assessments of research advancements in wetland plant diversity remain inadequate. This study pioneers a global bibliometric analysis of wetland plant diversity research (1986–2025), designed to systematically examine its worldwide patterns, knowledge architecture, and evolutionary trends. Bibliometric analysis was performed using CiteSpace V6.2.R4 (64-bit) software on 482 publications retrieved from the Web of Science Core Collection. Results indicate that the United States, Canada, China, and several European countries have collectively prioritized wetland plant diversity research, forming a close international collaboration network. Research themes initially centered on species composition, community structure, and diversity metrics have expanded to multiple dimensions such as ecosystem functions and services, environmental change impacts, and wetland management and restoration, forming several key research clusters. Keyword time-zone mapping reveals the trajectory of research themes from basic descriptions to applied and environmental relevance, while emergent analyses accurately identify hotspots and frontiers of current research such as ecosystem services, functional diversity, and climate change impacts. These findings contribute to comprehending the overall framework and developmental trajectories in wetland plant diversity research, and provide a reference for identifying potential research gaps and planning future research directions. Full article

Gonghe Basin is an important frontier of resource and energy development and environmental protection on the Qinghai–Tibetan Plateau and upper sections of the Yellow River. As a characteristic ecotone, this area exhibits complex and diverse ecosystem types while demonstrating marked ecological vulnerability. The response of ecosystem services (ESs) to human activities (HAs) is directly related to the sustainable construction of an ecological civilization highland and the decision-making and implementation of high-quality development. However, this response relationship is unclear in the Gonghe Basin. Based on remote sensing data, land use, meteorological, soil, and digital elevation model data, the current research determined the human activity intensity (HAI) in the Gonghe Basin by reclassifying HAs and modifying the intensity coefficient. Employing the InVEST model and bivariate spatial autocorrelation methods, the spatiotemporal evolution characteristics of HAI and ESs and responses of ESs to HAs in Gonghe Basin from 2000 to 2020 were quantitatively analyzed. The results demonstrate that: From 2000 to 2020, the HAI in the Gonghe Basin mainly reflected low-intensity HA, although the spatial range of HAI continued to expand. Single plantation and town construction activities exhibited high-intensity areas that spread along the northwest-southeast axis; composite activities such as tourism services and energy development showed medium-intensity areas of local growth, while the environmental supervision activity maintained a low-intensity wide-area distribution pattern. Over the past two decades, the four key ESs of water yield, soil conservation, carbon sequestration, and habitat quality exhibited distinct yet interconnected characteristics. From 2000 to 2020, HAs were significantly negatively correlated with ESs in Gonghe Basin. The spatial aggregation of HAs and ESs was mainly low-high and high-low, while the aggregation of HAs and individual services differed. These findings offer valuable insights for balancing and coordinating socio-economic development with resource exploitation in Gonghe Basin. Full article

This review provides a comprehensive overview of the closed-form expressions developed for estimating the soil water retention curve (SWRC) from 1964 to the present. Since the concept of the SWRC was introduced in 1907, numerous closed-form functions have been proposed to describe the relationship between soil matric suction and volumetric water content, each with distinct strengths and limitations. Given the variability in SWRC shapes influenced by soil texture, structure, and organic matter, models in the form of sigmoidal, multi-exponential, lognormal, hyperbolic, and hybrid functions have been designed to fit experimental SWRC data. Based on the number of adjustable parameters, these models are categorized into three main groups: three-, four-, and five-parameter models. They can also be classified as one-, two-, or three-segment functions depending on their structural complexity. A review of the developed models indicates that most are effective in representing the SWRC between the residual and saturated water content range. To capture the full range of the SWRC, hybrid functions have been proposed by combining traditional models. This review presents and discusses these models in chronological order of publication. Full article

Across the centuries, trees have been recognised as one of the oldest lifeforms on earth, witnessing and subject to the passage of time on a scale that far exceeds human life, telling us who we are in the world. This paper explores the intricate nature of human interactions with trees across a broad chronological and conceptual range, and the cultural, symbolic, and ecological meaning of “iconic” trees, drawing upon a selection of case studies to explore and analyse the relationship between the tree as a living organism and its cultural, textual, and mnemonic meaning. In conducting this, it reflects upon the cultural geographies of presence and absence, and the role of emblematic trees as places of memory and structures of belief. The relationship between human life and the life of trees is shown to be symbiotic; multiple cultural values and symbolic forms are ascribed to trees, and those same trees shape the physical, ecological, and human environment. The social and cultural construction of the landscape and sites of memory is presented as a key component in the formation of narratives and mentalities that define the relationship between humans and iconic trees, material and imagined. Physical, ecological, and cultural erosion, it is suggested, have the capacity of memorialising forgetfulness and creating a space in which the absence of presence and the presence of absence co-exist. The iconic image of the fallen tree, in its presence and absence, exposes the extent to which trees are also human objects, constructed and understood in human terms, and subject to a range of personal, political, and pragmatic impulses. A tree can be iconic not simply because of what it was but because of what it was believed to be, integrating a physical, historical, memory, and ecological or cultural space into our relationship with the natural world. Full article

As a globally critical carbon reservoir, the response mechanism of wetland ecosystems to climate change on the Qinghai–Tibet Plateau (QTP) has attracted significant scientific scrutiny. This study investigated the temperature sensitivity of cbbL-harboring carbon-sequestering microbial communities and their coupling with carbon–nitrogen cycle dynamics through a simulated field warming experiment conducted in the Wayan Mountains’ river source wetland in the northeastern QTP. Key findings revealed that warming markedly elevated Alpha diversity (ACE and Chao1 indices), whereas Shannon and Simpson indices remained stable, indicating that temperature increases primarily altered community composition by enhancing species richness rather than evenness. Taxonomic analysis demonstrated significant increases in the relative abundances of Cyanobacteria and Actinobacteria, while Proteobacteria retained dominance but exhibited reduced relative abundance. At the genus level, Thioflexothrix, Ferrithrix, and Rhodospirillum dominated the community, with Thioflexothrix and Ferrithrix showing warming-induced abundance increments. Functional predictions indicated that warming preferentially stimulated heterotrophic and photoheterotrophic functional guilds. Soil physicochemical analyses further revealed warming-driven increases in nitrate nitrogen (NN), total carbon (TC), and total nitrogen (TN), concurrent with decreased soil moisture. Redundancy analysis identified TC as the predominant determinant of microbial community structure (followed by TN > NN), while pH and ammonium nitrogen (AN) exerted comparatively limited influence. Strong positive correlations between microbial communities and carbon/nitrogen indicators suggested that enhanced carbon–nitrogen resource availability served as the central driver of community succession. These findings elucidate the temperature-responsive mechanisms of cbbL-type carbon-sequestering microorganisms in alpine wetlands, offering critical insights for the adaptive management of carbon cycling in high-altitude ecosystems and advancing strategies toward achieving carbon neutrality goals. Full article

This study examines the training and didactic proposals used to teach flood-related topics in Primary (5–12 years old) and Secondary Education (13–18 years old). This research employs a survey methodology, gathering responses from 726 trainee teachers across two Spanish universities (582 in Primary Education and 144 in Secondary Education). The findings highlight a significant gap in training, as more than half of the participants reported having received no instruction on floods, either during their school years or university studies. However, Secondary Education trainee teachers demonstrated a higher level of preparedness compared to their Primary Education counterparts. Regarding didactic proposals, two approaches stood out: activities based on real experiences (32.6%) and drills/talks led by experts (21.5%). Notably, Primary Education trainee teachers preferred expert-led sessions (24.7%), suggesting a lack of confidence in teaching these topics independently. This study underscores the crucial role of educators in risk reduction. Given their ethical responsibility to equip students with critical thinking skills, proper training is essential to fostering informed citizens capable of making sound decisions in the face of climate-related challenges. Full article