Earth, Volume 6, Issue 3 (September 2025) – 51 articles

Natural reserves in the West African drylands play a critical role in sustaining livelihoods and preserving ecological integrity. However, these ecosystems face growing threats from climate variability and anthropogenic pressure. This study assesses the potential of Nature-based Solutions (NbSs) to enhance climate resilience and mitigate human-induced degradation in Bontioli Natural Reserve (BNR), one of the region’s key biodiversity hotspots. We employed an integrated approach combining ecological assessments, climate and anthropogenic pressures analysis, and participatory governance framework. Generalized additive modeling (GAM) is applied to assess the long-term vegetation response to climate stressors. A conceptual framework that integrates climate resilience with socio-ecological systems is developed for synergies conservation. Our findings indicate a consistent vegetation decline at a rate of 0.051 ± 0.043/year, driven by rising temperatures, and declining rainfall, which is exacerbated by anthropogenic land use pressure since 2000. Human population growth is strongly correlated with cropland expansion (R² = 0.903) and vegetation loss (R² = 0.793). As a result, 53.85% of species populations are declining, with 30.77% classified as endangered or vulnerable. Based on the scientific evidence, NbSs have emerged as cost-effective and sustainable strategies to restore ecological function and strengthen communities-based conservation. The proposed NbS framework offers a holistic pathway for safeguarding long-term ecosystem resilience in dryland reserves, directly contributing to Sustainable Development Goals (SDGs) 13 and 15. Full article

Mercury (Hg) pollution from artisanal and small-scale gold mining (ASGM) is a global environmental and public health concern. In Indonesia, ASGM remains widespread, yet assessments of multimedia contamination and health risks are limited. This study quantified Hg concentration in water, sediment, soil, fish, and cassava to evaluate environmental pollution and potential health risks in Waluran, Sukabumi, Indonesia. Mercury concentration in ASGM was higher than in the reference area, especially in fish (median: 4.76 mg/kg dw), cassava leaves (median: 15.7 mg/kg dw), and tailing sediments (median: 171 mg/kg dw). A remarkably high Hg concentration (9760 mg/kg dw) was detected in soil from amalgam-burning spots. An elevated Hg concentration was observed in the reference area, suggesting widespread contamination and potential for long-range dispersion. Over 85% of ASGM samples were categorized as heavily to extremely contaminated by the geo-accumulation index (I_geo). Bioaccumulation assessment indicated a high bioconcentration factor (BCF) in fish and moderate bioaccumulation factor (BAF) in cassava roots. Hazard Quotients (HQ) were greater than 1 for most exposure pathways in both adults and children, with the greatest risk deriving from cassava leaf consumption. These findings indicate severe Hg contamination within ASGM-affected communities and underscore the urgent need for public health interventions, environmental monitoring, and strengthened regulations to reduce Hg exposure in Indonesia. Full article

The Port of Veracruz, the main port in the Gulf of Mexico, has experienced a significant increase in its import and export operations, such as petroleum coke (Petcoke), a solid waste, mainly used in the steel industry. During the period of 2010–2023, approximately 7,401,594 tons of coke were stored outdoors, generating PM₁₀ particulate emissions due to wind erosion. These particles were dispersed to urban areas, reaching an estimated total emission of 5077 tons. The study used geospatial analysis and environmental modeling tools (ALOHA^®) to evaluate the dispersion and concentration of PM₁₀ in the atmosphere, comparing them with the limits established by the Mexican Official Standard NOM-025-SSA1-2021. The results indicate that in years with high port activity, such as 2014, PM₁₀ concentrations exceeded the normative values, representing a potential risk to public health and urban infrastructure. This study provides critical evidence on the environmental impacts of coke handling in ports and suggests mitigation strategies, including processes for the confinement of materials and the implementation of advanced emissions monitoring systems. Full article

The study examines the relationship between water surface loss and deforestation in the Brazilian Amazon, focusing on the expansion of farming (crops and agriculture, as well as pasture and livestock) and the impact of inadequate legislation from 1985 to 2023. The Amazon biome is vital for the global hydrological cycle and is home to about 10% of the known species. Data from MapBiomas and multivariate statistical techniques revealed that forest and water surface areas decreased significantly while pasture and agricultural regions increased. Environmental legislation has shown progress, with Center and Left-leaning governments implementing environmental protection measures. In contrast, Center–Right and Right-leaning governments prioritized economic interests, resulting in significant setbacks in forest protection and increased deforestation. The study further highlights the importance of developing integrated and sustainable strategies that balance economic development and environmental conservation in the Amazon biome. Full article

Lavender is a plant widely used in the cosmetic, pharmaceutical, and food industries, and it is also well known for producing nectar and pollen that bees use to make honey. However, due to increasingly adverse atmospheric conditions in recent years, characterized by prolonged dry spells or intense rainfall focused in short periods, the production of monofloral honey, such as lavender honey, has become increasingly challenging. Therefore, accurate mapping of monofloral zones in order to support beekeepers in placing their beehives in the best location is required. In this context, the town of Kuyucak in Isparta Province (Turkey), renowned for its extensive lavender fields, was selected. Using true orthophoto images from 2020 with a ground sampling distance (GSD) of 30 cm, machine learning classification methods and deep learning techniques were applied to identify and map the correspondent lavender fields. Lavender plants within the region were detected using Maximum Likelihood (ML), Support Vector Machine (SVM), and Random Forest (RF) classifiers, as well as the Mask R-CNN deep learning method. The classification achieved an overall accuracy of 95% and a kappa coefficient of 0.94. Subsequently, assuming a bee foraging range of 3 km, a moving squared window (sizing 3 × 3 km) was used to estimate local areas with potential forage resources and the corresponding honey production potential. The resulting honey potential production maps then used to identify optimal location for beekeepers’ hives in order to maximize lavender honey production. Full article

This study investigates the relationship between land use and soil organic carbon (SOC) storage in tropical Andean ecosystems, introducing a socio-ecological perspective to assess how community conservation perceptions influence SOC storage and contribute to climate change mitigation strategies. Background and Objectives: Land-use change reduces carbon stocks in tropical ecosystems. Focusing on the Las Piedras River basin (Popayan, Colombia), we evaluated SOC storage under four plant cover types—riparian forests (RFs), ecological restoration (ER), natural regeneration (NR), and livestock pastures (LSs)—and examined its association with local conservation perceptions. Materials and Methods: SOC storage at 30 cm depth, carbon inputs and outputs, and soil physicochemical properties were measured across land-use types. Conservation perceptions were assessed through 65 community surveys. Data analyses included ANOVA, principal component analysis, and multinomial logistic regression. Results: SOC storage was highest in RFs (148.68 Mg ha⁻¹), followed by ER and LSs, and lowest in NR (97.30 Mg ha⁻¹). A positive relationship was observed between high conservation perception and greater SOC content. Conclusions: SOC storage is strongly influenced by land use and community conservation values. Active restoration efforts, coupled with environmental education, are essential for enhancing the socio-ecological resilience of these ecosystems. Full article

Backward Lagrangian stochastic modeling is widely used to estimate emission rates from land surfaces to the atmosphere. It is also applied to calculate concentrations of pollutants due to known emission sources. A key component of this modeling technique is the concentration estimator, which relies on tracer particle trajectories to establish the relationship between concentration, emission rate, and meteorological condition. A commonly used concentration estimator is closely examined and shown to have potential inaccuracies. An alternative estimator is derived and compared with the existing one. The new estimator is tested using backward Lagrangian stochastic modeling in both Gaussian and non-Gaussian turbulence. The results demonstrate that, in many cases, the two estimators are equivalent, which explains the general success of the popular estimator. However, if the vertical velocities of some tracer particles are extremely slow when hitting the source, a significantly higher ratio of concentration to emission rate will be obtained. This spuriously high ratio will result in overestimation of the concentration if the purpose is to calculate concentrations from a known emission rate and underestimation of the emission rate if the model is used to calculate the emission rate from measured concentrations. The new estimator can avoid this unjustifiable behavior and therefore exhibits superior performance. Full article

In the exploitation of ion-adsorption rare earth ores, the environmental effects of leaching agents are key constraints for green mining. Understanding the release behavior of typical heavy metals from soils under leaching conditions is of great significance. Laboratory column leaching experiments were conducted to systematically investigate the effects of three leaching agents—(NH₄)₂SO₄, Al₂(SO₄)₃, and MgSO₄—as well as varying concentrations of Al₂(SO₄)₃ on the release and speciation transformation of heavy metal Pb in mining-affected soils. The results revealed a three-stage pattern in Pb release—characterized by slow release, a sharp increase, and eventual stabilization—with environmental risks predominantly concentrated in the middle to late stages of leaching. Under 3% (NH₄)₂SO₄ and 3% Al₂(SO₄)₃ leaching conditions, Pb concentrations in soil increased significantly, with a higher proportion of labile fractions, indicating pronounced activation and risk accumulation. Due to its relatively weak ion-exchange capacity, MgSO₄ exhibited a lower and more gradual Pb release profile, posing substantially lower pollution risks compared to (NH₄)₂SO₄ and Al₂(SO₄)₃. Pb release under varying Al₂(SO₄)₃ concentrations showed a nonlinear response. At 3% Al₂(SO₄)₃, both the proportion of bioavailable Pb and the Risk Assessment Code (RAC) peaked, while the residual fraction declined sharply, suggesting a threshold effect in risk induction. All three leaching agents promoted the transformation of Pb in soil from stable to more labile forms, including acid-soluble, reducible, and oxidizable fractions, thereby increasing the overall proportion of active Pb (F1 + F2 + F3). A combined analysis of RAC values and the proportion of active Pb provides a comprehensive framework for assessing Pb mobility and ecological risk under different leaching conditions. These findings offer a theoretical basis for the prevention and control of heavy metal risks in the green mining of ion-adsorption rare earth ores. Full article

Understanding the dispersion of volcanic sulphur dioxide (SO₂) plumes is crucial for assessing their environmental and climatic impacts. This study integrates satellite-based and reanalysis datasets to simulate as well as visualise the dispersion patterns of volcanic SO₂ under diverse atmospheric conditions. By incorporating data from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2), CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), and OMI (Ozone Monitoring Instrument) datasets, we are able to provide comprehensive insights into the vertical and horizontal trajectories of SO₂ plumes. The methodology involves modelling SO₂ dispersion across various atmospheric pressure surfaces, incorporating wind directions, wind speeds, and vertical column mass densities. This approach allows us to trace the evolution of SO₂ plumes from their source through varying meteorological conditions, capturing detailed vertical distributions and plume paths. Combining these datasets allows for a comprehensive analysis of both natural and human-induced factors affecting SO₂ dispersion. Visual and statistical interpretations in the paper reveal overall SO₂ concentrations, first injection dates, and dissipation patterns detected across altitudes of up to ±20 km in the stratosphere. This work highlights the significance of combining satellite-based and global atmospheric reanalysis datasets to validate and enhance the accuracy of plume dispersion models while having a general agreement that OMI daily data and MERRA-2 reanalysis hourly data are capable of accurately accounting for SO₂ plume dispersion patterns under varying meteorological conditions. Full article

Frequent grassland fires have severely affected regional ecosystems as well as the production and living conditions of local residents. Grassland fire prevention capabilities constitute an integral part of the disaster prevention and mitigation system and play an important role in improving grassroots governance. To gain a deeper understanding of the practical foundation and influencing mechanisms of grassland fire prevention capabilities, establish an evaluation index system for prevention capabilities covering the four dimensions of disaster prevention, disaster resistance, disaster relief, and recovery. Combining micro-level survey data, a quantile regression model is used to analyze the influencing factors. The research findings indicate that (1) disaster resistance (0.49) plays a prominent role in grassland fire prevention capabilities, with economic foundations and individual disaster relief capabilities being particularly critical for overall improvement. Although residents have strong fire prevention awareness, their organizational collaboration capabilities are relatively weak, and there are significant differences in prevention capabilities across regions, necessitating tailored, precise enhancements. (2) There are significant differences in prevention capabilities among residents of different agricultural and pastoral production types, with semi-agricultural and semi-pastoral areas having the strongest comprehensive capabilities and pastoral areas relatively weaker. (3) A significant analysis of factors influencing grassland fire prevention capabilities: effective and diverse risk communication is a prerequisite for enhancing residents’ prevention capabilities; the level of panic regarding grassland fires and road infrastructure are important influencing factors, but residents’ understanding of climate change and grassroots organizations’ capacity for mechanism construction have insignificant impacts. Therefore, in future grassland fire disaster prevention and mitigation efforts, it is essential to strengthen risk communication, improve infrastructure, monitor environmental changes and the spatiotemporal patterns of grassland fires, enhance residents’ understanding of climate change, reinforce the emergency response capabilities of grassroots organizations, and stimulate public participation awareness to collectively build a multi-tiered grassland fire prevention system. Full article

Existing drainage systems have limited capacity to mitigate future climate change-induced flooding problems effectively. However, some studies have evaluated the effectiveness of integrating Water-Sensitive Urban Design (WSUD) with existing drainage systems in mitigating flooding in tropical regions. This study examined the performance of drainage systems and integrated WSUD options under current and future climate scenarios in a sub-catchment of Saltwater Creek, a tropical catchment located in Cairns, Australia. A combination of one-dimensional (1D) and two-dimensional (1D2D) runoff generation and routing models (RORB, storm injector, and MIKE+) is used for simulating runoff and inundation. Several types of WSUDs are tested alongside different climate change scenarios to assess the impact of WSUD in flood mitigation. The results indicate that the existing grey infrastructure is insufficient to address the anticipated increase in precipitation intensity and the resulting flooding caused by climate change in the Engineers Park sub-catchment. Under future climate change scenarios, moderate rainfall events contribute to a 25% increase in peak flow (95% confidence interval = [1.5%, 0.8%]) and total runoff volume (95% confidence interval = [1.05%, 6.5%]), as per the Representative Concentration Pathway 8.5 in the 2090 scenario. Integrating WSUD with existing grey infrastructure positively contributed to reducing the flooded area by 18–54% under RCP 8.5 in 2090. However, the efficiency of these combined systems is governed by several factors such as rainfall characteristics, the climate change scenario, rain barrel and porous pavement systems, and the size and physical characteristics of the study area. In the tropics, the flooding problem is estimated to increase under future climatic conditions, and the integration of WSUD with grey infrastructure can play a positive role in reducing floods and their impacts. However, careful interpretation of results is required with an additional assessment clarifying how these systems perform in large catchments and their economic viability for extensive applications. Full article

The Elfeija Dune Field (EDF) is a continental aeolian system in an arid region of southeastern Morocco. Studying this system is critical for understanding the effects of mounting climatic and anthropogenic pressures. This study provides a comprehensive characterization of the EDF’s morphology, sedimentology, aeolian dynamics, genesis, and recent evolution. A multi-scale, multidisciplinary approach was adopted, integrating field observations, sedimentological analyses, MERRA-2 reanalysis wind data, cartographic analysis, digital terrain modeling, and morphometric measurements. The results reveal an active 30 km² dune field, elongated WSW-ENE, which is divisible into three morphodynamic zones with a high dune density (80–90 dunes/km²). The wind regime is predominantly from the W to WSW, driving a net ENE sand transport and creating conditions conducive to barchan formation (RDP/DP > 0.78). Sediments are quartz dominated, with significant calcite and various clay minerals (illite, kaolinite, and smectite). Dune sands are primarily fine- to medium-grained and well sorted, in contrast to the more poorly sorted interdune deposits. The landscape is dominated by barchans (mean height H = 2.5 m; mean length L = 50 m) and their coalescent forms, indicating sustained aeolian activity. The potential sand flux was estimated at 1.7 kg/m/s, with a dune collision probability of 32%. The field’s genesis is hypothesized to be controlled by a topographically induced Venturi effect, with an initiation approximately 1000 years ago, potentially linked to the Medieval Climatic Optimum. Significant anthropogenic impacts from expanding irrigated agriculture are observed at the dune field margins. By providing a detailed characterization of the EDF and its sensitivity to natural and anthropogenic forcings, this study establishes a critical baseline for the sustainable management of arid environments. Full article

Accurate soil moisture estimation is critical for precision agriculture and water resource management, yet traditional sampling methods are time-consuming, destructive, and provide limited spatial coverage. Ground Penetrating Radar (GPR) offers a promising non-destructive alternative, but optimal machine learning approaches for GPR-based soil moisture prediction remain unclear. This study presents a comparative analysis of regression tree and boosted tree algorithms for predicting soil moisture content from Ground Penetrating Radar (GPR) histogram features across 21 sites in Eastern Thailand. Soil moisture content was measured at multiple depths (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m) using samples collected during Standard Penetration Test procedures. Feature extraction was performed using 16-bin histograms from processed GPR radargrams. A single regression tree achieved a cross-validation RMSE of 5.082 and an R² of 0.761, demonstrating superior training accuracy and interpretability. In contrast, the boosted tree ensemble achieved significantly better generalization performance, with a cross-validation RMSE of 4.7915 and an R² of 0.708, representing a 5.7% improvement in predictive performance. Feature importance analysis revealed that specific histogram bins effectively captured moisture-related variations in GPR signal amplitude distributions. A comparative evaluation demonstrates that while single regression trees offer superior interpretability for research applications, boosted tree ensembles provide enhanced predictive performance that is essential for operational deployment in precision agriculture and hydrological monitoring systems. Full article

Since 2005, landscape ecology has emerged as a structured scientific field in the Democratic Republic of Congo, notably shaped by the contributions of Professor Jan Bogaert. The evolution of research in this field can be divided into three main phases. The first phase (2005–2012) focused on the quantitative analysis of forest fragmentation using Geographic Information Systems and landscape metrics. From 2013 to 2019, research approaches broadened to include the social sciences, marking a shift toward a socio-ecological perspective on landscapes. Since 2020, the field has increasingly adopted holistic frameworks that integrate climatic factors and forward-looking modeling. Key research themes now include ecological flows across landscape mosaics, land-use dynamics, and the anthropogenic transformation of ecosystems. However, several challenges persist, including the lack of long-term temporal datasets, uneven geographic coverage, and limited integration of local knowledge systems. Notable advances have been made through high-resolution remote sensing and participatory methods, although their application is still limited by technical and financial constraints. This manuscript advocates for stronger interdisciplinary collaboration, improved field methodologies, and the development of context-appropriate tools to support sustainable and locally grounded landscape management in the Congolese context. Full article

Land degradation in drylands represents a critical environmental challenge, with persistent bare soil serving as a key indicator of ecosystem vulnerability, including in the Caatinga biome. This study maps and analyzes the spatial and temporal dynamics of persistent bare soils over three decades using multi-temporal remote sensing data. We applied Spectral Mixture Analysis (SMA), temporal metrics, and machine learning classifiers within Google Earth Engine to process long-term Landsat datasets and to derive the Normalized Difference Fraction Index Adjusted (NDFIa). The results indicate a widespread increase in bare soil, with over 63% of mapped hexagons showing expansion, particularly in the São Francisco Basin. Peaks in soil exposure coincided with severe drought events, highlighting the link between climate variability and land degradation. Moreover, abandoned agricultural lands and pasturelands emerged as the dominant contributors to persistent bare soils. These findings reinforce the need for targeted policies to mitigate land degradation and to promote sustainable land management in semi-arid ecosystems. This research provides a robust framework for long-term environmental monitoring in drylands by integrating satellite data with advanced analytical techniques. These advancements support more effective land management and conservation strategies in semi-arid ecosystems. Full article

The water–energy–food (WEF) nexus provides a critical framework for addressing the interconnected challenges of resource scarcity and sustainability in the face of global population growth and climate variability. This study investigates the application of a WEF nexus approach within the operation and management of a hydroponic greenhouse unit in Central Greece, with the aim of enhancing the unit’s energy autonomy and resource sufficiency. Hydroponics, a soilless cultivation method, optimizes water and land use but relies heavily on energy inputs, necessitating integrated solutions. Through the case study approach, we analyze the unit’s resource dynamics per hectare of water (68 MWh equivalent from desalination), energy (125 MWh or 321 GJ/ha plus 74.5 GJ/ha for fertigation), and food production (~295 tons, which contains 50,250,000 kcal and corresponds to 210 GJ) and propose technical solutions: photovoltaic panels as greenhouse coverings and water rain harvesting regulated with a small reservoir. These innovations could reduce external energy dependency by 90–95% and water use by 25–35%. Energy efficiency is quantified using the energy ratio (ER) and net energy gain (NEG), while resilience is assessed via system reliability under resource variability. Conclusively, this study illustrates how a nexus-based approach can effectively upgrade systems into climate-resilient, resource-efficient models as the abundance or scarcity of one source affects the availability or limitation of the others. Overall, the approach presented in this study could also be used to safeguard the supply chains in megacities. Full article

Quantifying land–atmosphere transport remains crucial for advancing climate prediction and weather forecasting efforts. To improve turbulent flux estimation, the anisotropy of turbulence is taken into consideration. The parameters $x_B$ and $y_B$, which quantify anisotropy degrees across motion scales, form trajectories in the barycentric map. Using the Hilbert–Huang transform, the scale-dependent properties of anisotropy in observational data from multiple sites are investigated. Analysis reveals consistent patterns in the average $y_B-x_B$ trajectories across stratification conditions: as scale increases, $x_B$ increases from 0.4 to 0.9, while $y_B$ initially climbs from 0.5 to 0.7 before declining to 0. Meanwhile, individual case trajectories sometimes deviate from this pattern, indicating contamination by non-turbulent motions that typically cause turbulent flux overestimation. Crucially, identifying the scale at which deviations occur allows effective separation of atmospheric turbulence from non-turbulent motions, which enables the reconstruction of turbulence data. Results demonstrate that corrected fluxes reduce overestimation inherent in traditional eddy covariance systems by approximately 30%, with enhancements for CO₂ and air pollutants reaching 45–83%. Furthermore, the correlation between anisotropy and stratification suggests potential for refining similarity theories into a broader scope, such as carbon cycle assessment and pollution control. Therefore, anisotropy shows promise in quantifying the land–atmosphere transport. Full article

Water-related ecosystem services provide a broad range of benefits, including the mitigation of extreme hydrometeorological events, the provision of water for various uses, the support of tourism, and the provision of cultural services. This study assesses the perceptions and accessibility of these services among communities located near the Alafiarou and Okpara dams in Benin and the Bagré dam in Burkina Faso. The methodology involved designing and implementing a questionnaire in KoboCollect, with trained agents deployed to conduct data collection at each of the three sites. Data analysis indicates that respondents identified biodiversity conservation and the provision of drinking water as the most crucial ecosystem services. Over two-thirds of participants reported observing both positive and negative changes in the services provided by rivers and in socio-economic activities since the construction of the dams. While the majority noted improvements in agriculture, irrigation, water quality, fisheries, and flow rates, other changes included biodiversity loss, a decrease in vegetation cover (notably trees and shrubs), an increase in the population of mosquitoes and other insects, and a decline in fishery resources downstream. Despite these challenges, local communities were strongly willing to participate in initiatives aimed at protecting and restoring river ecosystems and their related services. Full article

The Tibetan Plateau (TP) is experiencing higher warming rates than elsewhere, which may affect regional vegetation growth. Particularly on the Eastern Tibetan Plateau (ETP), where the topography is diverse and rich in biodiversity, it is necessary to clarify the drivers of climate and topography on vegetation cover. In this research, we selected the Shaluli Mountains (SLLM) in the ETP as the study area, monitored the spatial and temporal dynamics of the regional vegetation cover using remote sensing methods, and quantified the drivers of vegetation change using Geodetector (GD). The results showed a decreasing trend in annual precipitation (PRE) (−2.4054 mm/year) and the Palmer Drought Severity Index (PDSI) (−0.1813/year) in the SLLM. Annual maximum temperature (TMX) on the spatial and temporal scales showed an overall increasing trend, and the regional climate tended to become warmer and drier. Since 2000, fractional vegetation cover (FVC) has shown a fluctuating upward trend, with an average value of 0.6710, and FVC has spatially shown a pattern of “low in the middle and high in the surroundings”. The areas with non-significant increases (p > 0.05) and significant increases (p < 0.05) in FVC accounted for 46.03% and 5.76% of the SLLM. Altitude (q = 0.3517) and TMX (q = 0.3158) were the main drivers of FVC changes. As altitude and TMX increased, FVC showed a trend of increasing and then decreasing. The results of this study help us to clarify the influence of climate and topography on the vegetation ecosystem of the ETP and provide a scientific basis for regional biodiversity conservation and sustainable development. Full article

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

Floods are among the most devastating hydrometeorological natural disasters worldwide, causing massive infrastructure and economic loss in low-lying, flood-prone developing countries like Bangladesh. Effective disaster mitigation relies on organized and detailed flood damage information to facilitate emergency evacuation, coordinate relief distribution, and formulate an effective disaster management policy. Nevertheless, the nation confronts considerable obstacles due to insufficient historical flood damage data and the underdevelopment of near-real-time (NRT) flood monitoring systems. This study addresses this issue by developing a replicable methodology for flood damage assessment and NRT monitoring systems. Using the Google Earth Engine (GEE) platform, we analyzed flood events from 2019 to 2023, integrating geospatial layers such as roads, cropland, etc. Analysis of flood events over the five-year period revealed substantial impacts, with 21.60% of the total area experiencing inundation. This flooding affected 6.92% of cropland and 4.16% of the population. Furthermore, 18.10% of the road network, spanning over 21,000 km within the study area, was also affected. This system has the potential to enhance emergency response capabilities during flood events and inform more effective disaster mitigation policies. Full article

This study presents a hybrid approach to hydrological forecasting by integrating the physically based Soil and Water Assessment Tool (SWAT) model with Prophet time-series modeling and machine learning–based multi-output regression. Applied to the Cahaba watershed, the objective is to predict key environmental variables (precipitation, evapotranspiration (ET), potential evapotranspiration (PET), and snowmelt) and their influence on hydrological responses (surface runoff, groundwater flow, soil water, sediment yield, and water yield) under present (2010–2022) and future (2030–2042) climate scenarios. Using SWAT outputs for calibration, the integrated SWAT-Prophet-ML model predicted ET and PET with RMSE values between 10 and 20 mm. Performance was lower for high-variability events such as precipitation (RMSE = 30–50 mm). Under current climate conditions, R² values of 0.75 (water yield) and 0.70 (surface runoff) were achieved. Groundwater and sediment yields were underpredicted, particularly during peak years. The model’s limitations relate to its dependence on historical trends and its limited representation of physical processes, which constrain its performance under future climate scenarios. Suggested improvements include scenario-based training and integration of physical constraints. The approach offers a scalable, data-driven method for enhancing monthly water balance prediction and supports applications in watershed planning. Full article

In early May 2025, extreme wildfires swept across Manitoba, Canada, fueled by unseasonably warm temperatures, prolonged drought, and stressed vegetation. We explore how multi-source satellite indicators—such as anomalies in snow cover, precipitation, temperature, vegetation indices, and soil moisture in April–May—jointly signal landscape preconditioning for fire, highlighting the potential of these compound anomalies to inform fire risk awareness in boreal regions. Results indicate that rainfall deficits and diminished snowpack significantly reduced soil moisture, which subsequently decreased vegetative greenness and created a flammable environment prior to ignition. This concept captures how multiple moderate anomalies, when occurring simultaneously, can converge to create high-impact fire conditions that would not be flagged by individual thresholds alone. These findings underscore the importance of integrating climate and biosphere anomalies into wildfire risk monitoring to enhance preparedness in boreal regions under accelerating climate change. Full article

Urban vegetation monitoring is essential for sustainable city planning but is often constrained by the high cost and limited frequency of field-based inventories. This study evaluates the use of the Normalized Difference Vegetation Index (NDVI), derived from Sino-Brazilian CBERS-4A satellite imagery, as a spatially explicit and low-cost proxy for urban tree census data. CBERS-4A provides medium-resolution multispectral data freely accessible across South America, yet remains underutilized in urban environmental applications. Focusing on Aracaju, a metropolitan region in northeastern Brazil, we compared NDVI-based classification results with official municipal tree census data from 2022. The analysis revealed a strong spatial correlation, supporting the use of NDVI as a reliable indicator of canopy presence at the urban block scale. In addition to mapping vegetation distribution, the NDVI results identified areas with insufficient canopy coverage, directly informing urban greening priorities. By validating remote sensing data against field inventories, this study demonstrates how CBERS-4A imagery and vegetation indices can support municipal tree management and serve as scalable tools for environmental planning and policy. Full article

Climate change has accelerated the retreat of Andean glaciers, with significant recent losses in the tropical Andes. This study evaluates the extinction of the Carihuairazo volcano glacier (Ecuador), quantifying its area from 1312.5 m² in September 2023 to 101.2 m² in January 2024, its thickness (from 2.5 m to 0.71 m), and its volume (from 2638.85 m³ to 457.18 m³), before its complete deglaciation in February 2024; this rapid melting and its small size classify it as a glacierette. Multivariate analyses (PCA and biclustering) were performed to correlate climatic variables (temperature, solar radiation, precipitation, relative humidity, vapor pressure, and wind) with glacier surface and thickness. The PCA explained 70.26% of the total variance, with Axis 1 (28.01%) associated with extreme thermal conditions (temperatures up to 8.18 °C and radiation up to 16.14 kJ m⁻² day⁻¹), which probably drove its disappearance. Likewise, Axis 2 (21.56%) was related to favorable hydric conditions (precipitation between 39 and 94 mm) during the initial phase of glacier monitoring. Biclustering identified three groups of variables: Group 1 (temperature, solar radiation, and vapor pressure) contributed most to deglaciation; Group 2 (precipitation, humidity) apparently benefited initial stability; and Group 3 (wind) played a secondary role. These results, validated through in situ measurements, provide scientific evidence of the disappearance of the Carihuairazo volcano glacier by February 2024. They also corroborate earlier projections that anticipated its extinction by the middle of this decade. The early disappearance of this glacier highlights the vulnerability of small tropical Andean glaciers and underscores the urgent need for water security strategies focused on management, adaptation, and resilience. Full article

Refining the land use structure can boost land utilization efficiency and curtail regional carbon emissions. Nevertheless, prior research has predominantly concentrated on static linear planning analysis. It has failed to account for how future dynamic alterations in driving factors (such as GDP and population) affect simulation outcomes and how the land use spatial configuration impacts the attainment of the carbon-neutrality goal. In this research, 1 km spatial resolution LULC products were employed to meticulously simulate multiple land use scenarios across China at the national level from 2030 to 2060. This was performed by taking into account the dynamic changes in driving factors. Subsequently, an analysis was carried out on the low-carbon land use spatial structure required to reach the carbon-neutrality target. The findings are as follows: (1) When employing the PLUS (Patch—based Land Use Simulation) model to conduct simulations of various land use scenarios in China by taking into account the dynamic alterations in driving factors, a high degree of precision was attained across diverse scenarios. The sustainable development scenario demonstrated the best performance, with kappa, OA, and FoM values of 0.9101, 93.15%, and 0.3895, respectively. This implies that the simulation approach based on dynamic factors is highly suitable for national-scale applications. (2) The simulation accuracy of the PLUS and GeoSOS-FLUS (Systems for Geographical Modeling and Optimization, Simulation of Future Land Utilization) models was validated for six scenarios by extrapolating the trends of influencing factors. Moreover, a set of scenarios was added to each model as a control group without extrapolation. The present research demonstrated that projecting the trends of factors having an impact notably improved the simulation precision of both the PLUS and GeoSOS-FLUS models. When contrasted with the GeoSOS-FLUS model, the PLUS model attained superior simulation accuracy across all six scenarios. The highest precision indicators were observed in the sustainable development scenario, with kappa, OA, and FoM values reaching 0.9101, 93.15%, and 0.3895, respectively. The precise simulation method of the PLUS model, which considers the dynamic changes in influencing factors, is highly applicable at the national scale. (3) Under the sustainable development scenario, it is anticipated that China’s land use carbon emissions will reach their peak in 2030 and achieve the carbon-neutrality target by 2060. Net carbon emissions are expected to decline by 14.36% compared to the 2020 levels. From the perspective of dynamic changes in influencing factors, the PLUS model was used to accurately simulate China’s future land use. Based on these simulations, multi-scenario predictions of future carbon emissions were made, and the results uncover the spatiotemporal evolution characteristics of China’s carbon emissions. This study aims to offer a solid scientific basis for policy-making related to China’s low-carbon economy and high-quality development. It also intends to present Chinese solutions and key paths for achieving carbon peak and carbon neutrality. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Fresh water is vital for human activities; however, an increase in the contamination of water bodies has been observed, so it is necessary to monitor the degree of contamination and take measures to preserve it. In Altamira, Tamaulipas, the Guayalejo-Tamesí River basin has three estuaries and seven lagoons, including Laguna El Conejo, of which the National Water Commission only monitors one. The objective of this research is to determine water quality on the basis of the parameters COD, BOD₅, T, pH, and sediment characteristics. The open reflux method was used according to NMX-AA-030-SCFI-2012 for COD, BOD Track II, HACH equipment for BOD₅, and the granulometric characterization recommended by the Unified Soil Classification System ASTM D-2487-17. The water was found to be uniformly contaminated throughout its length in the range of 117.3–200 mg/L COD, and BOD₅ ranged from 15.8–112.75 mg/L throughout the study period, with sediments dominated by poorly graded soil and fine clay. Comprehensive management is needed because the BOD₅/COD ratio varies between 0.11and 0.56, indicating that it contains recalcitrant organic matter, which is difficult to biodegrade. Full article

Amid global environmental changes, urbanisation erodes nature connectedness, an important driver of pro-environmental behaviours and human well-being, exacerbating human-made risks like biodiversity loss and climate change. This study introduces a novel hybrid agent-based model (ABM), calibrated with historical urbanisation data, to explore how urbanisation, opportunity and orientation to engage with nature, and intergenerational transmission have shaped nature connectedness over time. The model simulates historical trends (1800–2020) against target data, with projections extending to 2125. The ABM revealed a significant nature connectedness decline with excellent fit to the target data, derived from nature word use in cultural products. Although a lifetime ‘extinction of experience’ mechanism refined the fit, intergenerational transmission emerged as the dominant driver—supporting a socio-ecological tipping point in human–nature disconnection. Even with transformative interventions like dramatic urban greening and enhanced nature engagement, projections suggest a persistent disconnection from nature through to 2050, highlighting locked-in risks to environmental stewardship. After 2050, the most transformative interventions trigger a self-sustaining recovery, highlighting the need for sustained, systemic policies that embed nature connectedness into urban planning and education. Full article