Search Results (114)

This research paper presents an assessment of geohazards on Lefkas Island, Greece, using Geographic Information System (GIS) technology to map risk and enhance public awareness through an interactive web application. Natural hazards such as landslides, floods, wildfires, and desertification threaten both the safety of residents and the island’s tourism-dependent economy, particularly due to its seismic activity and Mediterranean climate. By combining the Sendai Framework for Disaster Risk Reduction with GIS capabilities, we created detailed hazard maps that visually represent areas of susceptibility and provide critical insights for local authorities and the public. The web application developed serves as a user-friendly platform for disseminating hazard information and educational resources, thus promoting community preparedness and resilience. The findings highlight the necessity for proactive land management strategies and community engagement in disaster risk reduction efforts. This study underscores GIS’s pivotal role in fostering informed decision making and enhancing the safety of Lefkas Island’s inhabitants and visitors in the face of environmental challenges. Full article

Desertification presents a significant ecological challenge in arid and semi-arid regions, posing a severe threat to regional ecological security and sustainable development. This study introduces an integrated framework for desertification vulnerability assessment, combining the MEDALUS model with the XGBoost algorithm, to evaluate desertification dynamics across the Mongolian Plateau from 2000 to 2020 and project future trends under four Shared Socioeconomic Pathways (SSPs) for 2030. The findings are as follows: (1) Between 2000 and 2020, desertification vulnerability was most pronounced in the southern and western regions of the plateau, with lower vulnerability observed in the northern and eastern areas. High-vulnerability zones expanded over time, highlighting the need for targeted and prioritized management efforts. (2) Climate factors—particularly temperature, wind speed, and precipitation—emerged as the dominant drivers of desertification, followed by soil characteristics and vegetation (NDVI). The influence of human activities on desertification became increasingly significant, stressing the need for improved land management and sustainable practices. (3) Future risks show that desertification vulnerability in the Mongolian Plateau will intensify under high-emission scenarios (SSP3-7.0, SSP5-8.5), with significant expansion of high vulnerability areas. Lower-emission scenarios (SSP1-2.6, SSP2-4.5) may reduce some impacts, but high vulnerability will persist, highlighting the need for urgent climate mitigation and adaptation efforts. Full article

Climate change poses substantial threats to natural ecosystems and human livelihoods, particularly in coastal regions, by intensifying coastal erosion. This process leads to land loss, infrastructure damage, and habitat destruction while amplifying challenges such as sea-level rise, flooding, desertification, and salinization. In Vietnam’s Red River Delta (RRD), the dynamic interplay between erosion and accretion presents a highly complex challenge, necessitating effective risk assessment and management to safeguard communities and resources. Using the principles of natural disaster risk assessment and comprehensive analysis, this study develops a coastal erosion risk assessment framework incorporating hazard, exposure, and vulnerability dimensions. The framework integrates 17 indicators, including human activities, socioeconomic factors, shoreline type, and vegetation cover, with indicator weights determined through expert evaluation and the analytic hierarchy process. The application of this framework reveals that coastal erosion risk in the RRD is relatively high, with greater risk concentrated in the central and northern segments of the coastline compared to the flanking areas. This framework offers valuable insights for coastal erosion prevention, mitigation strategies, and the optimization of coastal spatial planning. The application of coastal erosion risk assessment methods provides a relatively complete foundation for developing comprehensive prevention and adaptation solutions in the future. Through the system of parameters and corresponding weights, it provides an overview of potential responses to future impacts while identifying current high-risk zones specifically and accurately, thereby assessing the importance of each parameter on that impact. Based on specific analysis of assessment results, a reasonable resource use and management policy can be developed to minimize related natural disasters. Therefore, two main groups of solutions proposed under the “Protection—Adaptation” strategy are proposed to prevent natural disasters, minimize risks and sustainably develop the coastal area of the RRD. Full article

Based on the rapid development of urbanization and the increasing severity of extreme heat disasters caused by global warming, it has become increasingly important to enhance the assessment of heat risk. In this study, in response to the urgent need for fine-grained assessment of urban heat risk in arid zones in the context of climate change, an analytical method of dividing Local Climate Zones (LCZs) into street blocks combined with the Hazard–Exposure–Vulnerability–Adaptability (HEVA) heat risk assessment framework is used in Urumqi, a representative city of China’s arid zones. In addition, Shapley Additive Explanations (SHAP) was introduced to quantitatively resolve the driving mechanisms of heat risk in different types of LCZs. The results show that the study area has the largest proportion of bare soil (LCZ F) (37.6%), which is distributed around the built-up types of LCZs, while water (LCZ G) has a very small proportion (0.39%) and only exists in the outskirts of the city. Heat risk was significantly higher in the urban core than in the peri-urban areas, but LCZ F had a very high hazard due to the unique surface characteristics of arid zones, which elevated the heat risk in the peri-urban desertification fringe; SHAP analyses demonstrated that in arid zones, land surface temperature (LST) became a determinant of heat risk for all low-density built-up types of LCZs. This study proposes targeted mitigation strategies for heat risk in arid zones based on the LCZ framework. Full article

Desertification, accompanied by the loss of perennial grasses and bush encroachment, affects more than 10% of the world’s drylands, thereby placing increasing pressure on rangelands and farmers’ livelihoods. In Namibia, rangeland desertification is exacerbated by external shocks such as droughts, market changes, and new regulatory frameworks that have led to profound social and ecological changes within this tightly coupled social–ecological system (SES). In this context, the interrelationship among system components, drivers, and external factors, as well as the impact of strategies to halt desertification, remain poorly understood. The present study employed a retrospective mixed-methods approach to investigate the drivers of desertification on Namibia’s freehold farms by applying the social–ecological trap (SET) concept. Our approach combined remote sensing methods with semi-structured interviews and a literature review. The aim was to decipher the underlying processes and self-reinforcing feedback loops and to identify associated changes in the social and ecological subsystem. Our results revealed that inadequate grass availability, coupled with income deficits, serves as a pivotal catalyst for rangeland desertification, perpetuating a self-reinforcing feedback loop. To avoid or mitigate the effects of ecological regime shifts and to help farmers escape the SET of desertification, it will be necessary to implement changes in the dominant feedback loops through long-term risk mitigation strategies, such as rangeland restoration measures, as well as on- and off-farm income diversification. These strategies will provide a foundation for subsequent research on effective long-term mitigation strategies to prevent further rangeland desertification and to secure the livelihoods of farmers. Full article

Atmospheric fine particles (PM_2.5) pose significant health risks by penetrating deep into the lungs and causing respiratory and cardiovascular issues. In Saudi Arabia, high PM_2.5 levels are driven by its geographic location and extreme climate. Therefore, analysis of PM_2.5 spatiotemporal variability is crucial for understanding its causes, impacts, and effective management. This study analyzed MERRA-2 reanalysis PM_2.5 data for 23 years (2001–2023). MERRA-2 data were validated with in situ observations in terms of several statistical metrics, including RMSE, FAC2, MAE, and Correlation Coefficient. The results revealed a significant spatial variation in PM_2.5 levels, with higher concentrations observed in the eastern and southeastern regions and lower concentrations observed in the western and northwestern regions, a trend confirmed by ground-level observations. Employing the robust Theil–Sen technique, temporal trends in PM_2.5 concentrations indicated an overall decreasing trend over the study period. At most sites, PM_2.5 levels increased until 2010 and then started decreasing, probably due to government interventions for reducing emissions, combating desertification, and enhancing tree plantations. Non-linear modeling provided a more accurate representation of complex trends compared to simple linear models. The findings underscore the need for continued national and regional efforts to mitigate PM_2.5 pollution by addressing its emission sources. Full article

Ecological risk evaluation is a prerequisite for the rational allocation of land resources, which is of great significance for safeguarding ecosystem integrity and achieving ecological risk prevention and control. However, existing research lacks analysis of the ecosystem state after land use simulation within the restricted conversion zone, making it impossible to determine whether ecological risks have been mitigated under these constraints. Therefore, we selected the Dianchi basin as the study area, extracted the ecological defense zone as the restricted conversion zone, and used the PLUS (Patch-generating Land Use Simulation) model to simulate land use for 2030 under multiple scenarios. We then evaluated ecological risks based on landscape pattern indices, and analyzed ecological risks under multiple scenarios with and without the restricted conversion zone. By comparing ecological risks across scenarios with and without constraints, we clarified the critical role of ecological risk evaluation in the rational allocation of land resources. The results show the following: (1) The ecological defense zone was obtained by overlaying no-development zones (such as forest parks and nature reserves), areas of extreme importance in the evaluation of water resource protection, soil and water conservation, and biodiversity, as well as areas of extreme importance in the evaluation of soil and water erosion and rocky desertification sensitivity. (2) Cultivated land and woodland cover significant portions of the Dianchi basin. Overall, ecological risk deterioration was more pronounced in the economic scenario (ES), while the ecological scenario (PS) exhibited lower ecological risk compared to the natural scenario (NS). (3) After importing the ecological defense zone into the PLUS model as the restricted conversion zone for land use simulation, ecological risks in all scenarios showed a trend of improvement. The improvement trend was strongest in the NS, followed by the PS, and weakest in the ES. The results of this study can help to identify the most suitable land use planning model and provide a more effective strategy for ecological risk prevention and control. Full article

The increasing global demand for food caused by a growing world population has resulted in environmental problems, such as the destruction of ecologically significant biomes and pollution of ecosystems. At the same time, the intensification of crop production in modern agriculture has led to the extensive use of synthetic fertilizers to achieve higher yields. Although chemical fertilizers provide essential nutrients and accelerate crop growth, they also pose significant health and environmental risks, including pollution of groundwater and other bodies of water such as rivers and lakes. Soils that have been destabilized by indiscriminate clearing of vegetation undergo a desertification process that has profound effects on microbial ecological succession, impacting biogeochemical cycling and thus the foundation of the ecosystem. Tropical countries have positive aspects that can be utilized to their advantage, such as warmer climates, leading to increased primary productivity and, as a result, greater biodiversity. As an eco-friendly, cost-effective, and easy-to-apply alternative, biofertilizers have emerged as a solution to this issue. Biofertilizers consist of a diverse group of microorganisms that is able to promote plant growth and enhance soil health, even under challenging abiotic stress conditions. They can include plant growth-promoting rhizobacteria, arbuscular mycorrhizal fungi, and other beneficial microbial consortia. Bioremediators, on the other hand, are microorganisms that can reduce soil and water pollution or otherwise improve impacted environments. So, the use of microbial biotechnology relies on understanding the relationships among microorganisms and their environments, and, inversely, how abiotic factors influence microbial activity. The recent introduction of genetically modified microorganisms into the gamut of biofertilizers and bioremediators requires further studies to assess potential adverse effects in various ecosystems. This article reviews and discusses these two soil correcting/improving processes with the aim of stimulating their use in developing tropical countries. Full article

Central Asia, as a chronically water-stressed region marked by extreme aridity, faces significant environmental challenges from intensifying desertification and deteriorating ecological stability. The region’s vulnerability to shifting precipitation regimes and extreme hydrometeorological events has been magnified under anthropogenic climate forcing. Although global climate models (GCMs) remain essential tools for climate projections, their utility in Central Asia’s complex terrain is constrained by inherent limitations: coarse spatial resolution (~100–250 km) and imperfect parameterization of orographic precipitation mechanisms. This investigation advances precipitation modeling through deep learning-enhanced statistical downscaling, employing convolutional neural networks (CNNs) to generate high-resolution precipitation data at approximately 10 km resolution. Our results show that the deep learning models successfully simulate the high center of precipitation and extreme precipitation near the Tianshan Mountains, exhibiting high spatial applicability. Under intermediate (SSP-245) and high-emission (SSP-585) future scenarios, the increase in extreme precipitation over the next century is significantly more pronounced compared to mean precipitation. By the end of the 21st century, the interannual variability of mean precipitation and extreme precipitation will become even larger under SSP-585, indicating an increased risk of extreme droughts/floods in Central Asia under high greenhouse gas emissions. Our findings provide technical support for climate change impact assessments in the region and highlight the potential of CNN-based downscaling for future climate change studies. Full article

Contiguous destitute areas (CDAs) in China, characterized by deep poverty and difficulty in alleviating it, have garnered attention for their spatial–temporal development. Using nighttime light (NTL) data from 2000 to 2020, we developed a novel method to identify spatiotemporal changes in CDAs. This is the first classification method based on multi-period continuous threshold judgement to objectively classify counties into expansion, shrinkage, or stability types, and quantify the intensity and ratio of these changes. The results showed that: (1) From 2000 to 2020, 345 counties expanded, 176 remained stable, and 11 shrank, accounting for 64.85%, 33.08%, and 1.07%, respectively. Dabie Mountains (VI), Luoxiao Mountains (XI), and Wuling Mountains (VII) had higher proportions of expanding counties, while shrinkage was concentrated in Tsinling-Daba mountains (V), VII, Lvliang Mountains (III), and Rocky Desertification Area (X). (2) The peak of expansions occurred between 2008 and 2011. (3) Both expansion and shrinkage intensities and ratios were generally low, with strong expansion primarily in IV and VI. We also found that there is the risk of returning to poverty in the development of the CDAs, such as space shrinking, lack of development impetus, coexisting expansion and shrinkage, and low intensity. It provides reference information for China’s future targeted and sustained poverty reduction policies and systems. Full article

This study focuses on the ecological security of the Guangxi–Vietnam karst border region, introducing an innovative framework that integrates traditional ecological knowledge (TEK) with modern GIS-based ecological modeling to promote sustainable development. Using remote sensing, geographic information systems (GIS), and ecological sensitivity assessments, this research identifies key ecological sources, corridors, pinch points, and barriers. Unlike conventional approaches that rely solely on biophysical indicators, this study incorporates TEK-derived ecological practices into ecological network optimization, ensuring that conservation strategies align with local knowledge and cultural sustainability. Ecological sensitivity was evaluated through indicators such as soil erosion, rocky desertification, and geological disaster risks to guide the optimization of ecological networks. TEK practices, including afforestation, rotational farming, and biodiversity conservation, were systematically integrated into the construction of an innovative “three axes, two belts, and six zones” ecological security pattern. The results revealed 55 ecological corridors, 80 ecological pinch points, and 14 ecological barriers, primarily located in areas with high human activity intensity. This study advances ecological security planning by demonstrating a replicable model for TEK-based conservation in transboundary karst landscapes. By integrating traditional knowledge with modern ecological methodologies, it enhances biodiversity conservation, ecosystem connectivity, and resilience. The proposed framework provides actionable insights for sustainable urban–rural coordination and ecological restoration in karst landscapes, contributing to the long-term sustainability of ecologically vulnerable and culturally diverse regions. Full article

In desertified regions, monitoring vegetation phenology and elucidating its relationship with climatic factors are of crucial significance for understanding how desertification responds to climate change. This study aimed to extract the spatial-temporal evolution of land surface phenology metrics from 2001 to 2020 using MODIS NDVI products (NASA, Greenbelt, MD, USA) and explore the potential impacts of climate change on land surface phenology through partial least squares regression analysis. The key results are as follows: Firstly, regionally the annual mean start of the growing season (SOS) ranged from day of year (DOY) 130 to 170, the annual mean end of the growing season (EOS) fell within DOY 270 to 310, and the annual mean length of the growing season (LOS) was between 120 and 180 days. Most of the desertified areas demonstrated a tendency towards an earlier SOS, a delayed EOS, and a prolonged LOS, although a small portion exhibited the opposite trends. Secondly, precipitation prior to the SOS period significantly influenced the advancement of SOS, while precipitation during the growing season had a marked impact on EOS delay. Thirdly, high temperatures in both the pre-SOS and growing seasons led to moisture deficits for vegetation growth, which was unfavorable for both SOS advancement and EOS delay. The influence of temperature on SOS and EOS was mainly manifested during the months when SOS and EOS occurred, with the minimum temperature having a more prominent effect than the average and maximum temperatures. Additionally, the wind in the pre-SOS period was found to adversely impact SOS advancement, potentially due to severe wind erosion in desertified areas during spring. The findings of this study reveal that the delayed spring phenology, precipitated by the occurrence of a warm and dry spring in semi-arid desertified areas of northern China, has the potential to heighten the risk of desertification. Full article

Desertification presents major environmental challenges in Central Asia, driven by climatic and anthropogenic factors. The present study quantifies desertification risk through an integrated approach using Bayesian networks and the ESAS model, offering a holistic perspective on desertification dynamics. Four key variables—vegetation cover, precipitation, land-use intensity, and soil quality—were incorporated into a Bayesian model to evaluate their influence on desertification. A probabilistic model was developed to gauge desertification intensity, with simulations conducted at 200 geospatial points. Hazard maps for 2030–2050 were produced under climate scenarios SSP245 and SSP585, incorporating projected land-use changes. All procedures for desertification risk assessment, land-use mapping, and climate downscaling were performed using the Google Earth Engine platform. The findings suggest a 4% increase in desertification risk under SSP245 and an 11% increase under SSP585 by 2050, with the greatest threats observed in western regions such as Kazakhstan, Uzbekistan, and Turkmenistan. Sensitivity analysis indicated that vegetation quality exerts the strongest influence on desertification, reflected by a Vegetation Quality Index (VQI) ranging from 1.582 (low in Turkmenistan) to 1.692 (very low in Kazakhstan). A comparison of the Bayesian and ESAS models revealed robust alignment, evidenced by an R² value of 0.82, a Pearson correlation coefficient of 0.76, and an RMSE of 0.18. These results highlight the utility of Bayesian networks as an effective tool for desertification assessment and scenario analysis, underscoring the urgency of targeted land management and proactive climate adaptation. Although reclaimed land presents opportunities for afforestation and sustainable agriculture, carefully considering potential trade-offs with biodiversity and ecosystem services remains essential. Full article

In the context of rapid urbanization and extreme climate change globally, balancing ecological resources and economic development for land spatial planning has become one of the pressing issues that need to be addressed. This study proposes a composite model to construct a spatial ecological security pattern. It identifies restoration areas with different risk levels based on the spatial distribution of land use, offering suggestions for optimizing spatial configuration. Focusing on the central Shaanxi region of the Yellow River Basin in China, ecological sources are identified by integrating ecological factors, and ecological corridors and restoration zones are extracted using the minimum cumulative resistance difference and circuit theory. The results indicate significant improvements in ecological quality and desertification in the study area from 2000 to 2020. Currently, the core area covers 51,649.71 km², accounting for 62.18% of all landscape types; the total ecological source area covers 31,304.88 km², representing 18.84% of the entire area. These ecological source areas are mainly distributed in the northern Loess Plateau and the southern mountainous regions. The area has 26 important ecological corridors, identifying 16 ecological pinch points and 12 ecological barriers, presenting an ecological security pattern characterized by a grid-like structure in the northern region and a dispersed pattern in the southern region. Additionally, 273.72 km² of ecological restoration priority areas and 197.98 square kilometers of ecological restoration encouragement areas are proposed as key planning regions for ecological environmental protection. This study provides references for optimizing spatial configuration to promote the sustainable development of urban and rural living environments in the Yellow River Basin. Full article

This research explores the dynamics of vegetation patterns under changing environmental conditions, considering the United Nations Sustainable Development Goal 15: “Protect, restore, and promote the sustainable use of terrestrial ecosystems; combat desertification; halt and reverse land degradation; and prevent biodiversity loss”. In this context, this study presents a modeling and nonlinear analysis framework for plant–soil-moisture interactions, including Holling-II functional response and hyperbolic mortality models. The primary goal is to explore how nonlinear soil–water interactions influence vegetation patterns in semi-arid ecosystems. Moreover, the influence of nonlinear soil–water interaction on the establishment of population patterns is investigated. The formation and evolution of these patterns are explored using theoretical analysis and numerical simulations, as well as important factors and critical thresholds. These insights are crucial for addressing desertification, a key challenge in semi-arid regions that threatens biodiversity, ecosystem services, and sustainable land management. The model, which includes environmental parameters such as rainfall, plant growth rates, and soil moisture, was tested using both theoretical analysis and numerical simulations. These characteristics are carefully adjusted to find important thresholds influencing the danger of desertification. Simulation scenarios, run under set initial conditions and varying parameters, yield useful insights into the pattern of patch development under dynamically changing environmental conditions. The findings revealed that changes in environmental conditions, such as rainfall and plant growth rates, prompted Hopf bifurcation, resulting in the production of three distinct patterns: a dotted pattern, a striped pattern, and a combination of both. The creation of these patterns provides essential information about the sustainability of environmental equilibrium. The variation curve of the average plant biomass reveals that the biomass fluctuates around a constant period, with the amplitude initially increasing, then decreasing, and gradually stabilizing. This research provides a solid foundation for addressing desertification risks, using water resources responsibly, and contributing to a better understanding of ecosystem stability. Full article