Search Results (101)

The Inner Mongolia Plateau (IMP), situated in the arid and semi-arid ecological transition zone of northern China, is particularly vulnerable to both climate change and human activities. Understanding the spatiotemporal vegetation dynamics and their driving forces is essential for regional ecological management. This study employs Sen’s slope estimation, BFAST analysis, residual trend method and Geodetector to analyze the spatial patterns of Normalized Difference Vegetation Index (NDVI) variability and distinguish between climatic and anthropogenic influences. Key findings include the following: (1) From 1982 to 2022, vegetation cover across the IMP exhibited a significant greening trend. Zonal analysis showed that this spatial heterogeneity was strongly regulated by regional hydrothermal conditions, with varied responses across land cover types and pronounced recovery observed in high-altitude areas. (2) In the western arid regions, vegetation trends were unstable, often marked by interruptions and reversals, contrasting with the sustained greening observed in the eastern zones. (3) Vegetation growth was primarily temperature-driven in the eastern forested areas, precipitation-driven in the central grasslands, and severely limited in the western deserts due to warming-induced drought. (4) Human activities exerted dual effects: significant positive residual trends were observed in the Hetao Plain and southern Horqin Sandy Land, while widespread negative residuals emerged across the southern deserts and central grasslands. (5) Vegetation change was driven by climate and human factors, with recovery mainly due to climate improvement and degradation linked to their combined impact. These findings highlight the interactive mechanisms of climate change and human disturbance in regulating terrestrial vegetation dynamics, offering insights for sustainable development and ecosystem education in climate-sensitive systems. Full article

Anthropogenic activities and climate change have significantly altered runoff generation in the upper Han River basin, posing a challenge to the water supply sustainability for the Middle Route of the South-to-North Water Diversion Project. Land use/cover changes (LUCCs) affect hydrological processes by modifying evapotranspiration, infiltration and soil moisture content. Based on hydro-meteorological data from 1961 to 2023 and LUCC data series from 1985 to 2023, this study aimed to identify the temporal trend in hydro-meteorological variables, to quantify the impacts of underlying land surface and climate factors at different time scales and to clarify the effects of LUCCs and basin greening on the runoff generation process. The results showed that (1) inflow runoff declined at a rate of −1.71 mm/year from 1961 to 2023, with a marked shift around 1985, while potential evapotranspiration increased at a rate of 2.06 mm/year within the same time frame. (2) Annual climate factors accounted for 61.01% of the runoff reduction, while underlying land surface contributed 38.99%. Effective precipitation was the dominant climatic factor during the flood season, whereas potential evapotranspiration had a greater influence during the dry season. (3) From 1985 to 2023, the LUCC changed significantly, mainly manifested by the increasing forest area and decreasing crop land area. The NDVI also showed an upward trend over the years; the actual evapotranspiration increased by 1.163 billion m³ due to the LUCC. This study addresses the climate-driven and human-induced hydrological changes in the Danjiangkou Reservoir and provides an important reference for water resource management. Full article

In recent years, the world has seen a significant increase in extreme weather events, such as floods, hurricanes, and storms, which have caused extensive damage to infrastructure and communities. These events result from natural phenomena and human-induced factors, including climate change and natural climate variations. For instance, the floods in Europe in 2024 and the hurricanes in the United States have highlighted the vulnerability of urban and rural areas. These extreme events are often unpredictable and pose considerable challenges for spatial planning and risk management. This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. By utilizing data such as weather records, land use and land cover (LULC) information, topographic LIDAR data, and advanced predictive models, the study aims to identify the most vulnerable areas and provide recommendations for risk mitigation. The results indicate that integrating these technologies can improve forecasting accuracy, thereby supporting more informed decisions in land management. Given the effects of climate change and the increasing frequency of extreme events, adopting advanced forecasting and planning tools is crucial for protecting communities and reducing economic and social damage. This method was applied to the Calopinace area, also known as the Calopinace River or Fiumara della Cartiera, which crosses Reggio Calabria and is notorious for its historical floods. It can serve as part of an early warning system, enabling alerts to be issued throughout the monitored area. Furthermore, it can be integrated into existing emergency protocols, thereby enhancing the effectiveness of disaster response. Future research could investigate incorporating additional data and AI techniques to improve accuracy. Full article

The Upper Yellow River Region plays an irreplaceable role in water conservation and ecological protection in China. Due to both natural and human-induced factors, this area has experienced significant grassland deterioration, land desertification, and salinization. Consequently, evaluating the region’s environmental status plays a vital role in promoting ecological conservation and sustainable growth in the Upper Yellow River Basin. This study constructed an ecological index based on remote-sensing data and examined its spatiotemporal changes from 1990 to 2020. Future ecological dynamics were predicted using the Hurst index, while key influencing factors were examined through an optimal-parameter-based GeoDetector and geographically weighted regression. The findings revealed the following: (1) RSEI values were generally lower in the north and increased progressively toward the south, indicating a notable spatial disparity. (2) Ecological conditions remained largely stable, with notable improvements observed in 65.47% of the study area. (3) It was anticipated that 52.76% of the region would continue to improve, whereas 24% is expected to experience further degradation. (4) Precipitation, temperature, elevation, and land cover were major factors contributing to ecological variation. Their impact on ecological quality varies across different geographic locations. These research findings provided references for the sustainable development and ecological civilization construction of the Upper Yellow River Region. Full article

Forest cover dynamics are studied on a routine basis, but how changes in forest cover impact forest fragmentation has rarely been studied over a long time period resolution. This is, however, important because forest fragmentation critically impacts ecosystem services, such as biodiversity and cooling effects. Here, we apply a long time series of Landsat images from 1986–2018 and study how forest fragmentation has changed along with forest cover dynamics in southern China. Furthermore, we attribute drivers and study the impact on local air temperature changes. The region is particularly relevant as it was largely deforested three decades ago, and most of the current forests are the result of protection and forestation measures. We found a reduction in the forest fragmentation index FFI (−34.4%) from 1986 to 2018. In 81.2% of the area, forest cover increased and fragmentation decreased, while 18.5% of the area showed increases in both forest cover and fragmentation. The contribution of human activities to forest fragmentation increased by 9%, with a distinct spatial correlation between areas of increasing forest fragmentation and high levels of human disturbance. Furthermore, we found that the average level of cooling effects in areas with increased forest cover of less than 40% is heavily dominated by forest fragmentation, whereas the cooling effects are primarily controlled by changes in forest cover. These findings underscore the role of human disturbance in driving forest fragmentation, which in turn affects the functioning of forest ecosystems. The results emphasize the need for integrated land management strategies that balance forest restoration with the mitigation of human-induced fragmentation to sustain ecosystem services in the face of ongoing environmental change. Full article

Fire-driven land cover change has generated a paradox: while habitat fragmentation from agriculture, livestock, and urban expansion has reduced natural fire occurrences, human-induced ignitions have increased wildfire frequency and intensity. In northern Colombia’s Magdalena Department, most of the territory faces moderate to high wildfire risk, especially during recurrent dry seasons and periods of below-average precipitation. However, knowledge of wildfire spatiotemporal occurrence and its drivers remains scarce. This work addresses this gap by identifying fire-prone zones and analyzing the influence of climate and vegetation in the Magdalena Department. Fire-prone zones were identified using the Getis–Ord Gi* method over fire density and burned area data from 2001 to 2023; then, they were analyzed with seasonally aggregated hydroclimatic indices via logistic regression to quantify their influence on wildfires. Vegetation susceptibility was assessed using geostatistics, obtaining land cover types most affected by fire and their degree of fragmentation. Fire-prone zones in the Magdalena Department covered ~744.35 km² (3.21%), with a weak but significant (τ = 0.20, p < 0.01) degree of coincidence between classification based on fire density, as pre-fire variable, and burned area, as a post-fire variable. Temporally, fire probability increased during the dry season, driven by short-lagged precursors such as Dry Spell Length and precipitation from the preceding wet season. Fire-prone zones were dominated by pastures (62.39%), grasslands and shrublands (19.61%) and forests (15.74%), and exhibited larger, more complex high-risk patches, despite similar spatial connectedness with non-fire-prone zones. These findings enhance wildfire vulnerability understanding, contributing to risk-based territorial planning. Full article

The China Baijiu Golden Triangle (BGT) serves as the core production hub of China’s Baijiu industry, where the ecological environment plays a pivotal role in ensuring the industry’s sustainable development. However, urbanization, industrial expansion, and climate change pose potential threats to the region’s vegetation dynamics. Utilizing Landsat remote sensing data from 2002 to 2022, this study integrates Theil–Sen trend analysis, the Mann–Kendall (MK) test, coefficient of variation (CV) analysis, and the Geodetector model (GD model) to investigate the spatiotemporal evolution of the Normalized Difference Vegetation Index (NDVI) and its underlying driving mechanisms within the BGT. The findings reveal an overall upward trend in vegetation NDVI, with the annual mean NDVI increasing from 0.45 to 0.67, corresponding to a growth rate of 0.49%. Spatially, areas of high vegetation cover are predominantly located in mountainous forest zones with favorable ecological conditions, whereas regions of low vegetation cover are concentrated in zones of urban expansion. Precipitation and topographic factors (elevation and slope) emerge as the primary natural drivers of vegetation change, while land use change and the night-time light index stand out as the most influential human-induced factors. Further analysis uncovers a nonlinear interactive enhancement effect between natural and anthropogenic factors, with the interaction between the night-time light index and precipitation being particularly pronounced. This suggests that urbanization not only directly impacts vegetation but may also exert indirect effects on the ecosystem by altering regional hydrological and climatic processes. The results indicate that ecological protection policies in the BGT have yielded some success; however, vegetation fragmentation and ecological pressures stemming from urban expansion remain significant challenges. Moving forward, optimizing land use policies and promoting eco-friendly development models will be essential to achieving ecosystem stability and sustaining industrial growth. Full article

The interplay between terrestrial water storage and vegetation dynamics in arid regions is critical for understanding ecohydrological responses to climate change and human activities. This study examines the coupling between total water storage anomaly (TWSA) and vegetation greenness changes in the Hexi Corridor, an arid region in northwestern China consisting of three inland river basins—Shule, Heihe, and Shiyang—from 2002 to 2022. Utilizing TWSA data from GRACE/GRACE-FO satellites and MODIS Enhanced Vegetation Index (EVI) data, we applied a trend analysis and partial correlation statistical techniques to assess spatiotemporal patterns and their drivers across varying aridity gradients and land cover types. The results reveal a significant decline in TWSA across the Hexi Corridor (−0.10 cm/year, p < 0.01), despite a modest increase in precipitation (1.69 mm/year, p = 0.114). The spatial analysis shows that TWSA deficits are most pronounced in the northern Shiyang Basin (−600 to −300 cm cumulative TWSA), while the southern Qilian Mountain regions exhibit accumulation (0 to 800 cm). Vegetation greening is strongest in irrigated croplands, particularly in arid and hyper-arid regions of the study area. The partial correlation analysis highlights distinct drivers: in the wetter semi-humid and semi-arid regions, precipitation plays a dominant role in driving TWSA trends. Such a rainfall dominance gives way to temperature- and human-dominated vegetation greening in the arid and hyper-arid regions. The decoupling of TWSA and precipitation highlights the importance of human irrigation activities and the warming-induced atmospheric water demand in co-driving the TWSA dynamics in arid regions. These findings suggest that while irrigation expansion cause satellite-observed greening, it exacerbates water stress through increased evapotranspiration and groundwater depletion, particularly in most water-limited arid zones. This study reveals the complex ecohydrological dynamics in drylands, emphasizing the need for a holistic view of dryland greening in the context of global warming, the escalating human demand of freshwater resources, and the efforts in achieving sustainable development. Full article

Physical soil crust (PSC) is a dense structural layer formed on the surface of bare or very low-cover land due to raindrop splashes or runoff. The formation of crust changes the properties of the soil and strongly affects water infiltration and runoff and sediment production processes on slopes. The irrational use of soil and water resources and frequent human production activity under the influence of urbanization increase the possibility of inducing erosion. Studying the formation and structural characteristics of PSC to predict terrestrial hydrological processes and improve models for predicting erosion is very important. Many studies of PSC have been carried out in China and abroad, but they are mainly unilateral discussions of the basic properties and characteristics of crust and its effects on runoff and sediment yield on slopes. Studies systematically analyzing and synthesizing the progress of crust research, however, are lacking. By reading the literature and analyzing the developmental history of PSC, we provide a comprehensive review of the following: (1) the meaning, main types, and classification of PSC, (2) the mechanism of formation and the characteristics and dynamic development of crust, (3) the factors affecting the formation of crust, including natural and anthropogenic factors and comprehensive effects, and (4) the development and formation of crust in the soil environment, i.e., hydrological processes and erosion. We also summarize the potential directions for future research on PSC: (1) studying the dynamics of soil structure during the development of crust, (2) developing an objective and standardized quantitative method for studying crust formation, (3) using models of erosion influenced by crust development, (4) improving the scale of the degree of crust development and structural characteristics, and (5) rationalizing the management of crust to optimize land structure and increase crop yield. Full article

Studying natural hazards in the context of human-induced landscape transformation is complex, especially in regions with limited information. The narratives of the elderly can play a role in filling these knowledge gaps at the multi-decadal timescale. Here, we build upon a citizen-based elderly approach to understanding natural hazard patterns and landscape transformation in a tropical mountainous environment, the Kigezi Highlands (SW Uganda). We engaged 98 elderly citizens (>70 years old) living in eight small watersheds with different characteristics. Through interviews and focus group discussions, we reconstructed historical timelines and used participatory mapping to facilitate the interview process. We cross-checked the information of the elderly citizens with historical aerial photographs, archives, and field visits. Our results show that major land use/cover changes are associated with a high population increase over the last 80 years. We also evidence an increase in reported natural hazard events such as landslides and flash floods from the 1940s until the 1980s. Then, we notice a stabilization in the number of hazard events per decade, although the two most impacted decades (1980s and 2000s) stand out. Despite this new information, an increase in natural hazard frequency due to land use/cover change cannot yet be quantitatively validated, especially when the probable modulator effect of climate variability is considered. Nevertheless, the increase in the exposure of a vulnerable population to natural hazards is clear, and population growth together with poor landscape management practices are the key culprits that explain this evolution. This study demonstrates the added value of historical narratives in terms of understanding natural hazards in the context of environmental changes. This insight is essential for governments and non-governmental organizations for the development of policies and measures for disaster risk reduction that are grounded in the path dependence of local realities. Full article

Climate change and human activities are reshaping the structure and function of terrestrial ecosystems, particularly in vulnerable regions such as agro-pastoral ecotones. However, the extent to which climate change impacts vegetation growth in these areas remains poorly understood, largely due to the modifying effects of human-induced land cover changes on vegetation sensitivity to climatic variations. This study utilizes satellite-derived vegetation indices, land cover datasets, and climate data to investigate the influence of both land cover and climate changes on vegetation growth in the agro-pastoral ecotone of northern China (APENC) from 2001 to 2022. The results reveal that the sensitivity of vegetation productivity, as indicated by the kernel Normalized Difference Vegetation Index (kNDVI), varies depending on the land cover type to climate change in the APENC. Moreover, ridge regression modeling shows that pre-season climate conditions (i.e., pre-season precipitation and temperature) have a stronger positive impact on growing-season vegetation productivity than growing season precipitation and temperature, while the effect of vapor pressure deficit (VPD) is negative. Notably, the kNDVI exhibits significant positive sensitivity (p < 0.05) to precipitation in 34.12% of the region and significant negative sensitivity (p < 0.05) to VPD in 38.80%. The ridge regression model explained 89.10% of the total variation (R² = 0.891). These findings not only emphasize the critical role of both historical and contemporary climate conditions in shaping vegetation growth but also provide valuable insights into how to adjust agricultural and animal husbandry management strategies to improve regional climate adaptation based on climate information from previous seasons in fragile regions. Full article

Watersheds are critical ecosystems that provide essential services, but they face increasing threats from deforestation, land use changes, and climate variability. The Mata Allo Sub-Watershed, which is characterized by steep topography and high rainfall, is particularly vulnerable to erosion, landslides, and habitat loss, necessitating robust conservation strategies. This study used principal component analysis (PCA) to assess ecological sensitivity, focusing on slope, rainfall, vegetation density, and land cover. The PCA results identified land cover as the most influential positive factor in F1 (loading value: 0.588), increasing sensitivity due to human-induced land use changes, while rainfall contributed most negatively (−0.638) by potentially mitigating extreme ecological risks. These contrasting roles underscore the complexity of interactions shaping watershed sensitivity. Slope strongly influenced F2 (−0.795), explaining 26.48% of the variance and highlighting the critical role of steep slopes in exacerbating erosion risks. Vegetation density in F3 (−0.679) and rainfall in F4 (−0.724) played significant roles in stabilizing soil and mitigating ecological risks, emphasizing their importance in reducing watershed sensitivity. The “Extremely Sensitive” class covers 48.79% of the watershed, primarily in areas with steep slopes and sparse vegetation, while “High Sensitivity” areas occupy 34.93%. Projections for 2032 suggest a reduction in “Extremely Sensitive” zones to 41.00%, reflecting improvements from targeted management interventions. These findings provide a foundation for promoting sustainable watershed management, enhancing climate resilience, and supporting biodiversity conservation efforts in vulnerable regions. Full article

Soil salinity is considered one of the biggest constraints to crop production, particularly in arid and semi-arid regions affected by recurrent and long periods of drought, where high salinity levels severely impact plant stress and consequently agricultural production. Climate change accelerates soil salinization, driven by factors such as soil conditions, land use/land cover changes, and water deficits, over extensive spatial and temporal scales. Continuous monitoring of areas at risk of salinization plays a critical role in supporting effective land management and enhancing agricultural production. For these purposes, this work aims to propose a spatiotemporal method for monitoring soil salinization using spectral indices derived from Earth observation data. The proposed approach was tested in the Zaghouan Region in northeastern Tunisia, a region where soils are characterized by alarming levels of salinization. To address this concern, remote sensing techniques were applied for the analysis of satellite imagery generated from Landsat 5, Landsat 8, and Landsat 9 missions. A comprehensive field survey complemented this approach, involving the collection of 229 geo-referenced soil samples. These samples were representative of distinct soil salinity classes, including non-saline, slightly saline, moderately saline, strongly saline, and very strongly saline soils. Soil salinity modeling using Landsat-8 OLI data revealed that the SI-5 index provided the most accurate predictions, with an R² of 0.67 and an RMSE of 0.12 dS/m. By 2023, 42.3% of the study area was classified as strongly or very strongly saline, indicating a significant increase in salinity over time. This rise in salinity corresponds to notable land use and land cover (LULC) changes, as 55.9% of the study area experienced LULC shifts between 2000 and 2023. A decline in vegetation cover coincided with increasing salinity, showing an inverse relationship between these factors. Additionally, the results highlight the complex interplay among these variables demonstrating that soil salinity levels are significantly impacted by climate change indicators, with a negative correlation between precipitation and salinity (r = −0.85, p < 0.001). Recognizing the interconnections between soil salinity, LULC changes, and climate variables is essential for developing comprehensive strategies, such as targeted irrigation practices and land suitability assessments. Earth observation and remote sensing play a critical role in enabling more sustainable and effective soil management in response to both human activities and climate-induced changes. Full article

Background/Objectives: Improved global data allow for a new understanding of what impact the food we produce, eat and dispose of has on the environment, human health and Nature’s resources. The overall goal is to guide decision-makers and individuals by providing in-depth knowledge about the effects of their dietary preferences on human and environmental health. Methods: The method is to investigate ways to reduce environmental degradation and to secure healthy food supplies in an urbanizing world, and to quantify the options. Results: Reviewed articles show that by eating less meat-based food and more plant-based and soilless food, as well as reducing food waste and recycling urban-disposed nutrients as fertilizers, we could reduce agriculture’s land requirement by 50% to 70% while still securing a healthy food supply. Less land under cultivation and pasture would reduce global emissions to air and water to a similar extent, and allow Nature to reclaim freed areas in order to catch more carbon and rejuvenate biodiversity. Thus, we could avoid further environmental degradation such as the current clearing of new fields needed under a business-as-usual regime. Presently, some 17 million people die each year due to poor diets, which is more than double the 7 million deaths since the onset of the COVID-19 pandemic. A return to more plant-based diets with unchanged intake of proteins but less calories, sugar, salt and fat combined with less red meat and ultra-processed food would reduce foremost non-communicable diseases by up to 20% and prolong life. The article suggests that the international focus has gradually turned to the food sector’s big contribution to climate change, biodiversity loss and harmful chemicals as well as to poor human health. It argues that this century’s rapid population growth and urbanization give urban residents a pivotal role in food’s impact on agricultural areas, which today cover half of the globe’s inhabitable areas. Their food demand, rather than the activities of farmers, fishermen or loggers, will guide remedial measures to be taken by individuals, industry and the public sector. A tool to calculate the potential environmental footprints of individual or societal measures is presented. Conclusions: Measures to make the agrifood sector more sustainable are still pending full recognition in international fora such as the UN COP Summits. Smart cities fitted with infrastructures to recycle macro- and micro-nutrients and organic matter have the potential to ameliorate human-induced impacts such as emissions to air and water bodies, crossing planetary boundaries, and polluting extraction of N (nitrogen), P (phosphorus) and K (potassium). Rapid results are within reach since dietary change and the turn-around time of nutrients in food is short compared to decades or centuries for recycled materials in cars or buildings. Full article

Rapid urbanization can change local climate by increasing land surface temperature (LST), particularly in metropolitan regions. This study uses two decades of remote sensing data to investigate how urbanization-induced changes in land use/land cover (LULC) affect LST in the Beijing Region, China. By focusing on the key issue of LST and its contributing variables through buffer zones, we determined how variables influence LST across buffer zones—core, transit, and suburban areas. This approach is crucial for identifying and prioritizing key variables in each zone, enabling targeted, zone-specific measures that can more effectively mitigate LST rise. The main driving variables for the Beijing Region were determined, and the spatial-temporal relationship between LST and driving variables was investigated using a geographically weighted regression (GWR) model. The results demonstrate that the Beijing Region’s LST climbed from 2002 to 2022, with increases of 0.904, 0.768, and 0.248 °C in core, transit, and suburban areas, respectively. The study found that human-induced variables contributed significantly to the increase in LST across core and transit areas. Meanwhile, natural variables in suburban areas predominated and contributed to stabilizing local climates and cooling. Over two decades and in all buffer zones, GWR models slightly outperformed ordinary least squares (OLS) models, suggesting that the LST is highly influenced by its local geographical location, incorporating natural and human-induced variables. The results of this study have substantial implications for designing methods to mitigate LST across the three buffer zones in the Beijing Region. Full article