Search Results (9,628)

Because of their wide distribution, short life cycle, rapid reproduction, and sensitivity to the environment, rodents can indicate changes in habitat quality and climate variables. Long-term studies are needed to verify these changes and assumptions about their causes. We analyzed small mammal trapping data in Lithuania, covering the period 1975–2023, with 1821 trapping sites and 57,426 small mammal individuals, with a focus on the bank vole (Clethrionomys glareolus). The aim of this study was to assess temporal changes in the relative abundance and proportion of this species in small mammal communities in relation to their habitats. With 21,736 captured individuals, C. glareolus was a dominant species in the country; its proportion in general was 37.9%, with 60.0% in forests. Open habitats, meadows and agricultural land were characterized by the lowest species proportions. Our main findings were the confirmation of decreasing abundances and proportions of C. glareolus since the 1990s, the absence of cyclical fluctuations in the relative abundances of the species in general and in forest habitats, and the introduction of a south–north cline in species proportions. The status of this temperate and boreal forest species is subject to change, with implications for the diversity of the mid-latitude small mammal community. Full article

Climate change will have various impacts on the survival and development of species, and it is important to explore whether plants can adapt to future climate conditions. Dipteronia dyeriana is an endangered species with a narrow distribution in Yunnan, characterized by a small population size. However, studies on its current distribution and the impact of climate change on its future survival and distribution are very limited. In this study, the current and future (2050 and 2090) potential habitats under the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios were predicted using both maximum entropy (MaxEnt) and random forest (RF) models based on the current range points of D. dyeriana, soil, topographical, land cover, and climate data. The results showed that the RF model demonstrated significantly higher AUC, TSS, and Kappa scores than the MaxEnt model, suggesting high accuracy of the RF model. Isothermality (bio_3), minimum temperature of the coldest month (bio_6), and annual precipitation (bio_12) are the main environmental factors affecting the distribution of D. dyeriana. At present, the high suitability area of D. dyeriana is mainly concentrated in the eastern part of Yunnan Province and part of southern Tibet, covering an area of 3.53 × 10⁴ km². Under future climate change scenarios, the total area suitable for D. dyeriana is expected to increase. Although, the highly suitable area has a tendency to decrease. With regards to land use, more than 77.53% of the suitable land area (29.67 × 10⁴ km²) could be used for D. dyeriana planting under different SSP scenarios. In 2090, the centroid shifts of the two models exhibit a consistent trend. Under the SSP1-2.6 scenario, the centroids transfer to the southeast. However, under the SSP3-7.0 and SSP5-8.5 scenarios, the centroids of high suitability areas migrate toward the northwest. In summary, this study enhances our understanding of the influence of climate change on the geographic range of D. dyeriana and provides essential theoretical backing for efforts in its conservation and cultivation. Full article

The growing deforestation of tropical forests requires the implementation of restoration actions capable of assisting the recovery of biodiversity and the functioning of these ecosystems. This research aimed to identify the environmental factors that influence the abundance and diversity of woody plant recruitment in an Andean forest restoration project in Medellin (Colombia). Data from woody plant individuals taller than 80 cm were collected in 22 plots of 200·m⁻². The environmental factors selected were edaphic variables, plantation structure, slope, elevation, prior land use, and landscape forest cover. Generalized linear models (GLM) were used to analyze recruitment densit and Linear Mixed Models (LMM) to assess recruited species richness, diversity, and dominance. Woody plant recruitment attributes in our study area were similar to those of secondary succession in an Andean forest, but planted trees contributed little to recruitment density and diversity. While recruitment density was affected by slope, canopy closure, and landscape forest cover, recruitment diversity was influenced by physical (bulk density) and chemical (pH, aluminum, Cation Exchange Capacity) edaphic factors, planted tree diversity (species richness and composition), canopy closure, and the mortality rate of planted trees. We conclude that sites with lower mortality rates of planted trees and denser canopies enhance both recruitment density and diversity, indicating a synergy between active restoration and passive regeneration processes. Full article

Advances in deep learning and computer vision techniques have made impacts in the field of remote sensing, enabling efficient data analysis for applications such as land cover classification and change detection. Convolutional neural networks (CNNs) and transformer architectures have been utilized in visual perception algorithms due to their effectiveness in analyzing local features and global context. In this paper, we propose a hybrid transformer architecture that consists of a CNN-based encoder and transformer-based decoder. We propose a feature adjustment module that refines the multiscale feature maps extracted from an EfficientNet backbone network. The adjusted feature maps are integrated into the transformer-based decoder to perform the semantic segmentation of the remote sensing images. This paper refers to the proposed encoder–decoder architecture as a semantic feature adjustment network (SFA-Net). To demonstrate the effectiveness of the SFA-Net, experiments were thoroughly conducted with four public benchmark datasets, including the UAVid, ISPRS Potsdam, ISPRS Vaihingen, and LoveDA datasets. The proposed model achieved state-of-the-art accuracy on the UAVid, ISPRS Vaihingen, and LoveDA datasets for the segmentation of the remote sensing images. On the ISPRS Potsdam dataset, our method achieved comparable accuracy to the latest model while reducing the number of trainable parameters from 113.8 M to 10.7 M. Full article

Anthropogenic heat emissions, which are quantified as anthropogenic heat flux (AHF), have attracted significant attention due to their pronounced impacts on urban thermal environments and local climates. However, there remains a notable gap in research regarding the distinctions in the distribution of anthropogenic heat emissions (AHEs) along urban–rural gradients. To address this gap, the present study introduces a new concept—the anthropogenic urban heat island (ArUHI)—where the AHF within urban areas is higher than that in background areas. To quantitatively describe the magnitude and spatial extent of the ArUHI effect, two metrics—namely, ArUHI intensity (ArUHII) and ArUHI footprint (ArUHIFP)—are introduced. We conducted a comprehensive study across 208 cities in China to investigate the spatiotemporal patterns of AHF variations along urban–rural gradients during the period of 2000–2016. In addition, we explored how the complex interactions between land cover and building form components affect changes in the AHF along urban–rural gradients. Additionally, we analyzed how economic zones and city sizes alter the ArUHI intensity and ArUHI footprint. The results showed that 97% (201/208) of Chinese cities exhibited a significant ArUHI effect from 2000 to 2016. The modeled ArUHI intensity value exhibited a substantial increase of nearly fivefold, increasing from 5.55 ± 0.19 W/m² to 26.84 ± 0.99 W/m² over time. Regarding the spatial distribution of the ArUHI footprint, the analysis revealed that, for the majority of cities (86% or 179 out of 208), the ArUHI footprint ranged from 1.5 to 5.5 times that in urban areas. City sizes and economic zones yielded significant influences on the ArUHI intensity and ArUHI footprint values. Building forms were significantly positively correlated with AHF, with R² values higher than 0.94. This study contributes to the understanding of ArUHI effects and their driving factors in China, providing valuable insights for urban climate studies and enhancing our understanding of surface urban heat island mechanisms. Full article

The accurate prediction of soil organic carbon (SOC) is important for agriculture and land management. Methods using remote sensing data are helpful for estimating SOC in bare soils. To overcome the challenge of predicting SOC under vegetation cover, this study extracted spectral, radar, and topographic variables from multi-temporal optical satellite images (high-resolution PlanetScope and medium-resolution Sentinel-2), synthetic aperture radar satellite images (Sentinel-1), and digital elevation model, respectively, to estimate SOC content in arable soils in the Wuling Mountain region of Southwest China. These variables were modeled at four different spatial resolutions (3 m, 20 m, 30 m, and 80 m) using the eXtreme Gradient Boosting algorithm. The results showed that modeling resolution, the combination of multi-source remote sensing data, and temporal phases all influenced SOC prediction performance. The models generally yielded better results at a medium (20 m) modeling resolution than at fine (3 m) and coarse (80 m) resolutions. The combination of PlanetScope, Sentinel-2, and topography factors gave satisfactory predictions for dry land ($R^2$ = 0.673, MAE = 0.107%, RMSE = 0.135%). The addition of Sentinel-1 indicators gave the best predictions for paddy field ($R^2$ = 0.699, MAE = 0.114%, RMSE = 0.148%). The values of $R^2$ of the optimal models for paddy field and dry land improved by 36.0% and 33.4%, respectively, compared to that for the entire study area. The optical images in winter played a dominant role in the prediction of SOC for both paddy field and dry land. This study offers valuable insights into effectively modeling soil properties under vegetation cover at various scales using multi-source and multi-temporal remote sensing data. Full article

Remote sensing allows us to conduct large-scale scientific studies that require extensive mapping and the amalgamation of numerous images. However, owing to variations in radiation, atmospheric conditions, sensor perspectives, and land cover, significant color discrepancies often arise between different images, necessitating color consistency adjustments for effective image mosaicking and applications. Existing methods for color consistency adjustment in remote sensing images struggle with complex one-to-many nonlinear color-mapping relationships, often resulting in texture distortions. To address these challenges, this study proposes a convolutional neural network-based color consistency method for remote sensing cartography that considers both global and local color mapping and texture mapping constrained by the source domain. This method effectively handles complex color-mapping relationships while minimizing texture distortions in the target image. Comparative experiments on remote sensing images from different times, sensors, and resolutions demonstrated that our method achieved superior color consistency, preserved fine texture details, and provided visually appealing outcomes, assisting in generating large-area data products. Full article

In light of international climate agreements and the Sustainable Development Goals (SDGs), there is a growing need to enhance the understanding of the linkages among land use/cover change (LUCC) and its carbon effects (CEs), as well as human well-being (HW). While existing studies have primarily focused on the impacts of LUCC on CEs or ecosystem services, there remains a gap in systematically elucidating the complex relationships among LUCC, CEs, and HW. This paper presents a comprehensive review of the nexus between land-based CEs and HW, examining: (1) the correlation between LUCC and CEs, encompassing methodologies for investigating LUCC CEs; (2) the association between CEs and HW, introducing the concept of “low-carbon human well-being” and evaluation framework; and (3) the proposed framework of “LUCC-CEs-HW,” which delves into the intricate connections among three elements. The study identifies research gaps and outlines potential future directions, including assessments of LUCC CEs and low-carbon HW, exploration of the “LUCC-CEs-HW” nexus, and the development of standardized measurement approaches. Key opportunities for further investigation include establishing a unified evaluation index system and developing scalable methods. This paper elucidates the relationships among LUCC, CEs, and HW, offering insights for future works. Full article

The quality and level of groundwater tables have rapidly declined because of intensive pumping in Punjab (Pakistan). For sustainable groundwater supplies, there is a need for better management practices. So, the identification of potential groundwater recharge zones is crucial for developing effective management systems. The current research is based on integrating seven contributing factors, including geology, soil map, land cover/land use, lineament density, drainage density, slope, and rainfall to categorize the area into various groundwater recharge potential zones using remote sensing, geographic information system (GIS), and analytical hierarchical process (AHP) for Punjab, Pakistan. The weights (for various thematic layers) and rating values (for sub-classes) in the overlay analysis were assigned for thematic layers and then modified and normalized using the AHP. The result indicates that about 17.88% of the area falls under the category of very high groundwater potential zones (GWPZs). It was found that only 12.27% of the area falls under the category of very low GWPZs. The results showed that spatial technologies like remote sensing and geographic information system (GIS), when combined with AHP technique, provide a robust platform for studying GWPZs. This will help the public and government sectors to understand the potential zone for sustainable groundwater management. Full article

Coastal saline soils are an important soil resource that, when restored, can enhance arable land and preserve the natural ecology. With the aim of improving the use of coastal saline soils, we conducted a spot survey at Bohai coastal saline land to investigate the differences in soil properties between different vegetation types. The soil physical and chemical properties of various vegetation types, including Aeluropus sinensis, Imperata cylindrica, Tamarix chinensis, Lycium chinense, Hibiscus moscheutos, Helianthus annuus, Gossypium hirsutum, and Zea mays, were examined at two depth layers: 0–20 cm and 20–40 cm, and in two seasons, spring and autumn. The soil properties were compared with bare land as a control. The results indicated that the electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density of soils with vegetation cover were lower than those with bare land. On the other hand, soil pH, organic matter content, mean weight diameter, and saturated hydraulic conductivity were higher. The redundancy analysis results revealed a substantial positive correlation between soil pH, saturated hydraulic conductivity, water content, mean weight diameter, and organic matter content, as well as a significant positive correlation between soil electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density. Soil pH, saturated hydraulic conductivity, water content, mean weight diameter, organic matter content, and soil electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density were negatively correlated. The results of the structural equation model and variance decomposition showed that soil organic matter and bulk density were the key factors affecting the degree of soil salinization, and compared with their independent effects, their combined effect on soil salinization was greater. This study’s conclusions can provide a point of reference for further research on the mechanisms of soil improvement and desalinization in coastal saline land. Full article

The diversity of land use and consolidation is fundamental to ensuring sustainable development. However, the impact of diverse land uses and consolidation on the well-known shallow accumulation pattern of soil nitrogen (N) remains unclear. This existence of this knowledge gap severely constrains the sustainable production of newly created farmland. Therefore, the objective of this study was to investigate the effects of land use and gully land transformation on the vertical distribution of soil N in agricultural and nature catchments. Methodologically, soil nitrate (NO₃⁻), ammonium (NH₄⁺) and total nitrogen (TN) were measured to a depth of 100 cm in the hillslope forestland, grassland and gully cropland areas of the treated (gully landform reshaping) and untreated (natural gully) catchments on the Chinese Loess Plateau (CLP). The results indicated that soil N in the hillslope forestland and grassland exhibited a shallow accumulation pattern, while the vertical distribution of soil N in the gully cropland areas displayed a homogeneous, random or deep accumulation pattern. In the hillslope areas, vegetable cover was the dominant factor controlling N variation in the topsoil. In contrast, in the gully areas, the interaction of landform transformation and hydrology was the primary factor influencing the distribution of soil N. In the treated catchment, soil NO₃⁻ exhibited a significant deep accumulation pattern in the newly created farmland through gully landform reshaping. In the untreated catchment, soil NH₄⁺ showed a significant deep accumulation pattern in the undisturbed natural gully. This study provides valuable insights into how land use and gully landform transformation affect the soil N profile. This information is crucial for the sustainable development and scientific management of valley agriculture at the catchment scale. Full article

Against a backdrop of rapid environmental degradation and increasing pressures on natural resources, a broad list of innovations has emerged to support the vision of the post-2020 Kunming-Montreal Global Biodiversity Framework and strengthen regional and country-level biodiversity strategies along the interface of protected areas and non-protected lands. The success of these strategies depends in large part on science-informed consideration and approaches to the underlying and indirect drivers of change for natural systems and ecosystem services. This paper aims to inform future strategies and action plans for conservation efforts and sustainable practices globally and regionally, with a specific focus on Latin America’s environmental challenges. Bibliometric analysis, covering two decades from 2003 to 2023, focused on global and Latin American research trends related to the indirect drivers of change for natural systems and ecosystem services at the interface of protected and non-protected lands. Through structured analysis, key opportunities for increased collaboration, impact, and research focus are identified, highlighting the need to expand research collaboration strategies and reach, enhance research dissemination through open and equitable innovations, and strengthen capacity to the complex and interrelated challenges underlying accelerated change in natural systems, which affects biodiversity and ecosystem services. Full article

The isle of Gavdos, and its wider area, is one of the few places worldwide where the calibration and validation of altimetric satellites has been carried out during the last, more than, two decades using dedicated techniques at sea and on land. The sea-surface calibration employed for the determination of the bias in the satellite altimeter’s sea-surface height relies on the use of a gravimetric geoid in collocation with data from tide gauges, permanent global navigation satellite system (GNSS) receivers, as well as meteorological and oceanographic sensors. Hence, a high-accuracy and high-resolution gravimetric geoid model in the vicinity of Gavdos and its surrounding area is of vital importance. The existence of such a geoid model resides in the availability of reliable, in terms of accuracy, and dense, in terms of spatial resolution, gravity data. The isle of Gavdos presents varying topographic characteristics with heights larger than 400 m within small spatial distances of ~7 km. The small size of the island and the significant bathymetric variations in its surrounding marine regions make the determination of the gravity field and the geoid a challenging task. Given the above, the objective of the present work was two-fold. First, to collect new land gravity data over the isle of Gavdos in order to complete the existing database and cover parts of the island where voids existed. Relative gravity campaigns have been designed to cover as homogenously as possible the entire island of Gavdos and especially areas where the topographic gradient is large. The second focus was on the determination of a high-resolution, $1\prime \times 1\prime$, and high-accuracy gravimetric geoid for the wider Gavdos area, which will support activities on the determination of the absolute altimetric bias. The relative gravity campaigns have been designed and carried out employing a CG5 relative gravity meter along with geodetic grade GNSS receivers to determine the geodetic position of the acquired observations. Geoid determination has been based on the newly acquired and historical gravity data, GNSS/Leveling observations, and topography and bathymetry databases for the region. The modeling was based on the well-known remove–compute–restore (RCR) method, employing least-squares collocation (LSC) and fast Fourier transform (FFT) methods for the evaluation of the Stokes’ integral. Modeling of the long wavelength contribution has been based on EIGEN6c4 and XGM2019e global geopotential models (GGMs), while for the contribution of the topography, the residual terrain model correction has been employed using both the classical, space domain, and spectral approaches. From the results achieved, the final geoid model accuracy reached the ±1–3 cm level, while in terms of the absolute differences to the GNSS/Leveling data per baseline length, 28.4% of the differences were below the $1\mathrm{c}\mathrm{m}\sqrt{S_{ij} \left[\mathrm{k}\mathrm{m}\right]}$ level and 55.2% below the $2\mathrm{c}\mathrm{m}\sqrt{S_{ij} \left[\mathrm{k}\mathrm{m}\right]}$. The latter improved drastically to 52.8% and 81.1%, respectively, after deterministic fit to GNSS/Leveling data, while in terms of the relative differences, the final geoid reaches relative uncertainties of 11.58 ppm (±1.2 cm) for baselines as short as 0–10 km, which improves to 10.63 ppm (±1.1 cm) after the fit. Full article

Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI > 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km² of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km² would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures. Full article

The growing population and expansion of rural activities, along with changing climatic patterns and the need for water during drought periods, have led to a rise in the water demand worldwide. As a result, the construction of water storage structures such as dams has increased in recent years to meet the water needs. However, dam construction can bring significant alterations to the natural flow regime of rivers, and it is therefore essential to understand the potential effects of human structures on the hydrological regime of rivers to reduce their destructive impacts. This study analyzes the hydrological changes in the Shahrchai River in response to the Shahrchai Dam construction in Urmia, Iran. The study period was from 1950 to 2017 at the Urmia Band station. The Indicators of Hydrological Alteration (IHA) were used to analyze the hydrological changes before and after regulating, accounting for land use changes and climatic factors. The results revealed the adverse effects of the Shahrchai Dam on the hydrological indices. The analysis showed an increase in the average flow rate during the summer season and a decrease in other seasons. However, the combined effects of water transferring for drinking purposes, a decrease in permanent snow cover upstream of the dam, and an increase in water use for irrigation and agricultural purposes resulted in a decrease in the released river flow. Furthermore, the minimum and maximum daily flow rates decreased by approximately 85% and 65%, respectively, after the construction of the Shahrchai Dam. Additionally, the number of days with maximum flow rates increased from 117 days in the pre-dam period to 181 days in the post-dam period. As a concluding remark, the construction of the Shahrchai Dam, land use/cover changes, and a decrease in permanent snow cover had unfavorable effects on the hydrological regime of the river. Therefore, the hydrological indicators should be adjusted to an acceptable level compared to the natural state to preserve the river ecosystem. The findings of this study are expected to guide water resource managers in regulating the sustainable flow regime of permanent rivers. Full article