Search Results (535)

Drought is one of the most complex natural hazards, characterized by its slow onset, persistent nature, diverse sectoral impacts (e.g., agriculture, water resources, ecosystems), and dependence on meteorological, hydrological, and socioeconomic factors. Over the years, significant scientific effort has been devoted to developing methodologies that address its multifaceted nature, reflecting the interdisciplinary challenges of drought analysis. However, previous reviews have typically focused on individual methods, while this study presents a unified, multidisciplinary framework that integrates multiple drought analysis methods and links them to key factors guiding method selection. To address this gap, five widely used methods—index-based, remote sensing, threshold-level methods (TLM), impact-based methods, and the storyline approach—are critically evaluated from a multidisciplinary perspective. In addition, the study examines spatial and temporal trends in scientific publications, illustrating how the application of these methods has evolved over time and across regions. The primary objective of this review is twofold: (1) to provide a holistic, state-of-the-art synthesis of these methods, their applications, and their limitations; and (2) to evaluate and prioritize the critical decision-making factors, including drought type, data type/availability, study scale, and management objectives that influence method selection. By bridging this gap, the paper offers a conceptual decision-support framework for selecting context-appropriate drought analysis methods. However, challenges remain, including the vast diversity of methods beyond the scope of this review and the limited consideration of less influential factors such as user expertise, computational resources, and policy context. The paper concludes with insights and recommendations for optimizing method selection under varying circumstances, aiming to support both drought research and effective policy implementation. Full article

Although measurements of urban shrinkage in China have received much attention, most have relied on statistical yearbook data based on political–administrative city boundaries, and remote-sensing-based quantification is mainly one-dimensional. This has caused problems in incorporating rural areas and spatiotemporal inconsistencies, as well as an inadequate understanding, which has subsequently resulted in an inaccurate shrinkage identification. This study merely utilized the latest multisensory and time-series remote sensing data, including nighttime light, land use, and population grids, to quantify the spatiotemporal patterns of multidimensional shrinkage based on the county-level urban entity mapping of Yangtze River Delta urban agglomerations (YRD-UAs) from 2003 to 2023. County-level urban entities were acquired from a pioneering mapping effort that utilized city-specific commuting distance and land use maps. The results demonstrated that urban entities in 215 counties grew at a generally slowing pace. The degree of economic, population, and space shrinkage was mainly slight, and the shrinking trajectory was dominated by temporary shrinkage. Most counties experienced population shrinkage in their coastal-oriented distribution, whereas economic shrinkage affected the fewest counties, with the lowest spatial clustering occurring northward. Population shrinkage also displayed the highest spatial autocorrelation, but its agglomeration weakened against space shrinkage clustering. This study concluded that the exclusive utilization of remote sensing products to measure urban-entity-based multidimensional shrinkage reduced the uncertainty associated with rural area inclusion and resulted in satisfactory assessment accuracy. The spatiotemporal patterns of multidimensional shrinkage suggested strengthening ecological land allocation within urban entities across the entire region, implementing polycentric development strategies in the north, as well as enhancing county-level economic governance in the northwest. This study presents a spatiotemporally comparable methodology for quantifying the multidimensional shrinking of county-level urban entities at a large scale and contributes to further optimizing the developments of YRD-UAs. Full article

Voltage-gated potassium channels Kv7.1, encoded by the gene KCNQ1, play critical roles in various physiological processes. In cardiomyocytes, the complex Kv7.1-KCNE1 mediates the slow component of the delayed rectifier potassium current that is essential for the action potential repolarization. Over 1000 KCNQ1 missense variants, many of which are associated with long QT syndrome, are reported in ClinVar and other databases. However, over 600 variants are of uncertain clinical significance (VUS), have conflicting interpretations of pathogenicity, or lack germline information. Computational prediction of the damaging potential of such variants is important for the diagnostics and treatment of cardiac disease. Here, we collected 1750 benign and pathogenic missense variants of Kv channels from databases ClinVar, Humsavar, and Ensembl Variation and tested 26 bioinformatics tools in their ability to identify known pathogenic or likely pathogenic (P/LP) variants. The best-performing tool, AlphaMissense, predicted the pathogenicity of 195 VUSs in Kv7.1. Among these, 79 variants of 66 wildtype residues (WTRs) are also reported as P/LP variants in sequentially matching positions of at least one hKv7.1 paralogue. In available cryoEM structures of Kv7.1 with activated and deactivated voltage-sensing domains, 52 WTRs form intersegmental contacts with WTRs of ClinVar-listed variants, including 21 WTRs with P/LP variants. ClinPred and paralogue annotation methods consistently predicted that 21 WTRs of KCNE1 have 34 VUSs with damaging potential. Among these, 8 WTRs are contacting 23 Kv7.1 WTRs with 13 ClinVar-listed variants in the AlphaFold3 model. Analysis of intersegmental contacts in CryoEM and AlphaFold3 structures suggests atomic mechanisms of dysfunction for some VUSs. Full article

Globally, ecosystems are affected by climate change, human activities, and natural disasters, which impact ecosystem quality and stability. Vegetation plays a crucial role in ecosystem material cycle and energy transformation, making it important to monitor its resilience under disturbance stress. The Critical Slowing Down (CSD) indicates that as ecosystems near collapse, the autocorrelation of lag temporal increases and resilience decreases. We used the lag Temporal Autocorrelation (TAC) of long-term remote sensing Leaf Area Index (LAI) to monitor vegetation resilience in the Three Gorges Reservoir Area (TGRA). The Disturbance Event Model (DEM) was used to validate the CSD. The results showed the following: (1) The eastern TGRA exhibited high and increasing vegetation resilience, while most areas showed a decline. (2) Among the various vegetation types, forests demonstrated higher resilience than other vegetation types. (3) Precipitation, temperature, and soil moisture significantly influenced vegetation resilience dynamics within the TGRA. (4) For model accuracy, the CSD’s results were consistent with the DEM, confirming its applicability in the TGRA. Overall, the CSD when applied to long-term remote sensing data, provided valuable quantitative indicators for vegetation resilience. Furthermore, more CSD-based indicators are needed to analyze vegetation resilience dynamics and better understand the biological processes determining vegetation degradation and restoration. Full article

The rapid and accurate prediction of crop yield and the construction of optimal yield prediction models are important for guiding field-scale agronomic management practices in precision agriculture. This study selected the leaf area index (LAI) of winter wheat (Triticum aestivum L.) at four different stages, and collected canopy spectral information and extracted vegetation indexes through unmanned aerial vehicle (UAV) multi-spectral sensors to establish the yield prediction model under the condition of slow-release nitrogen fertilizer and proposed optimal fertilization strategies for sustainable yield increase in wheat. The prediction results were evaluated using random forest (RF), support vector machine (SVM) and back propagation neural network (BPNN) methods to select the optimal spectral index and establish yield prediction models. The results showed that LAI has a significantly positive correlation with yield across four growth stages of winter wheat, and the correlation coefficient at the anthesis stage reached 0.96 in 2018–2019 and 0.83 in 2019–2020. Therefore, yield prediction for winter wheat could be achieved through a remote sensing estimation of LAI at the anthesis stage. Six vegetation indexes calculated from UAV-derived reflectance data were modeled against LAI, demonstrating that the red-edge vegetation index (CI_{red edge}) achieved superior accuracy in estimating LAI for winter wheat yield prediction. RF, SVM and BPNN models were used to evaluate the accuracy and precision of CI_{red edge} in predicting yield, respectively. It was found that RF outperformed both SVM and BPNN in predicting yield accuracy. The CI_{red edge} of the anthesis stage was the best vegetation index and stage for estimating yield of winter wheat based on UAV remote sensing. Under different N application rates, both predicted and measured yields exhibited a consistent trend that followed the order of SRF (slow-release N fertilizer) > SRFU1 (mixed TU and SRF at a ratio of 2:8) > SRFU2 (mixed TU and SRF at a ratio of 3:7) > TU (traditional urea). The optimum N fertilizer rate and N fertilizer type for winter wheat in this study were 220 kg ha⁻¹ and SRF, respectively. The results of this study will provide significant technical support for regional crop growth monitoring and yield prediction. Full article

Scientifically and accurately assessing the interaction between changes in human activity intensity and the surrounding ecological environment along the Qinghai–Tibet Railway is of great significance for the optimized construction of the railway and the restoration of the regional ecological environment. Based on different spatial distribution scales and construction phases of the Qinghai–Tibet Railway, this study integrates multi-source remote sensing data to construct a long-term spatiotemporal dataset of human activity intensity in the region. Drawing on analytical methods from production theory, a coupling theoretical framework based on remote sensing ecological models is proposed to quantitatively reveal the coupling relationships between the ecological environment and human activities across varying spatiotemporal scales along the Qinghai–Tibet Railway. The study finds that (1) the spatiotemporal distribution of human activity intensity along the Qinghai–Tibet Railway demonstrates clear patterns, with expansion primarily radiating from transportation corridors and their intersections, and marked spatial heterogeneity across different segments. Overall, human activity intensity increased slowly between 1990 and 2002, followed by a significant rise during the construction and opening of the Golmud–Lhasa section (2001–2007). From 2013 to 2020, the growth rate began to slow. Within a 0–30 km buffer zone centered on railway station locations (with a 15 km radius), the growth rate of human activity intensity generally decreased with increasing distance from the railway. In the 30–60 km buffer zone, this trend tended to stabilize. (2) The coupling process between ecological quality and human activity intensity across different spatiotemporal scales along the railway exhibits considerable spatial and temporal heterogeneity and complexity. The decoupling relationship is dominated by strong and weak decoupling patterns, with strong decoupling being the most prevalent. Weak decoupling is mainly distributed along the sides of the railway. Overall, in most areas along the railway, ecological quality has shown a certain degree of improvement alongside increasing human activity intensity; however, the rate of ecological improvement is generally lower than the rate of increase in human activity intensity. In some areas adjacent to the railway, intensified human activities have led to a decline in ecological quality, though the resulting ecological pressure remains relatively low. Full article

DEtection TRansformer (DETR) introduced an end-to-end object detection paradigm using Transformers, eliminating hand-crafted components like anchor boxes and Non-Maximum Suppression (NMS) via set prediction and bipartite matching. Despite its potential, the original DETR suffered from slow convergence, poor small object detection, and low efficiency, prompting extensive research. This paper systematically reviews DETR’s technical evolution from a “problem-driven” perspective, focusing on advancements in attention mechanisms, query design, training strategies, and architectural efficiency. We also outline DETR’s applications in autonomous driving, medical imaging, and remote sensing, and its expansion to fine-grained classification and video understanding. Finally, we summarize current challenges and future directions. This “problem-driven” analysis offers researchers a comprehensive and insightful overview, aiming to fill gaps in the existing literature on DETR’s evolution and logic. Full article

Food contaminants, including harmful microbes, pesticide residues, heavy metals and illegal additives, pose significant public health risks. While traditional detection methods are effective, they are often slow and require complex equipment, which limits their application in real-time monitoring and rapid response. Surface-enhanced Raman scattering (SERS) technology has gained widespread use in related research due to its hypersensitivity, non-destructibility and molecular fingerprinting capabilities. In recent years, Au-Ag bimetallic nanoparticles (Au-Ag BNPs) have emerged as novel SERS substrates, accelerating advancements in SERS detection technology. Au-Ag BNPs can be classified into Au-Ag alloys, Au-Ag core–shells and Au-Ag aggregates, among which the Au-Ag core–shell structure is more widely applied. This review discusses the types, synthesis methods and practical applications of Au-Ag BNPs in food contaminants. The study aims to provide valuable insights into the development of new Au-Ag BNPs and their effective use in detecting common food contaminants. Additionally, this paper explores the challenges and future prospects of SERS technology based on Au-Ag BNPs for pollutant detection, including the development of functional integrated substrates, advancements in intelligent algorithms and the creation of portable on-site detection platforms. These innovations are designed to streamline the detection process and offer guidance in selecting optimal sensing methods for the on-site detection of specific pollutants. Full article

Landslides pose significant risks to human life and infrastructure, driven by a complex interplay of geological and hydrological factors. This study investigates the ongoing slope instability affecting the village of Borrano, in Central Italy, where large-scale landslides are triggered or reactivated by extreme rainfall and seismic activity. A multidisciplinary approach was employed, integrating traditional geological surveys, direct investigations, and advanced geophysical techniques—including electrical resistivity tomography (ERT) and seismic refraction tomography (SRT)—to characterize subsurface structures. Additionally, Sentinel-1 interferometric synthetic aperture radar (InSAR) was employed to parametrize the deformation rates induced by the landslide. The results reveal a complex geological framework dominated by the Teramo Flysch, where weak clayey facies and structurally controlled dip-slopes predispose the area to gravitational instability. ERT and SRT identified resistivity and velocity contrasts associated with shallow and depth sliding surfaces. At the same time, satellite-based synthetic aperture radar (SAR) data confirmed persistent slow movements, with vertical displacement rates between −10 and −24 mm/year. These findings underscore the importance of lithological heterogeneity and structural settings in the evolution of landslides. The integrated geophysical and remote sensing approach enhances the understanding of slope dynamics. It can be used to cross-check interpretations, capture displacement trends, characterize the internal structure of unstable slopes, and resolve the limitations of each method. This synergy provides a more comprehensive assessment of complex slope instability, offering valuable insights for hazard mitigation strategies in landslide-prone areas. Full article

Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. This study constructs a Stefan CatBoost-ET (SCE) model through machine learning and Blending integration, leveraging multi-source remote sensing data, the Stefan equation, and measured ALT data to focus on the ALT in the Qinghai-Tibet Plateau (QTP). Additionally, the SCE model was verified via ten-fold cross-validation (MAE: 20.713 cm, RMSE: 32.680 cm, R²: 0.873, and MAPE: 0.104), and its inversion of QTP’s ALT data from 1958 to 2022 revealed 1998 as a key turning point with a slow growth rate of 0.25 cm/a before 1998 and a significantly increased rate of 1.26 cm/a afterward. Finally, based on multiple model input factor analysis methods (SHAP, Pearson correlation, and Random Forest Importance), the study analyzed the ranking of key factors influencing ALT changes. Meanwhile, the importance of Stefan equation results in SCE model is verified. The research results of this paper have positive implications for eco-hydrology in the QTP region, and also provide valuable references for simulating the ALT of permafrost. Full article

The increase of population and economic activities has brought a series of problems to coastal areas, such as ecosystem pollution, overdevelopment, and climate change. The frequent occurrence of natural disasters is threatening the ecosystem of coastal areas, but also seriously affecting coastal populations. Under these circumstances, the construction of coastal ecological corridors, integrated with resilience protection and disaster reduction systems, has emerged as a critical strategy for enhancing the stability of ecosystems. This study combines big data analysis technology, remote sensing technology, and geographic information system (GIS) to establish a real-time dynamic monitoring for ecological corridors. The experimental results show that the average flow velocity in the ecological corridor area significantly slows down after a rainstorm compared to the control area. After the construction of the ecological corridor, the soil erosion rates decreased significantly, while air and water quality showed significant improvements. These findings show that ecological corridors improve the quality and protection efficiency of the ecological environment in coastal areas. Full article

Landslides triggered by extreme rainfall often cause severe casualties and property losses. Therefore, it is essential to accurately assess and predict building vulnerability under landslide scenarios for effective risk mitigation. This study proposed a quantitative framework for vulnerability assessments of structures. It integrated extreme rainfall analysis, landslide kinematic assessment, and the dynamic response of structures. The study area is located in the northern mountainous region of Tianjin, China. It lies within the Yanshan Mountains, serving as a key transportation corridor linking North and Northeast China. The Sentinel-1A satellite imagery consisting of 77 SLC scenes (from October 2014 to November 2023) identified a slow-moving landslide in the region by using the SBAS-InSAR technique. High-resolution topographic data of the slope were first acquired through UAV-based remote sensing. Next, historical rainfall data from 1980 to 2017 were analyzed. The Gumbel distribution was used to determine the return periods of extreme rainfall events. The potential slope failure range and kinematic processes of the landslide were then simulated by using numerical simulations. The dynamic responses of buildings impacted by the landslide were modeled by using ABAQUS. These simulations allowed for the estimation of building vulnerability and the generation of vulnerability maps. Results showed that increased rainfall intensity significantly enlarged the plastic zone within the slope. This raised the likelihood of landslide occurrence and led to more severe building damage. When the rainfall return period increased from 50 to 100 years, the number of damaged buildings rose by about 10%. The vulnerability of individual buildings increased by 10% to 15%. The maximum vulnerability value increased from 0.87 to 1.0. This model offers a valuable addition to current quantitative landslide risk assessment frameworks. It is especially suitable for areas where landslides have not yet occurred. Full article

The increase in the carbon dioxide (CO₂) concentration is a major driver of global warming, presenting significant challenges to ecosystems and human societies. Satellite remote sensing technology can monitor the continuous spatial variation of the atmospheric CO₂ column concentration (XCO₂), but its global application is limited by the narrow observational swath. To address this, this study effectively integrates XCO₂ data retrieved from the GOSAT and OCO-2 satellites using atmospheric profile adjustment and spatial grid integration techniques. Based on this, a multi-machine learning ensemble algorithm (MLE) was developed, which successfully estimated the spatially continuous XCO₂ concentration in China from 2010 to 2022 (ChinaXCO₂-MLE). The results indicate that, compared to individual satellite observations, the integration of multi-source satellite XCO₂ data significantly improves the spatiotemporal coverage. The overall R² of the MLE model was 0.97, with an RMSE of 0.87 ppmv, outperforming single machine learning models. The ChinaXCO₂-MLE shows good consistency with the observational records from two background stations in China, with R² values of 0.93 and 0.78, and corresponding RMSEs of 1.00 ppmv and 1.32 ppmv. This study also reveals the seasonal and regional variations in China’s XCO₂ concentration: the highest concentration occurs in spring, the lowest concentration occurs in northern regions during summer, and the lowest concentration occurs in southern regions during autumn. From 2010 to 2022, the XCO₂ concentration continued to rise, but the growth rate has slowed due to the implementation of air pollution prevention and energy conservation policies. The spatially continuous XCO₂ data provide a more comprehensive understanding of carbon variation and offer a valuable reference for achieving China’s carbon neutrality goals. Full article

We present and demonstrate a highly birefringent fiber Bragg grating (Hi-Bi FBG) that was fabricated using a femtosecond laser to induce a sawtooth stress region near the FBG. The FBG is fabricated with a femtosecond laser point-by-point method, while the sawtooth stress region is generated in fiber cladding using the femtosecond laser along a sawtooth path. This sawtooth stressor can introduce an anisotropic and asymmetric refractive index profile in the cross-section of the fiber, resulting in additional birefringence up to 2.97 × 10⁻⁴ along the axial direction of the FBG. The central wavelengths of the Hi-Bi FBG at the fast and slow axes exhibit different sensitivities to temperature and strain, allowing simultaneous measurement of the strain and temperature by tracking the resonant wavelength shifts in the two axes. The experimental results show that the temperature sensitivities of the fast and slow axes are 10.32 pm/°C and 10.42 pm/°C, while the strain sensitivities are 0.91 pm/µε and 0.99 pm/µε. The accuracy of this proposed sensor in measuring strain and temperature is estimated to be 2.2 µε and 0.2 °C. This approach addresses the issue of cross-sensitivity between temperature and strain and offers some advantages of low cost, compact size, and significant potential for advancements in practical multi-parameter sensing applications. Full article

In the mechanical harvesting process, pineapple fruits are prone to damage. Traditional detection methods struggle to quantitatively assess pineapple damage and often operate at slow speeds. To address these challenges, this paper proposes a pineapple mechanical damage detection method based on machine vision, which segments the damaged region and calculates its area using multiple image processing algorithms. First, both color and depth images of the damaged pineapple are captured using a RealSense depth camera, and their pixel information is aligned. Subsequently, preprocessing techniques such as grayscale conversion, contrast enhancement, and Gaussian denoising are applied to the color images to generate grayscale images with prominent damage features. Next, an image segmentation method that combines thresholding, edge detection, and morphological processing is employed to process the images and output the damage contour images with smoother boundaries. After contour-filling and isolation of the smaller connected regions, a binary image of the damaged area is generated. Finally, a calibration object with a known surface area is used to derive both the depth values and pixel area. By integrating the depth information with the pixel area of the binary image, the damaged area of the pineapple is calculated. The damage detection system was implemented in MATLAB, and the experimental results showed that compared with the actual measured damaged area, the proposed method achieved an average error of 5.67% and an area calculation accuracy of 94.33%, even under the conditions of minimal skin color differences and low image resolution. Compared to traditional manual detection, this approach increases detection speed by over 30 times. Full article