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SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings. [ABSTRACT FROM AUTHOR]

Accurate and complete digital elevation models (DEMs) play an important fundamental role in geospatial analysis, supporting various engineering applications, human activities, and scientific research. Interferometric synthetic aperture radar (InSAR) plays an increasingly important role in DEM generation. Nonetheless, owing to its inherent characteristics, gaps often appear in regions marked by significant topographical fluctuations, necessitating an extra void-filling process. Traditional void-filling methods have operated directly on preexisting data, succeeding in relatively flat terrain. When facing mountainous regions, there will always be gross errors in elevation values. Regrettably, conventional methods have often disregarded this vital consideration. To this end, this research proposes a DEM void-filling method based on incorporating elevation outlier detection. It accounts for the detection and removal of elevation outliers, thereby mitigating the shortcomings of existing methods and ensuring robust DEM restoration in mountainous terrains. Experiments were conducted to validate the method applicability using TanDEM-X data from Sichuan, China, Hebei, China, and Oregon, America. The results underscore the superiority of the proposed method. Three traditional methods are selected for comparison. The proposed method has different degrees of improvement in filling accuracy, depending on the void status of the local terrain. Compared with the delta surface fill (DSF) method, the root mean squared error (RMSE) of the filling results has improved by 7.87% to 51.87%. The qualitative and quantitative experiments demonstrate that the proposed method is promising for large-scale DEM void-filling tasks. [ABSTRACT FROM AUTHOR]

Until the Mw 6.6 Luding earthquake ruptured the Moxi section of the Xianshuihe fault (XSHF) on 5 September 2022, the region had not experienced an Mw >6 earthquake since instrumental records began. We used Global Positioning System (GPS) and Sentinel-1 interferometric synthetic aperture radar (InSAR) observations to image the coseismic deformation and constrain the location and geometry of the seismogenic fault using a Bayesian method We then present a distributed slip model of the 2022 Mw6.6 Luding earthquake, a left-lateral strike-slip earthquake that occurred on the Moxi section of the Xianshuihe fault in the southwest Sichuan basin, China. Two tracks (T26 and T135) of the InSAR data captured a part of the coseismic surface deformation with the line-of-sight displacements range from ∼−0.16 m to ~0.14 m in the ascending track and from ~−0.12 m to ~0.10 m in the descending track. The inverted best-fitting fault model shows a pure sinistral strike-slip motion on a west-dipping fault plane with a strike of 164.3°. We adopt a variational Bayesian approach and account for the uncertainties in the fault geometry to retrieve the distributed slip model. The inverted result shows that the maximum slip of ~1.82 m occurred at a depth of 5.3 km, with the major slip concentrated within depths ranging from 0.9–11 km. The InSAR-determined moment is 1.3 × 1019 Nm, with a shear modulus of 30 GPa, equivalent to Mw 6.7. The published coseismic slip models of the 2022 Luding earthquake show apparent differences despite the use of similar geodetic or seismic observations. These variations underscore the uncertainty associated with routinely performed source inversions and their interpretations for the underlying fault model. [ABSTRACT FROM AUTHOR]

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas. [ABSTRACT FROM AUTHOR]

Fermented vegetable products play a significant role in various cuisines, and understanding the fermentation dynamics of lactic acid bacteria (LAB) strains is essential for optimizing their production and quality. Here, we sought to investigate the fermentation performance of five LAB strains isolated from Sichuan paocai as starters for paocai. Sensory evaluation revealed that the inoculation of radish paocai samples with LAB strains effectively improved the overall liking and sensory satisfaction of participants, increasing the scores to varying degrees in terms of taste, flavor, texture, and coloration. Lactiplantibacillus plantarum and Lacticaseibacillus rhamnosus exhibited a good salt resistance in radish juice and could grow in a medium containing 10% NaCl. Four indicator strains commonly found in contaminated paocai were effectively inhibited by fermented LAB broths, which improved the edibility and safe production of paocai. Compared to spontaneous fermentation (CK), radish paocai inoculated with LAB showed a significantly accelerated acid production rate, shortening the fermentation period by approximately two days. The contents of titratable total acids, organic acids, and free amino acids were higher in the inoculated samples and were enriched in the taste of radish paocai. The content of volatile organic compounds in the inoculated samples was higher than that in CK. Based on OPLS-DA analysis, 31 key indicators of paocai quality were screened and used to rank the fermentation performances of the five strains using the TOPSIS method; here, Lpb. plantarum and Lcb. rhamnosus achieved the highest scores. This study provides a reference for selecting LAB strains as efficient and secure fermentation starters to optimize paocai quality. [ABSTRACT FROM AUTHOR]

In recent years, the rapid expansion and development of the shale gas industry in the Sichuan Basin has coincided with a series of unexpected moderate-sized earthquakes. Given that the Sichuan Basin is situated within a stable interior block, the focal mechanism of the 2019 earthquake sequence (ML4.7, ML5.4, and ML5.2) in the Weiyuan-Rongxian area remains a subject of debate. In this study, we propose a joint InSAR- and PCA- based inversion method utilizing the distributed Mogi model to investigate the spatial-temporal characteristics of a gas reservoir and evaluate the induced Coulomb stress change. The surface deformation derived from Sentinel-1 data between 2015 and 2021 was consistent with the spatial distribution of production wells, and it correlated with the temporal changes in reservoir volume associated with the shale gas operating process. The Coulomb stress loading on the regional faults suggests that human activities associated with shale gas operation likely triggered the three moderate earthquakes. Furthermore, our results indicate Coulomb stress loadings of 10 kPa, 15 kPa, 5 kPa, 3 kPa, and 87 kPa on the Dongxingchang fault, Gaoqiao fault, Dayaokou fault, Niujingao fault, and Lijiachang fold, respectively. Consequently, fluid injection and extraction during shale gas development could be contributing to the elevated seismic activity in the Weiyuan-Rongxian area. [ABSTRACT FROM AUTHOR]

Landslides constitute a significant hazard to human life, safety and natural resources. Traditional landslide investigation methods demand considerable human effort and expertise. To address this issue, this study introduces an innovative landslide segmentation framework, EMR-HRNet, aimed at enhancing accuracy. Initially, a novel data augmentation technique, CenterRep, is proposed, not only augmenting the training dataset but also enabling the model to more effectively capture the intricate features of landslides. Furthermore, this paper integrates a RefConv and Multi-Dconv Head Transposed Attention (RMA) feature pyramid structure into the HRNet model, augmenting the model's capacity for semantic recognition and expression at various levels. Last, the incorporation of the Dilated Efficient Multi-Scale Attention (DEMA) block substantially widens the model's receptive field, bolstering its capability to discern local features. Rigorous evaluations on the Bijie dataset and the Sichuan and surrounding area dataset demonstrate that EMR-HRNet outperforms other advanced semantic segmentation models, achieving mIoU scores of 81.70% and 71.68%, respectively. Additionally, ablation studies conducted across the comprehensive dataset further corroborate the enhancements' efficacy. The results indicate that EMR-HRNet excels in processing satellite and UAV remote sensing imagery, showcasing its significant potential in multi-source optical remote sensing for landslide segmentation. [ABSTRACT FROM AUTHOR]

It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method for calculating the probability of failure in HVDC transmission lines during wildfire disasters is proposed to quantify the risk of HVDC transmission line failures caused by wildfire disasters. Using the ArcGIS 10.7. platform, the study examined the quantity of fire points within the buffer zone of each HVDC transmission line from 2001 to 2022. The results indicate significant variations in the number of fire incidents in the buffer zones of various transmission lines. Notably, there has been a noticeable increase in the number of fire incidents along several HVDC transmission lines, including Xizhe, Baihetan-Jiangsu, Baihetan-Zhejiang, and Fufeng, in recent years. Based on the number of fire points in the buffer zone obtained through ArcGIS processing and the proposed failure probability calculation model, six HVDC hydropower transmission channels in the Sichuan Province were analyzed. At the same time, the proposed probability calculation model was simplified, and a corresponding linear evaluation index was introduced. The regression analysis results indicate that the proposed index can effectively assess the failure risk of HVDC transmission lines during wildfire disasters. [ABSTRACT FROM AUTHOR]

Re-evaluating how urban and rural development can be integrated is a necessary step towards achieving the "dual-carbon" objective and facilitating a thorough transition towards a green and low-carbon economy and society. This study empirically investigates the geographical disparities, evolving patterns, and determinants of the effectiveness of urban–rural integration development in Sichuan and Chongqing. Results of the study indicate that (1) the effectiveness of urban–rural integration development in Sichuan and Chongqing is generally poor, and external environmental factors adversely affect the urban–rural integration of economically developed cities; (2) the urban–rural integration development efficiency in Sichuan and Chongqing does not show a more obvious polarization phenomenon, but the gap between the cities gradually widens; and (3) regarding influencing factors, market dynamics are favorable to overall urban–rural integration development in the Sichuan and Chongqing regions, while the development of the digital economy and the level of financial development can exacerbate the imbalance of regional urban–rural integration development. Based on this premise, pertinent policy suggestions are offered to facilitate the merger of urban and rural areas and foster efficient development in the regions of Sichuan and Chongqing. [ABSTRACT FROM AUTHOR]

Cropland is a vital resource intricately connected to food security. Currently, the issue of cropland abandonment poses a serious threat to food production and supply, presenting a significant challenge to rural economies and the stability of the food supply chain. The hilly and cloudy regions of southwest China are particularly affected by cropland abandonment, presenting significant challenges in accurately mapping the distribution of abandoned cropland due to fragmentation and heavy cloud pollution. Therefore, this study focuses on Mingshan County, located in Ya'an City, Sichuan Province, China, as the study area. Utilizing Google Earth Engine (GEE) and a random forest algorithm, a method integrating multi-source data from Landsat 8, Sentinel-2, and Sentinel-1 is proposed to extract abandoned cropland spanning from 2018 to 2022. This study analyzes spatial and temporal characteristics, employing the Geodetector with optimal parameters to explore the underlying mechanisms. The findings reveal the following: (1) The method achieves an overall accuracy of land use classification surpassing 88.67%, with a Kappa coefficient exceeding 0.87. Specifically, the accuracy for identifying abandoned cropland reaches 87.00%. (2) From 2018 to 2022, the abandonment rate in Mingshan County fluctuated between 4.58% and 5.77%, averaging 5.03%. The lowest abandonment rate occurred in 2019–2020, while the highest was observed in 2020–2021. (3) Cropland abandonment is influenced by both natural and social factors. Elevation and slope are the main driving factors, alongside factors such as distance to road, town, and residential settlement that all significantly contribute to abandonment trends. These five factors exhibit positive correlation with the abandonment rate, with distance to the river showing relatively weaker explanatory power. [ABSTRACT FROM AUTHOR]

The aerosol–cloud–precipitation correlation has been a significant scientific topic, primarily due to its remarkable uncertainty. However, the possible modulation of aerosol on the precipitation capacity of clouds has received limited attention. In this study, we utilized multi-source data on aerosol, cloud properties, precipitation, and meteorological factors to investigate the impact of aerosols on precipitation efficiency (PE) in the Sichuan Basin (SCB) and Yun-nan-Guizhou Plateau (YGP), where the differences between terrain and meteorological environment conditions were prominent. In the two study regions, there were significant negative correlations between the aerosol index (AI) and PE in spring, especially in the YGP, while the correlations between the AI and PE in other seasons were not as prominent as in spring. In spring, aerosol significantly inhibited both the liquid water path (LWP) and the ice water path (IWP) in the YGP, but negatively correlated with the IWP and had no significant relationship with the LWP in the SCB. Aerosol inhibited precipitation in the two regions mainly by reducing cloud droplet effective radius, indicating that warm clouds contributed more to precipitation in spring. The suppressive impact of aerosols on precipitation serving as the numerator of PE is greater than that of the cloud water path as the denominator of PE, resulting in a negative correlation between aerosol and PE. The AI–PE relationship is significantly dependent on meteorological conditions in the YGP, but not in the SCB, which may be related to the perennial cloud cover and stable atmosphere in the SCB. In the future, as air quality continues to improve, precipitation efficiency may increase due to the decrease in aerosol concentration, and of course, the spatio-temporal heterogeneity of the aerosol–cloud–precipitation relationship may become more significant. [ABSTRACT FROM AUTHOR]

Old landslides in the Loess Plateau, Northwest China usually occurred over a relatively long period, and their sizes are usually smaller compared to old landslides in the alpine valley areas of Sichuan, Yunnan, and Southeast Tibet. These landslide areas may have been changed either partially or greatly, and they are usually covered with vegetation and similar to their surrounding environment. Therefore, it is a great challenge to detect them using high-resolution remote sensing images with only orthophoto view. This paper proposes the optimal-view and multi-view strategic hybrid deep learning (OMV-HDL) method for old loess landslide detection. First, the optimal-view dataset in the Yan'an area (YA-OP) was established to solve the problem of insufficient optical features in orthophoto images. Second, in order to make the process of interpretation more labor-saving, the optimal-view and multi-view (OMV) strategy was proposed. Third, hybrid deep learning with weighted boxes fusion (HDL-WBF) was proposed to detect old loess landslides effectively. The experimental results with the constructed optimal-view dataset and multi-view data show that the proposed method has excellent performance among the compared methods—the F1 score and AP (mean) of the proposed method were improved by about 30% compared with the single detection model using traditional orthophoto-view data—and that it has good detection performance on multi-view data with the recall of 81.4%. [ABSTRACT FROM AUTHOR]

This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as 'exposures', which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, 'exposure risk' denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method's efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from O   (m × n × p) to O   (m × n) . [ABSTRACT FROM AUTHOR]

Whether China can achieve the United Nations' Sustainable Development Goals (SDGs) largely depends on the ability of main food-producing areas to cope with multiple land use change challenges. Despite the fact that the Yangtze River basin is one of the key regions for China's food security, the spatiotemporal dynamics of cropland abandonment and recultivation remain largely unexplored in this region. The present study assesses the evolution of the agricultural system within the Yangtze River basin between 2000 and 2020 by mapping cropland abandonment and recultivation using MODIS time series and multiple land cover products. The results highlight a widespread cropland abandonment process (i.e., 10.5% of the total study area between 2000 and 2020), predominantly in Western Sichuan, Eastern Yunnan, and Central Jiangxi. Although 70% of abandoned cropland is situated in areas with slopes less than 5°, the highest rates of abandonment are in mountainous regions. However, by 2020, 74% of this abandoned cropland had been recultivated at least once, whereas half of the abandoned croplands got recultivated within three years of their initial abandonment. Hence, as this is one of the first studies that unravels the complex interaction between cropland abandonment and recultivation in a spatiotemporal explicit context, it offers (i) scientists a novel methodological framework to assess agricultural land use issues across large geographical entities, and (ii) policy-makers new insights to support the sustainable transition of the agricultural sector. [ABSTRACT FROM AUTHOR]

This study investigated the convective weather features, precipitation microphysical characteristics, and water vapour transport characteristics associated with a southwest vortex precipitation event that occurred on the eastern edge of the Qinghai–Tibet Plateau, coinciding with a southwest vortex event, from 15 to 16 July 2021, using conventional observations of raindrop spectra, ERA5 reanalysis data, CMORPH precipitation data, and the HYSPLIT_v4 backward trajectory model. The findings aim to provide theoretical insights for improving the forecasting and numerical simulations of southwest vortex precipitation events. The findings revealed that the precipitation event induced by the southwestern vortex at Emeishan Station on 15–16 July 2021 was characterised by high rainfall intensity and significant precipitation accumulation. The raindrop spectrum exhibited a broad distribution with a notable bimodal structure. Both the Sichuan Basin and the Tibetan Plateau were dominated by the South Asian high pressure at higher altitudes, while a pronounced low-pressure system developed at mid and low altitudes within the basin, establishing a meteorological context marked by upper-level divergence and lower-level convergence. Throughout the event, notable vertical uplift velocities were recorded across the Sichuan Basin and Tibetan Plateau, along with distinct positive vorticity zones in the lower and middle strata of the Sichuan Basin, indicating that the atmosphere was in a state of thermal instability. The majority of moisture was in the mid and lower troposphere with evident convergence movements, which played a crucial role in the southwest vortex's development. WRF numerical simulations of the Emeishan precipitation event more accurately modelled the weather conditions for this precipitation but tended to overestimate the level of precipitation. It was observed that the region around Emei Mountain primarily received moisture influx from the southern Bay of Bengal and the South China Sea, with moisture transport chiefly originating from the Sichuan Basin and in a south-westward trajectory. [ABSTRACT FROM AUTHOR]

The extraction of real geological environment information is a key factor in accurately evaluating the vulnerability to geological hazards. Yanghe Township is located in the mountainous area of western Sichuan and lacks geological survey data. Therefore, it is important predict the spatial and temporal development law of landslide debris flow in this area and improve the effectiveness and accuracy of monitoring changes in landslide debris flow, this article proposes a method for extracting information on the changes in landslide debris flows combined with NDVI variation, which is based on short baseline interferometry (SBAS-InSAR) and optical remote sensing interpretation. In this article, we present relevant maps based on six main factors: vegetation index, slope, slope orientation, elevation, topographic relief, and formation lithology. At the same time, different remote sensing images were compared to improve the accuracy of landslide debris flow sensitivity assessments. The research showed that the highest altitude of the region extracted by multi-source remote sensing technology is 2877 m, and the lowest is 630 m, which can truly reflect the topographic relief characteristics of the region. The pixel binary model's lack of regional restrictions enables a more accurate estimation of the Normalized Difference Vegetation Index (NDVI), bringing it closer to the actual vegetation situation. The study uncovered a bidirectional relationship between vegetation coverage changes and landslide deformation in the study area, revealing spatial–temporal evolution patterns. By employing multi-source remote sensing technology, the research effectively utilized changes in multi-period imagery and feature extraction methods to accurately depict the development process and distribution characteristics of landslide debris flow. This approach not only offers technical support but also provides guidance for evaluating the vulnerability of landslide debris flow in the region. [ABSTRACT FROM AUTHOR]

Flagship species' conservation strategies hold significant prominence in biodiversity preservation. The giant panda, a globally recognized species, has drawn attention to its benefits and constraints as a flagship species. This study aimed to assess the potential benefits of a dual flagship strategy using both the giant panda and snow leopard, compared to an approach solely using the giant panda. We identified the number of potential beneficiary species based on their habitat overlap with the giant panda and snow leopard in Sichuan and Gansu, China. Subsequently, we examined public preferences for these two flagships and their influencing factors through questionnaire surveys within and outside China. The dual flagship strategy covered the habitats of more species and amplified existing protection for those species already benefiting from giant panda conservation efforts. The giant panda was commonly perceived as "Adorable", "Innocent", and "Rare", while perceptions of the snow leopard leaned towards "Mighty", "Mysterious", and "Rare". Though the giant panda is widely favored, the survey indicates a notable preference for snow leopards among a proportion of respondents. The dual flagship strategy offers expanded wildlife habitat coverage and benefits a broader range of species. Moreover, the combined appeal of the snow leopard and giant panda, each possessing unique charm and symbolism, holds the potential to garner broader societal interest and support. This study may serve as a reference for policy decisions in the Giant Panda National Park and other similar protected areas, optimizing conservation management and outreach initiatives for flagship species strategies. It may also benefit conservation strategies centered on other flagship species. [ABSTRACT FROM AUTHOR]

Chlorophyll is a vital indicator of vegetation growth; exploring its relationship with external influencing factors is essential for studies such as chlorophyll remote sensing retrieval and vegetation growth monitoring. However, there has been limited in-depth exploration of the spatial distribution of leaf chlorophyll content (LCC) and its influencing factors across large-scale areas with varying climates and terrains. To investigate the primary influencing factors and degrees of various environmental factors on LCC, this study employed the Geodetector Model (GDM) and the LCC satellite products in Sichuan Province in 2020 to investigate the impact of relationships between nine environmental factors (meteorology, topography, and vegetation types) and the ecosystem LCC at a regional scale. The results indicated the following: (1) Elevation (q-value = 49.31%) is the primary factor determining photosynthesis in Sichuan Province, followed by temperature (46.10%) and vegetation types (40.73%). The impact of topographical factors on LCC distribution is higher than that of meteorological factors and vegetation types in terrain with complex topography. The elevation effectively distinguishes the variations in climate factors and vegetation types. (2) Combining the influencing factors pairwise increased the combined q-values. The combination of elevation with other factors yielded the highest combined q-value. (3) The q-values for all influencing factors are higher in winter and spring and lowest in summer. Different influencing factors exhibited more substantial constraints on vegetation photosynthesis during winter and spring, significantly reducing influence during summer. (4) The different primary factors drive or constrain vegetation photosynthesis in different climate zones due to their distinct temperature and humidity characteristics. The findings of this study provide a basis for future research on vegetation change analysis and dynamic monitoring of vegetation LCC in different terrains. [ABSTRACT FROM AUTHOR]