Search Results (104)

Groundwater level (GWL) in dry areas is an important parameter for understanding groundwater resources and environmental sustainability. Remote sensing data combined with machine learning algorithms have become one of the important tools for groundwater level modeling. However, the effectiveness of the above-based model in the plains of the arid zone remains underexplored. Fortunately, soil salinity and soil moisture may provide an optimized solution for GWL prediction based on the application of apparent conductivity (ECa, mS/m) using electromagnetic induction (EMI). This has not been attempted in previous studies in oases in arid regions. The study proposed two strategies to predict GWL, included an ECa-based GWL model and a remote sensing-based GWL model (RS_GWL), and then compared and explored their performances and cooperation possibilities. To this end, this study first constructed the ECa prediction model and the RS_GWL with ensemble machine learning algorithms using environmental variables and field observations (474 ECa reads and 436 groundwater level observations from a mountain–oasis–desert system, respectively). Subsequently, a strategy to improve the prediction accuracy of GWL was proposed by comparing the correlation between GWL observations and the two models. The results showed that the RS_GWL prediction model explains 30% and 90% of the spatial variability in the two value domain intervals, GWL < 10 m and GWL > 10 m, respectively. The R² of the modeling and the validation of the ECa was 79% and 73%, respectively. Careful analysis of the scatter plots between predicted ECa and GWL revealed that when ECa varies between 0–600 mS/m, 600–800 mS/m, 800–1100 mS/m, and >1100 mS/m, the fluctuation ranges of the corresponding GWL correspond to 0–31 m, 0–15 m, 0–10 m, and 0–5 m. Finally, combining the spatial variability of ECa and RS_GWL spatial distribution map, the following optimization strategies were finally established: GWL < 5 m (in natural land with ECa > 1100 mS/m), GWL < 5 m (occupied by farmland from RS_GWL) and GWL > 10 m (from RS_GWL), and 3 < GWL < 10 m (speculated). In conclusion, this study has demonstrated that the integration of EMI technology has significantly improved the precision of forecasting shallow GWL in oasis plain regions, outperforming the outcomes achieved by the use of remote sensing data alone. Full article

Climate change has caused substantial shifts in species’ ranges and vegetation distributions in local areas of the Himalayas. However, the spatial patterns and dynamic changes of the vegetation lines in the Himalayas remain poorly understood due to the lack of comprehensive vegetation line dataset. This study developed a method to identify vegetation lines by combining the Canny edge detection algorithm with elevation parameters and produced comprehensive vegetation line datasets with 30 m resolution in the Himalayas. First, the Modified Soil-Adjusted Vegetation Index (MSAVI) was applied to indicate vegetation presence. The image was then smoothed by filling (or removing) small non-vegetated (or vegetated) patches scattered within vegetated (or unvegetated) areas. Subsequently, the Canny edge detection algorithm was applied to identify vegetation edge pixels, and elevation differences were utilized to determine the upper edges of the vegetation. Finally, Gaussian function-based thresholds were used across 24 sub-basins to determine the vegetation lines. Field surveys and visual interpretations demonstrated that this method can effectively and accurately identify vegetation lines in the Himalayas. The R² was 0.99, 0.93, and 0.98, respectively, compared with the vegetation line verification points obtained through three different ways. The mean absolute errors were 11.07 m, 29.35 m, and 13.99 m, respectively. Across the Himalayas, vegetation line elevations ranged from 4125 m to 5423 m (5th to 95th percentile), showing a trend of increasing and then decreasing from southeast to northwest. This pattern closely parallels the physics-driven snowline. The method proposed in this study enhances the toolkit for identifying vegetation lines across mountainous regions. Additionally, it provides a foundation for evaluating the responses of mountain vegetation to climate change in the Himalayas. Full article

Building extraction in landslide-affected scattered mountainous areas is essential for sustainable development, as it improves disaster risk management, fosters sustainable land use, safeguards the environment, and bolsters socio-economic advancement; however, this process entails considerable challenges. This study proposes a Res-Unet-based model to extract landslide-affected buildings from unmanned aerial vehicle (UAV) data in scattered mountain regions, leveraging the feature extraction capabilities of ResNet and the precise localization abilities of U-Net. A landslide-affected, scattered mountainous region within the Three Gorges Reservoir area was selected as a case study to validate the model’s performance. Experimental results indicate that Res-Unet displays high accuracy and robustness in building recognition, attaining accuracy (ACC), intersection-over-union (IOU), and F1-score values of 0.9849, 0.9785, and 0.9892, respectively. This enhancement can be attributed to the combined model, which amalgamates the skip connections, the symmetric architecture of U-Net, and the residual blocks of ResNet. This integration preserves low-level detail during recovery at higher levels, facilitating the extraction of multi-scale features while also mitigating the vanishing gradient problem prevalent in deep network training through the residual block structure, thus enabling the extraction of more complex features. The proposed Res-Unet approach shows significant potential for the accurate recognition and extraction of buildings in complex terrains through the efficient processing of remote sensing images. Full article

Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment. Full article

Knowing the spatial structure of traditional villages is required to promote and preserve these villages. These traditional villages are an essential part of China’s farming legacy and hold substantial historical and cultural significance. Therefore, this article analyzed 30 nationally recognized traditional villages in Liaoning Province, selected from the 6819 traditional villages in the province, as samples. These were divided into three types based on elevation: plain-type (below 200 m above sea level), hilly-type (200–500 m), and mountain-type (above 500 m) villages. Two villages of each type were selected for a total of six villages as the study objects; for these, quantitative comparative research on the spatial structure of these villages was carried out. The results of the study show that: (1) plain-type traditional villages are little affected by the terrain, the overall presentation of the surface space, the village traffic is well developed, able to form a commercial street as the core of the road interruptions in the head of the road more; (2) hilly-type traditional villages are influenced by mountains and water systems, forming a linear space with main roads as the core and crossroads, their core areas are more remote and lack space for public activities, and the villages rely on religious venues or the former residences of celebrities to attract tourists; (3) mountain-type villages are greatly influenced by the mountains, making it difficult to form a commercial area, the distribution of each natural town is relatively scattered and forms a point-like space, each point is developed with public space as the core, and there is a lack of characteristics within the village. The above quantitative characteristics are compared and three targeted conservation strategies for national-level traditional villages in Liaoning are proposed. Full article

A pilot ichthyological index was developed for use within the Water Framework Directive in the area of Central and Eastern Europe for dam reservoirs, which are heavily modified water bodies. This is the first approach to assessing this water body type based on ichthyofauna in Poland. Various fishing gear types were used. The tested dam reservoirs were scattered throughout the country, from lowland to mountainous areas, with very diverse hydrological and morphological characteristics and pressure ranges based on the TSI index. In preliminary work, a correlation matrix with the TSI index’s pressure indicator was tested based on the abundance or biomass of fish species, fish families present, fishing gear used, and fishing depth range for a total of 588 cases. As a result of the tests carried out, the preliminary indicator was based on the ratio of the number of the two families Cyprinidae and Percidae. The correlation between the developed indicator and the pressure index was strong (r = 0.77; p < 0.001). The Percidae family exhibited a strong correlation with the most connections in the matrix. Based on the obtained results, the principle of using already confirmed relationships, such as the ratio between Cyprinidae and Percidae fish families, in the assessment of eutrophication was confirmed to be effective, guaranteeing the effective initial assessment of ecological potential. Full article

The Changhe Basin is located in the earth–rock mountainous area in southeastern Shanxi, China, and represents a characteristic northern coal-bearing hilly area. The terrain is complex, and the area is rich in coal mines. It plays an indispensable role in maintaining ecological balance and sustainable development in North China. To investigate the changes in ecological quality in the Changhe Basin, as well as the impact of human mining activities and natural topography on ecological quality, this study constructs the Remote Sensing Ecological Index (RSEI) based on Landsat remote sensing images from 2001, 2008, 2015, and 2022, undertaking an analysis of the spatial–temporal distribution characteristics of the ecological quality and its changing trends over the past 20 years. Additionally, spatial autocorrelation distribution features are revealed using Moran’s I. The exploration extends to examining the relationship between mining activities and the surrounding ecological quality. Subsequently, we study the relationship between Topographic Wetness Index (TWI) and RSEI. The results indicate the following: (1) On the temporal scale, the average proportion of RSEIs categorized as excellent and good from 2001 to 2022 is 46.78%. Types showcasing stable ecological conditions average 52.49%. The level of overall ecological quality of the basin has remained consistently high. On the spatial scale, the western part of the Changhe River, particularly in mountainous areas, exhibits higher ecological quality. Poorer areas concentrate in Chuandi Town in the southwestern part, and are significantly impacted by mining activities. The eastern region manifests areas undergoing either rapid or gradual degradation. (2) The four-phase Moran index results reveal a robust positive correlation in the spatial distribution of ecological quality within the basin. High–High and Low–Low clusters dominate, while High–Low and Low–High distributions are scattered. (3) Mining activities exert a discernible impact on the surrounding ecological quality. As the distance from the buffer zone outside the mining area increases, RSEI gradually decreases. The impact level exhibits an initial increase and subsequent decrease from 2001 to 2022. Full article

In recent decades, wildfires have become common disasters that threaten people’s lives and assets, particularly in wildland–urban interfaces (WUIs). Developing an effective evacuation strategy for a WUI presents challenges to emergency planners because of the spatial variations in biophysical hazards and social vulnerability. The aim of this study was to map priority WUIs in terms of evacuation. The factors considered were the seriousness of the risk of wildfire exposure, and the population centres whose greatest constraints on the evacuation process stemmed from the nature of the exposed population and the time required to travel to the nearest shelter/refuge. An integrated framework linking wildfire hazard, social vulnerability, and the time taken to travel by foot or by car to the nearest refuge/shelter was applied. The study area includes two municipalities (Lousã and Sertã) in the mountainous areas of central Portugal that are in high-wildfire-risk areas and have very vulnerable and scattered pockets of exposed population. The combination of wildfire risk and travelling time to the nearest shelters made it possible to identify 20% of the WUIs that were priority areas for evacuation in the case of Sertã. In the case of Lousã, 3.4% were identified, because they were highly exposed to wildfire risk and had a travelling time to the nearest shelter of more than 15 min on foot. These results can assist in designing effective pre-fire planning, based on fuel management strategies and/or managing an effective and safe evacuation. Full article

(1) Background: Public space is an important carrier for maintaining the cultural values of a village and the production and living customs of the villagers, but the use rights and boundaries are in an unstable and ambiguous state, and it is not a completely open and inclusive public space. The study aims to deepen the understanding of the publicness of public space in traditional villages from the perspective of subjective and objective combination, which reveals the relationship between the space and villagers’ behavior. (2) Methods: The research established an evaluation framework for assessing the “publicness” of public spaces in traditional villages by integrating space syntax and cognitive surveys. This framework facilitates the analysis of the extent and dimensions of publicness, along with corresponding behavioral patterns, and explores the underlying mechanisms influencing publicness. (3) Results: The study reveals significant variations in the publicness of traditional village spaces. High-publicness areas tend to cluster, whereas low-publicness areas are more scattered, and riverfront regions exhibit greater publicness compared to mountain-adjacent ones. Villagers exhibit notable differences in their evaluations of public spaces, and individuals aged 14–18 and those over 66 rate the highest. The utilization rate of high-publicness spaces is significantly high, catering to a diverse array of activities. In spaces with lower publicness, the duration and variety of activities tend to be more constrained, often limited to rapid exchanges or brief respites, exhibiting a narrower scope of activities. (4) Conclusions: The study underscores the variability and complexity of publicness in traditional village spaces, which manifest not only in spatial layouts and types but also in villagers’ usage patterns and behavioral preferences. This may be influenced by objective factors such as spatial accessibility, social interaction, and richness of cultural activities. Full article

Artificial corner reflectors are widely used in the vegetated landslide for time series InSAR monitoring due to their permanent scattering features. This paper investigated the RCS features of a novel dihedral CR under multi-resource SAR datasets. An RCS reduction model for the novel dihedral corner reflector has been proposed to evaluate the energy loss caused by the deviation between the SAR incident angle and the CR’s axis. On the Huangtupo slope, Badong county, Hubei province, tens of dihedral CRs had been installed and the TSX–spotlight and Sentinel-TOPS data had been collected. Based on the observation results of CRs with more than ten deviation angles, the proposed reduction model was tested with preferable consistency under a real dataset, while 2 dBsm of systematic bias was verified in those datasets. The maximum incident angle deviation in the Sentinel data overlapping area is over 12°, which leads to a 2.4 dBsm RCS decrease for horizontally placed dihedral CRs estimated by the proposed model, which has also been testified by the observed results. The testing results from the Sentinel data show that in high, vegetation-covered mountain areas like the Huangtupo slope, the dihedral CRs with a 0.4 m slide length can be achieve 1 mm precision accuracy, while a side length of 0.2 m can achieve the same accuracy under TSX–spotlight data. Full article

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole. Full article

The nephrite belt in the Altun Mountain–Western Kunlun Mountain region, which extends about 1300 km in Xinjiang, NW China, is the largest nephrite deposit in the world. The Qiemo region in the Altun Mountains is a crucial nephrite-producing area in China, with demonstrated substantial prospects for future exploration. While existing research has extensively investigated secondary nephrite deposits in the Karakash River and native black nephrite deposits in Guangxi Dahua, a comprehensive investigation of black nephrite from original deposits in Xinjiang is lacking. Margou black-toned nephrite was recently found in primary deposits in Qiemo County, Xinjiang; this makes in-depth research on the characteristics of this mine necessary. A number of technical analytical methods such as polarizing microscopy, Ultra-Deep Three-Dimensional Microscope, electron microprobe, back-scattered electron image analysis, X-ray fluorescence, and inductively coupled plasma mass spectrometry were employed for this research. An experimental test was conducted to elucidate the chemical and mineralogical composition, further clarifying the genetic types of the black and black cyan nephrite from the Margou deposit in Qiemo, Xinjiang. The results reveal that the nephrite is mainly composed of tremolite–actinolite, characterized by Mg/(Mg + Fe²⁺) ratios ranging from 0.86 to 1.0. Minor minerals include diopside, epidote, pargasite, apatite, zircon, pyrite, and magnetite. Bulk-rock rare earth element (REE) patterns exhibit distinctive features, such as negative Eu anomalies (δEu = 0.00–0.17), decreasing light REEs, a relatively flat distribution of heavy REEs, and low total REE concentrations (1.6–38.9 μg/g); furthermore, the Cr (6–21 μg/g) and Ni (2.5–4.5 μg/g) contents are remarkably low. The magmatic influence of granite appears to be a fundamental factor in the genesis of the magnesian skarn hosting Margou nephrite. The distinctive black and black cyan colors are attributed to heightened iron content, mainly associated with FeO (0.08~6.29 wt.%). Analyses of the chemical composition allow Margou nephrite to be classified as typical of magnesian skarn deposits. Full article

Urban modernization and economic globalization have led to significant changes in traditional natural landscapes. The unregulated and large-scale expansion of rubber plantations in Xishuangbanna has resulted in water and scenic forests being replaced by rubber forests and the complex rainforest ecosystem being replaced by simple artificial forests. This has resulted in increasingly prominent ecological problems such as soil erosion, regional microclimate changes, and sharp declines in biodiversity. The ecological security pattern is an important way to protect regional ecological sustainability. Taking the tropical rainforest in Xishuangbanna as an example, this study identified ecological sources through the evaluation of the importance of ecosystem services, constructed resistance surfaces through ecological sensitivity evaluation, and used circuit theory to simulate ecological processes in heterogeneous landscapes by calculating “electricity” or “resistance”, thereby identifying ecological corridors and key ecological nodes. The results identified 31 ecological source areas, 65 key ecological corridors, 7 potential ecological corridors, 37 ecological pinch points, and 99 ecological barriers. The overall distribution of ecological sources was scattered, with higher density in the northwest and southeast regions. Ecological corridors were distributed along high mountains, and both ecological sources and corridors were mainly composed of forest land. Based on circuit theory, this study filled the gap in the MCR model’s inability to identify the true width of corridors due to ignoring the randomness of biological migration. It determined the spatial range of ecological corridors and the specific locations of ecological nodes and barriers, providing a reference for solving ecological problems in Xishuangbanna, such as “rainforest fragmentation”. Full article

Landslides recurrently cause severe damage and, in some cases, the full disruption of many highways in mountainous areas, which can last from a few days to even months. Thus, there is a high demand for monitoring tools and precipitation data to support highway alignment selections before construction. In this study, we proposed a new system highway alignment selection method based on coherent scatter InSAR (CSI) and ~1 km high-spatial-resolution precipitation (HSRP) analysis. Prior to the CSI, we calculated and analyzed the feasibility of Sentinel-1A ascending and descending data. To illustrate the performance of the CSI, CSI and SBAS–InSAR were both utilized to monitor 80 slow-moving landslides, which were identified by optical remote-sensing interpretation and field investigation, along the Barkam–Kangting Highway Corridor (BKHC) in southwestern China, relying on 56 Sentinel-1A descending images from September 2019 to September 2021. The results reveal that CSI has clearer deformation signals and more measurement points (MPs) than SBAS-InSAR. And the maximum cumulative displacements and rates of the landslides reach −75 mm and −64 mm/year within the monitoring period (CSI results), respectively. Furthermore, the rates of the landslides near the Jinchuan River are higher than those of the landslides far from the river. Subsequently, to optimize the highway alignment selection, we analyzed the spatiotemporal evolution characteristics of feature points on a typical landslide by combining the −1 km HSRP, which was calculated from the 30′ Climatic Research Unit (CRU) time-series datasets, with the climatology datasets of WorldClim using delta spatial downscaling. The analysis shows that the sliding rates of landslides augment from the back edge to the tongue because of fluvial erosion and that accelerated sliding is highly related to the intense precipitation between April and September each year (ASP). Consequently, three solution types were established in our method by setting thresholds for the deformation rates and ASPs of every landslide. Afterward, the risk-optimal alignment selection of the BKHC was finalized according to the solution types and consideration of the construction’s possible impacts. Ultimately, the major problems and challenges for our method were discussed, and conclusions were given. Full article

The municipality of Fusagasugá is located 50 kilometers from the city of Bogotá, Colombia, in the eastern cordillera of the Andes in South America. Due to its geographical location, a mountainous area with heights between 1000 and 2000 meters above sea level and two rainy seasons a year, it is affected by processes of instability and surface deformations. The objective of the present investigation was to identify and quantify the displacement speeds of the zones affected by processes of instability and superficial deformation. In this study, 20 radar satellite images from the Sentinel-1 program were used in the SLC format between 30 January 2020 and 19 April 2022 in descending orbit, applying the Small Base Line (SBAS) technique. On the other hand, 21 SAR images were also used in descending orbit between 6 January 2020 and 14 December 2021, applying the persistent scatterers (PS) technique. With the above information, it was possible to map and update the data of the municipality of Fusagasugá in order to include them in the monitoring processes at the regional level. Full article