Search Results (110)

Canopy chlorophyll content (CCC) is a key indicator for assessing the carbon sequestration capacity and material cycling efficiency of ecosystems, and its accurate retrieval holds significant importance for analyzing ecosystem functioning. Although numerous destructive and remote sensing methods have been developed to estimate CCC, the accurate estimation of CCC remains a significant challenge in mountainous regions with complex terrain and heterogeneous vegetation types. Through the synergistic analysis of ground hyperspectral and Sentinel-2 data, this study employed Pearson correlation analysis and spectral resampling techniques to identify Sentinel-2 blue band B1 (443 nm) and red band B4 (665 nm) as chlorophyll-sensitive bands through spectral matching with the hyperspectral reflectance of typical grassland vegetation. Based on this, we developed a new four-band vegetation index (VI), the Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI), for estimating the CCC of heterogeneous grasslands in the middle section of the Tianshan Mountains. DRECAVI incorporates red-edge anti-saturation modules (bands B4 and B7) and aerosol correction modules (bands B1 and B8). In order to test the performance of the new index, we compared it with eight commonly used indices and a hybrid model, the Sentinel-2 Biophysical Processor (S2BP). The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R² = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R² = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. In contrast, DRECAVI maintained similar precision while reducing algorithmic complexity, making it more suitable for regional-scale grassland dynamic monitoring. This study confirms that the synergistic use of multi-source data effectively overcomes the limitations of the spectral–spatial resolution of a single data source, providing a novel methodology for the precision monitoring of mountain ecosystems. Full article

Interferometric Synthetic Aperture Radar (InSAR) is an invaluable tool for deformation monitoring. However, potential geological disaster hazards occurring in different elevation regions exhibit distinct surface deformation trends and distributions. The applicability of InSAR techniques at different elevations for monitoring potential geohazards remains uncertain. In this paper, the study area is firstly divided into typical geological disaster hazard zones based on mountainous elevation definition and SAR image elevation distribution, including areas below 1000 m, between 1000 m and 3500 m, and above 3500 m. Secondly, the spatial–temporal evolution characteristics of surface deformation from 2018 to 2020 in the study area are investigated, and potential geohazards are monitored by employing time-series InSAR techniques such as Persistent Scatterer InSAR (PS-InSAR), Small Baseline Subset InSAR (SBAS-InSAR), and Distributed Scatterer InSAR (DS-InSAR). Finally, the potential geological hazards detected by different InSAR monitoring algorithms are interpreted, and the characteristics of different InSAR monitoring algorithms in different elevation intervals are compared and analyzed. The results show that potential geological hazards are more frequent in areas between 1000 m and 3500 m in elevation, and DS-InSAR shows the best performance and accuracy in monitoring potential geological hazards in different elevation intervals. Full article

Land-use transition has diverse influences on habitat quality. At present, land-use patterns and habitat quality in the ecologically fragile Yellow River Basin are undergoing significant change. However, the relationship between land-use transition and habitat quality and the driving factors of habitat quality dynamics across the whole basin remain unclear. In this study, we utilized a land-use transition matrix and an InVEST model to analyze the dynamics of land use, habitat quality, and the relationship between the two in the Yellow River Basin from 2005 to 2020. The driving factors of habitat quality dynamics were explored with a spatial econometric model. The results showed the following: (1) The areas of farmland and grassland accounted for more than 70%, but decreased by 14,600 km² and 2500 km², respectively. The areas of forest and construction land increased by 1800 km² and 16,900 km², respectively. (2) The habitat quality showed a trend of decrease-then-increase. The areas of low value (0–0.2) were the largest, accounting for about 50% of the total area; the regions of relatively high (0.6–0.8) and high value (0.8–1) were small and scattered in the mountainous forest area, accounting for about 10%. (3) The habitat quality was the lowest in the land categorized as transitioning to construction, and highest in unchanged forest and in the land characterized as transitioning to forest. The coupling coordination degree of land-use degree and habitat quality showed a steady upward trend. (4) The growth rate in the value added by secondary industries, GDP per capita, population density, ecological-protection policy score, average annual temperature, and average annual precipitation were the primary factors affecting habitat quality. This study fills the gap in the analysis of the relationship between land-use transition and habitat quality across the whole Yellow River Basin; the work assists in the understanding of the ecological effects of land-use transition in the region and provides suggestions for the development of other densely populated and ecologically fragile areas. Full article

Timber-adorned and rich in heritage, the traditional villages of central Hunan are famed for their wooden architecture, which is both a cornerstone of their cultural identity and a key driver of local tourism. The aim of this study is to evaluate the tourism development level and current status of these villages, providing insights for the enhancement and sustainability of tourism in similar ethnic settlements. This paper scrutinizes 18 villages in central Hunan, considering their resources, development context, and market conditions. A factor analysis-based evaluation system with 30 indicators was developed to assess tourism development potential. The findings indicate that the villages’ potential can be divided into high, medium, and low tiers. Tourism conditions are identified as the main stimulant for regional tourism growth. High-potential villages are scattered, with Da’an Village standing out due to its excellent transportation links; others are clustered in burgeoning tourism areas, notably around the Ziqujie terrace tourism district and Anhua County. Medium-potential villages are largely found in the Daxiong Mountain region, while low-potential villages lack a discernible distribution pattern. In light of these insights, this paper proposes development strategies tailored to the potential of each village, aimed at boosting tourism in central Hunan’s traditional villages and securing their long-term sustainable development. Full article

Mountains are crucial for essential ecosystem services that are foundational to ecological restoration and conservation. The Taihang Mountains are a key water recharge zone and ecological barrier in northern China. Yet, research on the spatial heterogeneity of ecosystem service trade-offs and synergies in this region remains scarce. This study addresses this gap by examining the spatiotemporal evolution, spatial heterogeneity, and the dynamic interplay between ecosystem service trade-offs and synergies in the Taihang Mountains, employing the multidimensional analysis method of time and space. Key findings from 2005 to 2020 show a significant CNY 2.665 billion increase in overall ecosystem service value in the Taihang Mountains. Spatially, soil conservation increased in the central and eastern regions, while water supply similarly increased in the northern region. Regarding spatial autocorrelation, the spatial distribution of these services was predominantly characterized by clusters of high–high and non-significant values. Regarding the spatiotemporal differentiation of trade-offs and synergies in ecosystem services, synergies prevail, with significant spatial disparities between trade-off and synergistic areas, where trade-offs are relatively scattered. Comprehending the interactions, trade-offs, and synergies among ecosystem services is crucial for natural resource allocation in the Taihang Mountains. This understanding facilitates resolving conflicts between economic and environmental goals, promoting harmonious regional development. Full article

The semi-humid evergreen broadleaved forest (SEBF) is the zonal vegetation type of western subtropical regions in China. Under human and natural disturbance, the area of SEBFs is severely shrinking, with remaining fragments scattered across mountains of the Central Yunnan Plateau. To explore the mechanisms of community assembly and species maintenance in the severely fragmented SEBFs, we selected three sites—Jinguangsi Provincial Nature Reserve, Huafoshan Scenic Area, and Qiongzhusi Forest Park—across the range of this vegetation type, and sampled a total of 42 plots of forest dominated by Castanopsis orthacantha Franch., the most widely distributed community type of SEBFs. We compared the species richness and composition of the communities of different age classes, employed the net relatedness index to characterize the phylogenetic structure of communities, and used Mantel tests and partial Mantel tests to quantify the impacts of spatial distance, age class, and habitat factors (including climate, topography, and soil) on species turnover across different spatial scales (i.e., intra- and inter-site) for trees, shrubs, and herbs, respectively. The results indicated the following: (1) In the young stage, the C. orthacantha communities exhibited a species richness statistically lower than those in middle-aged and mature communities. Notably, the difference in species richness among age classes was merely significant for shrub and herb species. Moreover, the phylogenetic structure changed towards over-dispersion with increasing community age. (2) The age class of the community played a pivotal role in determining taxonomic β diversity in the tree layer, while climate and soil factors significantly influenced β diversity in the shrub and herb layers of the communities. (3) Environmental filtering emerged as the predominant force shaping community assembly at the intra-site scale, whereas spatial distance was the primary determinant at the inter-site scale. Meanwhile, dispersal limitation versus biological interaction seemed to dominate the community dynamics of the C. orthacantha communities in the early versus middle and old ages, respectively. Our results highlight the variability in community assembly processes across different spatial and temporal scales, providing insights into the priority of the conservation and restoration of severely degraded zonal SEBFs. Expanding research to broader scales and other SEBF types, as well as considering the impacts of climate change and human activities, would provide further insights into understanding the mechanisms of community assembly and effective conservation strategies. Full article

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