Search Results (59)

Elevational gradients in temperature, moisture, and vegetation strongly influence soil nutrient content and stoichiometry in mountainous regions. However, exactly how total, microbial, and enzymatic carbon (C), nitrogen (N), and phosphorus (P) stoichiometry vary with elevation in karst peak-cluster depressions remains poorly understood. To address this, we studied soil total, microbial, and enzymatic C:N:P stoichiometry in seasonal rainforests within karst peak-cluster depressions in southwestern China at different elevations (200, 300, 400, and 500 m asl) and depths (0–20 and 20–40 cm). We found that soil organic carbon (SOC), total nitrogen (TN), and the C:P and N:P ratios increased significantly with elevation, whereas total phosphorus (TP) decreased. Microbial phosphorus (MBP) also declined with elevation, while the microbial N:P ratio rose. Activities of nitrogen- (β-N-acetylglucosaminidase and L-leucine aminopeptidase combined) and phosphorus-related enzymes (alkaline phosphatase) increased markedly with elevation, suggesting potential phosphorus limitation for plant growth at higher elevations. Our results suggest that total, microbial, and enzymatic soil stoichiometry are collectively shaped by topography and soil physicochemical properties, with elevation, pH, and exchangeable calcium (ECa) acting as the key drivers. Microbial stoichiometry exhibited positive interactions with soil stoichiometry, while enzymatic stoichiometry did not fully conform to the expectations of resource allocation theory, likely due to the functional specificity of phosphatase. Overall, these findings enhance our understanding of C–N–P biogeochemical coupling in karst ecosystems, highlight potential nutrient limitations, and provide a scientific basis for sustainable forest management in tropical karst regions. Full article

The Lijiang River Basin (LRB) is a karst ecosystem that presents unique challenges for agricultural land planning. Evaluating cultivated land suitability based on natural factors is critical for ensuring food security in this region. This study was based on the cultivated land distribution data of the LRB in the China Land-Use and Land-Cover Chang dataset, selecting 22 restriction factors across five dimensions: climate, topography, soil, hydrology, and social conditions, and the suitability of cultivated land (paddy fields and drylands) in the LRB was evaluated using the MaxEnt model to further identify the main restricting factors affecting the spatial distribution. The research showed that (1) For paddy fields, high-suitability areas covered 2875.05 km², medium-suitability 1670.58 km², low-suitability 3187.25 km², and non-suitable 9368.46 km². The main restriction factors were distance to villages, slope, surface gravel content, soil thickness, soil pH, and total phosphorus content. (2) For drylands, high-suitability areas covered 3282.3 km², medium-suitability 2260.93 km², low-suitability 4536.27 km², and non-suitable 6836.85 km². The main restriction factors were soil thickness, distance to roads, surface gravel content, elevation, soil pH, and soil texture. This research can provide a scientific basis for the layout of food security and planning agricultural land use in the LRB. Full article

Climate change and intensified human activities have increasingly threatened the sustainability of groundwater resources, especially in ecologically fragile karst regions. To address these challenges, this study proposes a karst spring discharge prediction model that integrates BiLSTM (Bidirectional Long Short-Term Memory) and the Transformer Encoder. The BiLSTM component captures both forward and backward information in spring discharge data, extracting trend-related features. The Transformer’s attention mechanism is employed to identify key precipitation factors influencing spring discharge. A patching preprocessing strategy divides monthly scale sequences into annual segments, reducing input length while enabling local modeling and global interaction. Experiments on Shentou Spring discharge show that the BiLSTM–Transformer Encoder outperforms other deep learning models across multiple evaluation metrics, with notable advantages in short-term forecasting. The patching strategy effectively reduces model parameters and improves efficiency. Attention visualization further confirms the model’s ability to capture critical hydrological drivers. This study not only provides a novel approach to sustainable water management in karst spring basins but also demonstrates an effective use of deep learning for long-term hydrological sustainability. Full article

Selenium (Se) is essential for various metabolic and physiological functions in the human body. However, the mechanisms of Se cycling in soils, particularly under different parent materials and land uses, remain understudied. This study investigates the spatial distribution and influencing factors of total Se in surface soils derived from limestone and sandstone in paddy and dryland systems in a Se-rich karst region of Southwest China. The mean Se content was 0.5 mg/kg, with 100% of samples exceeding national and global background levels, confirming Zheng’an County as a newly recognized Se-rich area. Soil Se concentrations, along with environmental variables such as soil organic matter (SOM), pH, elevation, slope, and trace elements (V, Cr, and Zn), were analyzed. One-way ANOVA revealed significant differences in Se content between parent materials and land-use types. Stepwise multiple regression identified SOM as the strongest predictor of Se, while Spearman correlation showed significant associations with topographic and chemical factors. These findings highlight the complex interactions between geology, land use, and topography in Se dynamics. Given the global distribution of karst landscapes, this research provides valuable insights into Se behavior in similar environments worldwide, with implications for land management and nutritional security. Full article

Karst topography comprises a fragile ecological environment with a significant potential for carbon sequestration. It is characterized by severe rocky desertification, particularly in China’s karst fault basin. Therefore, there is a crucial need to scientifically evaluate the variations in carbon storage over time and space in this area to ensure effective land space planning and regional ecological security, especially considering the dual carbon target. Using land use data (1985–2020) from the karst fault basin in Southwest China, the study employed the InVEST model to evaluate temporal and spatial variations in carbon storage. A time span of 35 years was examined, and predictions regarding carbon storage in 2050 were formulated under three different conditions: natural evolution, ecological protection, and cultivated land protection. These predictions were based on natural, social, and economic driving factors. The results revealed a fluctuating downward trend in regards to carbon storage in the study area from 1985 to 2020, with a total decrease of 2.1 × 10⁶ t. After 2000, there has been significant improvement in the dynamic degree of land use for forest land, grassland, and construction land compared to the levels before 2000. Additionally, many land use types with high carbon density transitioned into those with lower carbon density. Spatially, the carbon density in the karst fault basin was higher in the north and lower in the central and southern basins. At the county spatial scale, except for the northern and central parts of the study area, there was a decrease in total carbon storage in the remaining counties. By 2050, under the ecological protection scenario, total carbon storage is projected to increase by approximately 6 × 10⁶ t, whereas under the natural evolution and cultivated land protection scenarios, it is expected to decrease by 2 × 10⁶ t and 3 × 10⁶ t, respectively. Specifically, under the natural evolution scenario, only five counties will experience an increase in carbon storage, while the other counties will witness a decrease. The findings of this study offer a scientific basis for enhancing ecosystem carbon services through land management practices and the control of rocky desertification in the karst fault basin. They can inform decision-making processes regarding carbon sequestration, ecosystem restoration, and sustainable land use planning in the region. Full article

Vegetation phenology has lately gained attention in the context of studying human-induced climate change and its effects on terrestrial ecosystems. It is typically studied on various regional and temporal scales. This research focused on the microscale in dolines on the Northernmost part of the Dinaric Alps. The aim was to determine the timing of flowering onset and relate it to topographic and ecological conditions. We studied (1) the floristic gradient along N–W transects divided in 2 m × 2 m plots, from top slopes to the bottom of dolines, and identified discrete groups in relation to this gradient and (2) provided their diagnostic species and communities. The results indicate that the early spring onset of flowering of ground vegetation in the bottom and lower slopes of dolines is stimulated by high spring moisture and nutrient availability, as well as the open canopy of the mesophilous deciduous forests. The flowering onset on the upper slopes and karst plateau starts later, which is due to the precipitation peak in May/June and higher temperatures and light availability of the open canopy of thermophilous deciduous forests. The delayed onset of flowering in late summer in rocky crevices and rocky places is due to a particular physiology stimulated by the harsh site conditions. The phenology pattern along the doline topographic gradient is inverse to general patterns in vegetation phenology. Further study on the role of doline soils should be made to study their impact on phenology. Full article

Global warming has exacerbated the impact of regional drought on vegetation ecosystems, especially in typical karst areas with fragile ecosystems that are more severely affected by drought. However, the response mechanisms of vegetation ecosystems in karst areas to drought stress are still uncertain. With drought stress in the summer of 2022, we examined the spatiotemporal patterns of drought in a World Heritage karst site, Guilin, China, and revealed the exacerbated drought impacts on vegetation ecosystems in karst areas with various vegetation indices. Firstly, we analyzed the spatiotemporal characteristics of drought from 2000 to 2022, utilizing the temperature vegetation dryness index (TVDI), highlighting the intra-annual variability of drought in 2022. Additionally, we compared the responses of different vegetation types to drought stress in karst and non-karst areas and explored the exacerbated impacts of drought stress on vegetation ecosystems within the same year with three vegetation indices, namely, the Normalized Difference Vegetation Index (NDVI), Leaf Area index (LAI), and Gross Primary Production (GPP) in karst areas. The results showed that drought started in July and persisted from August to November at moderate to severe levels (with severe drought in September), eventually easing in December. Karst areas exhibited severe drought (TVDI = 0.76), which more significantly impacted regional vegetation ecosystems than those in non-karst areas. Different vegetation types also experienced greater drought stress in karst areas compared to non-karst areas. The vegetation indices increased at the early- to mid-stages of drought (July to September) compared to those in the baseline year (2020–2021), mainly due to the increase in non-karst areas. However, vegetation indices decreased at the late drought stage (October to November), primarily due to the decrease in karst areas, indicating that the karst topography exacerbated the impact of drought on regional vegetation ecosystems. Since LAI and GPP exhibited similar changing patterns to TVDI, with GPP showing particularly strong alignment, they can be used to reveal the response mechanisms of ecosystems to drought stress in karst areas. We emphasize the importance of monitoring the responses of vegetation ecosystems to climate-induced droughts stress and enhancing their resilience to future climatic challenges, particularly in karst areas. Full article

Accurate soil total phosphorus (TP) prediction is essential to support sustainable agricultural practices and formulate ecological conservation protection policies, particularly in complex karst landscapes with high spatial variability and high phosphorus and cadmium content and interactions, complicating nutrient management. This study uses GIS and geostatistical methods to analyze the spatial distribution, influencing factors, and predictive modeling of soil TP in the karst region of northern Mashan County, Guangxi, China. Using 427 surface soil samples, we developed five predictive models: ordinary kriging (OK), regression kriging (RK) and geographically weighted regression kriging (GWRK) combined with environmental variables such as land uses, soil types, and topographic factors; residual mean-centered kriging (MM_OK), and residual median-centered kriging (MC_OK). Our results indicate that higher TP levels were observed in agricultural lands (paddy fields and dry land, at 766 and 913 mg·kg⁻¹, respectively) may due to fertilization, while forests and shrublands showed lower TP levels (383 and 686 mg·kg⁻¹, respectively), reflecting natural phosphorus cycling. The high-value areas of soil TP concentration are in the karst areas in the west and east of the study area, and the low-value area is in the Hongshui River valley in the north of Mashan. The spatial distribution of soil TP is affected by land use, soil type, and topography. The GWRK model exhibited superior accuracy (80.6%), with predicted concentration of TP closely aligning with observed TP values, effectively capturing fine spatial variations, and showing the lowest mean standardized error, average standard error, and mean absolute error. GWRK also achieved the highest R² (0.67), demonstrating robust predictive capability. MM_OK and MC_OK models performed well and showed smoother spatial transitions, while the OK model displayed the lowest predictive accuracy (62%). By utilizing spatially adaptive weighting, GWRK and its residual-centered kriging method improve soil TP’s prediction accuracy and smoothness in karst areas, providing a reference for targeted soil conservation and sustainable agricultural practices in spatially complex karst environments. Full article

The topographies of soil erosion and deposition are critical factors that significantly influence soil quality, subsequently impacting the erodibility of soils in karst regions. However, the investigation into the effects of erosion and deposition topographies on soil erodibility across different stages of vegetation succession in karst trough valleys is still at a preliminary stage. Therefore, three distinct topographic features (dip slopes, anti-dip slopes, and valley depressions) were selected at erosion (dip/anti-dip slope) and deposition sites (valley) to investigate the spatial heterogeneity of soil physicochemical properties, infiltration capacity, aggregate stability, and erodibility in karst trough valleys. Additionally, five different stages of vegetation succession in karst forests were considered: Abandoned land stage (ALS), Herb stage (HS), Herb-Shrub stage (HES), Shrub stage (SHS), and Forest stage (FS). Additionally, the relationships among these factors were analyzed to identify the key driving factors influencing soil erodibility. The results revealed that soil physicochemical properties and soil aggregate stability at the deposition site were significantly superior to those at the erosion site. The FS resulted in the best soil physicochemical properties, whereas the HS resulted in the highest soil aggregate stability within the deposition site. However, the soil infiltration capacity at the erosion site was significantly greater than that at the deposition sites. The ALS had the strongest soil infiltration capacity at both the erosion and deposition sites. The soil erodibility at erosion sites (0.064) was significantly greater than that at deposition sites (0.051), with the highest soil erodibility observed on anti-dip slopes during the HES at erosion sites (0.142). The structural equation model reveals that erosion and deposition topographies, vegetation succession, soil physicochemical properties, soil aggregates, and soil infiltration characteristics collectively account for 88% of the variation in soil erodibility under different conditions. Specifically, both direct and indirect influences on soil erodibility are most significantly exerted by soil aggregate stability and vegetation succession. This study provides scientific evidence to support the management of soil erosion and ecological restoration in karst trough valleys while offering technical assistance for regional ecological improvement and poverty alleviation. Full article

Forest restoration landscapes are vital for restoring native habitats and enhancing ecosystem resilience. However, field monitoring (lasting months to years) in areas with complex surface habitats affected by karst rocky desertification is time-consuming. To address this, forest structural parameters were introduced, and training samples were optimized by excluding fragmented samples and those with a positive case ratio below 30%. The U-Net instance segmentation model in ArcGIS Pro was then applied to classify five forest restoration landscape types: intact forest, agroforestry, planted forest, unmanaged, and managed naturally regenerated forests. The optimized model achieved a 2% improvement in overall accuracy, with unmanaged and intact forests showing the highest increases (7%). Incorporating tree height and age improved the model’s accuracy by 3.5% and 1.9%, respectively, while biomass reduced it by 2.9%. RGB imagery combined with forest height datasets was most effective for agroforestry and intact forests, RGB imagery with aboveground biomass was optimal for unmanaged naturally regenerated forests, and RGB imagery with forest age was most suitable for managed naturally regenerated forests. These findings provide a practical and efficient method for monitoring forest restoration and offer a scientific basis for sustainable forest management in regions with complex topography and fragile ecosystems. Full article

This study addresses the challenges posed by the complex topography and forest structure in karst mountainous areas, as well as the difficulties in estimating forest stock using traditional methods. We propose a method that integrates optical remote sensing data from Sentinel-2 into airborne LiDAR data to estimate forest stock in karst areas. First, an Allometric Growth Model correlating tree height and diameter at breast height (DBH) in karst areas was developed based on field measurements. Tree height information extracted from LiDAR data was then combined with the binary wood volume model specific to fir trees in Guizhou Province to calculate the individual tree biomass of fir trees. In addition, this study evaluated the robustness of three machine learning methods, the Random Forest Regression Model, K-Nearest Neighbors Regression Model, and Backpropagation Neural Network Model, in estimating forest stock in karst mountainous areas. The results indicate the following: (1) The Allometric Growth Model based on field data showed strong predictive power for DBH and can be used for large-scale estimation. (2) The distribution characteristics of individual tree biomass and plot biomass under different site conditions revealed the distribution pattern of fir trees in the study area, providing important information for understanding the growth status of forest stock in the region. (3) The Random Forest Regression Model demonstrated exceptional accuracy, generalization capability, and robustness in the estimation of forest stock within karst mountainous regions. This study provides an effective technical tool for estimating forest stock in karst areas and under complex terrain conditions and has significant scientific value and practical implications for the monitoring and management of forest ecosystem carbon sinks. Full article

The Muji spring travertines, located in the Muji Basin in the eastern Pamirs Plateau, represent a typical spring deposit found on plateaus that is characterized by arid and semi-arid climatic conditions. However, its formation mechanisms remain poorly understood. This study aims to explore the recharge processes of the spring, the sedimentary environment, and the genetics of Muji spring travertines through a comparative analysis of conventional hydrochemistry, H-O stable isotope analysis of both spring and river water, and petrographic observation, as well as in situ analysis of major and trace elements present in calcite within travertines. The basin is surrounded by mountains with a topography that facilitates groundwater convergence within it. Carbonate-bearing strata are extensively developed around the basin, which serves as a crucial material foundation for travertine development. It infiltrates underground through fractures and faults, interacting with carbonate rocks to produce significant amounts of HCO₃⁻, Ca²⁺, and Mg²⁺. The observed range of isotopic compositions (δ²H, −102.27‰ to −96.43‰; δ¹⁸O, −14.90‰ to −14.36‰) in water samples suggests that their primary origin was from glacial and snowmelt sources. The concentration of HCO₃⁻ in spring water samples exhibits significant variability, with the highest value being 1646 mg·L⁻¹, which deviates significantly from the typical composition of karst groundwater. During its migration, groundwater undergoes the dissolution of gaseous CO₂ derived from deep metamorphic processes, leading to variable degrees of mixing with geothermal groundwater containing elevated concentrations of dissolved components that enhance the dissolution potential of carbonate rocks. Eventually, upwelling occurs along the Southwestern Boundary Fault of Muji, resulting in the formation of linear springs characterized by CO₂ escape. The Muji laminated travertines exhibit distinct white and dark laminae, and radial coated grains consisting of micritic and sparry layers. Chemical composition analyses reveal significant differences in the trace and rare-earth element composition, as well as the Mg/Ca ratio, of the two types of travertines. Specifically, the micritic laminae of the pisoid (Mg/Ca = 0.019; Sr = 530 × 10⁻⁶; Ba = 64.6 × 10⁻⁶) and the dark laminae of the laminated travertine (Mg/Ca = 0.014; Sr = 523 × 10⁻⁶; Ba = 48.1 × 10⁻⁶) exhibit generally higher Mg/Ca ratios and Sr, Ba contents than the neighboring sparry laminae (Mg/Ca = 0.012; Sr = 517 × 10⁻⁶; Ba = 36.6 × 10⁻⁶) and white laminae (Mg/Ca = 0.006; Sr = 450 × 10⁻⁶; Ba = 35.6 × 10⁻⁶). The development of laminated travertines and radial coated grains here is attributed to periodic changes in groundwater recharge induced by seasonal temperature fluctuations, as evidenced by the structural characteristics of the two types of travertines and the trace element analysis of different layers. Algae play a role in forming the dark laminae of laminated travertines and the micritic laminae of pisoids. Full article

The tobacco seedling stage is a crucial period for tobacco cultivation. Accurately extracting tobacco seedlings from satellite images can effectively assist farmers in replanting, precise fertilization, and subsequent yield estimation. However, in complex Karst mountainous areas, it is extremely challenging to accurately segment tobacco plants due to a variety of factors, such as the topography, the planting environment, and difficulties in obtaining high-resolution image data. Therefore, this study explores an accurate segmentation model for detecting tobacco seedlings from UAV RGB images across various geomorphic partitions, including dam and hilly areas. It explores a family of tobacco plant seedling segmentation networks, namely, U-Net, U-Net++, Linknet, PSPNet, MAnet, FPN, PAN, and DeepLabV3+, using the Hill Seedling Tobacco Dataset (HSTD), the Dam Area Seedling Tobacco Dataset (DASTD), and the Hilly Dam Area Seedling Tobacco Dataset (H-DASTD) for model training. To validate the performance of the semantic segmentation models for crop segmentation in the complex cropping environments of Karst mountainous areas, this study compares and analyzes the predicted results with the manually labeled true values. The results show that: (1) the accuracy of the models in segmenting tobacco seedling plants in the dam area is much higher than that in the hilly area, with the mean values of mIoU, PA, Precision, Recall, and the Kappa Coefficient reaching 87%, 97%, 91%, 85%, and 0.81 in the dam area and 81%, 97%, 72%, 73%, and 0.73 in the hilly area, respectively; (2) The segmentation accuracies of the models differ significantly across different geomorphological zones; the U-Net segmentation results are optimal for the dam area, with higher values of mIoU (93.83%), PA (98.83%), Precision (93.27%), Recall (96.24%), and the Kappa Coefficient (0.9440) than those of the other models; in the hilly area, the U-Net++ segmentation performance is better than that of the other models, with mIoU and PA of 84.17% and 98.56%, respectively; (3) The diversity of tobacco seedling samples affects the model segmentation accuracy, as shown by the Kappa Coefficient, with H-DASTD (0.901) > DASTD (0.885) > HSTD (0.726); (4) With regard to the factors affecting missed segregation, although the factors affecting the dam area and the hilly area are different, the main factors are small tobacco plants (STPs) and weeds for both areas. This study shows that the accurate segmentation of tobacco plant seedlings in dam and hilly areas based on UAV RGB images and semantic segmentation models can be achieved, thereby providing new ideas and technical support for accurate crop segmentation in Karst mountainous areas. Full article

Industrial heritage, recognized as a significant aspect of historical and cultural heritage, has garnered considerable attention from scholars globally. To elucidate the spatial morphological characteristics and the underlying influencing factors of industrial heritage within karst regions, this study employs methods such as the interstice index, fractal dimension analysis, and spatial syntax. It conducts research on the spatial morphological characteristics of nine typical industrial heritages in Guizhou Province. The primary factors contributing to the variations in layout forms are the intricate karst topography and the functional requirements of production. The functional zoning of industrial heritage aligns with its layout, characterized by straightforward functional zones that have not developed into composite spaces. The overall connectivity of industrial heritage is relatively low, exhibiting weak integration, significant disparities in control values, low average depth values, and a deficiency in comprehensibility and diversity of options. This indicates that the internal connectivity of industrial heritage spaces is generally inadequate, with low accessibility, strong interrelations, average convenience, limited connectivity, and generally acceptable passage. The overall spatial, architectural, and roadway configurations of industrial heritage predominantly exhibit a uniform pattern. Importantly, industrial heritage reveals a highly variable overall spatial form, with an average fractal dimension of 1.57, complex architectural layouts (average fractal dimension of 1.50), and simplistic road network designs (average fractal dimension of 1.43), which collectively suggest high spatial complexity and irregular characteristics. This study can provide a reference for the analysis of spatial characteristics and influencing factors of other material cultural heritages, and it is of great significance for the systematic protection and revitalization of industrial heritage. Full article

The differential distribution of original rock stress and stress concentration caused by the variation in coal seam depth in karst topography are critical factors influencing the selection of roof-cutting parameters. Based on this, this study explores a method to determine reasonable roof-cutting parameters by incorporating the characteristics of coal seam depth variation in karst mountainous areas. A mechanical model of the cantilever beam structure for roof cutting in gob-side entry retention (GSER) is constructed, and the critical values and reasonable ranges of roof-cutting height and angle under different burial depths are derived. Furthermore, the displacement and stress evolution characteristics of surrounding rocks in gob-side entry retention under different coal seam burial depths, roof-cutting heights, and roof-cutting angles within the reasonable range of roof-cutting parameters are analyzed. The results show that there is a positive correlation between roof-cutting height and tensile stress in the uncut portion of the main roof, while roof-cutting angle and coal seam depth are negatively correlated with tensile stress. From the perspective of impact, roof-cutting height has a greater impact than roof-cutting angle, followed by coal seam depth. As for the distribution characteristics of the reasonable roof-cutting parameter range, the fan-shaped area of reasonable roof-cutting parameters gradually decreases with increasing coal seam depth. Taking the geological conditions of Anshun Coal Mine as an example, when the burial depth increases from 350 m to 550 m, adjusting the roof-cutting height to 6 m, 7 m, and 8 m, respectively, and setting the roof-cutting angle at 10° can effectively achieve the stability of the surrounding rock in the GSER. The research findings can provide a scientific basis and engineering references for selecting roof-cutting parameters in mines with similar geological conditions. Full article