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Currently, the inversion of remote sensing satellite images of water environment indicators mostly stays in the indicators with active optical characteristics, while there is less research on the inversion of most water quality indicators with non-optical activity properties, weak scattering and absorption of optical radiation, the size of their concentration has little effect on the spectral characteristics of the water body, such as TOC(Total Organic Carbon). In this paper, based on Pingzhai Reservoir, a dammed river in the karst mountainous area, the inversion model of TOC concentration was established based on BP neural network (BPNN) and sentinel-2 satellite remote sensing images. The results showed that the single bands with high correlation with the measured TOC concentration data were two vegetation red-edge bands B6 (740 nm) and B7 (783 nm) and one NIR band B8 (842 nm), and finally b7, b6 + b7, b7 + b8, b7 ×b8 were selected as the input layers of BPNN for modeling through the combination of the bands, and their Pearson's coefficients were -0.667, -0.656, -0.655, - 0.675. The inverse model established could reach a minimum RMSE of 0.235 mg/L and a maximum R2 of 0.889, which was superior to that of the conventional empirical model. Demonstrate the feasibility of a TOC inversion method based on Sentinel-2 data and BPNN to monitor TOC concentrations in Pingzhai Reservoir. The study successfully established a BP neural network inversion model of TOC concentration in Pingzhai Reservoir with low error, meanwhile, we analyzed the correlation between common water quality indicators and TOC in the reservoir, in which TOC showed significant positive correlation with WT and significant negative correlation with TN and EC, with Pearson's coefficients of 0.655, -0.666, and -0.393, respectively. The article provides scientific theoretical foundation and technical support for water quality protection of water sources. [ABSTRACT FROM AUTHOR]

Dynamic features from remote sensing photos may be successfully extracted using deep learning and symmetric network structure, which can then be used to direct them to carry out accurate classification. The DBN model can more effectively extract features from photos since it uses unsupervised learning. It can be reduced to the many symmetric Restricted Boltmann Machines (RBM) training problem. In this paper, a soil rocky desertification (RD) assessment model based on a deep belief network (DBN) is created in light of the complicated influencing aspects of Karst RD risk assessment encompassing several geographical elements. The model builds upon the conventional RBM framework and incorporates the influence layer of related elements as an auxiliary requirement for retrieving Geographic Information System (GIS) score data. Then, in order to forecast the level of soil rocky desertification, it learns the features of many elements. The experimental results show that the proposed model proposed in this paper has better prediction performance and faster convergence speed, and its classification results for different degrees of RD are more consistent with the actual risk assessment results. [ABSTRACT FROM AUTHOR]

Remote sensing image with high spatial and temporal resolution is very important for rational planning and scientific management of land resources. However, due to the influence of satellite resolution, revisit period, and cloud pollution, it is difficult to obtain high spatial and temporal resolution images. In order to effectively solve the "space–time contradiction" problem in remote sensing application, based on GF-2PMS (GF-2) and PlanetSope (PS) data, this paper compares and analyzes the applicability of FSDAF (flexible spatiotemporal data fusion), STDFA (the spatial temporal data fusion approach), and Fit_FC (regression model fitting, spatial filtering, and residual compensation) in different terrain conditions in karst area. The results show the following. (1) For the boundary area of water and land, the FSDAF model has the best fusion effect in land boundary recognition, and provides rich ground object information. The Fit_FC model is less effective, and the image is blurry. (2) For areas such as mountains, with large changes in vegetation coverage, the spatial resolution of the images fused by the three models is significantly improved. Among them, the STDFA model has the clearest and richest spatial structure information. The fused image of the Fit_FC model has the highest similarity with the verification image, which can better restore the coverage changes of crops and other vegetation, but the actual spatial resolution of the fused image is relatively poor, the image quality is fuzzy, and the land boundary area cannot be clearly identified. (3) For areas with dense buildings, such as cities, the fusion image of the FSDAF and STDFA models is clearer and the Fit_FC model can better reflect the changes in land use. In summary, compared with the Fit_FC model, the FSDAF model and the STDFA model have higher image prediction accuracy, especially in the recognition of building contours and other surface features, but they are not suitable for the dynamic monitoring of vegetation such as crops. At the same time, the image resolution of the Fit_FC model after fusion is slightly lower than that of the other two models. In particular, in the water–land boundary area, the fusion accuracy is poor, but the model of Fit_FC has unique advantages in vegetation dynamic monitoring. In this paper, three spatiotemporal fusion models are used to fuse GF-2 and PS images, which improves the recognition accuracy of surface objects and provides a new idea for fine classification of land use in karst areas. [ABSTRACT FROM AUTHOR]

The special above-ground and underground dual structure in the karst area makes the capacity of surface water storage in this area poor. In water-limited ecosystems, soil moisture is the key to the survival of plants. Influenced by rainfall and soil texture, soil moisture variation characteristics of different vegetation types are different in slope land. Many researchers use rainfall simulation experiments to study the influence of soil water movement processes under rainfall patterns, but there are few studies under natural conditions. In this paper, wood trees, peach trees, flue-cured tobacco, and grassland were planted at the buffer zone boundary of Shibing, a World Heritage site, along different elevations in the depression of the research site. Peach trees and flue-cured tobacco were the main cash crops in the area. The soil layers of 0–0.15 m, 0.15–0.30 m, 0.30–0.45 m, 0.45–0.60 m, and 0.60–0.75 m were taken as research objects. Rainfall and soil moisture were monitored every ten minutes to study the variation of soil moisture of vegetation types under different rainfall patterns. Through the analysis of soil physicochemical properties and vegetation types, it was found that soil moisture changes were different in different land types. In general, shallow soil moisture (0~0.30 m) was more active in the wet season (CV > 10%) but tended to be stable in the dry season. The rainy season rainfall had the greatest influence on soil moisture change. The response level of soil moisture to rainfall of all vegetation types was grassland > peach trees > flue-cured tobacco. These results can provide ecological and economic suggestions for karst areas with large population distribution in China, so as to reduce the impact of rock desertification on this area. [ABSTRACT FROM AUTHOR]

Vegetation degeneration has become a serious ecological problem for karst regions in the Anthropocene. According to the deficiency of long serial and high-resolution analysis of karst vegetation, this paper reconstructed the variation of vegetation landscape changes from 1987 to 2020 in a typical karst region of China. Using Landsat time series data, the dynamic changes and driving factors of natural karst vegetation were identified at the landscape scale. On the premise of considering the time-lag effect, the main climatic factors that influence vegetation growth were presented at the interannual timescale. Then, the approach of residual analysis was adopted to distinguish the dominant factors affecting vegetation growth. Results of trend analysis revealed that 21.5% of the forestland showed an overall significant decline in vegetation growth, while only 1.5% showed an increase in vegetation growth during the study period. Precipitation and radiation were the dominant meteorological factors influencing vegetation at the interannual timescale, as opposed to temperature. More than 70% of the natural vegetation growth was dominated by climatic factors. The area percentage of negative human impact has increased gradually since 2009 and reached 18.5% in 2020, indicating the currently serious situation of vegetation protection; fortunately, in recent years, human disturbances on vegetation have been mitigated in karst areas with the promotion of ecological conservation and restoration projects. [ABSTRACT FROM AUTHOR]

The karst plateau is dominated by mountainous and hilly landforms, with low mechanization level of cropland, high difficulty of cultivation, and obvious phenomenon of cropland abandonment, which threatens regional food security. This study aims to analyze the spatial-temporal variation and its driving mechanisms of abandoned cropland in the Karst Plateau in Eastern Yunnan, China (KPEYC) between 2001 and 2020. To achieve this goal, 18 key factors from population, economic environment, cropland attributes, and farming conditions are selected. Moreover, correlation analysis, geodetector, and regression analysis methods are applied from three perspectives: temporal change, spatial distribution and spatial-temporal change. The results show that: (i) The cropland abandonment rate (CAR) in the KPEYC shows a fluctuating trend, with an average value of 9.78%, and the spatial distribution shows a pattern of "high in the center and low in the south and north". (ii) From the perspective of temporal change, gross value of agricultural production, and gross value of industrial production have the largest correlation coefficients with CAR. (iii) The explanatory power of gross tertiary industrial production, gross value of industrial production, followed by soil thickness. (iv) Gross value of agricultural production, and gross tertiary industrial production are the core driving forces for the spatial-temporal change of CAR. The higher the gross value of agricultural production and gross tertiary industrial production, the lower the CAR. elevation, soil thickness, and traffic mileage are the main driving factors for the spatial-temporal change of CAR. The study indicates that economic factors are decisive for cropland abandonment in the KPEYC. Based on the results, this study can provide decision-making support for local prevention and control of cropland abandonment, and the local community needs to promote land transfer and concentration and local urbanization according to local conditions, improve agricultural policies, improve farming conditions, etc. in order to increase farmers' enthusiasm for production, promote the rational use of cropland, and solidly push forward ecological restoration and management, optimize ecological spatial patterns, manage serious areas of rocky desertification, and appropriately alleviate the contradiction between people and land. [ABSTRACT FROM AUTHOR]

Regional ecosystem service value (ESV) is significantly influenced by factors such as land use/cover change (LUCC). In this study, from the perspective of spatio-temporal heterogeneity, we constructed a dynamic and zonal equivalence table of ecosystem service values using the equivalence factor method and analyzed the spatio-temporal changes in ecosystem service values of different agricultural plantation regions of the karst mountainous areas of southwestern China (Yunnan Province, YP) in the years from 1990 to 2020. Also, the ESV of YP in 2030 was simulated using the Patch-generating Land Use Simulation (PLUS) model. The results showed the following: (1) land use/land cover (LULC) in YP from 1990 to 2020 was dominated by needle-leaved forestland, broadleaved forestland, grassland, and rainfed cropland. (2) The total ESV in YP fluctuated between CNY 876.74 and 1323.68 B from 1990 to 2020, expanding at a rate of 50.98%. The largest portion of the total ESV comes from climate regulation. The ESV increased from east to west, and the positive spatial correlation of the ESV gradually weakened. (3) The ESV in YP was projected to reach CNY 1320.70 B by 2030, representing a decrease of ~CNY 2.98 B since 2020. The results showed a decline in the ecological environment's quality in YP. [ABSTRACT FROM AUTHOR]

Landscape change is a dynamic feature of landscape structure and function over time which is usually affected by natural and human factors. The evolution of rocky desertification is a typical landscape change that directly affects ecological environment governance and sustainable development. Guizhou is one of the most typical subtropical karst landform areas in the world. Its special karst rocky desertification phenomenon is an important factor affecting the ecological environment and limiting sustainable development. In this paper, remote sensing imagery and machine learning methods are utilized to model and analyze the spatiotemporal variation of rocky desertification in Guizhou. Based on an improved CA-Markov model, rocky desertification scenarios in the next 30 years are predicted, providing data support for exploration of the evolution rule of rocky desertification in subtropical karst areas and for effective management. The specific results are as follows: (1) Based on the dynamic degree, transfer matrix, evolution intensity, and speed, the temporal and spatial evolution of rocky desertification in Guizhou from 2001 to 2020 was analyzed. It was found that the proportion of no rocky desertification (NRD) areas increased from 48.86% to 63.53% over this period. Potential rocky desertification (PRD), light rocky desertification (LRD), middle rocky desertification (MRD), and severe rocky desertification (SRD) continued to improve, with the improvement showing an accelerating trend after 2010. (2) An improved CA-Markov model was used to predict the future rocky desertification scenario; compared to the traditional CA-Markov model, the Lee–Sallee index increased from 0.681 to 0.723, and figure of merit (FOM) increased from 0.459 to 0.530. The conclusions of this paper are as follows: (1) From 2001 to 2020, the evolution speed of PRD was the fastest, while that of SRD was the slowest. Rocky desertification control should not only focus on areas with serious rocky desertification, but also prevent transformation from NRD to PRD. (2) Rocky desertification will continue to improve over the next 30 years. Possible deterioration areas are concentrated in high-altitude areas, such as the south of Bijie and the east of Liupanshui. [ABSTRACT FROM AUTHOR]

The karst strata in the southwestern mountainous areas of China are extensively developed, creating a fragile geological environment. Landslide geological disasters occur frequently in these areas due to high annual rainfall, concentrated time periods, and frequent human engineering activities. Conventional ground geophysical methods face challenges in complex terrains, making it difficult to quickly and reliably obtain underground structures in landslide-prone areas. The semi-airborne transient electromagnetic method (SATEM) combines ground-based transmission and airborne reception of electromagnetic responses. This method, characterized by large emission magnetic moment and rapid data collection in the air, offers advantages in detecting deep geological structures in complex terrain areas. This article presents the application of a newly developed loop source SATEM system, integrated with multi-rotor unmanned aerial vehicles (UAVs), to conduct deep geological structure detection experiments on a large-scale landslide. The inversion results clearly depict the spatial distribution of the Feixianguan Formation and Longtan Formation, as well as vertical fractures developed in the strata due to underground coal mining. The detection test demonstrates the applicability of the SATEM method in detecting underground structures in Karst Plateau special landform areas, providing a new detection approach for similar regions. [ABSTRACT FROM AUTHOR]

This study aims to investigate the impact of vegetation restoration on soil humus and aggregate stability within the karst region of Southwest China. This study focused on soils at five vegetation succession stages (abandoned land, grassland, shrub rangeland, shrubland, and secondary forest) in the typical karst region, and the aggregate stability was determined using wet sieving and the Le Bissonnais method. Simultaneously, the Pallo method and separation extraction were used to determine the humus composition, aiming to analyze the distribution of humus content in the soil aggregates and its effect on aggregate stability. The results revealed the following: (1) The mean weight diameter of soil aggregates significantly increased with vegetation restoration stages. Soil water-stable aggregates at each vegetation stage mainly included particles over 2 mm in size. (2) The humic acid and fulvic acid contents consistently increased with vegetation restoration, and the precipitation quotient value of the humification degree showed an increasing trend. At each vegetation restoration stage, the percentage of each humus component was, from highest to lowest, as follows: insoluble HM, fulvic acid, humic acid, clay-bound HM, and iron-bound HM. (3) Through stepwise regression analysis, humic acid content in >2 mm aggregates, fulvic acid and clay-bound HM contents in 1–2 mm aggregates, and insoluble HM content in <0.25 mm aggregates were the dominant factors affecting soil aggregate stability in the karst region. These results aim to provide novel insights for a more in-depth comprehension of the restoration and rehabilitation of vegetation within the karst region of Southwest China, thereby laying a robust foundation for scientific theories and further investigations. [ABSTRACT FROM AUTHOR]

Rocky desertification (RD), a natural and human-induced process of land degradation in karst areas, has become the primary ecological disaster and one of the obstacles to sustainable ecological development in southwest China. Nevertheless, the variation of soil physical and chemical properties, bacterial and fungal communities, and their relationships in RD forests remains limited. Therefore, soil samples were collected from forests under four degrees of RD (NRD, non-RD; LRD, light RD; MRD, moderate RD; and SRD, severe RD) and subjected to high-throughput sequencing of 16S rRNA and ITS1 genes. The results showed a significant reduction in bacterial richness and diversity, while fungal richness and diversity decreased markedly and then showed a balanced trend with the increase in RD degree, indicating that bacteria and fungi did not present the same dynamics in response to the process of RD. The bacterial communities were dominated by Proteobacteria, Actinobacteria, Acidobacteria, and Chloroflexi, while the fungal communities were dominated by Basidiomycota, Ascomycota, and Mortierellomycota. The PCoA and NMDS demonstrated significant differences in microbial communities in study sites, among which the fungal communities in non-RD forest and LRD forest clustered together, suggesting that fungal communities were more stable than bacteria in RD forest. The db-RDA, Mantel test, and random forest model confirmed the important role of soil BD, pH, SOC, AN, and AP in driving microbial diversity and communities. The IndVal analysis suggested that Chloroflexi, Patescibacteria, Atheliales, and Cantharellales with high indicator values were identified as potential bio-indicators for RD forests. This study could not only improve our understanding of bacterial and fungal community dynamics across RD gradients, but also could provide useful information for the further use of microorganisms as indicators to reflect the environmental changes and ecosystem status during forest RD. [ABSTRACT FROM AUTHOR]

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Agricultural soils originating from carbonate rocks within karst regions exhibit inherent high concentrations of potentially toxic elements (PTEs) due to geogenic processes. However, the influencing factors of the elevated levels of PTEs in the naturally contaminated karst regions remain inadequately comprehended. This research investigates the chemical compositions of 278 soils derived from carbonate rocks. Descriptive statistics, stepwise multiple regression, and the random forest (RF) method were applied to screen the significant factors that affect the distribution, migration, and enrichment of the PTEs in soils. Cadmium (Cd) and arsenic (As) are the most highly contaminated PTEs in the soils of the study area, and the average contents of Cd and As in soils are 11.5 and 2.92 times the national soil background value, respectively. The pollution risk ranking of PTEs is Cd > As > Cr > Zn > Pb > Cu > Ni > Hg, using the proportion exceeding the risk screening thresholds of agricultural land as the standard. Soil Fe2O3 and Mn contents, soil pH and total organic carbon (TOC) values, and weathering intensity (characterized using the chemical index of alternation, CIA) are the most important factors influencing the PTE levels in agricultural soils. [ABSTRACT FROM AUTHOR]

Copyright of Acta Carsologica is the property of Scientific Research Centre of Slovenian Academy of Sciences & Arts and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Influenced by the topography, the spatial variation of soil thickness on karst slopes is very large, and accordingly the spatial variation of soil moisture is also large. Therefore, analyzing the spatial heterogeneity of soil moisture on hillslopes is important for maintaining ecosystem stability. Combining geostatistical methods and GIS technology, the spatial variability and distribution pattern of soil moisture and the influencing factors of spatial variation and surface soil moisture (0–7 cm) on a typical karst shrub–grass hillslope were analyzed. The results showed that the mean soil moisture and coefficient of variation (CV) ranged between 25.7–42.6% and 10.3–20.9%, respectively, showing a moderate variation. The soil moisture presented a moderate or strong spatial autocorrelation in the sampling scale. The occurrence of rainfall events can exert a great influence on reducing the spatial heterogeneity of soil moisture. The spatial distribution pattern of soil moisture showed roughly plaque or stripe distribution. When soil moisture was much lower, the patch space fragmentation of soil moisture was higher. The soil moisture was higher in the low and middle parts of the plot. We can conclude that factors such as topography, vegetation, and weather conditions will exert a significant effect on soil moisture spatial variability. Areas with lower slope and higher vegetation coverage were more conducive to the retention of soil moisture. [ABSTRACT FROM AUTHOR]

To explore soil nutrients and moisture changes in different karst mountain agroforestry, in the plateau mountains of Southern China Karst, we used secondary tree and irrigation forest (C) as a reference for our study and selected four mixed agroforestry species (walnut + maize + potato (HYM), walnut + maize (HTY), poplar + ryegrass (YSH), and maize + ryegrass (YMH)) for comparison. First, soil moisture change characteristics were monitored in situ in the field. Second, for soil samples, soil bulk density, porosity, and permeability were analyzed, soil nutrient (K, Na, Ca, and Mg) characteristics were tested and analyzed. Then, we explored the relationship between agroforestry and soil moisture, soil moisture and soil nutrients, soil moisture and precipitation, and agroforestry and soil nutrients. It is shown (1) during the monitored period, variation trends in soil nutrients in four types of agroforestry was small, but it increased/decreased significantly compared with the secondary forest, which the variation range was more than 5%; (2) the changes of soil water content were significantly affected by precipitation, soil porosity and permeability, the moisture content changes of HYM, HTY, YSH, and YMH agroforestry were significantly correlated with precipitation, soil porosity, and permeability; (3) under the same precipitation conditions, different types had different lags on soil water regulation, with the average HYM 0.8 h, HTY 0.6 h, YSH 0.3 h, and YMH 0.4 h, each type soil responded at 2–3 h after rain, and the soil moisture content returned to the normal level; and (4) the variation of soil moisture content fluctuated seasonally, and the most obvious was HYM and HTY agroforestry, their Cv value between winter and summer exceeded 21%. The results provide basic theoretical support for further exploring the relationship among agroforestry, soil, moisture, and nutrients and enrich the content of the development of agroforestry in karst areas. They are of importance to promote ecological restoration and agroforestry development in karst areas. [ABSTRACT FROM AUTHOR]

Three new species of the leafhopper genus Mitjaevia Dworakowska 1970, M. bijiensis sp. nov., M. solitaria sp. nov., and M. salaxia sp. nov., are described from the Karst region of Southwest China. Specimens studied were taken by sweep net. Morphological descriptions, depictions of habitus and illustrations of male terminalia are provided. A key and checklist to known species occurring in China are given. [ABSTRACT FROM AUTHOR]

Karst is a common engineering environment in the process of tunnel construction, which poses a serious threat to the construction and operation, and the theory on calculating the settlement without the assumption of semi-infinite half-space is lack. Meanwhile, due to the limitation of test conditions or field measurement, the settlement of high-speed railway tunnel in Karst region is difficult to control and predict effectively. In this study, a novel intelligent displacement prediction model, following the machine learning (ML) incorporated with the finite difference method, is developed to evaluate the settlement of the tunnel floor. A back propagation neural network (BPNN) algorithm and a random forest (RF) algorithm are used herein, while the Bayesian regularization is applied to improve the BPNN and the Bayesian optimization is adopted for tuning the hyperparameters of RF. The newly proposed model is employed to predict the settlement of Changqingpo tunnel floor, located in the southeast of Yunnan Guizhou Plateau, China. Numerical simulations have been performed on the Changqingpo tunnel in terms of variety of karst size, and locations. Validations of the numerical simulations have been validated by the field data. A data set of 456 samples based on the numerical results is constructed to evaluate the accuracy of models' predictions. The correlation coefficients of the optimum BPNN and BR model in testing set are 0.987 and 0.925, respectively, indicating that the proposed BPNN model has more great potential to predict the settlement of tunnels located in karst areas. The case study of Changqingpo tunnel in karst region has demonstrated capability of the intelligent displacement prediction model to well predict the settlement of tunnel floor in Karst region. [ABSTRACT FROM AUTHOR]

Soil microbial residues play an important role in the formation and stabilization of soil organic matter and can be quantitatively characterized by amino sugars. However, the response of soil microbial residues to agricultural cultivation in karst areas remains unclear. In this study, we collected soil samples from natural reserved land as well as five plantation forests dominated by Citrus trees cultivated for 0, 1, 5, 15, 30 years to examine the effects of agricultural cultivation on the content of microbial residues (amino sugar analysis). Results showed that: (1) Soil Amino Sugars (ASs) contents were significantly reduced after agricultural cultivation along with the sharp decrease in soil organic carbon (SOC). After 30 years of cultivation, the contents of total ASs, glucosamine (GluN), galactosamine (GalN), and muramic acid (MurA) in cultivated soils decreased by 58.22%, 55.30%, 27.11%, respectively, compared with 0 yr.; (2) Microbial residual carbon contribution to SOC increased from 34.11% to 81.33% after 30 years of cultivation, including fungal residual carbon (FRC) (25.79% to 48.6%) and bacterial residual carbon (BRC) (8.32% to 32.72%); (3) Soil GluN/MurA values tended to decrease with increasing cultivation years. The results highlight the significant effect of cultivation years on amino sugar accumulation. It indicates that the years of reclamation in karst areas have different impacts on the organic fractions derived from various microbial communities in the soil organic matter pool, and the microbial residues indicated by amino sugar are of great significance for the interception of soil organic matter. [ABSTRACT FROM AUTHOR]

Cave entrance data are crucial as the primary indicators in the underground water inventory of a karst area. The data collection was traditionally conducted by field survey, but it is very costly and not efficient. Remote sensing and Geographic Information System (GIS) can help estimate cave entrance locations more efficiently. In this study, variables for cave entrance identification were determined using remote sensing and GIS. In addition, the accuracy of the Cave Entrance Location Model (CELM) derived from binary logistic regression was examined. Several remote sensing and geological data were used including ALOS PALSAR Digital Elevation Model (DEM), Digital Elevation Model Nasional (DEMNAS), topographic and geological map. Topographic elements were extracted by using Toposhape and Topographic Position Index (TPI). Contours derived from the topographic map showed the highest accuracy for extraction of topographic elements compared to ALOS PALSAR DEM and DEMNAS, hence it was used for further analysis. Binary logistic regression was applied to estimate the probability of cave entrance locations based on the variables used. The result shows that three topographic variables: ravine, stream, and midslope drainage had a significant value for estimating cave entrance location. Using these variables, logit equation was formulated to generate a probability map. The result shows that cave entrances are likely to be located in a dry valley. The accuracy assessment using the field data showed that 52.77% of cave entrances are located in medium to high potential areas. This suggests that the moderatehigh potential area can indicate potential water resources in karst area. [ABSTRACT FROM AUTHOR]

Exploring the dynamics of soil erosion and identifying its driving mechanisms is key to understanding soil erosion processes, particularly in karst areas. In this study, the RUSLE model, optimized on the basis of rocky desertification factors, was used to estimate soil erosion in a karst plateau gorge area in SW China. The spatial and temporal dynamics of soil erosion in the region over the past 20 years were analyzed on the basis of slope units, while the relationship between soil erosion and elevation, slope, fractional vegetation cover (FVC), karst rocky desertification (KRD), rainfall, and land use cover/change (LUCC) was identified quantitatively by the geographical detector on the basis of spatial heterogeneity. The results showed that: (1) The no erosion area decreased from 2000 to 2020, with the highest proportion of light to medium erosion and an increasing trend of soil erosion. (2) Soil erosion conversion mainly occurred between no erosion, slight erosion, and light erosion. (3) The hotspots of erosion occurred in high slope–low elevation and high slope–high elevation units, while the coldspots of erosion occurred in low slope–low elevation units. (4) Soil erosion was positively correlated with FVC and slope, and negatively correlated with KRD. (5) The dominant factor of soil erosion changed from KRD-slope to LUCC-slope and finally to elevation-slope, while the q value of rainfall-elevation had the most significant increase throughout the study period. This study will help to advance the goal of sustainable development of soil and water conservation in karst areas. [ABSTRACT FROM AUTHOR]

Copyright of Acta Carsologica is the property of Scientific Research Centre of Slovenian Academy of Sciences & Arts and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Carsologica is the property of Scientific Research Centre of Slovenian Academy of Sciences & Arts and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research of human settlements on the karst area of East Sarajevo starts with a brief overview of the City's characteristic traits and the influence of relief on the distribution of the population. The karst terrain was mapped using GIS tools, after which the percentage of the population and its concentration on the karst area of the East Sarajevo was determined. Karst areas have a low population density caused by difficult living conditions due to the vertical and horizontal dissection of relief, lack of mineral resources, geodynamic disasters, water shortages, specific climate conditions, degraded land, and vegetation. The consequences are the fragmentation of property, migration and aging of the population, lack of labor, weakening of the economy, etc. The research focus is to determine the degree of the anthropogenic impact on the karst environment and to apply the suitable methodology in the determination of the settlements categories according to the degree of karst environment vulnerability. [ABSTRACT FROM AUTHOR]

Understanding the successional process from a disturbed forest to a mature forest is essential for species recovery and conservation initiatives. The resource acquisition and drought tolerance of plants can be instructive to predictions of species abundance and distribution for different forests. However, they have not been adequately tested at different successional stages in karst regions. Here, we selected seven dominant species in an early-succession forest and 17 species in a late-succession forest in a karst region of southwestern China. Resource acquisition-related traits such as hydraulic conductivity and photosynthetic rate, and drought tolerance-related traits, including turgor loss point and wood density, were measured. We found that species in the early-succession forest had a higher hydraulic conductance and photosynthetic rate than those in the late-succession forest, while leaf water potential at turgor loss point and wood density showed nonsignificant differences between the two forests. In addition, we observed a significant negative relationship between photosynthetic rate and drought tolerance in the early-succession forest, which was not identified in late-succession forests. Our study indicates that resource acquisition rather than drought tolerance was the key factor explaining plant distributions in forests at different successional stages in karst regions. We also suggest that the resource acquisition and drought tolerance trade-off hypothesis is not always supported for karst region species. Our study could inform about the design of species replacements in successional forests and provide forest management and restoration guidelines for karst regions. [ABSTRACT FROM AUTHOR]

In the framework of a collaboration between INAIL and University of Salento, an indoor radon survey in 54 buildings belonging to the UniSalento University (Southeast Italy) was carried out. The monitored buildings differ by type, construction period, materials, etc., and are located in an area with a morphology characterized mainly by marls, calcareous marls, and calcarenites (karst area). The sample of the survey includes 963 rooms at different floors: it consists in rooms mainly located at ground floor (67%), first floor (12%), and below ground (12%). SSNTD passive dosimeters measured the average radon activity concentration for two consecutive semesters (spring/summer and autumn/winter) from which annual radon averages were estimated for each room. The spatial variability within buildings was investigated in terms of variation between floors and among rooms at the same floor. Data analysis provides evidence that the distributions (in terms of arithmetic mean, standard deviation, median, and geometric mean) of indoor radon annual averages at ground floor and at first floor within building are very similar. This highlights that the karstic characteristics of soil and building materials affect radon levels not only below ground and at ground floor, but also at first floor. Moreover, to evaluate the spatial variability of radon among buildings or floors, the analysis of the distribution of coefficient of variation (CV) was carried out: the results show a low spatial variability with median and average values of CVs ≤ 30% both for the whole building and at different floor levels. [ABSTRACT FROM AUTHOR]

Understanding the changes in soil organic carbon (SOC) storage is important for accurately predicting ecosystem C sequestration and/or potential C losses, but the relevant information, especially for the evolvement of calcareous soil is limited in karst regions. Three calcareous soils with different evolvement intensities were sampled from an evergreen broadleaved forest in the subtropical region of southwest of China to investigate the changes in different SOC fractions and microbial communities. The results showed that: (1) The contents of SOC, dissolved organic carbon (DOC), mineral protected organic carbon (MOC), and recalcitrant organic carbon (ROC) significantly decreased with increasing evolvement intensity of calcareous soil, but pH and the chemical composition of SOC, including Alkyl C, O-alkyl C, Aromatic C, and Carbonyl C, did not significantly change, suggesting that various SOC fractions synergistically decrease with the evolvement of calcareous soil. (2) The evolvement of calcareous soil had a substantial negative effect on total phospholipid fatty acids (PLFA), bacteria (i.e., Gram positive bacteria and Gram negative bacteria), fungi, and actinomycetes, but did not affect the ratio of fungi to bacteria. This result supported the conclusion that various SOC fractions were synchronously loss with the evolvement of calcareous soil. (3) Results from the multivariate statistical analysis showed a significant correlation between SOC fractions (including SOC, DOC, MOC, and ROC) and soil base cations, mainly calcium (Ca), iron (Fe), and aluminum (Al). This strengthens the fact that SOC stability largely depends on the complex relationship between organic matter and mineral composition in soil. Taken together, the reduction of SOC during the evolvement of soil in the karst areas accords with some mechanisms of previous studies (e.g., microbial composition and soil geochemistry), and also has its own unique characteristics (e.g., the relative contribution of carbons to chemical shift regions of CPMAS 13C-NMR spectra and F:B ratio). [ABSTRACT FROM AUTHOR]