Soil organic carbon (SOC) in the active layer (0-2 m) of the Tibetan Plateau (TP) permafrost region is sensitive to climate change, with significant implications for the global carbon cycle. Environmental factors-including parent material, climate, vegetation, topography, soil, and human activities-inevitably drive SOC variations. However, vegetation and climate are likely the two most influential factors impacting SOC variations. To test this hypothesis, we conducted experiments using 31 environmental variables combined with the recursive feature elimination (RFE) algorithm. These experiments showed that RFE retained all vegetation variables [Land cover types (LCT), normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP)] as well as two climate variables [Moisture index (MI) and drought index (DI)], supporting our hypothesis. We then analyzed the relationship between SOC and the retained vegetation and climate variables using random forest (RF), Shapley additive explanations (SHAP), and GeoDetector models to quantify the independent and interactive drivers of SOC distribution and to identify the optimal conditions for SOC accumulation. The RF model explained 68% and 42% of the spatial variability in SOC at depths of 0-1 m and 1-2 m, respectively, with SOC stocks higher in the southeast and lower in the northwest. Additionally, SOC stock at 0-1 m was significantly higher (p 0.05). Spearman correlation coefficients results indicated that NDVI, LAI, GPP, and MI had highly significant positive correlations with SOC (p < 0.01), whereas DI had a highly significant negative correlation with SOC (p < 0.01). SHAP analysis revealed environmental thresholds for SOC variations, with notable shifts at NDVI (0.40), LAI (7), GPP (250 g C m(-)(2) year(-)(1)), MI (0.40), and DI (0.50). The spatial distribution of these thresholds aligns with the 400 mm equivalent precipitation line. Additionally, GeoDetector results emphasized that interactions between climate and vegetation factors enhance the explanatory power of individual variables on SOC variations. The swamp meadow type, with an NDVI range of 0.73-0.84, LAI range of 11.06-15.94, and MI range of 0.46-0.56, was identified as the most favorable environment for SOC accumulation. These findings are essential for balancing vegetation and climate conditions to sustain SOC levels and mitigate climate change-driven carbon release.
Deep-seated landslides can have significant and long-lasting impacts on surrounding ecosystem by altering topography and destabilizing the ground. This study presents a quantitative investigation of the impacts of deepseated landslides, which have actively supplied sediments for more than one hundred years, on damaged trees, incorporating various factors such as distance from landslide borders, slope gradient, factor of safety (FS), Wind Exposure Index (WEI), Convergence Index, and plan curvature in four landslides in Japan (Shichimenzan, Senmai, Sarugare, and Akakuzure). The study aims to enhance our understanding of the relationship between landslides and vegetation, providing valuable insights for landslide management and ecological restoration. Using a combination of field surveys and high-resolution UAV (Unmanned Aerial Vehicle) images from 2020 and 2023, a total of 2057 damaged trees were identified across all four study landslide areas. Statistical analysis (OneWay ANOVA) show there is significant differences among different damaged tree types in studied variables in 99 % of confidence level. Analysis of the distance from landslide borders revealed significant differences among damaged tree types. The average distance of Fallen and Green (FG) trees from the landslide borders was found to be 7.3 m, indicating their proximity to the affected area. Furthermore, the density of FG trees within the expanded area of the landslide was observed to be higher. Stand and Dead (SD) trees, in contrast, were situated at an average distance of 68.6 m from landslide border, suggesting their vulnerability to animal-related damages. Fallen and dead (FD) trees were associated with steep slope gradients, averaging 40.8 degrees, and exhibited low FS values (0.87), indicating their susceptibility to slope instability. Stand and Dead partially trees (SDP) and SD trees demonstrated higher FS values suggesting their presence in areas with superior slope stability. In addition, results show, FG, SD, and SDP trees were predominantly located in wind-exposed higher elevation areas. FD trees were primarily situated in areas with negative Convergence Index values (-0.7), indicating slope convergence. FG and SDP trees were found in areas with positive values, suggesting slope divergence. The findings enhance our understanding of the complex and long-lasting landslide impacts on forests, informing landslide management and ecological restoration strategies.
Flood is among the most disastrous natural disasters since they are responsible for massive damage to infrastructure, severe fatalities and injuries, innumerable economic losses, and social disruptions worldwide. These damages caused by floods have been worsening in recent years worldwide because of environmental degradation, climatic change, and high-speed urbanization. A rising precipitation rate increases the chances of floods in flood-vulnerable areas. A flash flood is a rapid flooding of geomorphic low-lying regions caused by remarkably high rainfall in a short duration. On September 23rd, 2023 a flooding event in the Nagpur, Maharashtra, it is directly impact on the human death and economic loss entire city. In the present study, the change in the dynamics of Nagpur city was analysed by employing remote sensing and GIS techniques to assess the change in the land use and land cover patterns. Landsat imagery of year 2000, 2010, 2020, and 2023 was used for land use and land cover classification. This analysis reveals that there is an increase in built-up area from 72.85 sq. km in year 2000 to 185.4 sq. km in year 2023. The built up land is increased this changes where directly affects the infiltration rate of rainwater into the soil. The total area covered by water bodies is reduced to 2.29 sq. km in 2023 which were 12.2 sq. km in year 2000. It is indicates the encroachment of built-up land on the water bodies. On the day of flash flood occurrence, it was observed that Nagpur city received 145 mm rainfall which is highest in the month of September, 2023. The Shannon entropy model was used to estimate the population dynamics and growth patterns of Nagpur city. Higher entropy values were obtained during the analysis which indicates the rapid transformation of city in all directions. Population dynamics of Nagpur city also indicate the inflation in population from 4,067,637 in 2000 to 4,653,570 in 2010. The SAR water index was calculated using Google Earth Engine to detect the water surges in residential areas during the flood. Precautionary measures should be taken by governing authorities to avoid such disasters. Proper city planning and improvements in drainage systems are recommended within the city. It is needed for an hour to develop a river monitoring system and early warning system, as well as preventive measures that should be implemented, like the construction of retaining walls to control the flood water.
Extreme flooding is becoming a more serious hazard to the world's infrastructure, especially in high-risk locations, and is linked to global warming and human activity. This research employs an analytical hierarchy process (AHP) model and geographic information system (GIS) analysis to delineate flood risk zones. An eight-factor multiparametric method to flood risk susceptibility mapping includes precipitation, distance to river, the slope, elevation, land use/cover, topographic wetness index, type of soil, and curvature. An urban flood risk index (UFRI) is established based on vulnerability mapping, revealing that approximately 33% of Haripur District, Khyber Pakhtunkhwa, Pakistan, is prone to floods. Additionally, land use cover analysis indicates that 23% of the crop area in Haripur District is at risk from flood disasters. Recognizing the potential for costly damage to infrastructure, flood hazard mapping serves as a valuable tool to prioritize risk areas for urban and agricultural development. The outcomes of this study are anticipated to significantly contribute to predisaster flood control management in the studied area.
Research on soil contamination has become increasingly important, but there is limited information about where to sample for pollutants. Thus, the use of three-dimensional (3D) spatial interpolation techniques has been promoted in this area of study. However, the application of traditional interpolation methods is limited in geography, especially in the expression of anisotropy, and it is not associated with geographical properties. To address this issue, we used a test site (a factory in Nanjing) to develop a new research method based on the geographical shading radial basis function (RBF) interpolation method, which considers 3D anisotropy and geographical attribute expression. Drilling and uniform sampling were used to sample the contaminated area at this test site. This approach included two steps: i) An ellipsoid with anisotropic properties was constructed. Thus, the first step was to determine the shape of the ellipsoid using principal component analysis (PCA) to determine the main orientations and construct a rotational and stretched matrix. The second step was determining the ellipsoid size by computing the range using the variogram method for orientations. ii) During field measurement, the geospatial direction influences soil attribute values, so a shadowing calculation method was derived for quadratic weight determination. Then, the weight of the attribute value of known points can be assigned to meet the field conditions. Lastly, the model was evaluated using the root mean square error (RMSE). For the 2D space, the RMSE values of Kriging, RBF, and the proposed method are 6.09, 7.12, and 5.02, respectively. The R2 values of Kriging, RBF, and the proposed method are 0.871, 0.832, and 0.946, respectively. For the 3D space, the RMSE values of Kriging, RBF, and the proposed method are 2.65, 2.23, and 2.58, respectively. The R2 values of Kriging, RBF, and the proposed method are 0.934, 0.912, and 0.953, respectively. The resulting fitted model was relatively smooth and met experimental needs. Thus, we believe that the interpolation method can be applied as a new method to predict the distribution of soil pollutants.
The conservation of Cultural Heritage in cave environments, especially those hosting cave art, requires comprehensive conservation strategies to mitigate degradation risks derived from climatic influences and human activities. This study, focused on the Polychrome Hall of the Cave of Altamira, highlights the importance of integrating remote sensing methodologies to carry out effective conservation actions. By coupling a georeferenced Ground Penetrating Radar (GPR) with a 1.6 GHz central-frequency antenna along with photogrammetry, we conducted non-invasive and high-resolution 3D studies to map preferential moisture pathways from the surface of the ceiling to the first 50 cm internally of the limestone structure. In parallel, we monitored the dynamics of surface water on the Ceiling and its correlation with pigment and other substance migrations. By standardizing our methodology, we aim to increase knowledge about the dynamics of infiltration water, which will enhance our understanding of the deterioration processes affecting cave paintings related to infiltration water. This will enable us to improve conservation strategies, suggesting possible indirect measures to reverse active deterioration processes. Integrating remote sensing techniques with geospatial analysis will aid in the validation and calibration of collected data, allowing for stronger interpretations of subsurface structures and conditions. All of this puts us in a position to contribute to the development of effective conservation methodologies, reduce alteration risks, and promote sustainable development practices, thus emphasizing the importance of remote sensing in safeguarding Cultural Heritage.
Karst regions represent fragile landscapes that are particularly vulnerable to environmental changes. The study aims to assess the soil quality in the karst basin of Ioannina, which is located in the north-western region of Greece. Factor analysis was employed to evaluate the concentrations of trace elements in the soil. Additionally, Geographical Information Systems (GIS) was utilized to visualize the spatial distribution of these trace elements and their potential sources in relation to the local geology and land use. The study findings underscored that most of the karst landscape in the research area is comprised of Quaternary deposits and it is predominantly occupied by agricultural land. The soil displays substantial levels of clay and silt, with noticeably elevated concentration of iron (Fe), manganese (Mn), nickel (Ni), chromium (Cr), lead (Pb), copper (Cu), vanadium (V), and phosphorous (P) compared to the median concentrations observed in European topsoil. The factor analysis is applied to the dataset of elements content in soil to identify the factors controlling their distribution. Factor 1 involves the geological contribution and the adsorption of Fe-Ni-Cr-Pb-V and lithium (Li) into clay minerals. Factor 1 may be termed as lithogenic factor. The cultivated land and road network showed a significant correlation with the higher positive loadings of Fe, Mn, Pb and Cu for Factor 2 which may be termed agricultural-road network factor. Agricultural activities and cultivated land presented a significant correlation with the higher positive loadings of nitrate (NO3-), nitrite (NO2-), organic matter (OM), ammonium (NH4+) and P for Factor 3 which may be termed as agricultural factor. The higher positive loadings of Factor 4 suggest a variation in the mechanical properties of the Quaternary deposits and may be termed as soil texture factor. Quaternary deposits and agricultural land exhibit a strong spatial relationship with factor scores of each factor. Combining factor analysis and GIS proved to be an effective method for identifying and confirming the sources of elements content in soil.
The Central Yakutian permafrost landscape is rapidly being modified by land use and global warming, but small-scale thermokarst process variability and hydrological conditions are poorly understood. We analyze lake-area changes and thaw subsidence of young thermokarst lakes on ice-complex deposits (yedoma lakes) in comparison to residual lakes in alas basins during the last 70 years for a local study site and we record regional lake size and distribution on different ice-rich permafrost terraces using satellite and historical airborne imagery. Statistical analysis of climatic and ground-temperature data identified driving factors of yedoma- and alas-lake changes. Overall, lake area is larger today than in 1944 but alas-lake levels have oscillated greatly over 70 years, with a mean alas-lake-radius change rate of 1.63.0 m/yr. Anthropogenic disturbance and forest degradation initiated, and climate forced rapid, continuous yedoma-lake growth. The mean yedoma lake-radius change rate equals 1.21.0 m/yr over the whole observation period. Mean thaw subsidence below yedoma lakes is 6.21.4 cm/yr. Multiple regression analysis suggests that winter precipitation, winter temperature, and active-layer properties are primary controllers of area changes in both lake types; summer weather and permafrost conditions additionally influence yedoma-lake growth rates. The main controlling factors of alas-lake changes are unclear due to larger catchment areas and subsurface hydrological conditions. Increasing thermokarst activity is currently linked to older terraces with higher ground-ice contents, but thermokarst activity will likely stay high and wet conditions will persist within the near future in Central Yakutian alas basins.
To better understand the linkage between lake area change, permafrost conditions and intra-annual and inter-annual variability in climate, we explored the temporal and spatial patterns of lake area changes for a 422382-ha study area within Yukon Flats, Alaska using Landsat images of 17 dates between 1984 and 2009. Only closed basin lakes were used in this study. Among the 3529 lakes greater than 1 ha, closed basin lakes accounted for 65% by number and 50% by area. A multiple linear regression model was built to quantify the temporal change in total lake area with consideration of its intra-annual and inter-annual variability. The results showed that 80.7% of lake area variability was attributed to intra-annual and inter-annual variability in local water balance and mean temperature since snowmelt (interpreted as a proxy for seasonal thaw depth). Another 14.3% was associated with long-term change. Among 2280 lakes, 350 lakes shrank, and 103 lakes expanded. The lakes with similar change trends formed distinct clusters, so did the lakes with similar short term intra-annual and inter-annual variability. By analysing potential factors driving lake area changes including evaporation, precipitation, indicators for regional permafrost change, and flooding, we found that ice-jam flooding events were the most likely explanation for the observed temporal pattern. In addition to changes in the frequency of ice jam flooding events, the observed changes of individual lakes may be influenced by local variability in permafrost distributions and/or degradation. Copyright (c) 2012 John Wiley & Sons, Ltd.