Landslides pose significant risks to human life and infrastructure, particularly in mountainous regions like Inje, South Korea. This study aims to develop detailed landslide susceptibility maps (LSMs) using statistical (i.e., Frequency Ratio (FR), Logistic Regression (LR)) models and a hybrid integrated approach. These models incorporated various factors influencing landslides, including aspect, elevation, rainfall, slope, soil depth, slope length, and landform, derived from comprehensive geospatial datasets. The FR method assesses the likelihood of landslides based on historical occurrences relative to specific factor classes, while the LR method predicts landslide susceptibility through the statistical modeling of multiple predictor variables. The results from the FR, LR, and hybrid methods showed that the cumulative area covered by high and very high landslide susceptibility zones was 13.8%, 13.0%, and 14.28%, respectively. The results were validated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC), revealing AUC values of 0.83 for FR, 0.86 for LR, and 0.864 for the hybrid method, indicating high predictive accuracy. Subsequently, we used K-mean clustering algorithms on the hybrid LSI to identify the higher LSI cluster of the region. Furthermore, sensitivity analysis based on landslide density confirmed that all methods accurately identified high-risk areas. The resulting LSMs provide critical insights for land-use planning, infrastructure development, and disaster risk management, enhancing predictive accuracy and aiding in the prevention of future landslide damage.

Landslides are recognized as major natural geological hazards in the mountainous region, and they are accountable for enormous human causalities, damage to properties, and environmental issues in the Teesta River basin, Sikkim, India. GIS approaches are widely used in landslide susceptibility mapping (LSM) that can help relevant authorities to mitigate landslide risk. The binary logistic regression is applied to estimate the landslide susceptibility zonation (LSZ) in the upper Teesta River basin areas. The landslide inventory data are subdivided into training data sets (70%) for applying algorithms in models and testing data sets (30%) for testing model accuracy. The LSZ mapping is designed after analyzing multicollinearity test of 14 landslide CFs and the result shows that the VIF value is less than 10, and TOL is greater than 0.1, respectively. There is no multicollinearity for the 14 conditioning landslides factors. The upper Teesta River basin is categorized into five groups: very low-to-very high landslide susceptibility zones. The results highlighted that most of the middle and southern parts of the study region are highly prone to landslides compared to the other parts. The susceptibility of landslide in the upper Teesta River basin areas validated by performing the Receiver Operating Characteristics (ROC) curve, which showed an 83% confidence level. The present research demonstrated landslide vulnerability circumstances for the Teesta River basin, Sikkim, an area prone to landslides, emphasizing the need for an effective mitigation and management roadmap.

Remote sensing plays an increasingly important role in agriculture, especially in monitoring the quality of agricultural crops. Optical sensing is often limited in Central Europe due to cloud cover; therefore, synthetic aperture radar data is increasingly being used. However, synthetic aperture radar data is limited by more difficult interpretation mainly due to the influence of speckles. For this reason, its use is often limited to larger territorial units and field blocks. The main aim of this study therefore was to verify the possibility of using satellite synthetic aperture radar images to assess the within-field variability of winter wheat. The lowest radar vegetation index values corresponded to the area of the lowest production potential and the greatest damage to the stand. Also for VH and VV polarizations, the highest values corresponded to the area of the lowest stand quality. Qualitative changes in the stand across the zones defined by frost damage and production potential were assessed with the help of the logistic regression model with resampled data for 10, 50, and 100 m pixel size. The best correlation coefficients were achieved at a spatial resolution of 50 m for both options. The F-score still yielded a promising result ranging from 0.588 to 0.634 for frost damage categories. The regression model of the production potential performed slightly better in terms of the F-score, recall, and precision at higher resolutions. It was proved that modern statistical methods could be used to reduce problems associated with speckles of radar images for practical purposes.

Ouagadougou, the capital city of Burkina Faso, is facing significant economic and social damages due to recurring floods. This study aimed to develop a flood susceptibility map for Ouagadougou using a logistic regression (LR) model and 14 flood conditioning factors, including elevation, slope, aspect, profile curvature, plan curvature, topographic position index (TPI), topographic roughness index (TRI), flow direction, topographic wetness index (TWI), distance to river, rainfall, land use/land cover (LULC), normalized difference vegetation index (NDVI) and soil type. A historical flood inventory map was created from household survey data, identifying 1026 flooded sites which were divided into a training dataset (70%) and a validation dataset (30%). The factors that had a statistically significant influence (p-value 1.96) at the 95% confidence level were, in order of importance, elevation, distance to river, rainfall, plan curvature and NDVI. The receiver operating characteristic (ROC) curve method was used to validate the model. The area under the curve (AUC) values of the model were 81% for the prediction rate and 82% for the success rate indicating its effectiveness in identifying areas susceptible to flooding. The results showed that 18.48% of the city is very high susceptible to flooding, 18.99% has high susceptibility, 18.43% has moderate susceptibility, and 19.98% and 24.18% have low and very low susceptibility, respectively. This research provides valuable information for policy makers to develop effective flood prevention and urban development strategies.

The Earth is currently experiencing severe economic and social consequences as a result of frequent floods. This study is crucial for effective risk management and mitigation, protecting lives and property from potential flood damage in the Deme watershed. This study endeavors to assess the efficacy of a logistic regression model in generating a flood susceptibility map for the Deme watershed in Ethiopia. Fourteen factors contributing to flooding were considered, including digital elevation model, slope, aspect, profile curvature, plane curvature, Topographic Position Index (TPI), Topographic Roughness Index (TRI), flow direction, Topographic WetnessIindex (TWI), distance to the river, rainfall, land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), and soil type. The receiver operating characteristic (ROC) curve method was employed to validate the model. The area under the curve (AUC) values for the model were determined to be 81% for the training dataset and 82% for the validation dataset, indicating its effectiveness in delineating flood-prone areas. The findings revealed that 18% of the watershed is very highly susceptible to flooding, 19% exhibits high susceptibility, 18% shows moderate susceptibility, while 20 and 24% have low and very low susceptibility, respectively. This research provides insights into comprehensive flood prevention and urban development strategies. HIGHLIGHTS center dot Flood susceptibility is determined by historical flood patterns and their influencing factors. center dot Logistic regression can be used to map flood-susceptible areas in a small watershed. center dot A multicollinearity test is necessary to ensure a linear relationship in flood conditioning factors. center dot Factors with high multicollinearity should be removed from models to improve prediction accuracy.

For remote communities in the discontinuous permafrost zone, access to permafrost distribution maps for hazard assessment is limited and more general products are often inadequate for use in local-scale planning. In this study we apply established analytical methods to illustrate a time- and cost-efficient method for conducting community-scale permafrost mapping in the community of Whati, Northwest Territories, Canada. We ran a binary logistic regression (BLR) using a combination of field data, digital surface model-derived variables, and remotely sensed products. Independent variables included vegetation, topographic position index, and elevation bands. The dependent variable was sourced from 139 physical checks of near-surface permafrost presence/absence sampled across the variable boreal-wetland environment. Vegetation is the strongest predictor of near-surface permafrost in the regression. The regression predicts that 50.0% (minimum confidence: 36%) of the vegetated area is underlain by near-surface permafrost with a spatial accuracy of 72.8%. Analysis of data recorded across various burnt and not-burnt environments indicated that recent burn scenarios have significantly influenced the distribution of near-surface permafrost in the community. A spatial burn analysis predicted up to an 18.3% reduction in near-surface permafrost coverage, in a maximum burn scenario without factoring in the influence of climate change. The study highlights the potential that in an ecosystem with virtually homogeneous air temperature, ecosystem structure and disturbance history drive short-term changes in permafrost distribution and evolution. Thus, at the community level these factors should be considered as seriously as changes to air temperature as climate changes.

The most dramatic permafrost degradation is expected to occur at the southern edge of permafrost distribution, which is difficult to detect directly on a large scale. Ecological indicators can be used to provide an early signal of changes in terrestrial ecosystems for regional near-surface permafrost habitats and potentially to monitor near-surface permafrost degradation. In this study, plant composition and community structure indicate the near-surface permafrost distribution at the southern edge of the boreal forest and permafrost in northeastern China. The plant species composition and structure of aboveground vegetation were linked to the belowground near-surface permafrost distribution in order to find indicators of changes in vegetation features from permafrost melting. These indicators are essential for assessing changes in permafrost vegetation systems under climate change. Carex schmidtii and C. appendiculata in the herb layer and Benda fruticosa in the shrub layer were found to be specific near-surface permafrost plant indicator species, especially for the wetland permafrost. Shrub cover, moss mat thickness and tree canopy cover are also strongly correlated with near-surface permafrost distribution. The active layer thickness (ALT) showed negative correlations with moss thickness and shrub cover because these features may act as buffers for regional climate warming. We chose the cover of each indicator species, near-surface permafrost-specific community features and geographical information as independent variables to predict the possible distribution of near-surface permafrost in our study region using logistic regression. The results showed that the prediction model had good performance and accuracy. Our study sheds light on early caution of deepening of regional-scale permafrost active layer with vegetation indicators that can further be identified from satellite images.

The Qinghai-Tibet Plateau (QTP), where is underlain by the highest and most extensive mid-altitude permafrost, is undergoing more dramatic climatic warming than its surrounding regions. Mapping the distribution of permafrost is of great importance to assess the impacts of permafrost changes on the regional climate system. In this study, we applied logistic regression model (LRM) andmulti-criteria analysis (MCA) methods to map the decadal permafrost distribution on the QTP and to assess permafrost dynamics from the 1980s to 2000s. The occurrence of permafrost and its impacting factors (i.e., climatic and topographic elements) were constructed from in-situ field investigation-derived permafrost distribution patterns in 4 selected study regions. The validation results indicate that both LRM and MCA could efficiently map the permafrost distribution on the QTP. The areas of permafrost simulated by LRM and MCA are 1.23 x 10(6) km(2) and 1.20 x 10(6) km(2), respectively, between 2008 and 2012. The LRM and MCA modeling results revealed that permafrost area has significantly decreased at a rate of 0.066 x 10(6) km(2) decade(-1) over the past 30 years, and the decrease of permafrost area is accelerating. The sensitivity test results indicated that LRM did well in identifying the spatial distribution of permafrost and seasonally frozen ground, and MCA did well in reflecting permafrost dynamics. More parameters such as vegetation, soil property, and soil moisture are suggested to be integrated into the models to enhance the performance of both models. (C) 2018 Published by Elsevier B.V.