Flood hazard has resulted in the loss of thousands of lives and large-scale damage to properties. This study has explored, analyzed, and categorized the flood hazard and risk levels of Arba Minch City in South Ethiopia by integrating geospatial and Analytical Hierarchy Process techniques. Data were acquired from DEM with 12.5 m resolution, Landsat 8 OLI, ortho-rectified, and surveyed data from the Municipality. Slope, Elevation, Rainfall, Aspect, Curvature, Topographic Wetness Index, Topographic Roughness Index, Drainage Density, Distance from River, Soil Types, Land Use Land Cover, and Population Density parameters were used. Standard classification criteria were set based on literature and experts' judgment. Data were rasterized, resampled, and reclassified into five classes through the natural break method and readjustment. The flood hazard map was produced using the weighted overlay technique with hazard levels of low (7.39%), moderate (56.13%), and high (36.48%). Whereas, very low and very high remained nil. The flood risk levels were produced ascendingly as 2.4%, 17.3%, 17%, 44%, and 19.4%, respectively. The validity of the model was confirmed by the ROC-AUC Value of 0.923 being fitted with flood damage sites of Shara, Limat, Airport, Agriculture Research Center, Konso Sefer, Ashewamado, Gurba, and Arba Minch University campuses. Slope, elevation, rainfall, aspect and curvature were the top priority flood hazard parameters. The hazard map, population density, and land use land cover inputs have significant weights for flood risks. Thus, the study findings urge that the stakeholders should take integrated and consistent flood risk reduction and management measures.

Non-grain production of cultivated land (NGPCL) threatened food security. Therefore, scholars have begun study this area in China and other countries, but most of the studies have focused on large scales, and few studies have focused on plot scale analysis. This study presents an analytical framework to shed light on the causes of NGPCL in the hilly mountainous regions of southern China. First, we categorized NGPCL into severe damage class and slight damage class according to the degree of damage of NGPCL to cultivated soils and the difficulty of restoring food production capacity. Then, we revealed the characteristics of spatial differentiation and causes of NGPCL in the southern hilly areas by using methods such as binary logistic regression model and spatial correlation analysis. Finally, the results in the study showed that: (1) the overall NGPCL areal in 2020 was 11288.46 hm2, accounts for 38.14%, of which the areas of NGPCL in the minor damage class and the serious damage class were 27.32% and 10.82%, respectively. (2) The spatial differentiation of NGPCL was obvious, which showed a clustered distribution pattern, with NGPCLs of the minor damage class clustered at high values in the topographically flat areas of the east-central zone, and NGPCLs of the severe damage class clustered at high values in the vicinity of the central urban area. (3) The levels of NGPCL for each type were significantly correlated with the three dimensions of natural, locational, and socio-economic factors, while topography, cultivated land infrastructure conditions and the degree of centralized and contiguous cultivated land were the important drivers of spatial differentiation of NGPCL. This paper reveals the distribution and influencing factors of NGPCL at the plot scale, that can provide theoretical reference and categorized governance suggestions for NGPCL governance in similar regions in China and even in the world.

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.

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.

Discerning the impact of anthropogenic impacts requires the implementation of bioindicators that quantify the susceptibilities and vulnerabilities of natural terrestrial and aquatic ecosystems to perturbation and transformation. Although legal regulations in Brazil recognize the value of bioindicators in monitoring water quality, the depreciation of soil conditions has yet to receive adequate attention. Thus, our study aimed to evaluate the potential of odonates (dragonflies and damselflies) as amphibiotic bioindicators to reflect the correlation between the degradation of aquatic and terrestrial habitats in pasture-dominated landscapes. We assessed the relationship between the biotic indices of Odonata and the conservation status of preserved riparian landscapes adjacent to anthropogenically altered pastures in 40 streams in the Brazilian savannah. Our results support the hypothesis that Odonata species composition may be a surrogate indicator for soil and water integrity, making them promising sentinels for detecting environmental degradation and guiding conservation strategies in humanaltered landscapes. Importantly, while the Zygoptera/Anisoptera species ratio is a useful bioindicator tool in Brazilian forest, it is less effective in the open savannah here, and so an alternative index is required. Importantly, while the Zygoptera/Anisoptera species ratio is a useful bioindicator tool in Brazilian forest, it is less effective in the open savannah here, and so an alternative index is required. On the other hand, our results showed the Dragonfly Biotic Index to be a suitable tool for assessing freshwater habitats in Brazilian savannah. We also identified certain bioindicator species at both ends of the environment intactness spectrum.

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.

The distribution of total soil nitrogen (TSN) and total soil phosphorus (TSP) plays a pivotal role in shaping soil quality, fertility, agricultural practices, and environmental balance, especially in ecologically sensitive regions like the North-Western Himalayas (NWH). The primary objectives of this study were to contribute to clarify the impact and the rationale of various land uses on the spatial variation of TSN and TSP in the corresponding soils. This study aimed to explore the relation of TSN and TSP distribution in NWH soils with various factors like landscape physiography and soil physical and chemical properties using random sampling and geostatistical analyses. Employing random sampling, 300 soil surface samples (at a depth of 0-20 cm) were collected across various 500 m x 500 m grids from agriculture, horticulture, forest and fallow lands in the NWH region. The spatial land heterogeneity of TSN and TSP were systematically analyzed using standard statistical and geostatistical approaches (Gaussian, spherical, exponential, and linear). Results revealed a decreasing order of TSN and TSP levels i.e., horticulture (0.410 and 0.723 mg/kg) > agriculture (0.314 and 0.597 mg/kg) > forest (0.236 and 0.572 mg/kg) > fallow (0.275 and 0.342 mg/kg). Stepwise multiple regression results demonstrated a correlation between TSN and soil organic carbon (SOC), while TSP was correlated with soil organic carbon (SOC) and fine-grained soil particles. Nugget % values indicated the following spatial variability for TSN: agricultural (1.4) > horticultural (3.2) > forest (3.9) > fallow land (4.8) > mixed land (5.8), whereas the spatial variability of TSP showed a similar trend for all land uses. The optimized conceptual framework and isotropy models varied for TSN and TSP on dependence on land use type. The results of this study revealed the spatial patterns and land userelated variations and improved the prediction of nutrient distribution, so contributing an optimized conceptual framework for future studies. Finally, this study provided crucial insights to enhance soil quality, fertility, agricultural sustainability, and environmental equilibrium in the ecologically fragile NWH region, contributing to solve a significant research gap in the global understanding of soil dynamics.

A hazard is a natural occurrence that might harm humans, animals or the environment. It may cause loss of life, illness or other health consequences, property damage, social and economic crisis or environmental degradation. Various regions around the world are vulnerable to one or more types of disasters. Flooding is one of the worst environmental catastrophes that impacts both civilisation and the environment globally. Various datasets and methods, such as meteorological data, satellite images and GIS, were used to create the hazard assessment map. For a particular region, flood hazards can be developed by integrating an assessment map for several parameter categories. The aim of the study was to evaluate the hazard of flooding and map the areas that will be flooded in Gujarat. This study develops and tests flood-hazard maps to visualise the spatial variation of hazards in Gujarat, India. The parameters for flood-hazard assessment are mainly considered as elevation, slope, aspect, curvature, lithology, soil, land use/cover, drainage density and distance from the river, and rainfall to create a map in the context of a GIS. The acquired data was evaluated using ArcGIS and fuzzy-logic techniques to build a flood hazard map. Five categories have been assigned to the computed flood hazard map: very low, low, moderate, high, and very high. Engineers, planners and local governments may find this study useful in the future when it comes to land use planning and the control of hazards. Flood hazard potential mapping is necessary to manage and mitigate flooding.

A hazard is a natural occurrence that might harm humans, animals or the environment. It may cause loss of life, illness or other health consequences, property damage, social and economic crisis or environmental degradation. Many places of the world are at risk from one or more disasters. Although many studies have concentrated on single hazards, there is a need for integrated evaluations of multi-hazards for more effective land management. A selection of datasets and methods, such as meteorological data, satellite images, and GIS, were used to create the risk assessment maps. The parameters for multi-hazard assessment are mainly considered as rainfall, slope, elevation, and land use/land cover and create a map in a GIS environment. For a particular region, multi-hazards can be produced by integrating maps of several hazard assessments. The objective of this study is an integration of geospatial and fuzzy-logic techniques for multi-hazard mapping. Extensive parts of Gujarat state (India) experience a wide range of natural hazards: floods, soil erosion, drought, and earthquakes. This research creates and evaluates individual and group multi-hazard maps to visualize the spatial variation of hazards in Gujarat state, India. The calculated four individual hazard maps have been categorised into five classes: very-low, low, moderate, high, and very high. The multi hazard map has been classified into sixteen classes using the GIS unsupervised. This study aims to improve disaster preparedness, enhance land management, or guide decisionmaking for disaster risk reduction. This study can be helpful in the future to engineers, planners, and local governments in the field of spatial planning and natural disaster management.