Topographic Index Research Articles

Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios

Flash floods are highly destructive, and their frequency and intensity are expected to escalate due to climatic changes. This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. To predict FFS, we evaluated twelve flood-influencing variables: elevation (EL), slope (SL), aspect (AS), drainage density (DD), distance to stream (DS), topography roughness index (TRI), stream power index (SPI), topographic wetness index (TWI), soil permeability (SP), precipitation (PR), land use and land cover (LULC) and normalized difference vegetation index (NDVI). Earth observation data, field surveys, and past flood records were used to create a detailed flood inventory. Among the machine learning models tested, the random forest (RF) algorithm outperformed others, including support vector machine (SVC), logistic regression (LR), and extreme gradient boosting (XGBoost), and was subsequently used for flood susceptibility mapping based on future precipitation projections under two Sixth Coupled model intercomparison project (CMIP6) climate change scenarios: SSP1-2.6 and SSP5-8.5. Our findings indicated that the areas at high to very high risk of flooding are projected to increase significantly under both the SSP1-2.6 and SSP5-8.5 scenarios. Initially, around 38 % of the studied region had high to very high flood susceptibility, but this is expected to rise to 40–42 % over the projected time periods. These spatial delineations of flood-prone areas can provide guidance for developing effective mitigation and adaptation strategies to address the adverse impacts of flash flooding in the hilly river basins of Bangladesh.

Rapid diagnosis of the geospatial distribution of intertidal macroalgae using large-scale UAVs

Macroalgae have been used as indicators of the health of coastal ecosystems, they function as sinks of CO2 and are essential contributors to primary production. With the increase in anthropogenic activities, it is crucial to assess the impact of such activities on these ecosystems. As traditional surveying techniques, although accurate, are time-consuming and their area coverage is limited, novel techniques are required to monitor the coverage and diversity of intertidal macroalgae. We propose a methodology using the free-source Semi-Automatic Classification Plugin from QGIS to use UAV and multispectral cameras for the spatiotemporal monitoring of intertidal macroalgae. We also compared the performance of six classifiers: Minimum Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapping (SAM), Multi-Layer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM), for three types of macroalgae classification: general, taxonomical groups and species. As proof of concept, an intertidal rocky shore in a marine protected area (NW Spain) was studied for four months. RF and SVM achieved similar results, with both being recommended for the general (OASVM = 97.4±1.7 and OARF = 98.3±1.7) and taxonomical groups (OASVM = 91.6±1.9 and OARF = 89.2±4.5). SVM and ML were found to be more suitable for species classification (OASVM = 77.4±11.4 and OAML = 74.2±9.7). SAM and MLP provided the least performant species classifiers because of the overlap in the macroalgae spectral signatures. The plugin showed limitations when tuning the input parameters of the MLP classifier and did not let to add a validation dataset. Additionally, we present an open-access GIS web application, Alganat 2000 GIS web, to facilitate the monitoring and management of coastal areas. We conclude that the proposed methodology using the SVM or ML classifiers is an effective tool for assessing intertidal macroalgal assemblages. Its easy and rapid implementation is beneficial for researchers who are not very familiar with coding and machine learning frameworks and reduces the time and cost of fieldwork. As future work, we propose the combination of the multispectral bands with topographic and spectral indices and to research the application of deep learning models to the classification of intertidal macroalgae.

Factors and effects of inter-individual variability in silver birch phenology using dense LiDAR time-series

Comprehending and quantifying local variability in plant phenology, alongside its impacts on tree growth, is challenging due to spatially and temporally heterogeneous environmental factors that interact to affect phenological events and periods. Although previous studies have focused on the climatic factors driving phenological events at the stand level, the influences of competition, neighborhood characteristics, species richness, and water availability on plant phenology remain unclear. In the present paper, we used hourly terrestrial LiDAR surveys performed between April 2020 and April 2021 to investigate the influence of local factors (neighborhood and topography) on the phenology of silver birch trees (Betula pendula Roth.). We also examined how phenological events affect growth in tree height and crown area. All response and explanatory variables were estimated using LiDAR time-series data and a field survey campaign. Our findings demonstrate a high within-species variability in plant phenology that is controlled by biotic and abiotic characteristics of the ecosystem. We found that between tree variation in leaf burst, completion of the leaf growth and length of canopy growth period were affected by tree size, neighborhood species richness, level of suppression and competition, which potentially indicate plant responses to light availability. The beginning of senescence, completion of leaf drop, and length of autumn phenology were mostly affected by topographic water index, which reflects water availability and can be linked to nutrient availability and exposure to wind. Furthermore, we demonstrated that an earlier leaf burst, and delayed beginning of senescence were associated with larger absolute growth in canopy area but found no clear relationship between height growth and phenology. Our study highlights the capability of dense, high-quality LiDAR time-series to detect significant phenological variation among individuals of the same species within the same locality, demonstrating the potential of LiDAR time-series as a tool for understanding phenology and its impacts.

Flood vulnerability map of the Bagmati River basin, Nepal: a comparative approach of the analytical hierarchy process and frequency ratio model

AbstractThe analytical hierarchy process (AHP) and frequency ratio model (FR), along with the integration of GIS, have proven to be successful approaches for assessing flood-prone areas. However, in Nepal flood vulnerability mapping based on GIS decision analysis is limited. Thus, this study focused on comparing the data-driven FR method and expert knowledge-based AHP technique in a GIS environment to prepare a flood vulnerability map for the Bagmati River basin, helping to explore the gap in flood vulnerability mapping methodologies and approaches. By combining all class-weighted contributing factors, like elevation, precipitation, flow accumulation, drainage density, soil, distance from the river, land use land cover, normalized difference vegetative index, slope and topographic wetness index, the study evaluated the efficiency of FR and AHP in assessing flood vulnerability maps. An inventory map of floods containing 107 flood points was created. Subsequently, the flood vulnerability maps generated using FR and AHP models revealed that 9.30% and 11.36% of regions were in highly vulnerable areas, respectively. Receiver operating characteristics validated the model outcomes, indicating that the FR model’s accuracy of 91% outperformed the AHP model’s 84% accuracy. The study findings will assist decision-makers in enacting sustainable management techniques to reduce future damage in the Bagmati basin.

Integration of geospatial techniques and analytical hierarchy process (AHP) ind demarcating groundwater potential zones in Lakhimpur district, Assam, India

Overexploitation and climate change have threatened the availability and sustenance of groundwater resources. A proper understanding of the regional distribution of groundwater is crucial to ensure long-term water security. The present study aims to identify the groundwater potential zones in the Lakhimpur district of Assam using the Analytical Hierarchy Process (AHP) in combination with geospatial technologies. The occurrence of groundwater in the region was determined by several factors including geomorphology, lithology, slope, distance from the river, drainage density, lineament density, rainfall, curvature, soil, land use, land cover, Normalized difference vegetation index (NDVI), and topographic wetness index (TWI). These factors organized as thematic layers were utilized to generate a groundwater potential zones (GWPZ) map in the GIS environment. The AHP, an effective decision-making technique, was adopted to assign weights to each thematic layer corresponding to their relative importance in influencing groundwater availability. The GWPZ map prepared using the weighted overlay techniques was categorized into three classes: good, moderate, and poor. The result revealed that the good potential zone comprises 1909.41 km2 (65.12%), moderate 1018.25 km2 (34.72%) and the poor zone comprises 4.22 km2 (0.14%) of the total geographical area. The obtained results of 73.33% (Overall accuracy), 0.708 (ROC-AUC), and 0.50 mbgl (groundwater level fluctuation) between pre-monsoon and post-monsoon prove that the model has performed satisfactorily in identifying groundwater potential zones. The findings provide a framework for the effective exploration and management of groundwater resources, ensuring their future availability in the region.

Delineation of groundwater potential zones using multi-criteria analysis (AHP), frequency ratios (RF), remote sensing and GIS: a case study of the Batcham municipality (west Cameroon)

Groundwater resource management and development should be done sustainably, which calls for accurate assessment based on new methods and scientific principles. There is an increasing demand for potable water supplies, to meet the woefully inadequate domestic water needs in the Batcham municipality (west of Cameroun). The dearth of knowledge on sustainable management of water resources is to blame for this predicament. As integrated methodologies are not used in groundwater exploration programs, the current problem has gotten worse. Innovative scientific principles of groundwater resource exploration are required to effectively counteract the non-rationalization of water and the problem of unacceptable failure rates in drilled water borehole development. This will improve the appropriate and sustainable use of these resources. Therefore, the goal of this research is to map groundwater potential zones by utilizing readily available datasets and remote sensing (RS), GIS, Frequency Ratios (FR), and multi-criteria analysis (AHP) techniques. The database used is made up of geological data, hydroclimatic data, well data, and satellite images. Each of the methods used is based on the study of the relationships between the factors controlling the groundwater potential. Among these factors, ten (10) were retained based on previous work; these are slope, elevation, land use, land cover, lineament density, drainage density, topographic wetness index, curvature, geology, rainfall, and the static water level. The AHP allowed, according to the user's expertise, to assign weights to each factor controlling the water potential. The FR made it possible to study the relationships linking each variable with high flow rates. The thematic layers obtained were integrated into a GIS environment to generate the groundwater potential map. Four classes of groundwater potential have been identified: bad, moderate, good, and excellent. The bad classes represent 8.32% and 11.21%; moderate 36.57% and 28.70%; good 43.55% and 38.93%; and excellent 11.56% and 21.16%, for the AHP and FR models, respectively. The overlying of wells with high flow rates on areas of excellent groundwater potential shows perfect concordance, which validated the final map. Likewise, the resulting map was validated by sensitivity curve analysis, and the receiver operating characteristic (ROC) curve from which result accuracy was obtained is 70.63% and 80.28% for the AHP method and FR method, respectively. Finally, the FR model outperformed the AHP model in terms of performance and it is possible to use these dependable models to describe groundwater potential zones in the other regions of Cameroon.

Associations between endothelial cell characteristics and corneal topography findings in different stages of keratoconus.

This study aimed to compare and correlate specular microscope indices and corneal topography indices in different stages of keratoconus. Two hundred forty-six eyes of 123 participants were enrolled in the study. Corneal topography was performed using Sirius (CSO, Italy), with a rotating Scheimpflug camera and a Placido disc topographer. Corneal endothelial cell indices were assessed using a specular microscope (Nidek CEM-530, Japan). Eyes were graded as keratoconus stages 0-4 according to the Amsler-Krumeich classification. Corneal topography and endothelial cell indices were compared among the groups, and the correlations between them were analyzed. The mean age of the patients was 23.26 ± 6.75years (range, 14-47years). Forty-eight cases were male (39%) and 75 were female (61%). There were no statistically significant age (p = 0.578) or sex ratio (p = 0.529) differences between the groups. Twenty-nine eyes were included in the control group (11.78%), while 41 (16.67%) had stage 1 keratoconus, 88 (35.77%) had stage 2, and 88 (35.77%) had stage 3. Measurement was not possible in stage 4 keratoconus. No statistically significant difference was determined in specular microscopy values according to the stage of keratoconus, except for the number of analyzed cells (NUM) (p > 0.05). The lowest NUM values were observed in stages 1, 2, and 3, with values of 184.34 ± 67.62 cells/mm2, 155.07 ± 59.48 cells/mm2, and 127.06 ± 64.39 cells/mm2, respectively (p = 0.001). In the keratoconus group, weak statistically significant negative correlations were observed between NUM and SimK1, SimK2, KVf, BCVf, KVb, and BCVb, while a weak positive correlation was noted between NUM and central corneal thickness (p < 0.05). NUM seems to decrease, while endothelial cell density exhibits no significant changes, with the progression of keratoconus. It appears that as keratoconus index values increase, NUM may decrease in different stages of keratoconus.

Flood Susceptibility Mapping Using Analytic Hierarchy Process and Geographic Information System in Baitarani Basin of Odisha, India

Baitarani, the 6th largest river basin in Odisha, eastern India, faces water related challenges in its catchments. As one of the most frequent environmental disasters, floods are consistently cited in hotspots of the eastern region in India. Hence, in this study, flood susceptibility maps were generated for Baitarani River basin by using analytical hierarchy process technique and geographic information system. Eleven thematic maps of topographic ruggedness index, topographic wetness index, stream power index, elevation, lithology, soil, slope, drainage density, distance from drainage network, land use/land cover and normalized difference vegetation index were prepared for flood vulnerability mapping. The themes were assigned suitable weights depending on relative influence of each of the themes on flood risk. The results indicated that 87% of the basin falls under intermediate flood susceptibility zone, while 10% is classified under high flood hazard zone. Areas in the lower elevation catchment, particularly those with flat topography near the coastline, were identified as being highly susceptible to flooding. The impacts of severe flooding in the basin are most pronounced in specific blocks in Jajpur district (Jajpur, Dasarathpur, and Korei blocks), Bhadrak district (Dhamnagar and Bhandaripokhari blocks), and Anandapur block in the Keonjhar district of Odisha. Additionally, changes in land use/land cover during 1995-2020 revealed 8% decline in agricultural land and 13.93% increase in fallow land in the area. The outcomes of this research can help researchers, planners and managers in managing natural resources, find locations that could be vulnerable to flooding, and lessen the damage.

Geospatial Intelligence for Landslide Susceptibility and Risk Analysis: Insights from NH31A and East Sikkim Himalaya Settlements

Slope instability is a serious concern in the Sikkim Himalayas. The town and numerous road segments along National Highway 31A were ravaged by multiple landslides that occurred in the nearby region. A bivariate statistical method known as frequency ratio (FR), information value (IV), and certainty factor (CF) analysis was employed in this work to examine landslide risk assessment (LRA) and landslide susceptibility zonation (LSZ) maps in the Rorachu watershed. This study represents the first comprehensive analysis of landslide risk in the populated areas of East Sikkim and along NH31A, offering a deeper understanding of the risks involved and contributing to the enhancement of local resilience against landslide hazards. A total of 153 different landslide locations were mapped using Google Earth and GIS software; 30% (46) of these locations were used to validate the models, and 70% of these (107) served as training data for the FR, IV, and CF models. The thirteen landslide causative factors (geology, soil, elevations, slope, curvature, drainage density (DD), road density (RD), rainfall, normalized difference vegetation index (NDVI), land use land cover (LULC), topographic position index (TPI), stream power index (SPI), and topographic wetness index (TWI)) were extracted from a spatial database for LSZ mapping. Landslides were most prevalent on slopes (35° to 50°), heights (2,500–4,100 m), and rainfall (2000–2,500 mm and 3,000–3,300 mm). The area under the curves (AUC) for the FR, IV, and CF models are 0.925 (92.50%), 0.846 (84.60%), and 0.868 (86.20%), respectively. The prediction rates are shown by the AUCs for the FR, IV, and CF models, which are 0.828 (82.8%), 0.750 (%), and 0.836 (83.60%), respectively. According to the landslide risk assessment (LRA), the FR (20.75%), IV (40.91%) and CF (18.78%) models showed high risk on Highway 31A, while the FR (9.05%), IV (38.59%) and CF (20.90%) models showed high risk in densely populated areas. These landslide risk and vulnerability maps can be used to develop land use planning strategies that can save lives and are useful for planners and mitigation measures. Special attention should be paid to urbanization, highway construction, and deforestation.

Assessment of shift in GWPZs in Kashmir Valley of Northwestern Himalayas

Groundwater is a critical resource for the Kashmir Valley, which is increasingly pressured by urbanization and climate change. This study aims to delineate Groundwater Potential Zones (GWPZs) in the Kashmir Valley using the Analytical Hierarchy Process (AHP) and Geographical Information Systems (GIS). The research integrates eight thematic layers, including geology, geomorphology, slope, drainage density, land use/land cover (LULC), lineament density, rainfall, and topographic wetness index (TWI), to assess GWPZs for 1995, 2010, and 2020. Weights for each layer were assigned based on their influence on groundwater dynamics. The generated GWPZs were classified into five classes: very high, high, moderate, low, and very low. Over 25 years, significant changes in GWPZs were observed: very low potential zones decreased by 45.17%, low potential zones by 26.17%, and very high potential zones by 72.95%. Conversely, moderate potential zones increased by 50.87%, and high potential zones saw a slight increase of 9.00%. Results indicated that high and very high GWPZs are predominantly located in the valley's alluvial plains, primarily covered by agricultural and partially horticultural land. Validation of the generated GWPZs using bore well locations, discharge data, and Receiver Operating Characteristic (ROC) curves demonstrated high accuracy, with Area Under the Curve (AUC) values of 0.915, 0.898, and 0.890 for the respective years. These findings underscore the adverse impact of urbanization on groundwater potential zonation and emphasize the need for sustainable water management practices. This study provides valuable insights into the long-term shifts in groundwater potential in response to urban expansion and climate change. It highlights the importance of continuous monitoring and adaptive management to safeguard groundwater resources.

Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India

Landslide susceptibility mapping is vital for disaster management and sustainable land-use planning. This research was conducted in Wayanad, Kerala, India, to identify landslide susceptible zones. The study used large geospatial datasets, such as elevation, slope, aspect, curvature, stream power index, topographic wetness index, land use and land cover, rainfall, flow accumulation, geology, and geomorphology. It is followed by the application of various machine learning and deep learning models such as the support vector machine, artificial neural networks, logistic regression, random forest, gradient boosting machine, recurrent neural networks long short-term memory, and deep neural network models to map the landslide susceptible zones. The model was trained and validated using the landslide inventory map, which contains 298 sites of landslides. The random forest model, with 97 % accuracy, performed best. It is possible to effectively mitigate landslides and plan long-term land use by identifying hazardous zones within the study region.

Changes in the Spatial Patterns of Near‐Surface Soil Moisture and Environmental Controlling Factors Before and After a Landslide

ABSTRACTRain‐induced landslides are common natural disturbances in forested headwaters that can strongly alter the spatial distributions of hydrological and environmental features. Although many studies have reported the impacts of landslides on the spatial patterns of soil moisture or their environmental controls, studies that have used detailed in situ data sets collected at the same location before and after a landslide are lacking. This study investigated the spatial distribution pattern of the near‐surface soil moisture and environmental controls, including topographic, soil and vegetation features, in a headwater catchment using ground‐based measurements with high spatial resolution after a landslide in 2016. The data set was compared to measurements taken at the same location before the landslide to explore whether the landslide altered the amount, spatial distribution, or potential controlling factors of near‐surface soil moisture. The mean soil moisture decreased across the site after the landslide, even under conditions of greater rainfall input than before the landslide. Spatial variation in soil moisture decreased in the high‐disturbance area but increased in the low‐disturbance area, although autocorrelation distances of soil moisture changed little. The relationships between the spatial mean and standard deviation of soil moisture markedly changed from a convex‐upward shape to a convex‐downward shape in the highly disturbed area. This indicates that the spatial mean of soil moisture exhibited its greatest spatial variation under moderate conditions before the landslide, with the lowest spatial variation occurring after the landslide. Most of the same controlling factors (i.e., slope gradient, topographic wetness index, vegetation density, soil porosity and saturated hydraulic conductivity) were explored after the landslide, but their influence levels greatly decreased or even disappeared. Thus, the landslide weakened the spatial connectedness between soil moisture and environmental features, which has not yet been restored 6 years after the landslide. We suggest that the connectedness between hydrological responses and environmental features is crucial for restoration. Their connectedness can serve as an indicator to identify the stage of ecological succession from the disturbances of a landslide.

GIS Application for Creating Potential Flood Map Using AHP: A Case Study in Chaq-Chaq Valley, Sulaymaniyah City, Kurdistan Region, Iraq

This study aims to analyze and classify the hazards of flooding within the Chaq-Chaq sub-basin, the Sharazoor-Piramagroon basin, Sulaymaniyah City, Iraqi Kurdistan Region. The selected area is exposed to flooding from time to time. The study adopted many thematic maps, namely the topographic wetness index (TWI), elevation, slope, precipitation, land use–land cover (LULC), normalized difference vegetation index (NDVI), distance from the river, distance from the road, and drainage density. Modern technologies like remote sensing, GIS, ENVI, and AHP programs are used to obtain a digital map showing the classification of the flooding hazard within the sub-basin area. The results showed that large parts, about 50% of the basin area, are exposed to high and very high classes of flood risk. In comparison, areas of medium risk constituted about 27% of the total area of the sub-basin. Parts with low and very low risk recorded a percentage estimated at 22% of the total area. Large parts of the city of Sulaymaniyah face a high and very high risk of flooding, significantly impacting the tourist areas and economic facilities situated in the valley basin.

Within‐field variation of crop yield loss from cover crops

AbstractThe amount of high‐resolution agricultural data has increased rapidly in the current decade. Integration of satellite multispectral imagery, combine harvester yield monitoring data, and soil moisture mapping allows managing for within‐field variation and better interpreting on‐farm experimentation. In this study, we investigated the effect of cover crops on yield in Finland by integrating Sentinel‐2 satellite imagery (normalized difference vegetation index), topographic soil moisture indexes, and high‐resolution yield data. The experiment was run by three farmers over 4 years and serves as an example for low‐cost on‐farm experimentation. Our results confirmed earlier findings that undersown cover crops result in approximately 5% yield loss. We also found that the effect is highly variable across farms and within fields. The highest yield losses were found in areas of the field, which were wetter in the spring seeding time. The competition between crop and cover crop could be observed in the vegetation maps for autumn and early summer. Combining NDVI and soil moisture maps allows delineating field zones, which require extra management to reduce the risk of yield loss from cover crop resource competition. Evaluating the overall effect of cover crops on yield would require replication on more farms. The within‐field variation results and workflow investigated in this study can guide placement of sampling areas within those fields.

A Multi-criterian Approach to Identify Groundwater Potential Zones in the Subarnarekha River Basin Using Integrated Analytical Hierarchy Process and Geospatial Technology

The intense extraction of groundwater and changing climate patterns have strained global groundwater supplies. As the demand for drinkable water rises worldwide, assessing groundwater availability and aquifer output becomes increasingly important. Geographic information systems (GIS) are being used more frequently for groundwater exploration due to their speed and provision of preliminary data. This study focuses on mapping groundwater availability in a minor tropical river basin in India, using a combination of GIS and the analytical hierarchy process (AHP). Ten thematic layers were generated and analyzed, including Geology, Geomorphology, Land use and land cover (LULC), Lineament density, Drainage density, Rainfall, Soil type, Slope, Topographic Wetness Index ( TWI), and curvature to delineate groundwater potential zones. AHP was employed to assign weights to each category based on their water retention properties. The resulting map was validated against existing groundwater data, demonstrating an accuracy of approximately 85%. The groundwater potential zones were classified as high, moderate, and, low. The moderate potential zone covered 59% of the basin, while the low and high potential zones constituted 29% and 11% respectively. High and low potential regions were limited to small sections of the basin. In conclusion, this research successfully mapped the groundwater availability in the Rarhu River watershed using GIS and AHP. The findings highlight the distribution of different groundwater potential zones, providing valuable information for sustainable water resource management in the area.

Flooding Hazard Vulnerability Assessment Using Remote Sensing Data and Geospatial Techniques: A Case Study from Mekkah Province, Saudi Arabia

Flash floods are catastrophic phenomena that pose a serious risk to coastal infrastructures, towns, villages, and cities. This study assesses the risk of flash floods in the ungauged Mekkah province region based on specific and effective morphometric and topographic features characterizing the study region. Shuttle Radar Topography Mission (SRTM) data were employed to construct a digital elevation model (DEM) for a detailed analysis, and the geographical information systems software 10.4 (GIS) was utilized to assess the linear, area, and relief aspects of the morphometric parameters. The ArcHydro tool was used to prepare the primary parameters, including the watershed border, flow accumulation, flow direction, flow length, and stream ordering. The study region’s flash flood hazard degrees were assessed using several morphometric characteristics that were measured, computed, and connected. Two different and effective methods were used to independently develop two models of flood vulnerability behaviors. The integrated method analysis revealed that most of the eastern and western parts of the studied province provide high levels of flood vulnerability. Due to it being one of the most helpful topographic indices, the integrated flood vulnerability final map was overlayed with the topographic position index (TPI). The integrated results aided in understanding the link between the general basins’ morphometric characteristics and their topographical features for mapping the different flood susceptibility locations over the entire studied province. Thus, this can be applied to investigate a surface-specific reduction plan against the impacts of flood hazards in the studied landscape.

Mitigating the extinction risk of globally threatened and endemic mountainous Orthoptera species: Parnassiana parnassica and Oropodisma parnassica

Abstract Orthoptera species are vulnerable to extinction on a global scale. Greece hosts 35% (380 species) of the European Orthoptera fauna with a high degree of endemic (37%) and threatened species (37%). We sampled 46 plots (100 m2) to investigate the distribution and ecological requirement of two Greek mountain endemic and red‐listed species: Parnassiana parnassica (Ramme, 1926; Orthoptera: Tettigoniidae; Critically Endangered [CR]) and Oropodisma parnassica (Scudder, 1897; Orthoptera: Caelifera; Endangered [EN]). Species had a restricted geographical range, with two isolated populations confined to high altitudes (1527–2320 m) of Mts. Parnassos and Elikonas. Species distribution models showed that slope affected their suitable habitat, together with the topographic position index and the annual temperature range (P. parnassica), and the amount of green vegetation and evapotranspiration (O. parnassica). Connectivity analysis showed that P. parnassica‐suitable habitat consisted of few larger and well‐connected patches (26 patches: effective mesh size of 1.57 km2) and that O. parnassica‐suitable habitat consisted of more but smaller and less connected patches (56 patches: effective mesh size of 0.3 km2). Generalised linear models showed that the population density of P. parnassica was negatively influenced by the height of herbaceous vegetation and that of O. parnassica was positively influenced by altitude. The species face three main imminent threats: land take, wildfires and global warming, whereas livestock grazing seems to have a positive impact and skiing a neutral impact on their populations. We assessed both species as EN after International Union for Conservation of Nature (IUCN) criteria and a suite of conservation measures are suggested for their status improvement.

Geospatial Modelling of Flood Susceptibility of the Calabar City, Cross River State, Nigeria

Flooding remains a major environmental problem in many parts of the world including Nigeria, causing untold losses. This study modelled the flood susceptibility of Calabar Metropolis using frequency ratio and geographical information system (GIS). Direct field observation and key informant approaches were used to obtain 127 previous and current flood locations in the study area from which a database of historical flood occurrence was developed. Using the geostatistical analyst tool within a geographical information environment the flood locations were split into two parts in the ratio of 70:30 percent for the training and testing processes, respectively. Eight flood conditioning factors (elevation, slope, aspect, curvature topographic position index, topographic wetness index, land cover and normalized difference vegetation index) were extracted from SRTM-30m DEM and Landsat 8 data accordingly and used in the geospatial analysis of flood occurrence within the GIS. The frequency ratio model was then developed using the training dataset. The final product was the flood susceptibility map of the study area. The results revealed that 9.3 percent of the study area was considered to have very high flood susceptibility, 19.97 percent was classified as high flood susceptibility while 31.79 percent was under moderate risk area. About 39 percent of the study area was classified at least under low flood susceptibility. The order of contribution of the conditioning factors to the model were topographic position index (TPI), elevation, normalized difference vegetation index (NDVI), land cover, curvature, aspect, slope, and topographic wetness index (TWI). The validation of the model using the receiver operating characteristics (ROC) curve returned an AUC value of 74.81 percent. Hence, the result was acceptable for the prediction of flood locations in the study area. The findings would assist urban planners, environmental managers, risk reduction and management agencies, land developers, policy makers, and indeed the general public.

Картографирование почв на основе аэрофотосъемки с БПЛА

The article presents the results of research on the creation of a detailed soil map (scale 1:2500) using the example of a land plot of the Federal State Budgetary Educational Institution of Higher Education Perm State Agrarian University. The purpose of the research is to improve the methodology of detailed soil mapping using an unmanned aerial vehicle (UAV) and methods of geoinformation analysis of aerial photographs. The total area of the plot is 13.4 hectares, on which a UAV survey was carried out on September 12, 2023, and a soil survey was carried out on a detailed scale from June 1 to 24, 2024. Soil mapping was carried out on the basis of field data and a digital elevation model created by photogrammetry methods based on UAV survey materials. The map creation process is automated by creating geoprocessing models in QGIS model builder. Based on the results of creating a detailed soil map, a conclusion was made about the close relationship of soils with characteristic relief elements, which are reflected in classes according to the topographic position index TPI. Keywords: UAV, GEOINFORMATIONAL MAPPING, SOIL MAP

Prediction of Landslide Susceptibility in the Karakorum under the Context of Climate Change

Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the spatial distribution of landslides, ignoring temporal dynamics in landslide development in the context of climate change. In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. The assessment and prediction were based on a catalog of climate change-related landslides over the past 20 years, the causative factors, and predicted climatic variables for the Shared Socioeconomic Pathways (SSP1-2.6) scenario. The results indicated that elevation, slope, groundwater, slope length gradient (LS) factor, Topographic Wetness Index (TWI), valley depth, and maximum precipitation were the key causes of slides below the snow line. The key factors causing debris flow above the snow line were elevation, slope, topographic relief, aspect, LS factor, distance to the river, and maximum temperature. The accuracy of slide and debris flow susceptibility was 0.92 and 0.89, respectively. The area of slides with medium, high, and very high susceptibility is 25.5% of the Taxkorgan. In addition, 82.6% of the slides happened in this region, and 49.5% of the entire area is covered by debris flows with medium, high, and very high susceptibility. Moreover, this area accounts for 91.8% of all debris flows. Until 2060, the region’s climate is anticipated to become warmer and wetter. Slides below the snow line will gradually decrease and shift eastward, and debris flows above the snow line will expand. Our findings will contribute to the management of landslide risks at the regional scale.