Topographic Position Index Research Articles

Dynamic simulation of landscape ecological risk in Gansu’s farming-pastoral ecotone based on topographic gradients

The farming-pastoral ecotone in Gansu Province, a crucial green barrier in northwestern China, faces a conflict between economic development and environmental fragility. Scientifically assessing the landscape ecological risks in this region is essential for developing an early warning system for ecological security and ensuring the sustainable management of regional ecosystems. This study systematically analyzes the dynamic evolution patterns and clustering characteristics of landscape ecological risk along the topographic gradient in the area, using Land-Use and Land-Cover Change (LUCC) data from five periods (each period representing 10 years) between 1980 and 2020, as well as Digital Elevation Model (DEM) data. Methods such as CA-Markov and the Topographic Position Index (TPI) were employed to assess these risks. Additionally, the study simulates the spatial distribution patterns of ecological risks in future years. The results reveal that from 1980 to 2020, the area of built-up land in the farming-pastoral ecotone of Gansu Province increased by approximately 82%, with roughly 21% of the expansion occurring at the expense of grasslands and 53% from arable land. Ecological risk in the study area exhibits a spatial distribution pattern characterized by lower risk in the southwest and higher risk in the northeast. Overall, ecological risk initially increased and then decreased. The area of high-risk zones has steadily decreased by approximately 50% since 2010, primarily in regions with low topographic gradients and high human activity. In other areas, high-risk levels are directly proportional to the topographic gradient, showing a high degree of spatial clustering. Consequently, land-use planning should be tailored to local conditions, and a long-term monitoring mechanism should be established. This study provides novel insights into the spatiotemporal changes in landscape ecological risk in ecologically vulnerable areas, focusing on topographic gradients.

Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML

Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims to evaluate the flash flood susceptibility in the Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by the H2O automated ML platform. The best-performing model was used to generate a flash flood susceptibility map, and its interpretability was analyzed using the Shapley Additive Explanations (SHAP) tree interpretation method. The results revealed that the top four models, including both single and ensemble models, demonstrated high accuracy in the tests. The flash flood susceptibility map generated by the best-performing eXtreme Randomized Trees (XRT) model showed that 8.92%, 12.95%, 15.42%, 31.34%, and 31.37% of the study area exhibited very high, high, moderate, low, and very low flash flood susceptibility, respectively, with approximately 74.9% of the historical flash floods occurring in areas classified as moderate to very high susceptibility. The SHAP plot identified topographic factors as the primary drivers of flash floods, with the importance analysis ranking the most influential factors in such descending order as DEM, topographic wetness index, topographic position index, normalized difference vegetation index, and average multi-year precipitation. This study demonstrates the benefits of interpretable machine learning, which can provide guidance for flash flood mitigation.

Chlorophyll and topographic patterns demonstrate stress conditions drive the brightness of autumn leaf colour

Abstract Autumn leaf colour brightness is an important cultural ecosystem service. As its spatial patterns and ecophysiological mechanisms remain unclear, we analysed relationships among autumn leaf colour brightness, late summer chlorophyll content, and topographic position in both canopy‐based micro‐scale analysis and site‐based macro‐scale analysis. Multispectral drone observations were made in three Fagus crenata forests at elevations of 300, 600, and 900 m in Niigata Prefecture, Japan. In a drone‐acquired digital surface model of canopy area distribution, we analysed the canopy‐averaged autumn leaf colour brightness, four vegetation indices, topographic position index, and elevation. The macro‐scale pattern showed brighter yellow leaves at lower elevations, suggesting the effect of temperature through productivity or evaporative stress. The micro‐scale pattern showed brighter yellow leaves in canopies that had a low late summer chlorophyll content, or that grew on ridges. The negative correlation between autumn leaf colour and chlorophyll content suggests that low‐chlorophyll trees experience stresses (e.g. irradiance, desiccation, senescence) that induce higher carotenoid content to defend against such stresses. Increased autumn leaf coloration on ridges is consistent with a stress gradient. Although further research is needed to reveal the underlying physiological and ecological mechanisms, autumn leaf colour brightness has different meanings at different scales and thus has potential not only as a cultural ecosystem service but also in forest management through early detection of stress.

Investigating soil erosion vulnerable zones based on clustered geoinformatics approach: a case study of Tyume River Catchment, Eastern Cape, South Africa

Rigorous field surveys, environmental specificity, and data paucity hamper detailed soil erosion assessments, model selection, ecological monitoring, and prioritization against soil erosion. To address this in a topographically complex environment, the present study presents a novel selection of physiographic factors integrated geospatially with the land use/cover and geology data to prioritize the soil erosion vulnerable areas within a watershed, using Tyume River Catchment, Eastern Cape, South Africa as a case study. A quantitative morphometric analysis involving parameters such as the drainage density, topographic wetness index, terrain ruggedness index, topographic position index, and vector roughness measure was computed using a digital elevation model based on their inference of watershed's morphogenetic response to anthropic factors and pluviometric processes. Based on expert judgment for thematic ranking and weightage, the soil erosion prioritization area map was generated through weighted overlay analysis of the morphometric parameters with land use land cover and surficial lithology themes. The results depicted a catchment-scale soil erosion vulnerability map, classified into very high (40 km2), high (135 km2), medium (209 km2), low (186 km2), and non-vulnerable (113 km2) zones. Using Google Earth image analyses through the coefficient of determination (R2 = 0.563) and Receiver Operating Characteristics Curve (AUC = 0.899), the model corroboration indicated that the soil erosion vulnerability assessment is reliable and highly predictive. The study identified free-range animal operation and hillslope overgrazing, especially in riparian zones, as the environmental practices aggravate the catchment's terrain susceptibility to soil erosion. The assessment showed that some of the selected morphometric parameters could be used to improve the validated soil erosion models in mountainous regions. Due to the high precision of the engaged approach and the identified environmental concerns, the method can be adopted in similar environments.

Hybrid delineation of landforms: Case of Bystrytsia-Pidbuzka drainage basin

Landforms, together with geological substrate and geomorphic processes, are essential for delineation of geoecosystems, which are indispensable for environmental management at landscape and regional levels. Geomorphometry is a geographic information system technology, which affords quantitative land-surface analysis and landform delineation using digital elevation models (DEM). Application of geomorphometry has been fostered by the emergence of free global high-resolution DEMs. However, automated geomorphometric extraction of landforms for flat areas, like wide river valleys, may be problematic owing to insufficient accuracy of the DEM. We selected the Bystrytsia-Pidbuzka drainage basin of 500 km², which has a transitional location between low flysch External Carpathian Mountains and wavy denudation-alluvial plains of the Fore-Carpathian Upland in Lviv Oblast (Ukraine), to test a hybrid methodology of landform delineation – manual and automated geomorphometric. We considered regional landforms as orotectonic units (morphostructures), which are used to characterize ecoregions, and morpholithotopes as the smallest local landforms – mesorelief elements together with surface deposits and current geomorphic processes. FABDEM V1-2 with a resolution of 30*30 m was used as a primary geodataset of elevation data. Ecoregions and morpholithotopes of wide flat valley bottoms were delineated via manual interpretation of the DEM and ancillary data, while the morpholithotopes of the hilly and mountainous interfluves were delineated automatedly using three topographic variables: topographic position index, slope, and flow accumulation. Within the study area, we singled-out six microecoregions and four mesoecoregions: Upper Dnister Depression, Upper Dnister Upland, Marginal Beskydy, and Dnister Beskydy. There are 21 classes of morpholithotopes distinguished belonging to flat valley bottoms with alluvial and lacustrine deposits, narrow valleys and big gullies, as well as lower concave and upper convex slopes formed by colluvial and eluvial-colluvial deposits respectively, divided into four inclination categories, and two lithological groups. Each morpholithotope class is attributed with a drainage status and probable current geomorphic processes. The obtained dataset is oriented on further ecological application. Key words: morpholithotope; ecoregion; geomorphometry; FABDEM; the Carpathian Mountains; the Fore-Carpathian Upland.

Niche based distribution modelling and mapping of Brutian pine in the Gölhisar district

The purpose of this work was to elucidate the fundamental characteristics and application areas of various modelling techniques that are widely employed in current ecological modelling research. Using five distinct distribution modelling techniques, possible species distribution modelling and mapping of the Brutian pine species in the Gölhisar district were conducted. The data was collected from Brutian pine species in 400 sampling plots in the area. The variables used in the models were elevation, slope, aspect, radiation index, heat index, topographic position index and bedrock types. Logistic regression, classification tree, random forest, generalized additive model and maximum entropy were used as the species distribution modelling methods. Receiver Operating Characteristics (ROC) curves were created and the performance of the species distribution models was evaluated with the Area Under the ROC curve (AUC). The statistical analyses revealed that the best models were generalized additive model, random forest, classification tree, maximum entropy and logistic regression, respectively. Elevation and bedrock types had the highest contribution to the Brutian pine distribution models. The outputs of the generalized additive model technique that had the highest AUC value were mapped. Some ecological and statistical differences were found between the models and their reasons were presented. Compared to the methods commonly used in species distribution modelling studies, generalized additive model technique has a specific smoothing function which ensures both fittings between the envirenvironmental changes and explanatory curves and more accurate ecological interpretation of the models obtained.

Contrasting topography-vegetation relationships at natural and human-influenced mountain treelines in the Peruvian Andes

ContextThe tropical Andes have a long history of human land use, which has affected vegetation patterns especially at high elevations. However, reference patterns to quantify these effects are missing due to lacking data from mountain ranges unaffected by humans.ObjectivesWe compared landscape-scale patterns of forest distribution in the largest tropical Andean mountain range without high-elevation human occupancy, the Cordillera de Vilcabamba, and a nearby range with a long land-use history, the Cordillera de San Miguel, in Peru.MethodsWe quantified topographical positions (exposition, slope, wetness index) of 12.5 × 12.5-m2 pixels with and without forest cover in 100-m elevational bands and compared the study regions.ResultsIn the undisturbed range (Vilcabamba), forest consistently occurred on steeper slopes than non-forest, differing little in other topographic metrics except a slight preference for the less sunny South- and West-facing slopes. In the disturbed range (San Miguel), forest showed little preference for any landscape position at elevations below 3500 m, but at higher elevations patterns were very clear: forest was more restricted to valley bottoms, as indicated consistently by higher topographic wetness and lower slope and topographic position indices, and to the less sunny South-facing slopes.ConclusionsLand-use has a strong influence on landscape-level forest distribution in this part of the tropical Andes. Similar effects are likely in other tropical mountains, although no reference areas are available in most of them. The patterns described here can support the use of remote sensing to detect minimally disturbed tropical alpine-treeline ecotones, which should be focal points for conservation and research.

A hybrid approach: remote sensing, analytic hierarchy process and geographic information system for groundwater potential mapping in Dediapada, India.

In recent years, the overexploitation of groundwater accompanied by a significant climatic shift has placed a burden on the world's groundwater supply. This has generated a rising need for potential groundwater zones. In recent year the ever-increasing demand for potable water across the world for various groundwater exploration investigation. The present study explores the utility of remote sensing (RS) and geographic information system (GIS) to identify potential water zones in the dry deciduous forest area of Dediapada Taluka, Narmada District, Gujarat, India. Twelve thematic layers of different environmental variables including geology, geomorphology, land use/land cover (LULC), drainage density, lineament density, rainfall, soil, slope, roughness, topographic wetness index (TWI), topographic position index (TPI), and curvature were integrated. GIS mode was subjected to weight analysis using Saaty's 9-point scale. The result was normalized using analytic hierarchy process (AHP), and five distinct groundwater potential zones (GWPZs) were identified namely very high, high, moderate, low, and very low. The area occupied by each category was 6.52%, 3.1%, 60.1%, 14.32%, and 15.99%. The output aids in validation assessment with the existing well data. The groundwater potential zones maps generated using AHP technique can prove beneficial for designing strategies for water management for the present taluka and can be extended to the different talukas also.

Automatic identification of streamlined subglacial bedforms using machine learning: an open‐source Python approach

Subglacial processes exert a major control on ice streaming. Constraining subglacial conditions thus allows for more accurate predictions of ice mass loss. Due to the difficulty in observing large‐scale conditions of the modern subglacial environment, we turn to geological records of ice streaming in deglaciated environments. Morphometric values of streamlined subglacial bedforms provide valuable information about the relative speed, direction, and maturity of past ice streams and the relationship between ice streaming and subglacial erosion and deposition. However, manually identifying streamlined subglacial bedforms across deglaciated landscapes, sometimes in clusters of several thousand, is an arduous task with difficult‐to‐control sources of variability and human‐biased errors. This paper presents a new tool that utilizes a machine learning approach to automatically identify glacially derived streamlined features. Slope variations across a landscape, identified by topographic position index, undergo analysis from a series of supervised machine learning models trained from over 600 000 data points identified across the deglaciated Northern Hemisphere. A filtered data set produced through the combination of scientifically driven preprocessing and statistical downsampling improved the robustness of our approach. After cross‐validation, we found that Random Forest detected the most true positives, up to 94.5% on a withheld test set, and an ensemble average of machine learning models provided the highest stability when applied within the range of applicable data sets, performing at up to 79% identification of true positives on an out of distribution area of interest. We build these models into an open‐source Python package, bedfinder, and apply it to new data in the Green Bay Lobe region, USA, finding the general ice‐flow direction and average streamlined subglacial bedform elongation with minimal effort. This type of open, reproducible machine learning analysis is at the leading edge of glacial geomorphology research and will continue to improve with integration of newly acquired and previously collected data.

Impact of Microtopography and Neighborhood Effects on Individual Survival Across Life History Stages.

Understanding drivers of plant community assembly and individual survival in forest ecosystems is crucial for effective conservation and management. While macro-scale factors influencing vegetation patterns are well documented, the combined impact of microtopographic variations and neighborhood effects at neighborhood scales, particularly in subtropical forests, requires further study. To contribute to this area of research, we established a 9.6 ha dynamic plot in a subtropical evergreen broad-leaved forest to examine the interplay between microtopographic factors and neighborhood effects on individual plant survival across different life stages. We conducted a comprehensive analysis of microtopographic variables and neighborhood effects, with individual plant survival censused through repeated surveys at 5-year intervals. Mixed-effects models were employed to assess the combined influence of these factors across life stages. Our results reveal that both microtopographic factors and neighborhood effects significantly influence plant survival, with their impacts varying across life stages. Water availability, represented by flow direction, emerged as a consistently critical factor throughout all life stages. Elevation and the topographic position index showed significant positive effects on survival, particularly in later life stages, possibly reflecting adaptations to light acquisition and water drainage. The influence of topographic factors intensified with succession, while the impact of neighborhood effects, particularly asymmetric competition and conspecific negative density dependence, changed as plants matured. This study enhances our understanding of forest community assembly, emphasizing the importance of considering abiotic and biotic factors across multiple scales for effective forest conservation and management. It provides insights into mechanisms driving spatial variation in community composition, crucial for preserving biodiversity in heterogeneous forest landscapes.

Rock glacier inventory and predictive modeling in the Mackenzie Mountains: predicting rock glacier likelihood with a generalized additive model

Rock glaciers have been the subject of extensive research in recent years due to their potential to serve as indicators of past and present climate conditions and their potential impacts on water resources. Location and descriptive rock glacier data within the Mackenzie Mountains were used to build a rock glacier inventory that will serve as a valuable resource for future research and monitoring efforts. Additionally, this study maps the likelihood of rock glacier presence using extracted variables in a generalized additive model (GAM). The model incorporates attribute data, including potential incoming solar radiation (PISR), topographic position index (TPI), slope, elevation, and lithology as controls for rock glacier development. Topographic data were compiled for three study regions of the Mackenzie Mountains from a 30 m digital elevation model (DEM). The analysis of the GAM showed that the most significant explanatory variables were PISR, elevation, slope, and TPI. The GAM model had an accuracy of 0.87 with a sensitivity of 0.92. This study provides important insights into the controls, distribution, and dynamics of rock glaciers in the Mackenzie Mountains, as well as both the limitations and the potential of statistical models in predicting their occurrence.

Geomorphic characterization by integrating automated approaches for sustainable land use planning in the earthquake-prone Himalayan region of Uttarakhand, India

This study leverages two cutting-edge automated landform classification approaches—Topographic Position Index (LCTPI) and Geomorphon (LCGm)—to map and categorize landforms in Uttarakhand, a strategically vital region in the Himalayas frequently impacted by natural hazards. Situated at the subduction boundary of the Indian and Eurasian tectonic plates, Uttarakhand's complex landscape demands innovative approaches for sustainable land use planning. Our analysis links classified landforms to critical factors such as slope, geology, soil types, land use land cover (LULC) patterns, fault density, and regional seismicity. Results reveal that high ridges, deep valleys (LCTPI), and slopes (LCGm), predominantly covered by trees and rangelands, are associated with lithosols and dystric cambisols soil types from the Precambrian period. In contrast, flat areas, shoulders, footslopes, U-shaped valleys, open slopes, and mild slope ridges, mainly used for agriculture and urban development, exhibit lower seismic activity. Notably, shallow earthquakes are more frequent in deep valleys and high ridges, which have low to moderate fault density and are majorly occupied by trees and rangelands. This research offers a robust framework for geomorphic characterization that can be adapted to other regions, despite the inherent limitations of automated studies, such as DEM resolution and neighbourhood cell size. Our approach supports sustainable land use planning, biodiversity preservation, natural resource management, and disaster risk reduction, ultimately fostering resilient futures in the built environment. This study emphasizes the necessity of integrating geomorphic insights into the planning process to mitigate natural hazards and enhance environmental sustainability in the Himalayan region by providing actionable insights for land use planners and policymakers.

Multi-Approaches for Flash Flooding Hazard Assessment of Rabigh Area, Makkah Province, Saudi Arabia: Insights from Geospatial Analysis

Flash flood hazard assessment is a critical component of disaster risk management, particularly in regions vulnerable to extreme rainfall and climatic events. This study focuses on evaluating the flash flood susceptibility of the Rabigh area, located along the Red Sea coast in Makkah province, Saudi Arabia. Using advanced GIS tools and a spatial multi-criteria analysis approach, the research integrates a variety of datasets, including remotely sensed satellite data, the SRTM Digital Elevation Model (DEM), and topographic indices. The main goal was to produce detailed flood susceptibility maps based on the morphometric characteristics of the region’s drainage basins. These basins were delineated and assessed for their flood vulnerability using three distinct modeling techniques, each highlighting different aspects of flood behavior. The results show that the northern basin (Dulaidila) and the central basins (Rabigh, Algud, and Al Nuaibeaa) exhibit the highest flood risk, with significant susceptibility also observed in the southern basins (Ofoq and Saabar). Other basins in the region display moderate susceptibility levels. A key aspect of this analysis was the overlay of the integrated flood susceptibility map with the Topographic Position Index (TPI), a crucial topographic indicator, which helped refine the understanding of flood-prone areas by linking basin morphometry with in-situ topographic features. This study’s comprehensive approach offers valuable insights that can be applied to other coastal regions where hydrological and climatic data are scarce, contributing to more effective flood risk mitigation and strategic planning.

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.

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

Mining, farming, and diplomacy. Understanding the human landscape of Bronze Age Sardinia (Italy) through geospatial analysis

Human agency on landscape modification and land use is often seen in terms of socio-economic opportunities vs. natural constraints. In the study of prehistoric cultures this is both a strong source of information about sustenance strategies and community behaviours, and a subject potentially easy to analyse within a limited set of physical and social parameters. The recent advancements in the use of spatial analysis tools in landscape archaeology allow to obtain ever more precise models. However, studies that compare at the same time the geological landscape and social elements are very scarce. We used Point Pattern Analysis and Modelling to investigate megalithic structures (nuraghes) in Bronze Age southwestern Sardinia (Italy) and identify correlations between their spatial patterns and a set of covariates encompassing both environmental (i.e. topography and geological resources) and cultural factors. The models which best represent pattern distribution come from the combination of covariates from both groups.The models highlight a close distance from known ore deposits and show a clear dependence of Nuragic populations to ore extraction and metallurgy. The availability of fertile soils with moderate permeability and moderately low pH is also significant, as well as a preference to prominent locations with a positive correlation with the Topographic Position Index and the Convexity Index. From a cultural standpoint, we observed a consistent aggregation of simple nuraghes around complex nuraghes at mid-short distances. The occurrence of polycentric patterns can be explained either by the former emerging from the presence of the latter or vice versa, and is typically associated with a loosely stratified social structure devoid of strong hierarchies. These results underscore the efficacy of spatial analysis in disentangling and juxtaposing the physical and social factors influencing the distribution of past culture, and offer new insight on the development of Bronze Age societies in their geographical context.

Landscape positioning of Neolithic mustatil stone structures along the margins of the Nefud Desert, Saudi Arabia

Monumental rectangular stone structures called mustatils are an important emerging feature of the Holocene archaeological record of northwestern Arabia. To date, few have been excavated, with available radiocarbon dates suggesting an age range of ca. 5400–4200 BC. Here we present a rigorous spatial analysis to identify the patterning and landscape context of 169 mustatils in the southern and western margins of the Nefud Desert. This included: (1) a systematic survey of satellite imagery to identify mustatils; (2) viewshed analysis to examine location and landscape visibility; (3) a point process model to understand how diverse environmental and landscape variables affect mustatil locations; (4) mark correlation function to assess spatial patterning of mustatils based on their size. Results indicate that mustatil locations are determined most by proximity to water (likely locations of enhanced surface water occurrence under the enhanced humidity of the Mid-Holocene), on east facing slopes, close to rocky areas, at elevations between 880 and 950 masl, and on or near topographic ridges (positive topographic position index). Viewshed analysis showed that mustatils are preferentially located in areas that have good views, but not the best that are available, indicating complex landscape positioning that balances a range of topographic and behavioural factors. Using a rank permutation method with size (length) of mustatils as a proxy for labour mobilization we show that mustatils within clusters are not arranged hierarchically based on size, and were likely built by non-stratified groups of people. Our analyses show that people were choosing multiple factors when deciding where to build mustatils, and that the distribution of mustatils may relate to different groups of people resulting in the construction of complex ritual landscapes. As one of the earliest examples of large-scale monumental stone structure construction in global prehistory, understanding mustatils can enlighten us on human-environment interaction during the Neolithic.