Terrain Elevation Research Articles

Quantitative characterization of geomorphological and topographical features of debris-flow channels at the Alpe di Succiso mountain, Northern Apennines (Italy)

ABSTRACT This study combines the traditional method of geological and geomorphological mapping with a quantitative GIS analysis to examine landforms and debris flows channels. Using a high-resolution DEM (5 m) as a base, the geomorphological map highlights the various landforms around the Alpe di Succiso (Northern Apennines, Italy), including glacial and cryoclastic features, slope landforms and deposits due to gravity (particularly debris flow) and deposits due to running waters. The methodology applied to the channels is based on the geomorphometric parameters derived from the DEM, including Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), Slope Angle, and Elevation extracted every 10 m along each channel and grouping the channels in four classes based on their lengths. Within each class, multiple ranking criteria were employed for the identification of channels showing anomalous or common patterns. Debris flow channels exhibit anomalous patterns in the geomorphometric parameters, indicating significant variations in their topographical characteristics..

Design, modeling and cost analysis of 8.79 MW solar photovoltaic power plant at National University of Sciences and Technology (NUST), Islamabad, Pakistan

Climate change, as a critical global concern, has fueled our efforts to address it through different strategies. In response to the critical worldwide issue of climate change, we suggested a Photovoltaic (PV) system at the National University of Sciences and Technology (NUST) in Islamabad, Pakistan (latitude: 33.724530 N, longitude: 73.046869, terrain elevation: 552 m). Islamabad is located in a region blessed with enormous solar resources, boasting a daily horizontal solar irradiance of 1503.45 kWh/m2 and an average daily solar irradiance of 5.89 kWh/m2, with an exceptional solar fraction of 98.99%. The ambient air temperature, averaging 23.21 °C, reaches its maximum in June and its minimum in December. Our research thoroughly evaluates the system’s performance, accounting for various losses and utilizing modern PVsyst software. Over the course of 18 years, our PV system is expected to save 75,478.60 tons of CO2, the equivalent of planting 348,754 teak trees. Furthermore, the cost of energy generation is an affordable 0.0141 US $/kWh, much lower than traditional rates, including the Sherif cost of 0.028$/kWh. Along with the performance research, we conducted a detailed cost analysis, projecting the starting cost and cash flow, and discovered that the plant would be in surplus within 12 years of installation. Our system is positioned to generate 11,270,771 kWh/year with a respectable performance ratio (PR) of 76.2% and a Capacity Utilization Factor (CUF) of 16%. Our findings not only highlight the potential of renewable energy but also provide important insights for future sustainable energy programs.

Extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments

Abstract Point cloud maps constructed using 3D LiDAR, are widely used for robot navigation and localization. Few studies have utilized point cloud maps to extract terrain elevationinformation in front of a vehicle, which can be used as active suspension inputs to reduce vehicle bumps. In addition, the trajectories of dynamic objects in point cloud maps and global navigation satellite system (GNSS) data loss can affect the extraction of elevation information. To solve these problems, this paper proposes a framework for extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments. The framework consists of two modules: point cloud map construction and vehicle front terrain elevation information extraction. In the point cloud map construction module, a system for simultaneous localization and mapping (SLAM) is proposed, which is capable of building point cloud maps without GNSS. Furthermore, a dynamic descriptor-based dynamic object filtering algorithm is proposed which is applied to SLAM. Therefore, the SLAM system overcomes the influence of dynamic objects on vehicle position and attitude estimation, and there are no trajectories of dynamic objects in the point cloud maps built by the system. In the vehicle front terrain elevation information extraction module, the unscented Kalman filter is utilized to predict the vehicle position at the next moment. Based on the geometric features of the tire-ground contact area, the terrain elevation information of the tire contact area at the predicted position on the point cloud map is extracted. Experiments show that the algorithm in this paper overcomes the effect of dynamic objects and builds a vehicle point cloud map without dynamic objects under GNSS data loss, which improves the accuracy of the extraction of terrain elevation information in front of the vehicle.

Mapping cellular coverage suitability: a GIS-Based approach to identifying poor service areas

Cellular coverage is a critical component of modern telecommunications, providing essential services for communication, commerce, and emergency response. Despite its importance, many regions, particularly those with challenging terrain or low population density, experience inadequate cellular service. This study focuses on the commune of Laghouat, Algeria, employing a GIS-based Multi-Criteria Analysis (MCA) to assess cellular coverage suitability. Using ArcGIS Pro, four key factors were integrated: cell tower locations, terrain elevation, population distribution, and restricted areas. The methodology includes performing a Viewshed analysis to identify dead zones, converting population data into raster format to map demand, and digitizing restricted areas where telecommunication infrastructure development is prohibited. All factors were standardized to a common scale to ensure comparability and a weighted overlay analysis was conducted to generate a comprehensive suitability map. The map highlights areas of poor cellular coverage, guiding the placement of new infrastructure. Additionally, the research considers regulatory restrictions on land use, ensuring compliance with governmental policies. The resulting suitability map provides valuable insights for telecommunications providers and policymakers, enabling them to target underserved regions for infrastructure improvements while adhering to legal constraints. This study offers a robust, scalable framework for identifying and addressing cellular coverage gaps, applicable to other regions with similar geographic and demographic challenges.

Evaluation of gridded precipitation datasets in mountainous terrains of Northwestern Mexico

Study regionThe complex mountainous terrains of the Sierra Madre Occidental in northwestern Mexico. Study focusAcquiring high-resolution precipitation data in regions with limited conventional rain gauge coverage poses significant challenges. Gridded precipitation (GP) datasets, including gauge-based, satellite, and reanalysis products, provide a potential solution, but their reliability in areas with complex terrain and intricate precipitation patterns remains uncertain. This study comprehensively evaluates the performance of four GP datasets—AORC, CHIRPS, Daymet, and ERA5—in estimating precipitation. The evaluation was conducted at a daily, monthly, and seasonal timescale, further analyzing extreme precipitation, the influence of elevation, and spatial averaging across hydrologic basins, using as reference the NERN rain gauge data from 2002 to 2004. New hydrological insights for the regionResults indicate that Daymet and AORC are the most accurate GP datasets for daily and monthly timescales, respectively. All datasets improve in accuracy over longer timescales but face challenges during the wet summer monsoon months and extreme events, with Daymet performing relatively better. Terrain elevation had a minimal impact on overall dataset accuracy, though a slight improvement in precipitation detection was noted as elevation increased. This work provides valuable insights into the strengths and limitations of GP datasets in regions with complex terrain and orographically-forced convective precipitation, offering practical outcomes for climate and hydrologic studies in similar regions.

Unveiling Anomalies in Terrain Elevation Products from Spaceborne Full-Waveform LiDAR over Forested Areas

Anomalies displaying significant deviations between terrain elevation products acquired from spaceborne full-waveform LiDAR and reference elevations are frequently observed in assessment studies. While the predominant focus is on “normal” data, recognizing anomalies within datasets obtained from the Geoscience Laser Altimeter System (GLAS) and the Global Ecosystem Dynamics Investigation (GEDI) is essential for a comprehensive understanding of widely used spaceborne full-waveform data, which not only facilitates optimal data utilization but also enhances the exploration of potential applications. Nevertheless, our comprehension of anomalies remains limited as they have received scant specific attention. Diverging from prevalent practices of directly eliminating outliers, we conducted a targeted exploration of anomalies in forested areas using both transmitted and return waveforms from the GLAS and the GEDI in conjunction with airborne LiDAR point cloud data. We unveiled that elevation anomalies stem not from the transmitted pulses or product algorithms, but rather from scattering sources. We further observed similarities between the GLAS and the GEDI despite their considerable disparities in sensor parameters, with the waveforms characterized by a low signal-to-noise ratio and a near exponential decay in return energy; specifically, return signals of anomalies originated from clouds rather than the land surface. This discovery underscores the potential of deriving cloud-top height from spaceborne full-waveform LiDAR missions, particularly the GEDI, suggesting promising prospects for applying GEDI data in atmospheric science—an area that has received scant attention thus far. To mitigate the impact of abnormal return waveforms on diverse land surface studies, we strongly recommend incorporating spaceborne LiDAR-offered terrain elevation in data filtering by establishing an elevation-difference threshold against a reference elevation. This is especially vital for studies concerning forest parameters due to potential cloud interference, yet a consensus has not been reached within the community.

Terrain preview detection system based on loosely coupled and tightly coupled fusion with lidar and IMU

The ride comfort of emergency rescue vehicles is considerably affected by the terrain preview accuracy. In this paper, a robust, precise terrain preview detection system that integrates lidar with IMU is presented based on loosely and tightly coupled fusion. To evaluate the terrain measurement error, lidar measurement error and vehicle motion error are considered to achieve centimeter-level mapping and localization accuracy in disparate scenes. To reduce the localization cumulative error, the frontend laser odometer and backend factor graph optimization are innovatively combined, and the cumulative error compensation strategy is designed. Experimental validation is conducted in different scenes to assess the performance of the prototype and to validate the simulation results. The vehicle localization accuracy is greater than 4–8 cm, and the simulated and measured terrain elevation waveforms exhibit strong correlations, which verifies the reliability and validity of the simulation method. The elevation measurement accuracy reaches 87 %.

Habitat availability decline for waterbirds in a sensitive wetland: Climate change impact on the Ebro Delta

The highly biodiverse, productive and vulnerable areas, such as coastal wetlands, are increasingly threatened by human-induced disturbances, resulting in habitat loss. This habitat loss is a critical driver of biodiversity decline and significantly impacts species distribution and behavior, increasing the risk of extinctions. To address these concerns, we developed a Species Distribution Model (SDM) using a decade-long dataset of waterbird functional group occurrences in the Ebro Delta, with a particular focus on the Ardeidae family, which represents the most prominent species in the wetland. This model aims to predict potential habitat loss under various climate change scenarios, specifically those outlined in the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCP) 4.5 and 8.5, across three distinct time periods.Our SDM revealed that habitat suitability for waterbirds was notably influenced by temperature variations, increased cumulative precipitation, and lower terrain elevation. Predictions indicate an increase in available habitat for these waterbird species over the study periods considering the specified climate scenarios. However, the incorporation of sea-level rise and flooded areas into the models unveiled a dramatic decline in available areas for waterbirds in the long-term, reaching up to 50 % (RCP 4.5) and 70 % (RCP 8.5) reduction.Our study highlights the urgent need for conservation efforts to mitigate the substantial reduction in available habitat for waterbirds. This need is especially crucial due to the Ebro Delta's importance as a vital coastal wetland for many thousands of waterbirds throughout their entire life cycle. Immediate and targeted conservation actions are imperative to safeguard the essential habitats for these species.

Investigation of dynamic effect of rapid impact compaction

Dynamic compaction is a soil improvement technique which involves the repeated application of an impact load to a contact area of the soil surface induced by heavy weights. Experimental measurements were prepared for evaluation of the ground wave propagation induced by the rapid impact compaction. Influence of increase of subsoil stiffness during compaction and terrain configuration were investigated. When the shear strength of soil is exceeded during the penetration of the compaction footing, increase of peak particle velocities is slower but still clearly visible along the measurement line. The peak particle velocity is damped by the subsoil below 5 mm s−1 within 40–50 m from the source of excitation. Attenuation radius is affected by the terrain configuration, when terrain elevations cause the increase of acceleration amplitudes. Rigid bodies in the ground, such as foundation structures, could have similar influence. Dominant frequencies are in the interval from 20 to 40 Hz regardless the distance from the source of excitation or the density of the treated subsoil. Obtained results are preferably related to the given boundary conditions. However, wave propagation pattern and change of its characteristics during densification of the subsoil are applicable at another construction sites considering their particularities.

Interactive effects of elevation difference, slope variation, and terrain formation on road traffic crashes occurrences using triangular irregular network

AbstractNumerous factors contribute to traffic crashes, which result in fatalities and injuries. Road traffic crashes and their severity level are likely caused by variations in elevation, slope, and terrain formation. As far as our knowledge is concerned, the triangular irregular network (TIN) was not used to analyze dangerous zones of the road network with respect to traffic crashes' location. The main purpose of this study was to investigate a dangerous road network that was affected by traffic crashes using TIN. To address the goal, this study used elevation differences slope variation, and terrain between road traffic crash locations. The study used the intersection zone road network coordinates of traffic crashes in the city of Budapest that were recorded from 2017 to 2021. Using tools like ArcGIS and MS Excel, the study organized, modeled, and analyzed the data. Geometrical interval classification was used to classify the data in accordance with the characteristics of elevation deference and slope variation. Both inferential and descriptive statistics were engaged to explain, analyze, and summarize the findings. The results of this study demonstrated that the majority of traffic crashes occurred in locations with lower elevations and their differences, steeper/cliff slopes between points, and flat terrain. The finding was in contrast to what was observed in other studies. This study advocates that TIN is the optimal method and substitute for analyzing the risky zone of the road network at macroscopic level.

A novel multi-model ensemble framework for fluvial flood inundation mapping

Floods pose a significant threat to communities and infrastructure, necessitating timely predictions for effective disaster management. Conventional hydrodynamic models often encounter limitations in data requirements and computational efficiency. To overcome these constraints, we propose a novel multi-model ensemble framework integrating the flood extent and depth models for fluvial flood mapping. Various flood conditioning factors, such as terrain elevation and slope, flow direction, distance from the river, and latitude-longitude, were selected as model inputs, considering their relevance. The proposed framework was evaluated for predictive, extrapolative, and generalization capabilities. Results indicate that the proposed model successfully captures flood dynamics across a wide range of streamflow values, including unforeseen events, making it a valuable tool for predicting flood extent and depth. Overall, our approach offers a promising alternative to conventional hydrodynamic models, providing robustness, computational efficiency, scalability, automation, and integration with existing tools for flood inundation mapping tasks.

Three-Dimensional Modeling and Analysis of Ground Settlement Due to Twin Tunneling Using GIS

Ground settlement occurs because of the surrounding ground behavior during tunnel excavation. A high chance of its occurrence could cause the collapse of buildings; therefore, the accurate prediction and assessment of ground settlement are necessary when structures are concentrated in urban regions. This study leverages Geographic Information Systems (GIS) and 3D modeling to evaluate the effects of tunnel excavation on the ground settlement and damage of buildings along the Mandeok–Centum underground highway in Busan. It integrates the field topography with building data to simulate and visualize construction-induced interactions. Numerical analysis is used to assess the effects of the terrain elevation, building presence, excavation sequences, and lag distance between the twin tunnels on the settlement. The results indicate that high terrain elevation, dense building layouts, and shorter distances between tunnels increase settlement. Furthermore, this study deduces that bidirectional excavation causes a rapid increase in settlement compared with parallel excavation, which is evident from the variations in the inflection points during the excavation process. Finally, this study estimates the damage to buildings and ground settlements and visualizes risk maps using GIS, emphasizing the practicality of 3D modeling based on GIS.

Simulate the elastic wavefields in media with an irregular surface topography based on staggered grid finite difference

Abstract Wave equation forward modeling is a useful method to study the propagation regulation of seismic wavefields. Finite difference (FD) is one of the most extensively employed numerical approaches for computing wavefields in earthquake and exploration seismology. However, the FD approach relying on regular grids often struggles to calculate wavefields in regions featuring surface topographies. The elastic wave equation can more accurately describe the propagation of seismic wavefields in elastic media compared to the acoustic wave equation. We introduce a new FD scheme to calculate the elastic wavefields in an isotropic model with a surface topography. The novel approach can use a conventional staggered grid FD (SGFD) approach based on regular grids. A new elastic model with a horizontal surface is first obtained from the nearby surface's elastic properties and the undulating terrain elevation. We subsequently employ a topography-related strategy to eliminate the effects of surface topographies on the seismic wavefields in models with irregular surface topographies. The merits of our proposed scheme lie in its ability to stable numerically compute wavefields in models with irregular surface topographies without altering the conventional SGFD relying on regular grids. To validate the effectiveness and practicality of our method, we utilize elastic models featuring complex surface topographies. Numerical experiments demonstrate that our approach efficiently calculates elastic wavefields in isotropic media with irregular topographies based on conventional SGFD.

Spatio-Temporal Variations in Soil Erosion and Its Driving Forces in the Loess Plateau from 2000 to 2050 Based on the RUSLE Model

Assessing the spatio-temporal variability and driving forces of soil erosion on the Loess Plateau is critical for ecological and environmental management. In this paper, the Revised Universal Soil Loss Equation (RUSLE) model, the patch-generating land use simulation, and the Geographical Detector are used to investigate the spatio-temporal variations of the Loess Plateau’s soil erosion from 2000 to 2050. The results showed that: (1) The primary categories of soil erosion from 2000 to 2020 were moderate, mild, and slight, and the average level of soil erosion exhibited a decreasing and then an increasing tendency during the last 20 years. (2) Soil erosion was directly impacted by changes in land use, with cropland and forest being the primary land use and land cover changes in the study region. Cropland and construction land being turned into woodland between 2000 and 2020 resulted in a significant decrease in the severity of soil erosion. Projected soil erosion is expected to increase significantly between 2020 and 2050 due to arable land being converted into construction land. (3) The key variables impacting the spatial distribution of soil erosion were LUCC (Land-Use and Land-Cover Change), NDVI (Normalized Difference Vegetation Index), and slope, and the interplay of these variables may increase their ability to explain soil erosion. Grasslands with an NDVI ranging from 0.9 to 1, rain ranging from 0.805 to 0.854 m, a slope above 35°, and a terrain elevation ranging from 1595 to 2559 m were identified as having a high risk of soil erosion. Soil erosion prevention and management efforts should focus on the ecological restoration of upland areas in the future.

A SHAP-enhanced XGBoost model for interpretable prediction of coseismic landslides

In the coseismic landslide hazard assessment (CLHA), advanced machine learning (ML) models have garnered significant attention due to their effectiveness in handling the complex relationships between landslides and various influencing factors. However, explaining the decision-making mechanisms of machine learning models that predict landslide spatial distribution based on these influencing factors remains challenging. This study compares the predictive performance of four models—XGBoost, RF, LR, and SVM—optimized using the TPE algorithm, and introduces the SHAP algorithm into the XGBoost model to achieve both global and local interpretations of CLHA. In various tests, the optimized XGBoost model demonstrated the best predictive performance, achieving an accuracy of 0.864 and an AUC value of 0.886. Global interpretations indicate that the occurrence of coseismic landslides is primarily influenced by triggering factors such as hypocentral distance and distance from the seismogenic fault. Terrain roughness and elevation, on the other hand, make significant contributions among the conditioning factors. Single-factor dependence plots indicate that the contribution of individual factors to landslides varies across different ranges of their feature values. Analysis of two-factor dependence plots reveals that interactions between factors are also crucial in influencing the occurrence of landslides. Combining field surveys with local interpretations confirms significant variations in the contributions of influencing factors within local ranges. The main innovation of this study lies in the integration of the SHAP algorithm into the CLHA model, revealing the decision-making mechanisms of the model for spatial prediction of coseismic landslides.

Spatial Variation of Rainfall Between Nineveh and Basra Governorates due to Terrain Elevation Using Digital Elevation Model–Geographic Information System

Spatial Variation of Rainfall Between Nineveh and Basra Governorates due to Terrain Elevation Using Digital Elevation Model–Geographic Information System

ANADEM: A Digital Terrain Model for South America

Digital elevation models (DEMs) have a wide range of applications and play a crucial role in many studies. Numerous public DEMs, frequently acquired using radar and optical satellite imagery, are currently available; however, DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, compromising the accuracy of models in representing terrain elevation. In this study, we developed a digital terrain model for South America using a novel methodology to remove vegetation bias in the Copernicus DEM GLO-30 (COPDEM) model using machine learning, Global Ecosystem Dynamics Investigation (GEDI) elevation data, and multispectral remote sensing products. Our results indicate considerable improvements compared to COPDEM in representing terrain elevation, reducing average errors (BIAS) from 9.6 m to 1.5 m. Furthermore, we evaluated our product (ANADEM) by comparison with other global DEMs, obtaining more accurate results for different conditions of vegetation fraction cover and land use. As a publicly available and open-source dataset, ANADEM will play a crucial role in advancing studies that demand accurate terrain elevation representations at large scales.

A Novel Flexible Geographically Weighted Neural Network for High-Precision PM2.5 Mapping across the Contiguous United States

Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this study introduces a Flexible Geographically Weighted Neural Network (FGWNN) to estimate PM2.5 based on multi-source remote sensing data. FGWNN incorporates the Flexible Geographical Neuron (FGN) and Geographical Activation Function (GWAF) within the framework of Artificial Neural Network (ANN) to capture the intricate spatial non-stationary relationships among predictive variables. A robust air quality remote sensing estimation model was constructed using remote sensing data of Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), and Terrain Elevation (HGT) as inputs, and Ground-Based PM2.5 as the observation. The results indicated that FGWNN successfully generates PM2.5 remote sensing data with a 2.5 km spatial resolution for the contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared to traditional ANN and Geographically Weighted Regression (GWR) models. FGWNN holds the potential for applications in high-precision and high-resolution remote sensing scenarios.

Accuracy assessment of GEDI terrain elevation, canopy height, and aboveground biomass density estimates in Japanese artificial forests

Global forests face severe challenges owing to climate change, making dynamic and accurate monitoring of forest conditions critically important. Forests in Japan, covering approximately 70% of the country's land area, play a vital role yet often overlooked in global forestry. Japanese forests are unique, with approximately 50% comprising artificial forests, predominantly coniferous forests. Despite the Japanese government's extensive use of airborne Light Detecting and Ranging (LiDAR) to assess forest conditions, these data need more availability and frequency. The Global Ecosystem Dynamics Investigation (GEDI), the first Spaceborne LiDAR data explicitly designed for vegetation monitoring, is expected to provide significant value for high-frequency and high-accuracy forest monitoring. To assess the accuracy of GEDI data in Japanese artificial coniferous forests, the reference data were gathered from 53,967,770 artificial coniferous trees via airborne LiDAR data in Aichi Prefecture, Japan. This data was then compared to the corresponding GEDI-derived terrain elevations, canopy heights (GEDI RH98), and aboveground biomass density (AGBD) estimates to assess the accuracy of GEDI data. This research also explored how different factors influence the accuracy of GEDI terrain elevation estimates, including the type of beam, time of acquisition (day or night), beam sensitivity, and terrain slope. Additionally, the effects of various forest structural parameters, such as the height-to-diameter ratio, crown length ratio, and the number of trees on the accuracy of the GEDI canopy height and AGBD, were investigated. The results showed that GEDI terrain elevation demonstrated high accuracy across various slope conditions, with rRMSE ranging from 2.28% to 3.25% and RMSE ranging from 11.68 m to 16.54 m. After geolocation adjustment, the comparison of canopy height estimates derived from GEDI to airborne LiDAR-derived canopy height also showed high accuracy, exhibiting a rRMSE of 22.04%. In contrast, the GEDI AGBD product showed moderate accuracy, with a rRMSE of 52.79%. The findings also indicated that the accuracy of GEDI RH98 was influenced by terrain slope and crown length ratio, whereas the accuracy of GEDI AGBD was mainly impacted by the number of trees and crown length ratio. This study provided the first baseline accuracy assessment of GEDI terrain elevation, RH98, and AGBD estimates in Japanese artificial forests. Furthermore, this study provided valuable insights into the accuracy of GEDI metrics by examining potential factors.

Distribution of some natural and artificial radionuclides in soil from the city of Bitola (Macedonia) and its environs.

The aim of the study is a thorough investigation of the radioactivity level in soils of the town of Bitola (Macedonia) and its environs. Topsoil samples collected from 58 locations within a 5×5 km grid were analysed. Serving as a screening, gross alpha and beta activity measurements were performed using gas-flow proportional counter. Gamma-spectrometric measurements revealed the presence of three natural (40K, 226Ra, 232Th) and one artificial radionuclide (137Cs) in the samples. The activity concentrations of these radionuclides were consistent with the results of similar studies in neighbouring areas. Spatial distribution maps and factor analyses have revealed that the activity concentrations of the natural radionuclides are strongly influenced by geology and have no significant influence from human activities. A correlation of 137Cs activity concentrations with terrain elevation was also observed. The aim of the study is a thorough investigation of the radioactivity level in soils of the town of Bitola (Macedonia) and its environs. Topsoil samples collected from 58 locations within a 5×5 km grid were analysed. Serving as a screening, gross alpha and beta activity measurements were performed using gas-flow proportional counter. Gamma-spectrometric measurements revealed the presence of three natural (40K, 226Ra, 232Th) and one artificial radionuclide (137Cs) in the samples. The activity concentrations of these radionuclides were consistent with the results of similar studies in neighbouring areas. Spatial distribution maps and factor analyses have revealed that the activity concentrations of the natural radionuclides are strongly influenced by geology and have no significant influence from human activities. A correlation of 137Cs activity concentrations with terrain elevation was also observed.