Journal of Geomatics

Urban Dynamics with Population Changes in Thanjavur City, Tamil Nadu, India - A Geospatial Approach

S. Sreekala — 2025-10-26

Globally, over 50% of the population lives in urban areas today. By 2045, the world's urban population will increase by 1.5 times to 6 billion. The urban planners must plan for providing the basic amenities and infrastructure for the expanding population’s need. Urban sprawl is unavoidable in accommodating the rising urban population, the influence of which can be limited through innovative land use planning techniques and community cooperation. Sustainable cities have been the leading global paradigm of urbanism. The present study analyses the urban dynamics in terms of decadal growth of population and the aerial expansion of built-up features in Thanjavur city from 2001 to 2021.The population increase was at lower rate during the period 2001 to 2011 and aerial expansion of built-up land was at higher rate. In the period 2011 – 2021, the population increase rate is high with slow rate of increase in built-up area inferring a stress in demand of land for future developments. The demand for land is assessed using the urban growth indicators of Land Consumption Rate (LCR) and Land Absorption Coefficient (LAC). The < 2 % of LCR values in the study area reveals a controlled and sustained urban growth. The LAC value of < 1 ha/ population shows an efficient land absorption with high density of urban development.

Comparing the Accuracy of Unmanned Aerial Vehicles and Ground Surveying Methods for Road Corridor Surveys: A Case Study of Parkoso Road

Benedict Okyere Asamoah Asante — 2025-10-26

Traditional ground-based surveying methods in highway engineering often fall short in meeting project timelines because they are slow. Consequently, researchers are exploring new techniques to deliver accurate and reliable data within project schedules. However, these new approaches must demonstrate their reliability and effectiveness across various scenarios. This study aims to compare the accuracy of Unmanned Aerial Vehicles (UAVs) and Real-Time Kinematic GPS (RTK GPS) in road corridor surveys. The research utilizes two main datasets: the first records point positions and elevations along the corridor using RTK GPS, while the second includes geometrically corrected aerial photographs from UAV surveys. Ground Control Points (GCPs) are used as benchmarks to ensure comparable accuracy between RTK GPS and UAV data. Notably, minimal positional shifts were observed between the two methods. Longitudinal profiles and cross-sections derived from both datasets were overlaid, showing negligible differences. Root Mean Square Errors (RMSEs) were calculated as 0.025m, 0.041m, and 0.065m for Eastings, Northings, and Elevations, respectively. The Arithmetic Mean Error (AME) and the Arithmetic Mean Standard Error (AMSE) were 0.032m and 0.0795m. Additionally, the Arithmetic Standard Deviation (ASD) between the survey methods was 1.1615E-16m. These statistical results indicate a strong agreement between UAV and RTK GPS measurements, suggesting UAVs can provide sufficient accuracy comparable to RTK GPS for road corridor topographic surveys.

Morphometric Analysis of the Tunga River Sub-catchment, Karnataka, India Using Remote Sensing and QGIS: Implications on Water Resource Allocation

Sangeeta Angadi — 2025-10-26

Tunga River Sub-catchment is situated in Western Ghats, Karnataka and has a humid climate. The qualitative morphometric analysis is significant to gauge the basin potential is essential for management of natural resource under increasing precipitation trends. Linear, areal and relief aspects are computed and evaluated using Quantum Geographical Information System (QGIS 3.28) plugins. Drainage network derived SRTM DEM 30m indicates Tunga River Sub-catchment is a 5^th Order basin with sub-dendritic drainage network. Areal features such as Elongation ratio (0.51), Circularity ratio (0.31) and Form factor (0.20) indicates the basin is elongated and the time of concentration for present study is 12.23 hours. Low Drainage density (0.61 km/km²) indicates that the basin is composed of permeable material having low to moderate relief. Low Infiltration number (0.15), high Length of overland flow (0.81) and high Constant of channel maintenance (1.62), indicate that there may be more opportunities for infiltration, potentially leading to higher groundwater recharge rates. Sub-catchment potential assessment using aspects such as Stream frequency and Drainage density in relationship between Bifurcation ratio ensures the catchment has high basin potential.

Morpho-tectonic Analysis of an Upstream Sub-basin of the Cauvery River from Bhagamandala to Shivanasamudra using Geomorphic Indices using GIS

M. Samarth Urs — 2025-10-26

The Cauvery sub-basin, with an elevation of 2029 meters, is a mountain range that has been uplifted during the Cretaceous period and is bordered by the Western Ghats. The two northern mountain fronts and the southern front of the Cauvery sub-basin are defined by faults near the origin point (catchment area). We conducted a morpho-tectonic analysis by assessing the properties of geomorphic indices to uncover variations in rock uplift. In particular, the study contrasts the northern and southern mountain fronts' valley height to floor ratio, area-height correlations (hypsometric curve), and mountain front sinuosity (Smf). The high land in the sub-basin and key faults in the northern part of the Western Ghats (with an average height of approximately 2029 meters), indicate the uplift of both geomorphic units as part of a single extensive crustal block. The asymmetry factor indicates that there are imbalances in the Cauvery River, with an increased slope toward the sub-basin's left side. Transverse topographic symmetry also suggests that the sub-basin experiences tectonic activity at both the source and endpoint, where Shivanasamudra falls is located. Likewise, other morpho-tectonic indices reinforce the observation that both the starting and ending points are tectonically active. Finally, river profiles reveal that the sub-basin's left section displays the most notable river entrenchment, likely due to uplift. Our geomorphic assessment indicates that the Western Ghats within the Cauvery sub-basin region exhibit tectonic activity, characterized by a series of faults along the mountain's leading edge. The presence of Pseudotachylites connected to the fault system indicates that neo-tectonic processes also have an impact on the Shivanasamudra falls, which are situated at the end of the research region.

Leveraging GIS and Mobile Technology for Efficient Road Data Collection: A Case Study with GetMap

M.S. Saran — 2025-10-26

Data is the basic requirement in information derivation across all fields of study. And this is especially true for transportation engineering, where road and traffic-related data are essential for getting meaningful results. The traditional methods of road data collection involve extensive preparation, manual works, and use of paper forms. These methods are time-consuming and expensive. With the growth of information technology and the widespread availability of the internet, we have entered an era of real-time data capture and sharing. This study introduces a user-friendly mobile application, GetMap, designed for real-time road data collection and sharing. The Android based app records users travel path and also collects road related data and uploads the information to Firebase cloud server. App’s key functionalities include recording travel tracks, adding road inventory and cross-sectional details, and marking points of interest with photographs. The app outputs are generated as KML files and Excel sheets, facilitating facile integration with GIS platforms. Getmap will be an effective tool for road data collection agencies like Public Works Departments (PWD), transportation planners, and road safety authorities.

Forest Fire Risk Mapping Using Analytical Hierarchy Process (AHP): A Case of Malkangiri, Odisha, India

Jintu Moni Bhuyan — 2025-10-26

The risk of forest fires is affected by various factors such as vegetation density, topography, human activities, and climate patterns. These factors remain relatively constant over time, at least during the fire season. To manage forests and ensure protection against fires, fire-cycle analysis is performed which includes creating a map of potential fire ignition and preparing a vulnerability map that can assist in controlling the spread of fire. Accurate data is crucial for forest management, and geospatial technology provides reliable information. By providing accurate information, geospatial technology can help prevent and mitigate damage caused by forest fires, while also promoting sustainable land use practices. The study focused on assessing forest fire risk in the Malkangiri district of Odisha, India, using geospatial technology and the AHP method. The final risk map was categorized into five zones, namely very high, high, moderate, low, and very low, which can help guide forest management and firefighting efforts in the area. To validate these forest fire risk zones, the study used fire points data from the office of PCCF, Odisha from FIRMS. The results showed that the forest fire risk was high in the low to moderate elevation ranges, with most fire points overlapping in the very high-risk zones of the map. Anthropogenic activities have been a major cause of forest fires in tropical regions. Overall, the study demonstrated the effectiveness of using geospatial technologies and the AHP method for assessing forest fire risk. The results can help in developing strategies to prevent and mitigate the impact of forest fires, particularly in areas with high-risk zones, such as the Malkangiri district of Odisha, India.

Comparative Performance of Multi-Platform DEMs and Topographic Sheets in Fluvial Morphometry: Insights from the Jiadhal River Basin, India

Ishanjyoti Chetia — 2025-10-26

This study evaluates the performance of various Digital Elevation Models (DEMs) and Survey of India (SOI) topographic sheets in fluvial morphometry using the upper part of the Jiadhal River basin as a case study. The primary objective is to compare and assess the interchangeability of data derived from SRTM, ASTER, AW3D, TanDEM-X, Cartosat 30m DEMs, and SOI 1:50,000 scale topographic sheets. Key morphometric parameters such as stream order, stream length, drainage density, drainage texture, basin area, perimeter, and relief aspects were derived from each dataset and compared to determine the influence of spatial resolution on hydrological studies. Results indicate that DEMs such as AW3D, Cartosat capture finer landform features better and provide added precision in stream delineation, mostly in flat terrains, as compared to other data sources. Despite variations in satellite’s spatial resolutions, parameters and sensor systems, the derived fluvial morphometric parameters and statistics from the DEMs and topographic sheets showed significant agreement overall. The study highlights that AW3D, Cartosat 30m DEM outperforms SRTM, ASTER and TanDEM-X in stream path delineation, and are recommended for future morphometric and river basin studies. This research highlights the significance of choosing appropriate DEMs based on their spatial resolutions as well as terrain characteristics of the river basin for improved morphometric and river basin analysis.

ReCXTUnet: A Novel Framework for Remote Sensing Image Classification using TransUnet and XGBoost

Diksha Kumar — 2025-10-26

Remote sensing image classification (RSIC) is crucial for many environmental and urban applications. RSIC can be difficult due to the high variability and dimensionality in remote sensing image data. This paper presents a novel framework that combines Transformer-based U-Net (TransUNet) and eXtreme Gradient Boosting (XGBoost) for RSIC. TransUNet, known for its powerful feature extraction capabilities, efficiently captures contextual and spatial information from remote sensing images. Additionally, XGBoost improves classification accuracy by efficiently managing high-dimensional data. TransUNet was originally designed for image segmentation tasks, instead of classification. Its architecture is designed to excel at segmenting complex details within images. In our proposed framework, we have adapted TransUNet by adding a classification layer. The fully connected layer of TransUNet serves as the base learner for XGBoost, forming a robust framework for efficient RSIC. This hybrid approach, which combines TransUNet and XGBoost, offers multiple benefits. TransUNet maintains complex details and spatial relationships in images, which improves feature representation. XGBoost provides high predictive accuracy and prevents overfitting with the help of gradient boosting algorithm. This combination tackles challenges in RSIC, such as variations in image quality and noise. We evaluated the proposed approach using high-resolution remote sensing images from the RSI-CB 256 and NWPU-RESISC45 datasets. Our findings show that our framework has outperformed other existing baseline models, attaining an impressive classification accuracy of 91% in RSIC. The experimental results indicate that our approach not only enhances classification accuracy but also remains robust against variations in image quality and noise.

On the Prediction of Cloud-to-Ground Lightning Occurrences over the Indian Region using Different Initial and Boundary Conditions

Degala Venkatesh — 2025-10-26

Atmospheric lightning, one of the deadliest natural disasters globally, poses significant risks, making research in this area crucial for risk reduction. This study evaluates the performance of the Weather Research and Forecasting (WRF)-Elec model for forecasting lightning occurrences over India during September 2023, utilizing initial and boundary conditions from the Global Forecast System (GFS) and the one from the modified GFS from the National Centre for Medium Range Weather Forecasting (NCMRWF), viz. NGFS. The WRF model outputs are compared with data from the National Remote Sensing Centre’s Lightning Detection Sensor Network (NRSC-LDSN). Results indicate that NGFS provides better forecasting accuracy compared to GFS, as reflected by higher Probability of Detection (POD) of 0.79 and lower False Alarm Ratio (FAR). We suggest that The NGFS data’s integration of advanced assimilation techniques and comprehensive observational data improves the model performance, emphasizing the importance of localized and enhanced inputs for accurate lightning forecasting, which is crucial for mitigating lightning-related risks.

Evaluating Multivariate Adaptive Regression Splines (MARS) for Modelling Observed Heights Above Mean Sea Level: A Case Study of the Tarkwa Local Geodetic Reference Network in Ghana

Michael Stanley Peprah — 2025-10-26

This study examines the potential of Multivariate Adaptive Regression Splines (MARS) in predicting recorded heights above mean sea level within the Tarkwa Local Geodetic Reference Network in Ghana. Logistical and computational constraints of conventional techniques, such as spirit levelling and geostatistical interpolation, drive the assessment of MARS as a strong soft computing substitute. The MARS model was trained and verified using field-measured data gathered using a Total Station DTM 122A, and its performance was compared against the Polynomial Regression model (PRM) and Kriging models. Each model technique was assessed based on statistical models such as arithmetic mean absolute error (AMAE), arithmetic mean squared error (AMSE), arithmetic root mean squared error (ARMSE), arithmetic standard deviation (ASD), correlation coefficient (R), and coefficient of determination (R²). Statistical measures showed MARS's better accuracy utilizing near-perfect correlation (AMAE: 1.7963E-06 m; AMSE: 8.6775E-12 m) and low error margins. The results show MARS to be a possible, high-precision solution for orthometric height calculation, hence improving Ghana's geodetic network uses in environmental management, building, and surveying. This work not only confirms the effectiveness of MARS but also provides a basis for improving height measurement methods in local geodetic systems.

Landscape Characterization of the Cauvery Basin, India using Geomorphic Indices Derived from the Digital Elevation Model

Sudhanshu Raghubanshi — 2025-10-26

Landscape characterization is essential for deciphering geomorphic evolution, resource potential, and sustainable management in river basins. While geomorphic indices are commonly used for quantifying terrain and assessing erosion, their integrated application with landform features remains underexplored. This study offers a comprehensive landscape assessment of the Cauvery River Basin, India, by integrating geomorphic indices with landform features to study spatial patterns of fluvial erosion. Using Shuttle Radar Topography Mission (SRTM-30 m) data, three geomorphic indices-Hypsometric Integral (HI), Hack’s Stream-Length Gradient (SL) Index, and Normalized Channel Steepness (K_sn) Index-were computed across six sub-basins, along with landform classification. The HI values ranged from 0.11 to 0.27, indicating varied stages of landscape development, with the Shimsha sub-basin showing the highest HI (0.27), and the Kabini, Noyil, and lower Cauvery basins the lowest (0.11–0.12), suggesting mature, eroded terrains. Knickpoints analysis showed intense incision in the Bhavani basin (3162 knickpoints), supported by high K_sn values (90), and high SL values (up to 160,884 gradient meters), particularly around waterfalls and dams shaped by lithological controls. Clustering of knickpoints at elevations between 0-120 m above sea level, suggests that Quaternary eustatic base-level fall triggered a phase of river rejuvenation across the basin. Landform classification shows plains dominate (52-89 % areal coverage), especially in lower sub-basins, while ridge and deeply incised streams show more active erosion zones. This integration of geomorphic indices and landforms shows a framework for linking fluvial erosion to sea-level history, providing insights into past geomorphic responses, lithological controls over river basin evolution, and implications for sustainable land and water resource management.

An Integrated Geospatial Framework for Monitoring Built-Up Area Growth in Historic Cities Using Archival Maps and Remote Sensing

Madhavan Sridhar — 2025-10-26

Indian historic cities serve as cultural anchors and are vital to heritage tourism, yet their unregulated urban expansion has become a major concern. Long-term monitoring of built-up area growth is crucial for informed and sustainable urban governance. However, the absence of satellite data before 1975 limits the ability to track historical urbanization trends. To bridge this temporal data gap and enhance the accuracy of future urban growth predictions, this study develops a semi-automated methodology that integrates georeferenced and vectorised historical maps with remote sensing data. Focusing on the historic cities of Varanasi and Hyderabad, the study reconstructs two centuries of built-up area growth. Varanasi exhibited an average annual built-up growth rate of approximately 3.35%. A discernible north-westward shift in the urban centroid was observed, with buffer analysis around the Kashi Vishwanath Temple indicating intensified urbanization within the 5–10 km and >20 km zones. Hyderabad showed an average annual built-up growth rate of about 3.04%. The city’s centroid exhibited a northward drift until 1995, followed by a south-eastward shift, aligning with the growth of the IT corridor and associated infrastructure in that region. Buffer analysis further revealed that urbanization in Hyderabad has been more prominent beyond the 20 km radius, underscoring peripheral expansion driven by economic clustering. This study demonstrates the efficacy of combining historical cartographic archives with satellite imagery for reconstructing long-term urban dynamics. The proposed methodology not only enhances the temporal depth of urban change analysis but also provides actionable insights for planners and policymakers to promote resilient, culturally sensitive urban development strategies.