Journal of Geomatics

GIS and Remote Sensing Analysis of Morphometric Characteristics in Karanthaimalai Hill, Dindigul District, Tamil Nadu

2024-08-12T05:33:28+00:00

The present study integrates GIS and remote sensing technology to do a morphometric analysis of Karanthaimalai Hill in Tamil Nadu's Dindigul district, India. Morphometric analysis is the measurement and quantitative study of many Earth surface features, including shape, length, height, and slope. Karanthaimalai Hill in the Natham region is mostly composed of granite and gneiss. The primary purpose of this study is to identify a wide range of morphometric parameters, including slope, contour, aspect, curvature, drainage, elevation, flow accumulation, flow direction, drainage density, dissection index, relative relief, roughness, and hill stream order. The data used in this study include Survey of India (SOI) toposheet maps and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) satellite photos with a resolution of 30 metres. The study found that Karanthaimalai Hill has steep and highly dissected topography, with heights ranging from 300 m to 900 m. Notably, the hill provides large relative relief, particularly at the summit and middle, with moderate relief throughout. The dissection index reveals extensive erosion and stream incision, notably on the hill's southern side. The topography roughness varies greatly across the area, indicating a diverse terrain. The slope aspect is primarily orientated west and east, while the hill's curvature displays steep slopes and dips. Furthermore, the hill's drainage density represents a complex network of streams and drainage patterns. These findings have significant implications for regional land-use planning, conservation, and management strategies. However, additional research is required to understand the fundamental principles that affect the landscape and to promote sustainable land management approaches.

Evaluating Flood Vulnerability with Remote Sensing and GIS: Urban Development Challenges in Chennai City Region, Tamil Nadu, India

2026-02-28T16:45:56+00:00

This study evaluates flood vulnerability in the Chennai City Region, Tamil Nadu, using remote sensing and GIS techniques to guide urban development planning. With rapid urbanization and recurrent flooding, Chennai faces heightened risks from heavy monsoon rains, inadequate drainage, and encroachment on natural floodplains. Sentinel-2 and Landsat satellite imagery, combined with GIS data such as digital elevation models (DEM) and land-use maps, were used to classify land cover, map flood extents, and assess flood vulnerability. A multi-criteria evaluation using Analytical Hierarchy Process (AHP) identified key vulnerability factors, including population density, elevation, land use, and proximity to water bodies and drainage infrastructure. The study also conducted sensitivity analyses, including map-removal sensitivity analyses, to quantify the impact of individual parameters on flood vulnerability mapping. The findings reveal significant urban expansion (85% of the area) and widespread impermeable surfaces contributing to high surface runoff and limited infiltration. Topographic Wetness Index (TWI), drainage density, slope, and distance from streams were used to assess flood-prone zones further. The Normalized Difference Vegetation Index (NDVI) was calculated to evaluate the extent and health of vegetation affected by flooding. At the same time, DEMs and terrain analysis provided insights into low-lying areas with higher flood vulnerability. The research identified flood-prone zones classified into low, medium, and high-risk areas, covering 24.4%, 50.2%, and 25.4% of the study region, respectively. These results underscore the need for sustainable land-use management, improved drainage infrastructure, and climate-resilient urban development strategies to mitigate flood vulnerability in Chennai. The comprehensive assessment aims to support flood vulnerability management efforts and urban resilience planning in the region.

Spatial Exploration of Rajgad Fort: An Integrated Approach of UAV Technology and Geographical Information System

2025-11-26T04:21:10+00:00

Rajgad Fort, situated in the Pune district of Maharashtra, stands as a testament to India's rich cultural and historical heritage. This study employs UAV technology and Geographical Information System (GIS) to conduct a detailed spatial exploration of Rajgad Fort, aiming to document its architectural intricacies, historical significance, and environmental context. High-resolution orthomosaic imagery, digital surface models (DSM), and point cloud data were integrated to map 52 distinct features within the fort, including towers, gates, bastions, temples, and water tanks. Field surveys complemented UAV data, providing crucial insights into the fort's layout and cultural landscape. The study reveals Rajgad Fort's strategic fortifications, such as the expansive Fort Wall and intricate water management systems, highlighting its historical importance and architectural grandeur. Despite challenges posed by terrain complexity and accessibility, the study demonstrates the efficacy of UAV and GIS technologies in heritage conservation and management. The findings underscore the significance of preserving Rajgad Fort as a cultural icon and advocate for informed conservation strategies to safeguard Maharashtra's historic forts for future generations.

Assessing Cyclone-Induced Landscape Transformation in the Coastal Town of Diu, India: Insights from Cyclone Tauktae using Sentinel-2

2026-03-17T05:05:49+00:00

Natural disasters occur often around the world, and their incidence and intensity appear to be increasing in recent years. The disasters, like cyclones and floods, usually cause vital loss of life, large-scale economic and social impacts, environmental damages, and drastic changes in land use and land cover (LULC). Cyclones can cause devastating impacts, including strong winds, heavy rainfall, storm surges, and flooding. The aftermath includes infrastructure damage, loss of life, displacement of communities, and ecological disruptions. Studying the impact of cyclones on LULC is crucial to inform the design and implementation of natural vegetation management, identify threatened habitats, prevent and/or counter environmental threats, and enhance conservation efforts and offers valuable insights for disaster preparedness, infrastructure planning, and climate resilience. Understanding and forecasting the consequences of climate change is critical to support the work of planners and decision makers. The coastal wetland of Diu, with numerous creeks and channels, is associated with shoals and vast tidal flats and has one of the richest zones for mangroves along the west coast of India. The study portrays the occurrence of a TAUKTAE, a super cyclonic storm of the 21st century, to land in Gujarat, Saurashtra, and Diu, the most-hit, which was formed over the Arabian Sea. The research uses Sentinel 2A to derive different indices such as NDVI, used in mapping vegetation change, and NDBI (Normalized Difference Built-up Index) to detect changes based on the spectral response of built-up areas for the pre- and post-cyclone Tauktae periods. This study can enhance understanding of vulnerability and aid in formulating strategies to mitigate cyclone impacts, ensuring sustainable development in the region.

The coastal wetland of the Diu with numerous creeks and channels is associated with shoals and vast tidal flats have one of the richest zones for mangroves along the west coast of India. The study portrays the occurrence of a TAUKTAE, super cyclonic storm of 21^st Century to Land in Gujarat; Saurashtra, Diu Most-Hit that was formed over Arabian Sea. The research uses Sentinel 2A is used to derive different indices such as NDVI used in mapping vegetation change and NDBI (Normalized Difference Built-up Index) to detect changes based on the spectral response of built-up areas for pre and post period of cyclone Tauktae. This study can enhance understanding of vulnerability and aid in formulating strategies to mitigate cyclone impacts, ensuring sustainable development in the region.

Geospatial Perspective for Groundwater Augmentation: A Case Study for Hunsur Taluk of India

2026-03-30T16:53:30+00:00

Groundwater augmentation is increasingly recognized as a critical strategy for addressing the global water crisis, particularly in regions experiencing groundwater depletion. This study aims to determine site suitability for Artificial Recharge Structures (ARS) in Hunsur taluk to support long-term groundwater sustainability. The integration of PAN (Panchromatic) and IRS-1D LISS (Linear Imaging and Self Scanning) satellite data improved the identification of suitable recharge locations using Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP). Key groundwater recharge controlling parameters, including slope, lithology, geomorphology, land use/land cover (LULC), lineament density, soil, drainage density, and stream order were integrated to delineate potential recharge zone. The analysis identified suitable locations for 44 check dams, 16 nalah bunds, and 10 percolation tanks as site-specific remedial measures to enhance groundwater recharge, reduce surface runoff, and improve aquifer storage. These interventions are particularly recommended along moderate drainage networks, fractured zones, and gentle slope regions to maximize infiltration and recharge efficiency. The findings demonstrate the effectiveness of integrating GIS and AHP for scientifically guiding groundwater augmentation planning and implementing location-specific remedial measures for sustainable groundwater management in Hunsur taluk.

A Comparative Analysis of Random Forest and Gradient Boosting Regression Techniques in Google Colab for Air Temperature Prediction in the Greater Accra Region, Ghana

2026-03-30T16:35:25+00:00

Accurate temperature prediction is essential for climate adaptation, environmental monitoring, and sustainable urban planning. This study evaluates the performance of two machine learning techniques Random Forest (RF) and Gradient Boosting Regression (GBR) for predicting near-surface air temperature in the Greater Accra Region of Ghana. Daily temperature observations obtained from the Ghana Meteorological Agency covering the period 1960–2018 were used for model development. The average daily air temperature was computed from minimum and maximum temperature observations. The predictive performance of the models was compared with a classical statistical time-series model, Autoregressive Integrated Moving Average (ARIMA). Model evaluation was performed using five-fold cross-validation to improve the robustness of the results. Performance metrics included Mean Squared Error (MSE) and the coefficient of determination (R²). The results show that the Random Forest model achieved the highest predictive accuracy with MSE = 0.0010 °C and R² = 0.9996, while the Gradient Boosting Regression model produced MSE = 0.0015 °C and R² = 0.9994. The ARIMA model showed significantly lower performance with MSE ≈ 0.598 °C and R² ≈ 0.30. The high predictive performance of the machine learning models is partly attributed to the deterministic relationship between the input variables and the computed target temperature. The study demonstrates the potential of machine learning approaches for climate-related prediction tasks and provides insights for environmental planning and climate resilience strategies in rapidly urbanizing regions such as Greater Accra.

Assessing Forest Fire Susceptibility in the Hindu Kush Himalaya: Implications for Biodiversity and Carbon Stock

2025-08-05T07:15:14+00:00

The Hindu Kush Himalaya (HKH) is a globally significant biodiversity hotspot, with extensive forests, protected areas (PAs) and substantial carbon reserves. However, increasing frequency and intensity of forest fires threaten its ecological integrity. Despite these concerns, there is a lack of comprehensive spatial assessment of forest fire susceptibility, necessitating a data-driven approach to evaluate environmental risks. This study evaluates forest fire susceptibility and its impact on biodiversity and carbon stocks across the HKH region using remote sensing and machine learning models. The models include Analytic Hierarchy Process, Certainty Factor, Maximum Entropy, and Random Forest (RF), based on thirteen ignition factors, representing environmental, meteorological, edaphic, socio-economic, and topographic factors. Active fire data from MODIS and VIIRS was used for training and testing of models. Model performance, evaluated using Area Under Curve (AUC) of Receiver Operating Characteristic curve, showed that RF (AUC = 0.95) outperformed other models. Results indicate that about 13.54–20.47% of HKH forested region is highly susceptible to forest fires, with higher risk in Himalayan belt, Bangladesh, and Myanmar. Key factors influencing fire risk include wind speed, solar radiation, elevation, and precipitation. Forest fires threaten biodiversity, with around 25,878.66 sq. km of PAs identified as highly vulnerable. Additionally, fire-induced carbon emissions from aboveground biomass, estimated at 32.22 million Mg, jeopardize carbon stocks by depleting stored carbon and increase atmospheric CO₂ levels. Forest fire susceptibility maps and risk assessments provide essential spatial insights for policymakers, supporting proactive fire mitigation, biodiversity conservation efforts, and carbon management.

Estimating Above-ground Biomass of Trees Outside Forests in the Thar Desert using Sentinel Data and Machine Learning

2025-11-03T04:10:51+00:00

Above-ground biomass estimation of Trees outside Forests is crucial as they play a significant role in carbon sequestration, biodiversity conservation, and microclimate regulation, especially in arid and semi-arid regions where tree cover is limited. The heterogeneous vegetation covers and highly scattered nature of trees add to the challenges in the accurate estimation of aboveground biomass employing remote sensing technology. This study aimed to estimate the AGB of TOF in the arid regional landscape of the Thar desert of Rajasthan, integrating Sentinel-1 (S1) SAR and Sentinel-2 (S2) optical datasets and field observations, applying the Random Forest (RF) model. The field calculated AGB in the sampled area ranged from a minimum of 0.19 t/ha to a maximum of 43.12 t/ha, with a mean of 8.03 t/ha. The backscattering coefficients at VV and VH polarizations and 5 SAR indices from S1 and the multispectral bands, vegetation indices, and biophysical variables from S2 were extracted as the predictor variables for the AGB model. After correlation and multi-collinearity analysis, three models were developed: the first model based on S1(M_S1), the second model with S2 (M_S2), and the third model is a combined model of S1 and S2 variables (M_S1S2). The correlation analysis revealed that the SAR indices have a higher relationship with field biomass. Further, the combined model (M_S1S2) achieved the highest accuracy (R² = 0.52, RMSE = 3.89 t/ha) in AGB estimation, followed by M_S1 (R² = 0.46) and M_S2 (R² = 0.43). The results of the study highlight the utility of Sentinel datasets and larger ecological plots at the landscape level in biomass mapping in sparsely vegetated arid environments. Moreover, the study highlights the ecological importance of TOF and emphasizes the need for biomass and carbon stock assessments in the ecologically sensitive arid regions.

Groundwater Exploration using Ground Magnetic Survey along the Contact of Crystalline and Sedimentary Rocks in Parts of Perambalur District, Tamil Nadu, India

2026-04-08T05:39:46+00:00

This study delineates groundwater potential zones along the contact between the Tiruchirappalli Cretaceous formations and the Archaean crystalline basement using ground magnetic surveys. Magnetic susceptibility data were collected with a Proton Precession Magnetometer along six NW–SE profiles at 1 km station spacing and 5 km profile intervals, covering key locations in Perambalur district, Tamil Nadu. A total of 60 measurements were obtained, with magnetic intensity values ranging from 967 to 7 gammas and averaging 297 gammas. Higher values were recorded in crystalline rocks (967–300 gammas) and lower values in sedimentary rocks (300–7 gammas), enabling the delineation of the basement–sedimentary litho contacts. Data processing in Geosoft and ArcGIS produced total magnetic intensity, reduction-to-pole, directional filter, regional, and residual maps, which highlighted lithological contacts and fracture systems. NE–SW fractures correspond to lithological contacts, while NW–SE fractures represent neo-tectonic structural elements. Groundwater potential zones were identified using rank and weightage method which shows that the possible potential zones are along the litho contact and the intersection of NW-SE fractures with litho contacts. The findings confirm that magnetic surveys are an effective tool for locating groundwater-bearing structures in basement–sedimentary terrains.

Dhristhi - Robust Change Detection, Monitoring, and Alert System on User-defined AOI using Multi-Temporal Sentinel-2 Satellite Imagery

2025-11-03T06:20:39+00:00

Unregulated and aggressive Land Use Land Cover (LULC) dynamics such as urban sprawl and informal deforestation require a monitoring system that is close to real time, accurate, and readily available. Currently available commercial and cloud-based systems often demand high levels of technical knowledge in geospatial and programming, posing a stumbling block to local practitioners and NGOs. We propose Dhristhi, an automated, end-to-end Web-GIS platform designed to overcome this expertise bottleneck. Dhristhi integrates a sophisticated hybrid methodology: it employs a pre-trained U-Net Deep Learning model for high-precision, pixel-wise semantic segmentation of multispectral imagery, followed by an Object-Based Post-Classification Comparison (OBC) approach to aggregate and validate changes into meaningful geographic regions. An important component of the framework is a Random Forest (RF) classifier with user defined dynamic thresholding mechanism & seasonal variation in values of spectral indices (NDVI, NDBI) to prevent false positive and improve change validation. This platform works on a fully automated, plug-and-play workflow, it automatically handles everything from data collection to pre-processing, so anyone can use the platform easily without having technical expertise. After any successful change detection system automatically sends alert and generates report in the user’s dashboard. Dhristhi successfully demonstrates advanced geospatial intelligence, providing a robust, noise-resilient tool for environmental and civil governance. On a validation dataset covering forest-sprawled and urban regions, the proposed system achieves an overall change detection accuracy of 92.1%, with a Kappa coefficient of about 0.88 and mean IoU of about 0.89 for binary anthropogenic change detection.

AHP-Based Mapping of Optimal Groundwater Recharge Sites in Tirupathur District, Tamil Nadu, India

2026-04-10T06:09:48+00:00

A study was conducted in the Tirupathur district of Tamil Nadu, India to identify suitable sites for groundwater recharge and to suggest appropriate site specific recharge mechanisms. The potential of groundwater depends on topography, lithology, geological structure, depth of weathering, slope, drainage pattern, landuse land cover, soil, rainfall, lineament density, drainage density, magnetic breaks and topographic wetness index. All thematic layers were prepared and assigned comparative weights using Saaty's 9-point scale and then normalized using the Analytical Hierarchy Process. According to the investigation, groundwater recharge zones are categorised into five classes; very low, low, moderate, high, and very high. The study found the region of Vaniyambadi and Natrampalli had very high and high potential zones, respectively, covering 6.22% (128.75km2) and 15.2% (312.79km2) area. Conversely, the region of south Natrampalli, Tirupathur, and eastern Ambur had moderate, low, and very low potentials, covering 29.31% (607.06km2), 24.35% (518.30km2), and 25.02% (504.41km2) area. The study mainly focused on moderate to very low potential zones for artificial recharge. High and very high zones were not considered as priority due to their high infiltration rates. This approach helped to identify 46 potential sites for artificial recharge based on the best execution of AHP to boost groundwater conditions and meet the shortage of water resources in agriculture and domestic use. This study reveals that Remote Sensing and GIS with AHP provide an efficient and effective platform for convergent analysis of various data for groundwater management and planning.

Satellite-based Crop Discrimination with Machine Learning on GEE Platform: Insights from Udham Singh Nagar

2026-04-15T16:07:08+00:00

Crop discrimination is crucial for environmental monitoring, agricultural planning, and sustainable development. This study assessed the performance of optical (Sentinel-2, 10 m, atmospherically corrected to surface reflectance) and microwave (Sentinel-1, 10 m, preprocessed with radiometric calibration and speckle filtering) remote sensing data for crop classification in Udham Singh Nagar district, Uttarakhand, India, during the June–October 2023 kharif season. Ground truth data for major crops, namely rice (815 samples) and sugarcane (62 samples), were collected through field surveys and split into 70% training and 30% validation subsets to ensure robust model evaluation. Five machine-learning classifiers Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Classification and Regression Tree (CART), and Gradient Boosted Machine (GBM) were applied to individual and fused datasets. GBM consistently achieved the highest classification accuracy at the monthly scale, likely due to its sequential error-correction mechanism that effectively exploits distinct phenological patterns captured in monthly temporal composites, while RF produced the highest overall accuracy (89.21%) for the season-long fused optical and microwave dataset. SVM and KNN showed comparatively lower performance, especially during transitional crop growth stages. The results highlight the effectiveness of ensemble learning methods and demonstrate the benefit of multi-sensor data fusion for accurate and reliable crop discrimination and land use/land cover mapping.