Journal of Geomatics

Watershed prioritization in some part of Wainganga river sub-basin, Central India inferred from remote sensing and GIS data

Bhujang Manjare, U.P. Meshram, A. A. Meshram — Wed, 30 Apr 2025 00:00:00 +0000

The study area is a part of Wainganga river sub-basin bounded by latitude and longitude 20⁰15'00"to 20⁰45'00"N and 79⁰30'00"E to 80⁰30'0"E which falls in Gadchiroli district of Maharashtra. Geologically, the study area is mainly constituted by the rocks of Paleoproterozoic age with some patches of Late Permian-Early Triassic age in the central area and the northern and southern area is covered by Archean age while Paleoproterozoic-Mesoproterozoic and Quaternary age covered only northern area. Geomorphologically, the study area is covered majorly by alluvial and pediplain while pediments, plateau and denudational hills covered the western area. The study area is divided into four sub-basins: SW-1, SW-2, SW-3 and SW-4. Based on the morphometry, the sub-basins have been grouped into three categories, i.e., high priority, medium priority and low priority. SW-2 shows medium priority and remaining SW-1, SW-3 and SW-4 shows high priority respectively. The comprehensive use of GIS resulted in the development of an efficient and effective methodology of spatial data management and manipulation. The integration and analyses of various thematic maps and image data proved useful for the delineation of zones of groundwater potential.

Geo-tagged video visualization using open source Web-GIS Techniques

Shashikant Patel, Ashwin Mohan, Baljit Kaur, Ajay Mathur, Brijendra Pateriya — Wed, 30 Apr 2025 00:00:00 +0000

The practice of identifying deterioration on paved or unpaved surfaces for roads is known as road surface monitoring (RSM). Road surface abnormalities, such as potholes, cracks, and bumps, which have an impact on driving comfort and on-road safety, must be found in order to effectively monitor the state of the road surface. The Road Surface Monitoring system is a web-based application designed to assist transportation authorities, maintenance crews, and engineers in assessing the condition of roads. The system utilizes GPS track data in GPX format and associated videos to provide a visual representation of road surface conditions. By synchronizing the GPS data with video playback, users can monitor and analyze road conditions in real-time. The system's main features include the selection of GPX files representing road segments, visualization of road paths on an interactive map, and playback of associated videos. The system enables users to navigate and explore road segments, enlarge or reduce, and switch between different map views. As the video playback progresses, the system updates the displayed GPS track data, allowing users to monitor road conditions with current locations and identify specific surface issues like roughness, cracks, or potholes. With its user-friendly interface and real-time monitoring capabilities, the Road Surface Monitoring system offers several benefits. Users can access road conditions accurately, make informed decisions regarding maintenance and repairs, and prioritize resources effectively. The system also facilitates data-driven analysis and reporting, enabling authorities to optimize road maintenance strategies and enhance overall transportation infrastructure. Overall, the Road Surface Monitoring system provides a valuable Web-GIS tool for road surface assessment and monitoring, enhancing the efficiency and effectiveness of road maintenance efforts.

The Morphometric Analysis of Raghunathapalli Watershed, Jangaon District, Telangana State, India

Prakash Prathapani, G. Prabhakar, G. Sreenivasa Reddy — Wed, 30 Apr 2025 00:00:00 +0000

A morphometric analysis involves the quantitative measurements and calculation of landforms. It enables the analysis of geohydrological characteristics of a drainage basin in relation to terrain features and flow patterns. The study area is located in Raghunathapalli watershed of Jangaon District. The majority of morphological characteristics show that the river basin features are subject to considerable geological and Geomorphological controls. For planning purposes and sustainable management of a study area the results of morphometric characteristics may be used.

Temporal Gap Filling of Nighttime Light Composites

Nalin Sharma, Prasun Kumar Gupta, Prabhakar Alok Verma — Wed, 30 Apr 2025 00:00:00 +0000

The temporal nighttime light (NTL) data generated by DMSP-OLS sensors was discovered to have large gaps (missing values) over time. The research aims to provide a scientifically valid gap-filling mechanism for having consistent DMPS-OLS time series data (1992-2013) and predicting the historic NTL (1991-1985) for long-term studies. A deep learning neural network, Long Short Term Memory (LSTM) has been proposed in the study for temporal gap filling and historic NTL prediction. The developed LSTM model is being tested in a time distributed wrapper way having window size (3-7) for the temporal gap filling and prediction of the historic NTL. According to the accuracy evaluation, the developed model has a testing accuracy of R² = 0.96 with a window size of 5. The historic population, Gross Domestic Product (GDP), and Electric Power Consumption per capita (EPC) data are utilized to validate the gap-filled and historic NTL. R² = 0.91 w.r.t population, R² = 0.71 w.r.t GDP, and R² = 0.69 w.r.t EPC, is been found during the assessment of these parameters with the sum of light of the year (1985-2013). The historic & gap-filled NTL data can be used in various studies to monitor temporal development.

Integration of Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data and Lightning Detection Sensor (LDS) data for lightning event prediction

Prisha Sharma, Vinod Kr Sharma, Abhinav Kr Shukla, Sameer Saran — Wed, 30 Apr 2025 00:00:00 +0000

Lightning, a complex and potentially destructive atmospheric phenomenon, poses significant risks to public safety and infrastructure resilience globally. In India, the frequency of lightning strikes, particularly during the monsoon season, underscores the importance of early warning systems. The development of accurate detection and timely warning infrastructures is essential to mitigate the impact of lightning events and enhance disaster preparedness. At present 46 lightning detection sensors (LDS) are installed across India, by the Indian Space Research Organisation (ISRO). Each LDS is having 300 Km range detection. The network is designed with a 50% overlap to ensure high geolocation accuracy and maintain redundancy in regions with a strong LDS presence. Though this study is focused on a region with a strong existing LDS network, we recognized that there are regions throughout the nation where the LDS network is weak or nonexistent moreover LDS focuses on detection rather than prediction. To address this disparity, the study has two primary objectives: first, to advance beyond detection by developing models that can predict lightning events before they occur; and second, to enhance lightning detection capabilities in areas where the LDS network is sparse or nonexistent. This research relies on data from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) dataset and the LDS dataset, focusing on the region between 86.68°E to 88.52°E longitude and 24.70°N to 26.42°N latitude during June 2022. To improve grid and temporal resolution, data refinement techniques were applied. Following this, statistical analyses, including the Chi-squared test, ANOVA, and Spearman-Rho correlation, were conducted to identify the parameters most strongly correlated with lightning occurrences. Humidity, pressure, and precipitation emerged as the most predictive factors.

Using these parameters, the Extra Trees model with bagging was employed to predict lightning occurrences, and a Random Forest classifier was used to predict lightning intensity based on the number of strikes. These models were validated using additional datasets. The findings from this study have the potential to significantly advance early warning systems, particularly in regions with limited LDS coverage, thereby enhancing resilience to natural hazards such as lightning across a wider area.

Evaluation of Artificial Neural Networks (ANNs) and Multivariate Adaptive Regression Splines (MARS) for Monthly Mean Land Surface Temperature (LST) Modelling– A Case Study of Aowin District, The Republic of Ghana

Wed, 30 Apr 2025 00:00:00 +0000

Accurate and precise estimations of Land Surface Temperature (LST) are essential in climatology, agribusiness, agronomy, urban planning, aviation, and hydrology studies. In this study, the feasibility of two soft computing methods, thus; fifteen different Artificial Neural Network (ANN) architectures and the data mining model of Multivariate Adaptive Regression Splines (MARS) is evaluated for predicting the monthly mean LST of Aowin District, Ghana. Various weather prediction variables, including precipitation, relative humidity, wind speed, and temperature time series historical data spanning 37 years (from 1st January 1985 to 31st December 2022), were used. The data was obtained from a satellite database repository and used in the ANN and MARS models' formulation as input (independent variables) and output (dependent variable), respectively. Five different statistical performance indicators, namely mean error (ME), root mean absolute error (RMAE), mean squared error (MSE), root mean squared error (RMSE), and standard deviation (SD), were used to assess the accuracy and precision of LST estimates from both the ANN and MARS models for the research area. The results demonstrate the capability of both techniques in predicting the monthly mean LST. However, the MARS model produced the best LST estimate, with statistical metrics of ME, RMAE, MSE, RMSE, and SD being 1.8705E-07 °C, 0.0004 °C, 3.3449 °C, 5.7835 °C, and 1.6000E-09 °C, respectively. Both ANN and MARS methods can be effectively applied for LST estimation in the research region and for studying the potential impacts of climate change dynamics globally.

Use of camera traps in parametric and non-parametric home range and utilization distribution estimation of tiger (Panthera tigris tigris Linn).

Varghese A O, Vishal Patil, Arun S. Suryavanshi, Rao Y.L.P. — Wed, 30 Apr 2025 00:00:00 +0000

Each wild animal possesses a home range specific to their trophic level, characterizing that home range provides valuable insight into the animal's habits, social structure, and lifestyle. Camera trapping is one of the methods for analyzing the home ranges of some wildlife species where the individual species can be identified through the stripes or spot characteristics. This method offers essential insights about the target species while minimizing animal disturbance. It operates continuously, silently, and cost-effectively. In the present study, the utilization of camera traps in deriving the home ranges of tigers was analyzed using parametric and non-parametric home range estimators like Minimum Convex Polygon (MCP), Kernel Density Estimator (KDE), Autocorrelated KDE (AKDE) and Low Convex Hull (LoCoH) methods. Melghat Tiger Reserve, Maharashtra, India, was taken as a study site with the camera trap information derived from CaTRAT (Camera Trap Data Repository and Analysis Tool) and ExtractCompare (pattern recognition program). LoCoH is constructed using the k-1 nearest neighbors of each data point. To obtain a utilization distribution in KDE, probability contours were derived as 0.95 as the outer layer. LoCoH hulls were ordered from the smallest to the largest to get the utilization distribution, where the smallest hulls indicate frequently used areas. The average size of the home range of tigers in tropical dry deciduous forests of India derived from MCP, KDE, AKDE, and LoCoH were 51± SD 24, 87± SD 36, 111± SD 33, and 45 ± SD 21km² in that order. Average male tiger territory for the above home range estimators recorded were 80±15, 131±29, 146±23, 71±11 km² and 40±15, 71±22, 97±26 & 36±15 km² for females. In MCP and LoCoH methods, the outer boundary exactly matches the camera trap locations where it is recorded, but in real scenarios, this may be extended further up to some more areas that could not be captured in MCP and LoCoH methods. Moreover, the different hulls generated using LoCoH methods are not continuous in nature and do not give a clear picture of the utilization distribution. Data derived from camera traps with realistic and autocorrelated movement, KDE, MCP, and LoCoH underestimate home range substantially. So, considering these facts, it is concluded that AKDE with 95% probability contours appears to be the best method for home range estimation of tigers using camera traps where the sample size is small.

Deep Learning based enhanced aerial object detection

Avinash Chouhan, Dibyajyoti Chutia, Biswarup Deb, S.P. Aggarwal — Wed, 30 Apr 2025 00:00:00 +0000

In congested urban environments, accurate detection and counting of humans and vehicles provide valuable insights for optimizing traffic flow, identifying congestion hotspots, and designing efficient transportation systems. By leveraging computer vision algorithms, such as deep learning based object detection models, real-time monitoring of pedestrian and vehicular traffic can be achieved with high accuracy and granularity. The ability to precisely quantify pedestrian and vehicle movements enables urban planners and policymakers to make data-driven decisions regarding infrastructure development, road maintenance, and public transit planning. In this work, we enhanced the existing deep learning based network architecture for object detection using UAV images. The enhanced network architecture can detect and give a count of the number of objects for any particular area in the image.

An AHP-GIS based approach for optimal metro route planning In Tiruchirapalli City, Tamilnadu

Suvish S — Wed, 30 Apr 2025 00:00:00 +0000

This study identifies optimal metro network locations in Tiruchirapalli city to address current and future transportation challenges. The research leverages Remote Sensing, Geographic Information System (GIS), and the Analytical Hierarchical Process (AHP) to assess and prioritize key factors influencing site selection. Criteria such as population density, traffic hubs, intersections, existing road networks, land use, and slope maps are systematically analyzed. Each factor is ranked based on its importance and weighted using AHP. A GIS-based weighted overlay method integrates these ranked criteria to identify potential routes for the metro system. The study proposes five routes spanning southwest-north and east-west directions, connecting critical origin and destination stations. The findings provide a strategic framework for sustainable urban transportation planning in Tiruchirapalli, ensuring efficient connectivity while addressing the city’s evolving transit demands.

Keywords: Metro network planning, Geographic Information System (GIS), Analytical Hierarchical Process (AHP), Transportation site selection

Mapping Lightning Strikes across Gujarat State for the Year 2022 and Development of Prediction Model

Wed, 30 Apr 2025 00:00:00 +0000

Under the influence of changing climate, extreme events are getting more frequent. Atmospheric lightning is considered a natural disaster which accounts for more than 40% of the weather-related deaths worldwide. Unlike the other weather-related phenomena, mitigation efforts pertaining to lightning need high resolution information on the vulnerable regions with region specific prediction. Present study is focused on mapping lightning incidences in the state of Gujarat, India, for the year 2022 and determines the vulnerable locations within a state.

Our studies have shown that during monsoon season, most of the lightning strikes happen where the elevation is less than 30 meters above sea level and post monsoon the built area poses a serious concern. Vulnerability of strikes also increases when the temperatures are between 21-30 ⁰C and curling wind speed is between 0-2 m/s. Our prediction model could predict 87% records correctly. The extended objective of the study is to develop a prediction model that aims to help implementing the appropriate nationwide mitigation efforts in the vulnerable geographical regions.

Predicting current and future habitat suitability of Cullenia exarillata A. Robyns – An endemic and keystone species of the Western Ghats

Bhavya M.S., Jyoti Kumari, Namitha L.H., Sudhakar Reddy — Wed, 30 Apr 2025 00:00:00 +0000

Accurate prediction of habitat suitability is crucial for species of conservation importance. Predictive distribution models play a key role in conservation by identifying current and future suitable habitat. Cullenia exarillata A. Robyns is an endemic and keystone tree species of the tropical wet evergreen forests of the Western Ghats of India. This study used a species distribution model to predict the current and future distribution of Cullenia exarillata. Various environmental variables and the MaxEnt model were used to assess the current potential distribution and shifts within different shared socio-economic pathways. The findings illustrate the potential reduction of the species ecological niche in certain landscapes of Karnataka, Kerala and Tamil Nadu under future climate change scenarios. The receiver operating characteristic area under the curve was used to evaluate the accuracy of the model. The Jackknife test was used to assess the significance of environmental factors. This study highlights the importance of targeted conservation and habitat management strategies for the conservation of Cullenia exarillata. This spatial approach can be applied to other species facing similar threats, making it an essential tool for broader conservation efforts.

Automated Farm Ponds Detection using Satellite Imagery and Deep Learning: Insights from Kadwanchi village, Maharashtra

Sumona Bera, Stutee Gupta, Dharmaveer Singh, T.P Singh — Wed, 30 Apr 2025 00:00:00 +0000

Farm ponds play a crucial role in rainwater harvesting and irrigation, making their accurate detection essential for effective water resource management. This study explores the application of deep learning models for detecting farm ponds in Kadwanchi village, Maharashtra, where water scarcity challenges agricultural productivity. Using satellite imagery, the study compares and evaluates four deep learning models—U-Net, Mask RCNN, DeepLabV3+, and Feature Classifier—based on precision, recall, and F1 score. The Feature Classifier emerged as the most accurate model, achieving a perfect precision score of 1.0, a recall of 0.863, and an F1 score of 0.927, detecting 296 farm ponds. U-Net also performed well, with an F1 score of 0.873, while Mask RCNN and DeepLabV3+ showed more moderate results. These findings can assist government agencies in making data-driven decisions about water resource management and promoting sustainable agriculture in water-scarce regions. Future research could focus on hybrid models and larger datasets to improve farm pond detection accuracy.

Land Surface Phenology for Africa - A Case of the Republic of Ghana

Wed, 30 Apr 2025 00:00:00 +0000

Understanding Earth’s surface phenology at different spatiotemporal scales is fundamental in evaluating the interaction between biogeographical distributions and climate dynamics. Despite remarkable achievements in remote sensing and Earth-observing technologies, there is a deficiency of African studies in land surface phenology (LSP). The article is a case of Ghana synthesizing studies of LSP between 2000 and 2024 using a systematic review and meta-analysis method. In a systemic review of the literature using the PRISMA protocol, this article critically examines methodological frameworks, spatiotemporal patterns, and drivers of LSP in diverse ecological zones of Ghana. The results indicate large discrepancies in ecological zone-based phenomenological patterns driven by climate variability, land use/cover change, and human pressures regarding deforestation, urban expansion, and agriculture expansion. Remote sensing observations using MODIS and Landsat imagery have been crucial in observing such processes, yet there is a limitation in using ground observations to gain better precision. Temperature and precipitation patterns indicate a trend in vegetation cycles such as the advancing start of the growing season and shortening vegetation duration of growth, having implications for biodiversity and agriculture productivity. In addition, extreme events in terms of heatwaves and droughts have heightened phenomenological anomalies. The article recommends more efficient remote sensing approaches, climate-resilient land management approaches, and environmentally friendly policy interventions to mitigate their impact. Future studies need to use high-resolution satellite observations in combination with local ground observations to calibrate models of LSP to provide useful information to support environmentally friendly management approaches and policy making. The article addresses knowledge gaps in African ecosystem processes and facilitates strategies to meet Sustainable Development Goals (SDGs).