Journal of Geomatics

The Geographic Information System (GIS) and Multi-purpose Household Survey (MPHS) Based Tax Assessment Approach for manifold revenue generation in Mandav, Dhar (M.P.), India: A pilot ward study

vineesha singh — 2024-10-30

Abstract: This study has been done under project entitled “Prepare Property Tax Register based on GIS & Multi-Purpose Household Survey its integration E-Nagarpalika with Technical Handholding Support” by M.P. Government. In Mandav, as a pilot ward 05 (Rampura Ward) were selected and that aims to find out the comparison of revenue generation in terms of taxation for individual household properties in the same ward. Study ward had 98 existing house hold properties according to Nagar Parishad, Mandav records and after MPH survey it became 164. Using GIS techniques and existing tax assessment approach, in a totality 191% demand growth has been observed. 100% demand growth has been found in commercial properties followed by 39% growth in residential properties and 16% growth in mixed properties. Land Use and Land Cover map has also been made using drone imagery (resolution 0.35cm) and GIS techniques. In study ward agriculture land were found 35.44% of the total area followed by built-up area 27.16%, waste land 17.63%, water bodies 14.36%.

Keywords: Manifold Revenue Generation, Demand Growth, Land Use and Land Cover, Multi-purpose Households Survey and GIS techniques.

Selection of Optimum Enhancement Technique for the Discrimination of Alkali Granites and Syenites around Suryamalai Batholith of Central Tamil Nadu, India

Muthamilselvan Alagan — 2024-10-30

Image processing techniques such as Band Ratio, Principal Component Analysis, Supervised, Unsupervised classification and Reclassification techniques are generally used as an enhancement technique for alteration zone demarcation and lithological discrimination in geosciences environment. In the present study, Landsat OLI satellite imagery has been utilized to produce various combinations of image enhancement to discriminate alkali syenite and alkali granite of Proterozoic age. These techniques were also used to differentiate three phases of granites reported in Suryamalai Batholith which is very difficult to map by conventional method due to its rugged topographic nature. 12 types of enhancement techniques were applied to differentiate rock alkali syenite from granite, however only few methods such as Kaufmanns ratio, ratio of 5/7, 3/1 and 3/5 and supervised classification were found to be suitable for the discrimination of those rocky types. Band ratio 5/7, 3/1 and 3/5, Crosta analysis and thermal reclassification techniques significantly brought to light the difference between the metamorphic rocks such as fissile hornblende biotite gneiss and hornblende biotite gneiss of Archaean age. Similarly, phase I and phase III granites were perfectly discriminated from all the image enhancement techniques. It is differentiated mainly based on its grain size and mineral variation between these two rock types. Study revealed that standard digital image processing technique can be effectively used for lithological mapping.

Analysis of Groundwater Quality in Semi-arid Region of Eastern - South Rajasthan on the Basis of Water Quality Index (WQI)

Pankaj Sen — 2024-10-30

Abstract: The present study was conducted to evaluate the groundwater quality for drinking purposes in selected locations of the Eastern- South Rajasthan of India. Total eight sights groundwater samples were collected during 2022 and the samples were analyzed for physicochemical parameters i.e., pH, total hardness, total dissolved solids, Nitrate, Sulphate, fluoride, Chloride, and alkalinity were calculated. The results of the study are illustrated with the help of descriptive statistics, diagrams, and the water quality index (WQI). The focus of this research was to assess the current state of groundwater quality and the spatial distribution of characteristics in order to determine the quality of drinkable water. The WQI was used to categorize the samples into categories based on its acceptability for consumption. It is observed that most of the samples fall in the water unsuitable for drinking purposes category and a few samples are in the very poor water category.

Early Season Crop Acreage Estimation for Pigeon Pea using SAR Data: A Case Study of Kalaburagi District, Karnataka

V Poompavai — 2024-10-30

Crop acreage estimates using space-based observations during mid-season and pre-harvest periods are operationally provided for major Kharif and Rabi crops in India. These are critical inputs for planning and decision-making concerning food security, storage, pricing, and procurement. This work explores the potential of Satellite C-band Synthetic Aperture Radar (SAR) data in VV and VH polarization, acquired during monsoon season, for early crop acreage assessment of Pigeon Pea. The present study evaluates two classification approaches based on multi-temporal SAR data to generate a spatial distribution map of the pigeon pea crop in the Kalaburagi region of Karnataka. The temporal profiles of sigma nought (backscatter coefficient) and Entropy vs. Alpha Angle were studied with reference to crop phenology. Random forest algorithm has been used to classify multi-temporal SAR Ground Range Detected (GRD) data from June to November 2019. Temporal SAR data from June to early November with VV+VH polarization was optimal for tur area assessment with 85% accuracy. This study also examines phase and intensity information from 3-date SAR Single Look Complex (SLC) data with an unsupervised Wishart Classification approach for improved crop acreage estimation. The results emphasize the effectiveness of dual-polarimetric SAR data for timely crop inventory of Pigeon Pea during the Kharif season.

Assessment of Groundwater Quality for Irrigation Suitability in Rajangaon Shenpunji and Surrounding Area, Aurangabad, Maharashtra, India.

Suren Kamble — 2024-10-30

The quality of irrigation water available to farmers has a significant impact on crop yield. Hence, it is need to better understand irrigation water quality. The present study mainly emphasizes the assessment of the suitability of 30 water samples from dug wells, bore wells and surface water bodies in Rajangaon Shenpunji and surrounding areas, Aurangabad, Maharashtra, India. The groundwater sample datasets of post-monsoon 2013 season were collected. The water quality parameters, viz. sodium adsorption ratio (SAR), percent sodium (%Na), residual sodium carbonate (RSC), residual sodium bicarbonate (RSBC), Kelly’s ratio (KR), magnesium adsorption ratio (MAR), permeability index (PI) have been calculated. Based on these calculated parameter results, prepared spatial distribution maps in Arc GIS 10.3 software. These parameters were correlated with standard permissible/desirable limits for irrigation use for the prevailing crops. Based on these factors, most of the samples fall into the category of water suitable for irrigation. A few samples that exceeded permissible limits were discovered in various geological and anthropogenic activities near the water samples in the study area. A complete investigation of groundwater quality may be suitable remedial action. Artificial recharge techniques are developed to increase the chemical concentrations in groundwater or suitable crops are working to continue the present water quality.

Assessment of Bleaching Stress Vulnerability of Lakshadweep Islands using Google Earth Engine (GEE)

Divya Mhalaskar — 2024-10-30

Coral reefs, among the Earth’s most diverse and valuable ecosystems, face unprecedented challenges due to climate change. Coral bleaching is a phenomenon wherein corals lose their symbiotic zooxanthellae owing to various stressors, leading to a whitening effect of the coral tissues. In recent decades, climate change has intensified coral bleaching events. Multiple stressors, including elevated Sea Surface Temperature (SST), extreme irradiance levels, and various biotic and abiotic factors trigger bleaching events. Coral bleaching is primarily driven by thermal stress caused by elevated SSTs. Climate change has worsened bleaching’s frequency and intensity. Global bleaching events are often linked to planetary ocean-atmospheric circulation processes such as El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD).

This study focused on assessing the vulnerability of coral reefs in the Lakshadweep region of India from 2016 to 2023 using National Oceanic and Atmospheric Administration’s Climate Data Record Optimum Interpolation Sea Surface Temperature (NOAA CDR OISST) daily data. GOOGLE EARTH ENGINE (GEE) is a cloud computing platform which is used to collect and generate the base data for this study. The vulnerability assessment utilized two bleaching indices: SST anomaly and Degree Heating Week (DHW). Analysis of DHW data reveals that 2020 experienced the highest SST anomaly residence time due to IOD event of 2019, as compared to 2016 and 2023 which are known El Niño years. All Lakshadweep islands exhibited vulnerability, although in varying degrees across different areas. Based on the magnitude, intensity, and frequency of bleaching stress, the islands are categorized into different categories of vulnerability. This study identifies Baliyapaniyam, Cheriyam-Kalpeni and Suhelipar reefs as very highly vulnerable reefs in Lakshadweep. This study highlights the urgent need for monitoring and management measures to mitigate the impacts of climate change on coral reef ecosystems using spatial vulnerability patterns.

Statistical Relationship between Satellite observed Land Surface Temperature and in-situ measured Surface Air Temperature over the Indian Region: An Exploratory Study

Utkarsh Tyagi — 2024-10-30

This paper presents an investigation of spatio-temporal estimation of daily minimum (Tmin) and maximum (Tmax) surface air temperature using satellite (INSAT and MODIS) derived Land surface temperature and in-situ observational data over the Indian region. Least Absolute Shrinkage and Selection Operator (LASSO) regression technique is used to identify influencing neighboring stations. To capture spatial and temporal variability of surface air temperature of a particular IMD station, Linear Regression and Auto-Regressive Integrated Moving Average, which is known as ARIMA, are used respectively. These models are statistically ensembled using stack generalization. Obtained station-by-station relationship is validated on an independent test data using Root Mean Squared Error (RMSE) to check the validity of the model under consideration. Results show ensembled model outperform (has lowest RMSE) the traditional methods for prediction of Tmin and Tmax with RMSE within a range of [1, 2] range for most of the regions and seasons.

Statistical approach towards watershed prioritization of Narmada River Basin

Prakher Mishra — 2024-10-30

The Narmada River basin, one of the most significant river systems in India, plays a vital role in the socio-economic and ecological development of the region. Understanding the basin's morphometric characteristics is essential for sustainable water resource management and environmental planning. This research work aims to conduct a comprehensive morphometric analysis of the Narmada River basin using GIS, by studying the parameters of geomorphologic significance. These parameters reflect the true nature of the response which the topography offers to the hydrological flow. In this study quantification of basins’s characteristics is carried out, to provide valuable insights into its hydrological behavior, erosion susceptibility, potential for water resource utilization by prioritizing river basins using PCA (Principal Component Analysis). The PCA approach is applied keeping in mind the scale and volume of parameters and also to recognize their individual contribution towards the behavioral pattern and response.

A Comparative use of UAV and Satellite Images in discrimination and estimation of cashew plantation areas

Alain Abi Kaberou — 2024-10-30

Cashew plantations generate significant interest in Benin due to their high socioeconomic value for the population. A thorough understanding of the spatial distribution of these plantations is crucial for comprehending their environmental and socioeconomic impacts. In this study, various types of multi-sensor imagery were compared to assess each sensor's capabilities in mapping plantation areas. The study was conducted in the Savè commune, a major industrial cashew-producing region. Multispectral sensors from Landsat-8 Operational Land Imager (OLI), Sentinel-2A, and UAV multispectral platforms, along with ground surveys, were fused and classified using the Random Forest algorithm. The study results allowed for the assessment of uncertainties associated with different platforms in detecting cashew plantations in the test area. Classification using Random Forest algorithms on UAV, Sentinel, and Landsat platform images yielded overall accuracies of 83%, 65%, and 48%, respectively. Producer and user accuracies were 94% and 75% for the UAV platform, 98% and 71% for the Sentinel platform, and 91% and 77% for the Landsat platform in cashew tree detection. This study demonstrates the complementarity among various platforms in detecting and mapping cashew plantations.

Covid-19 and its related environmental quality issues along ganga river, Varanasi, India

Kiruthika N N — 2024-10-30

COVID-19 made people around the world in terrifying and appalling circumstance which held back all collaborative stitches that is the government decreed people to stay in homes for suppressing the disease spread amid people. Pandemic situation due to Covid-19 has lead to worldwide lockdown. On the other hand, it has increased the quality of the environment especially in hydrosphere and atmosphere progressively throughout the world. The global shutdown from March to June 2020 made liable in assessing the environmental condition which is eccentric accomplishment when collating with past two decades. This study demonstrates change in the water and aerosol quality with the aid of remote sensing in Ganga River of Varanasi city, Uttar Pradesh state of India. Sentinel data obtained from February to July of 2020 used to determine the variation of pollution levels during different phases of lockdown. NO₂ levels derived from Sentinel data shows hazardous level of 5.64x10^-5mol m^-2 in March which is progressively decreased to 5.29 x10^-5mol m^-2 for the month of April. NO₂ level further gradually increased after the unlock 1.0. Similarly, CO levels obtained from Sentinel data shows hazardous level of 0.0517mol m^-2 in February which is progressively decreased to 0.0490 mol m^-2 for the lockdown periods and it was gradually increased after the unlock phase. Further, Landsat data has been used to show Suspended Particulate Matter and pollution variation between lockdown phases. The high concentration of SPM value for March month ranges from -48.7773 to -48.7812 ,there is drastic decrease in concentration of SPM in which reflection value ranges from -48.7772 to -48.7806 in month of April noticed in the study area.Again there is drastic increase in July values ranging from -48.7753 to -48.7796 which is very high when compared to the lockdown SPM levels.The present study brought to light significant variations in the pollution level during the lockdown phases.

Estimation of Soil Erosion by RUSLE Model Using Geoinformatics Techniques: A Case Study of Mulshi Reservoir Catchment, Pune District, Western Maharashtra.

Mitrajeet Khobragade — 2024-10-30

In the western part of Maharashtra, where the Western Ghat highlands are located, soil erosion is a serious and persistent environmental issue. To make matters worse, the majority of the dams in this region run the risk of sedimenting their reservoirs, which is a serious concern. This research was being conducted on the Mulshi reservoir catchment area, Pune district of western Maharashtra having area of 250.25 km². It is fact that the surface runoff of seasonal rainfall is more in this region due to the presence of western ghats. In this study, high resolution satellite images were used to estimate average annual soil erosion based on the five factors defined in the Revised Universal Soil Loss Equation (RUSLE) with the help of Geoinformatics techniques. Overlay of five parameters, viz., rainfall erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C) and support & conservations practices factor (P) has been done in ArcGIS (version 10.8) software. Annual average soil erosion of the catchment is 0 to 433.43 tons/hector/year and has been classified into Six categories, defined as very high, high, moderate, low, and very low and negligible according to the severity of erosion. The high values of soil erosion (>20 tons/hector/year) found on the highlands due to the high slope and bare lands. however, low values (<10 tons/hector/year) are found on the area occupied by reservoir and dense vegetation.

Integrating Geospatial and Real Time Technologies for Risk zone monitoring in Periyar Tiger Reserve, India.

Veeramani S — 2024-10-30

There are numerous monitoring technologies available today, owing to the rapid advancements in technology and the increasing demand for safety and security in forests. Real-time monitoring with AI cameras, which are commonly utilized for creating and updating real-time features through surveillance, stands out as one of the most effective monitoring solutions. The objective of this current research is to monitor various risk zones within the Periyar Tiger Reserve by integrating real-time AI camera with geographic data. AI cameras were strategically placed using spatial analysis techniques. Leveraging Geographic Information System (GIS) technology, the system facilitates the spatiotemporal management of multiple cameras and their associated data. The spatial distribution and monitoring range of the AI cameras are depicted on the GIS map, along with the layout densities of the cameras and other pertinent information stored in a geospatial database. Additionally, merging various risk areas identified from past incidents with the camera locations enhances the system's capability to establish accurate topological connections between cameras and other points of interest. The results revealed that only 13% of the risk zone was observable from the nine available Real Time Monitoring towers. However, with the addition of 51 more towers, the visibility of the risk zone would increase to 40%. The remaining 15% of the risk zones were not visible through the existing infrastructure. To address this visibility gap, if permitted by the Wildlife Protection Act, wired communication may need to be implemented instead of wireless for monitoring these areas.

Web-based Land Suitability Analysis for Horticulture Crop (Kiwi) in Champawat districts, Uttrakhand using MCDM and Geospatial Analysis

Kamal Pandey — 2024-10-30

Identifying the suitability of a horticulture fruit with a defined area is an active research area, it has been attempted through different approaches, surveying and geographic information system are the two most popular techniques utilized extensively for this activity across the globe. GIS has its proven advantage over the field surveys based approach and modern GIS tools offers automation of the entire work flow for this activity. This study presents a web-based land suitability analysis for kiwi fruit cultivation in the Champawat District of Uttarakhand, using AHP, TOPSIS, and Ensemble modeling in conjunction with Multi-Criteria Decision Making (MCDM) and Geospatial Analysis. Land suitability was evaluated across various classes based on nine criteria viz. elevation, chilling hours, land use/land cover (LULC), soil texture, soil depth, temperature, rainfall, slope, and aspect. The AHP method identified 26.09 hectares as highly suitable, 14,606.19 hectares as suitable, 31,899.77 hectares as moderately suitable, 8,807.99 hectares as less suitable, and 192.23 hectares as not suitable. TOPSIS results showed 4,294.32 hectares as highly suitable, 14,193.54 hectares as suitable, 11,782.52 hectares as moderately suitable, and 2,399.21 hectares as less suitable. The ensemble method indicated 24.15 hectares as highly suitable, 8,763.51 hectares as suitable, 13,785.13 hectares as moderately suitable, 2,472.13 hectares as less suitable, and 191.94 hectares as not suitable. Integrating these findings revealed that the ensemble approach closely aligns with AHP in identifying highly suitable and less suitable areas, while TOPSIS emphasizes moderate suitability. This synthesis provides valuable insights for land use planning for kiwi cultivation. Additionally, an online spatial decision support system (SDSS) was developed using the Django framework, offering a modular and secure environment for informed decision-making.

Identification of Sulfur Dioxide (SO2) Hotspots of Gujarat state using Sentinel 5P-TROPOMI

Tejas Turakhia — 2024-10-30

Sulphur dioxide (SO₂) is a widely recognized pollutant with far-reaching consequences for human health, climate, and the environment. This study aims to identify SO₂ hotspots within the state of Gujarat located in the western part of India using the Sentinel 5P TROPOMI satellite data. Based on the analysis of the satellite data from January 2019 to 2023, 16 hotspot regions were identified in Gujarat with significantly high concentration of SO₂. Majority of these hotspots were located in industrial areas and petroleum refineries, including Mundra port, Ahmedabad, Vadodara, Surat, and the Alang shipbreaking yard. Notably, some scattered hotspots were found near Eco-sensitive zones like Purna and Narayan Sarovar. The observed SO₂ concentrations in these hotspots vary between ~ 10 to ~ 1000 µmol/m², with an average concentration of ~ 300 µmol/m². It is also observed that SO₂ concentration is significantly elevated during winter and pre-summer months, with a marked reduction during the monsoon season. The average monthly SO₂ concentrations exhibit a distinct seasonal cycle, with the lowest levels during the monsoon and the highest during winter. Furthermore, impact of COVID induced lockdown is also perceived across the state.