Nowadays due to the change in climatic conditions and proliferation in sea level, the coastlines are under high threat. The Gujarat coastline is studied in the current work. It is the longest coastline in India and is highly vulnerable to... more
Nowadays due to the change in climatic conditions and proliferation in sea level, the coastlines are under high threat. The Gujarat coastline is studied in the current work. It is the longest coastline in India and is highly vulnerable to cyclones, earthquakes, floods, landslides, etc. These facts show the relevance of the present research. The parameters based on which the coastal vulnerability index is laid include seven physical parameters and one social parameter. The seven physical parameters are rate of shoreline change, coastal slope, coastal elevation, geomorphology, significant wave height, tidal range, sea level rise, and the social parameter is population density. The additional parameters used in this study, to increase the accuracy of the vulnerability index are coastal elevation, rise in sea level, and population density. The study is done using geospatial data and various other models and is analyzed with the help of geospatial tools. The high-resolution Cartosat DEM data is used to analyze the coastal elevation and makes this study stand out from the previous studies. Using the risk rating of each parameter, the coastal vulnerability index is prepared and it divides the coast into four zones, that is, very highly vulnerable, highly vulnerable, moderately vulnerable, and low vulnerable. According to the analysis, about 43.5% of the coastline is under highly vulnerable zone and about 1% is under very highly vulnerable zone. The study shows that the area under high erosion is basically tidal flats and mangroves.
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Coastal waters are showing deteriorating trend in its quality. This leads to the damage of marine ecosystems and interferes in its normal use. In order to tackle this issue, it is important to know about the extent of pollution.... more
Coastal waters are showing deteriorating trend in its quality. This leads to the damage of marine ecosystems and interferes in its normal use. In order to tackle this issue, it is important to know about the extent of pollution. Conventional method of water quality estimation includes analysis of water samples from various locations. This is a tiresome and costly process limiting its application to small scales and accessible sampling sites. In this paper, an attempt has been made to quickly estimate the concentration of Petroleum Hydrocarbons (PHC) and counts of Total Coliform (TC) which are important water quality parameters, along the south west coast of India. This study formulated satellite data-based multiple regression equations for determining the count of total coliform bacteria and concentration of petroleum hydrocarbons. The sea surface temperature and remote sensing reflectance values of different bands of MODIS sensor along with field values were used in the process. Th...
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AIM: The aim of the present study was to prepare natural adsorbent from sugar cane bagasse modified with propionic acid for the removal of basic dye ‘Methylene Blue’ from synthetic wastewater. OBJECTIVE: Adsorption experiments were... more
AIM: The aim of the present study was to prepare natural adsorbent from sugar cane bagasse modified with propionic acid for the removal of basic dye ‘Methylene Blue’ from synthetic wastewater. OBJECTIVE: Adsorption experiments were performed to determine the optimum conditions of time, adsorbent dosage, adsorbent size, agitation speed, dye concentration and pH. MATERIAL AND METHODS: The sugarcane bagasse was procured and washed thoroughly with water. The bagasse was dried in sun for two days (Nevine 2008). It was then oven dried for 24 hours at 120 °C temperature (Hajira et al. 2012). The bagasse was crushed in the mixer until it turned fine powder and sieved to the sizes of 0.6, 0.3 and 0.15 mm. The efficiency of adsorption was influenced by various factors such as contact time, adsorbent dosage, size of the adsorbent, concentration of dye, pH and rpm. Each factor was optimized experimentally. BACKGROUND: The release of coloured waste water in to the natural streams such as rivers causes severe problems in the aquatic environment. The presence of dyes will absorb and reflect the sunlight entering the water thus hindering the process of photosynthesis in plants. This will reduce the water quality in natural streams and it also affects the human health. The dyes can also cause allergic diseases, skin irritations, cancer and mutations. RESULTS: The results indicated that the adsorbent showed good sorption potential and maximum dye removal was observed at pH 7.Within 8 minutes of operation about 81.5% of the dye was removed from the solution. The sorption curve was well fitted to the Langmuir model. The adsorption capacity of dye at optimum conditions was found to be 60 mg/L. Langmuir adsorption isotherms have been analysed and it gives high correlation factor (R2 > 0.98). Kinetic study shows that the adsorption process follows pseudo second order reaction with good correlation factor (R2 > 0.99).
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GIS technology is used to estimate the spatial heterogeneity of the hydrological parameters of a watershed. Hydrological models help to overcome the spatial variability and parameter uncertainties. Runoff is important parameter of... more
GIS technology is used to estimate the spatial heterogeneity of the hydrological parameters of a watershed. Hydrological models help to overcome the spatial variability and parameter uncertainties. Runoff is important parameter of hydrological cycle. Soil and Water Assessment Tool (SWAT) which is a physical distributed model developed to forecast runoff, sediment, erosion and nutrient transport from agricultural watershed helps to understand the hydrology of a watershed with rainfall, temperature, solar radiation, wind speed and relative humidity. SWAT simulates better results in both gauged and ungauged watersheds. In the present paper, Krishna river catchment area known as Jurala watershed in Mahabubnagar district, Telangana state of South India is taken to study surface runoff from agricultural areas as this area receives less annual rainfall and agriculture is mostly dependent on seasonal rainfall. Soil has less water infiltration capacity and bottom layer calcium carbonate deposits make soil alkaline due to bore well irrigation. To suggest proper water conservation methods, understanding hydrology of this watershed is important. To simulate runoff from this agriculture watershed SWAT model is used for 11 years from 2000 to 2010. The results are calibrated with observed values.
Deltas and associated coastlines are amongst the most rapidly changing landscape features, as these are subjected to physical, geological, biological and environmental threats. One of the largest deltas of the world, Sundarbans is... more
Deltas and associated coastlines are amongst the most rapidly changing landscape features, as these are subjected to physical, geological, biological and environmental threats. One of the largest deltas of the world, Sundarbans is undoubtedly a vulnerable area from both the ecosystem and human sustainability angles. To protect such a sensitive ecosystem, a comprehensive strategy and action plan is therefore needed to ensure conservation of the environment while guaranteeing the inhabitants an adequate living standard. Such rigorous planning must follow reliable and scientific data and information. The present study aims to evaluate the primary physical and environmental parameters affecting the Indian Sundarbans, and it will help to assess the amount of erosion and accretion over the southern islands of Indian Sundarbans that directly faces the onshore tidal currents.
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Alteration in Land use land cover (LULC) and its causes have been measured using remote sensing while mapping of a range of LULC and their variations in spatial and temporal scales were studied using Geographical Information System (GIS).... more
Alteration in Land use land cover (LULC) and its causes have been measured using remote sensing while mapping of a range of LULC and their variations in spatial and temporal scales were studied using Geographical Information System (GIS). A maximum likelihood classification (MLC) algorithm has been used to classify five land cover classes. The study shows that from 2001 to 2011 water body, forest cover and barren land were decreased whereas, agriculture land and built up area was increased by 21.38% and 53.6% respectively. In between 2011 to 2015, there has been a significant increase in water body which led to an increase in agricultural land. The rate of decrease in forest cover observed in all these years was almost the same. The built-up area doubled in the year 2015 as compared to 2011. It was found that during 2015 to 2018 water body was increased by 5%. But built up area got increased almost twice and the barren land was decreased by 7.25% whereas, the forest cover reduced to...
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The AVIRIS-NG hyperspectral data consists of continuous spectral bands with low bandwidth, Sentinel-2 multispectral image has less number of bands with higher bandwidth. Several studies are carried out to calculate the Normalized... more
The AVIRIS-NG hyperspectral data consists of continuous spectral bands with low bandwidth, Sentinel-2 multispectral image has less number of bands with higher bandwidth. Several studies are carried out to calculate the Normalized Difference Vegetative Index (NDVI) of hyperspectral data. The studies considered a single band in the red and NIR region of hyperspectral data. In this present study, NDVI analysis is carried out by taking the mean reflectance of red and NIR wavelength region bands of the AVIRIS image. To choose the bands, a methodology is devised for AVIRIS image by analyzing and evaluating the NDVI between AVIRIS and Sentinel-2 image. The AVIRIS data consists of 7 red bands and 22 NIR bands. Root Mean Square Error (RMSE) between NDVI of all the 154 combinations of AVIRIS image bands and Sentinel-2 image is calculated for each Land Use Land Cover (LULC). Three mean NDVI are evaluated such as (i) mean of all bands reflectance; (ii) mean of band reflectance higher than [Mean...
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This article adopts Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of rainfall in Vellore (India). SVM is firmly based on the theory of statistical learning theory. RVM is a probabilistic basis model. SVM... more
This article adopts Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of rainfall in Vellore (India). SVM is firmly based on the theory of statistical learning theory. RVM is a probabilistic basis model. SVM and RVM use air temperature (T), sunshine, humidity and wind speed (V a) as input variables. This article uses SVM and RVM as a regression technique. Equations have been also developed for prediction of rainfall. The developed RVM gives variance of the predicted rainfall. This study shows the RVM is more robust model than the SVM.
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The wastewater released by poultry businesses are portrayed for the most part by high biochemical oxygen request, high suspended solids and complex blend of fats, proteins and fibers requiring orderly treatment before transfer. Due to the... more
The wastewater released by poultry businesses are portrayed for the most part by high biochemical oxygen request, high suspended solids and complex blend of fats, proteins and fibers requiring orderly treatment before transfer. Due to the increase in usage of water, waste water generation is high and also constitutes high concentration of pollutants comprising with wide range. Degree of treatment required for poultry processors and it have the option of utilising Physical, Chemical and Organic treatment frameworks. Every framework sort possesses unique treatment favourable circumstances and operational troubles. Among the diverse treatment, Reed Bed Treatment System is good alternative and effective system for treating the poultry waste water. This review article is focused performance of reed bed system, design consideration and remove methods.
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The study identifies various growing stages of rice crop using multispectral data through red edge analysis. The maximum reflectance values for 35, 66, 76, and 96 days which indicate vegetative phase, reproductive phase, reproductive... more
The study identifies various growing stages of rice crop using multispectral data through red edge analysis. The maximum reflectance values for 35, 66, 76, and 96 days which indicate vegetative phase, reproductive phase, reproductive phase and ripening phase are 0.17, 0.228, 0.231, and 0.266 respectively at the test site 1. For the test site-2, the same trends are followed. When the crop is in vegetative stage the reflectance values are less whereas, when the stage of crop is reproductive, adjacent to the vegetative, the values of reflectance are increasing significantly due to increase in trend in canopy. This type of spectral analysis approach can be adapted to generate spectral library which can be beneficial for future research purpose.
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Digital Elevation Models (DEMs) are useful digital representation of earth’s topographic relief that has developed as a result of interaction of earth’s internal and external process, over a long temporal scale. However, accuracy of the... more
Digital Elevation Models (DEMs) are useful digital representation of earth’s topographic relief that has developed as a result of interaction of earth’s internal and external process, over a long temporal scale. However, accuracy of the extracted information and parameters highly rests on the quality (resolution) of input DEMs. Therefore, this book is presented to study a comparison between 30m-ASTER and 90m-SRTM DEMs is carried out in order to ascertain their suitability and accuracy for geomorphological analysis. A comparison is also done with SRTM 90 resampled to 30m to emphasise the importance of resolution, where in the extracted parameters were found to be similar to that obtained with that of ASTER 30 DEM. Geomorphological parameters related to drainage patterns and basin morphology have been compared over varying topographic relief. Besides the resolution, the thresholding of the flow accumulation or drainage area was also found to strongly control the morphometric results, which was in turn controlled by the resolution of the DEM.
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ABSTRACT
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Models based on Rational Polynomial Coefficients (RPC) have recently sparked considerable interest within the remote sensing community because of their simplicity and accuracy. Indeed, some commercial, high-resolution, satellite imagery... more
Models based on Rational Polynomial Coefficients (RPC) have recently sparked considerable interest within the remote sensing community because of their simplicity and accuracy. Indeed, some commercial, high-resolution, satellite imagery data are now supplied with RPC even though they do not disclose their physical sensor model. RPC, with stereo pairs, enable full photogrammetric processing including 3-D reconstruction, generation of digital elevation models (DEMs), orthorectification, block adjustment and feature extraction. In the light of this we here present a complete methodology for generating a DEM from stereo satellite images by using rational polynomial coefficients of the imaging geometry. We also conduct a study of the accuracy and performance, in terms of generating a stereo images-based DEM using RPC within three well known software packages. Our results are evaluated using sample data that was captured by IKONOS.
Research Interests: Geography and Polynomial
Agriculture residue is a promising resource of energy. It can be seen as a source of power production. In India, there is a huge amount of biomass available, but it cannot be used in proper ways, and with the help of GIS it can be... more
Agriculture residue is a promising resource of energy. It can be seen as a source of power production. In India, there is a huge amount of biomass available, but it cannot be used in proper ways, and with the help of GIS it can be customised. In the present paper, it is estimated that biomass reserves are available for power generation. The biomass produced by the surplus agricultural crops is reflected as a source of fuel for electricity generation. The data taken by satellite are useful for assessment of the areas with the help of satellite images taken in high resolution, which increases the preciseness of estimation. An agriculture cropland map with agricultural statistics has been analyzed in GIS to discover the agricultural straw potential for bioenergy generation. Due to unawareness about the benefits and uses of GIS, the modern farming sector bears a loss of huge bioenergy potential every year. To overcome the above mentioned challenges, the agricultural system needs a major...
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Land surface temperature (LST) is an important for urban environment. Our research mainly based on the landuse and landcover (LULC) on LST. The research of our study tells how the LST variations based especially for a rapidly developing... more
Land surface temperature (LST) is an important for urban environment. Our research mainly based on the landuse and landcover (LULC) on LST. The research of our study tells how the LST variations based especially for a rapidly developing city such as Vellore, India. This study uses the techniques of remote sensing and geographic information system (GIS) to detect the temperature variation of LST. The spatial variability of texture in LST was done. These variations are also present in the images, and are responsible for the spatial patterns in an urban environment. The result values shows that both the spatial and temporal variation in surface temperature is associated with CO2 concentration levels and thus affects the local land use pattern
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SVM and SAM classifiers for the lithological mapping using Hyperion data in parts of Gadag schist belt of western Dharwar craton, Karnataka, India were used. The main objective of the present study is to assess and compare the potential... more
SVM and SAM classifiers for the lithological mapping using Hyperion data in parts of Gadag schist belt of western Dharwar craton, Karnataka, India were used. The main objective of the present study is to assess and compare the potential use of Hyperion data set for lithological mapping. Accuracy assessment of the derived thematic maps was based on the analysis of the confusion matrix statistics computed for each classification map. For consistency, the same set of validation points were used in evaluating the accuracy of the lithological thematic maps produced. On the basis of the accuracy assessment results, it appears that SVM generally outperformed the SAM classifier in both OA accuracy and individual classes' accuracies. OA accuracy and Kc for SVM is 96.93% and 0.9655, whereas for SAM it is 74.02% and 0.7085 respectively. SVM classification is the best in describing the spatial distribution and the cover density of each lithology, as was also indicated from the statistics of the individual class results. The individual class accuracy were also analyzed for the SVM and the result show that PA ranges from 87% to 100% and UA ranges from 91% to 100%, whereas for SAM ranges from 15% to 95%, and from 31% to 100% respectively. The SVM method could effectively classify and improve on the existing geological map for the Gadag schist belt (GSB) using hyperspectral data. The results could be validated through field visits. Therefore, it is concluded that hyperspectral remote sensing data can be efficiently used to improve existing maps, especially in areas where same rock types show variable degree of alteration over smaller spatial scales.
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Hyperspectral images have wide applications in the fields of geology, mineral exploration, agriculture, forestry and environmental studies etc. due to their narrow band width with numerous channels. However, these images commonly suffer... more
Hyperspectral images have wide applications in the fields of geology, mineral exploration, agriculture, forestry and environmental studies etc. due to their narrow band width with numerous channels. However, these images commonly suffer from atmospheric effects, thereby limiting their use. In such a situation, atmospheric correction becomes a necessary prerequisite for any further processing and accurate interpretation of spectra of different surface materials/objects. In the present study, two very advance atmospheric approaches i.e. QUAC and FLAASH have been applied on the hyperspectral remote sensing imagery. The spectra of vegetation, man-made structure and different minerals from the Gadag area of Karnataka, were extracted from the raw image and also from the QUAC and FLAASH corrected images. These spectra were compared among themselves and also with the existing USGS and JHU spectral library. FLAASH is rigorous atmospheric algorithm and requires various parameters to perform but it has capability to compensate the effects of atmospheric absorption. These absorption curves in any spectra play an important role in identification of the compositions. Therefore, the presence of unwanted absorption features can lead to wrong interpretation and identification of mineral composition. FLAASH also has an advantage of spectral polishing which provides smooth spectral curves which helps in accurate identification of composition of minerals. Therefore, this study recommends that FLAASH is better than QUAC for atmospheric correction and correct interpretation and identification of composition of any object or minerals. Ó 2016, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).