M. Arya ¹, P. Geetha ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidya Peetham, Coimbatore 641 – 112, Tamil Nadu, IN

Abstract

This paper describes how the wind farms effect the crop production of Kanyakumari District. Remote sensing technology along with GIS has been used here for finding the NDVI values. Paddy yield data were also used for finding the effect of wind farms. Along with the NDVI values, we use temperature, humidity and Rainfall data for finding the Crop production rate. Erdas imagine 8.3 along with ArcGis have been used as the software for image and geo-information analysis.

Keywords

ArcGIS, Crop Yield Assessment, Erdas, Kanyakumari, NDVI

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M. Abhiram ¹, N. Dhivya Priya ¹, P. Geetha ²

Affiliations
1 Department of Civil Engineering, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, IN
2 Department of Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, IN

Abstract

During the past few decades the world has witnessed a phenomenal rise in the growth of population, which has in turn resulted in an increase in the demand for fresh water. Thus, creating an exigency to identify an alternative sustainable freshwater resource to subdue the rising demand for fresh water. Fog, an often-overlooked aspect of the hydrological process, can be one such resource. Several experimental studies were conducted around the globe to gauge the potential of fog as an alternative freshwater resource in high altitude regions. Studies have indicated, that the sites, which were chosen to assess the fog potential of a region, have mostly been random, which might have undermined the fog harvesting potential of the region. The inaccessibility and the harsh environment of the high altitude terrain have also been significant impediment in the experimentation process. This paper is an endeavor to address these issues. In this paper, a hybrid approach between traditional fog quantifications and mathematical modeling, using physically based impaction model aided by complex terrain analysis has been used to quantify the process of fog collection and identification of potential fog harvesting sites of a region. A case study has been done using this approach to analyze the fog harvesting potential of Western Ghats in Ettimadai region and 10054’47.4”N76053’00.6”E has been identified as an ideal location for fog harvesting and has been observed to produce an average yield of 7.67 ± 0.7Lday−1.

Keywords

Complex Terrain Analysis, Fog Harvesting, Impaction Model

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R. Mamtha ¹, M. Jasmine Niketha ², P. Geetha ²

Affiliations
1 Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore − 641 112, Tamil Nadu, IN
2 Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore − 641 112, Tamil Nadu, IN

Abstract

The optimized method clearly explains about the consequences of deforestation and the land usage by human beings in Kotagiri Taluk of Nilgiris district. Deforestation is one of the main purposes for global warming and contributor of greenhouse effect. In recent years, Remote Sensing and Geographical Information System have the prospective to deliver precise information regarding terrestrial use and forest surface changes. The current analysis assesses the usefulness of high resolution satellite data and GIS techniques for analyzing the change of terrestrial use and forest surface change of Kotagiri Taluk of Nilgiris district for 2013-2016. The Landsat imageries of 2013 and 2016 were analyzed using software. A total of twelve land use regions were identified. The comprehensive study has revealed that the region under forest has increased from 973.34 km² and 996.45 km² and settlement from 44.29 km² to 50.28 km². The analysis shows that there was major alteration in the pattern of terrestrial use and deforestation of trees for urbanization. There has been a vast change in the strategy of forest surface and terrestrial usage throughout the region of Kotagiri which eventually results in loss of natural ecosystem.

Keywords

Deforestation, Kotagiri, Land use and Land Cover Modification, Remote Sensing and GIS Technique.

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M. Jocelyn Babu ¹, P. Geetha ¹, K. P. Soman ¹

Affiliations
1 Centre for Excellence in Computational Engineering and Networking Amrita School of Engineering, Amrita Vishwa Vidyapeetham Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Abstract

Background/Objectives: The recent times have observed an inflation in frequency of occurrence of Algal Blooms (ABs). In this work, seven most commonly occurring species (i.e., Trichodesmium erythraeum, Noctiluca scintillans/miliaris, Cocholodinium ploykrikoides, Chattonella marina, Karenia mikimotoi and Protoperidinium species) that have contributed to major ABs along the coastline of India in the years 2002 to 2015 are classified. Methods/Statistical Analysis: Processing the data procured by MODIS Aqua sensor, classification of seven species of algae is performed based only on the feature of Remote Sensing Reflectance (R_rs). In contrast to the existing algorithms like band-ratio and interpretation of water discoloration, classification of blooms is based on Support Vector Machine (SVM) and Regularized Least Squares (RLS) algorithms. Findings: Classification is executed using LIBSVM and GURLS library for fast and efficient performance. The classification accuracies achieved using both the classifiers are comparable; the overall accuracy using SVM classifier is 88.37%, whereas that obtained with RLS classifier is 89.98%. Applications/Improvements: These results reveal that the above mentioned algorithms are capable of effectively detecting these ABs which is of immense interest in fisheries and healthcare industries. The algorithms can be further trained with bloom parameters based on in-situ datasets from additional occurrences.

Keywords

Algal Bloom, MODIS Aqua Data, Rrs, RLS, SVM.

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M. Kaviarasan ¹, P. Geetha ¹, K. P. Soman ¹

Affiliations
1 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, IN

Abstract

Ground water is one of the most important resources which play a key role in sustainable development. Over the last few decade, the industrial development and population growth has increased the water utilization which creates stress on both water and land resources. In such scenario, assessment of water quality is essential for proper management and utilization. This paper presents the usage of statistical method like Principal Component Analysis and Pearson Correlation coefficient analysis for analysing temporal variations of the ground water. From Coonoor Taluk of Nilgiri district, various samples were collected to analyse physico-chemical factors. The quality of the ground water and the compositions are to be determined by Water Quality Index (WQI) calculation method. A comparison of each parameter with that of standard permissible limit as recommended by WHO.

Keywords

Ground Water, Principal Component Analysis, Water Quality Index, WHO

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S. Amar Balaji ¹, P. Geetha ¹, Soman K. P. ¹

Affiliations
1 Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore Amrita Vishwa Vidyapeetham Amrita University, Coimbatore - 641112, Tamil Nadu, IN

Abstract

Background/Objectives: Advancements in the field of remote sensing techniques and sensors used have made monitoring of forest resources an easier task. Forests are ecosystems that provide habitat and fodder for wild animals, timber and help maintain the global temperature balance. They face threats both by nature and mankind. So therein comes the need to monitor vegetation from time to time, for preserving the ecosystem. Methods/Statistical Analysis: The Kalakkad Mundanthurai Tiger Reserve (KMTR) area is posed to such threats leading to change in forest cover and type. Multi-temporal Landsat imageries were used to study the area. A change detection analysis was carried out to determine the disruptions in forest cover from 2005 to 2015. Findings: Overlaying the classified multi-temporal images indicated significant changes in forest cover. Statistical analysis shows the approximate amount of vegetation affected and afforested over the time period. Applications/Improvements: The findings can be included for the vegetation monitoring and conservation activities carried out by NGOs and governmental organizations.

Keywords

Change Detection, Forest, GIS, Kalakkad, Mundanthurai, NDVI, Remote Sensing, Vegetation.

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M. KaviNandhini ¹, P. Geetha ¹, K. P. Soman ¹

Affiliations
1 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, IN

Abstract

Objective: The main objective of this paper describes how the large scale windfarms affect the climate of south west monsoon region. Methods/Analysis: Method used for analysing climatic parameters of before and after installation of wind farms is Gaussian mixture model. ArcGIS and QGIS software is used for image and geo-information analysis. Data from the commercial wind turbine of south west monsoon region like temperature, relative humidity, precipitation, wind speed is used to find the climatic variation. Findings: Large scale wind farms significantly affect the various climatic parameters. These impacts depends on the static stability, increase or decrease in the climatic parameters. Conclusion/ Application: Improvements can be made by taking the ground temperature measured by satellite image and identify the warming effect of night and day time warming effect of large scale wind farm area of southwest monsoon regions.

Keywords

Gaussian Mixture Model, Humidity, Precipitation, Temperature, Wind Farm, Wind Speed

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S. Parvathy ¹, P. Geetha ¹, K. P. Soman ¹

Affiliations
1 Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641001, Tamil Nadu, IN

Abstract

In this paper we try to analyze what is the relationship between number of dengue cases reported with that of the different environmental factors like temperature, rainfall etc. For doing this, we make use of regression analysis along with GIS. This method also helps in predicting the future course of the disease spread as we will be in a position to identify the dependence on different factors. We got a Coefficient of determination R2 value of 0.9218, implying that our method is efficient for accurately identifying the relationship between the different parameters and number of cases reported.

Keywords

Coefficient of Determination, Dengue, GIS, Regression Analysis, Vector Borne Diseases

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