• Agribusiness and Information Management
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• v.10 no.2
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• pp.1-7
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• 2018

In the present day context of changing information needs of the farmers and diversified production systems there is an urgent need to look for the effective extension support system for the small and marginal farmers in the developing countries like India. The rapid developments in the collection and analysis of field data by using the spatial technologies like GPS&GIS were made available for the extension functionaries and clientele for the diversified information needs. This article describes the GIS and GPS based decision support system in precision agriculture for the resource poor farmers. Precision farming techniques are employed to increase yield, reduce production costs, and minimize negative impacts to the environment. The parameters those can affect the crop yields, anomalous factors and variations in management practices can be evaluated through this GPS and GIS based applications. The spatial visualisation capabilities of GIS technology interfaced with a relational database provide an effective method for analysing and displaying the impacts of Extension education and outreach projects for small and marginal farmers in precision agriculture. This approach mainly benefits from the emergence and convergence of several technologies, including the Global Positioning System (GPS), geographic information system (GIS), miniaturised computer components, automatic control, in-field and remote sensing, mobile computing, advanced information processing, and telecommunications. The PPP convergence of person (farmer), project (the operational field) and pixel (the digital images related to the field and the crop grown in the field) will better be addressed by this decision support model. So the convergence and emergence of such information will further pave the way for categorisation and grouping of the production systems for the better extension delivery. In a big country like India where the farmers and holdings are many in number and diversified categorically such grouping is inevitable and also economical. With this premise an attempt has been made to develop a precision farming model suitable for the developing countries like India.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.351-361
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• 2003

As the launches of a series of remote sensing satellites, there are various multiresolution and multi-spectral images available nowadays. This diversity in remotely sensed image data has created a need to be able to integrate data from different sources. The C-band imaging radar of ERS-2 due to its high sensitivity to coastal wetlands holds tremendous potential in mapping and monitoring coastal wetland features. This paper investigates the advantages of using ERS-2 SAR data combined with IRS-ID LISS-3 data for mapping complex coastal wetland features of Tamil Nadu, southern India. We present a methodology in this paper that highlights the mapping potential of different combinations of filtering and integration techniques. The methodology adopted here consists of three major steps as following: (i) speckle noise reduction by comparative performance of different filtering algorithms, (ii) geometric rectification and coregistration, and (iii) application of different integration techniques. The results obtained from the analysis of optical and microwave image data have proved their potential use in improving interpretability of different coastal wetland features of southern India. Based visual and statistical analyzes, this study suggests that brovey transform will perform well in terms of preserving spatial and spectral content of the original image data. It was also realized that speckle filtering is very important before fusing optical and microwave data for mapping coastal mangrove wetland ecosystem.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.3
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• pp.59-64
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• 2008

Soil erosion is a major problem in the case of forests in hilly terrains. Soil erosion removes the fertile topsoil, making unsuitable for growth and establishment of vegetation. In the present study, erosion prone areas in a forest region situated in the Moyar sub-watershed of Western ghats was identified using GIS with data collected from India. The thematic layers such as forest cover, slope and drainage density were used for analysis. In the erosion prone map, majority of area (48%) was under medium category, and about 35% of area was under high erosion prone category. Very high erosion prone category occupied 7% of the forest area. This erosion prone map would be an ideal spatial data to take up necessary management actions at appropriate places in this watershed to prevent erosion.

• Korean Institute of Interior Design Journal
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• v.23 no.6
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• pp.3-13
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• 2014

The purpose of this study is to compare and analyse the difference between courtyard housings of hot-humid region and hot-dry region in India, in order to identify the affection of climate on the space arrangement of housing. The study starts from the curiosity about similar space structure of Indian housings at different climate area. Indian housings usually have courtyard at the center of its plan, though the 'Courtyard housing' is typical form of dry region's house type. Research method is comparative analysis of traditional houses of India, and the samples are selected from hot-dry city, Ahmedabad and hot-humid city, Bangalore. The conclusion is drawn through comparing main houses with traditional houses of other dry city - Jaipur - and humid cities - Trivandrum and Nilambur. It shows that both dry and humid region's housings has courtyard in common, but their spatial structures are not same at all. Houses of dry region shows organically connected spatial form, in order to maximize the cooling effect of ventilation. In contrast, the plan of houses in humid region shows opened, but can be closed in any time to prevent the penetration of moisture. Both Parekh house(Ahmedabad) and Koramangala house(Bangalore) left inconvenience of its arrangement, though the ventilation of air is the most important point of sustainability in hot region. The study could be the practical reference data for advanced sustainable housings of India which may built in the future.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.4
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• pp.73-78
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• 2008

The present study was aimed to estimate the urban sprawl in a historical city of India using series of satellite data between 1968 and 2005(37 years) and GIS. The total area of the Tiruchirappalli city was 1991.96 ha during 1968 and it was expanded into 4335.98 ha(117.67%) during 2005. The average growth rate per year was 63.35 ha. This 117.67% growth was at the cost of agriculture land(97.81%) and water body(2.19%). The satellite data used in this study were found to be good source of information for this kind of analysis and further studies are need to estimate the impact of this city expansion on agriculture yield and ground water.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.4
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• pp.51-58
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• 2007

Information on forest type and cover density status of the present and past on large scale (1:50,000) is very much needed for conservation of any forest region. Such large-scale maps are not available for the Eastern Ghats (EG) of Tamil Nadu. This study deals with the preparation of forest type and cover density map of EG of Tamil Nadu during 2003 and the changes it has undergone between 1990 and 2003 using appropriate satellite data. About 10 forest types have been identified and mapped. Major changes have been observed in the forest types such as evergreen, and deciduous.

• Advances in environmental research
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• v.9 no.1
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• pp.19-39
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• 2020

Urban heat island (UHI) is one of the most important climatic implications of urbanization and thus a matter of key concern for environmentalists of the world in the twenty-first century. The relationship between climate and urbanization has been better understood with the introduction of thermal remote sensing. So, this study is an attempt to understand the influence of urbanization on local temperature for a small developing city. The study focuses on the investigation of intensity of atmospheric and surface urban heat island for a small urbanizing district of Punjab, India. Landsat 8 OLI/TIRS satellite data and field observations were used to examine the spatial pattern of surface and atmospheric UHI effect respectively, for the month of April, 2018. The satellite data has been used to cover the larger geographical area while field observations were taken for simultaneous and daily temperature measurements for different land use types. The significant influence of land use/land cover (LULC) patterns on UHI effect was analyzed using normalized built-up and vegetation indices (NDBI, NDVI) that were derived from remote sensing satellite data. The statistical analysis carried out for land surface temperature (LST) and LULC indicators displayed negative correlation for LST and NDVI while NDBI and LST exhibited positive correlation depicting attenuation in UHI effect by abundant vegetation. The comparison of remote sensing and in-situ observations were also carried out in the study. The research concluded in finding both nocturnal and daytime UHI effect based on diurnal air temperature observations. The study recommends the urgent need to explore and impose effective UHI mitigation measures for the sustainable urban growth.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.656-683
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• 2022

The COVID-19 pandemic has profoundly affected the world, inflicting nationwide lockdowns interrupting conventional schooling through schools, colleges and universities. Educational institutions are struggling to maintain learning continuity through remote learning solutions. Still, the students' perception of this 'new normal' mode and pace of learning needs to be examined to ensure the success of these efforts. This study aimed at examining the perception of higher education students in India and Croatia especially with respect to the association between cultural orientation and the e-learning. The period considered for the data collection was from March 2020 to September 2020. Correspondence analysis was attempted to create spatial maps to depict the respondent choices. Students from both the regions agreed to the high-power distance that existed in their cultures and considered the role of device and content to be an important dimension of e-learning for it to be effective, but the results also pointed out some differences in their choices on other culture dimensions as well as factors affecting e-learning which make this study unique and suggest in-depth future research for conclusive results.

• Journal of Biosystems Engineering
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• v.42 no.1
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• pp.23-43
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• 2017

Purpose: As application-specific wireless sensor networks are gaining popularity, this paper discusses the development and field performance of the GHAN, a greenhouse area network system to monitor, control, and access greenhouse microenvironments. GHAN, which is an upgraded system, has many new functions. It is an intelligent wireless sensor and actuator network (WSAN) system for next-generation greenhouses, which enhances the state of the art of greenhouse automation systems and helps growers by providing them valuable information not available otherwise. Apart from providing online spatial and temporal monitoring of the greenhouse microclimate, GHAN has a modified vapor pressure deficit (VPD) fuzzy controller with an adaptive-selective mechanism that provides better control of the greenhouse crop VPD with energy optimization. Using the latest soil-matrix potential sensors, the GHAN system also ascertains when, where, and how much to irrigate and spatially manages the irrigation schedule within the greenhouse grids. Further, given the need to understand the microclimate control dynamics of a greenhouse during the crop season or a specific time, a statistical assessment tool to estimate the degree of optimality and spatial variability is proposed and implemented. Methods: Apart from the development work, the system was field-tested in a commercial greenhouse situated in the region of Punjab, India, under different outside weather conditions for a long period of time. Conclusions: Day results of the greenhouse microclimate control dynamics were recorded and analyzed, and they proved the successful operation of the system in keeping the greenhouse climate optimal and uniform most of the time, with high control performance.