Szerző szerinti böngészés "Mustak, Sheikh"

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    Correction of Atmospheric Haze of IRS-1C LISS-III Multispectral Satellite Imagery: An Empirical and Semi-Empirical Based Approach
    (2016-06-14) Mustak, Sheikh; Baghmar, Naresh; Singh, Sudhir
    The atmospheric effect greatly affects the quality of satellite data and mostly found in the polluted urban area in the great extent. In this paper, the atmospheric correction has been carried out on IRS-1C LISS-III multispectral satellite image for efficient results for the Raipur city, India. The atmospheric conditions during satellite data acquisition was very clear hence very clear relative scattering model of improved dark object subtraction method for the correction of atmospheric effects in the data has been carried out to produce the realistic results. The haze values (HV) for green band (band 2), red band (band 3), NIR band (band 4) and SWIR (band 5) are 79, 53, 54 and 124, respectively; were used for the corrections of haze effects using simple dark object subtraction method (SDOS). But the final predicted haze value (FPHV) for these bands are 79, 49.85, 21.31 and 0.13 that were used for the corrections of haze effects applying improved dark object subtraction method (IDOS). We found that IDOS method produces very realistic results when compared with SDOS method for urban land use mapping and change detection analysis. Consequently, ATCOR2 model provides better results when compared with SDOS and IDOS in the study.
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    Prediction of industrial land use using linear regression and mola techniques: A Case Study of Siltara Industrial belt
    (2015-12-28) Mustak, Sheikh; Baghmar, Naresh; Singh, Sudhir
    The Siltara Industrial belt is an important industrial pocket of Chattisgarh state located in the northern part of the Raipur city, which is rapidly growing. In this process spatial, cultural, political and administrative factors are controlling its rate, direction and pattern. The Simple Linear Regression (SLR) and Multi-Objective Land Allocation (MOLA) techniques, which are embedded in SPSS and Idrisi Kilimanjaro software respectively, and have been used for the estimation of future scenario of the industrial growth. In this model, a suitable platform has been prepared in which future industrialization has been estimated by integrating physical, social, cultural factors and land acquisition policy. In this article, results have revealed that industrialization has occurred very fast during last one decade. The industrial land was 6.15 km2 in 2001 and 18.725 km2 in 2011 and estimated as 31.30 km2 in 2021 and 43.87 km2 in 2031 using SLR. The rapid industrial growth is very critical issues for agrarian society and fresh environment. This model very accurately estimating (overall accuracy=95.39%, Kno=97.24%, agreement=98.63 %) the future growth of industrial land. This work will be useful to the planners and policy makers of private and government sectors to regulate the sustainable planning practices and smart decision-making.