International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 9 - Issue 1, January 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Canopy Removal On Satellite Images Using Classification And Contrast Enhancement

[Full Text]



Dr. M. Prabu



Remote Sensing, Image Processing, Canopy removal, Land use, Data mining.



The increasing the usage of satellite remote sensing for a civilian purpose has proved to be the most cost-effective mapping environmental changes with regard to natural resources, particularly in developing countries. Forests as one part of the wildlife of human societies in economic growth and permanency of natural resources in the countries of the world. But because of various details such as the growth of population, progressively varying forest to the other unfitting applications such as agriculture, providing energy and fuel, millions of hectares from the natural means are destroyed every year, and the remainder of the surface changes quantitatively and qualitatively. For better management of the forests, the evolution of forest area and rate of forest concentration should be examined. It is achievable that, there isn’t any change in the field of the forest during the time, but the density of forest canopy is changed. Estimation of forest canopy cover has recently become an essential part of the forest. Therefore, the research study is to develop Forest Canopy Remover, which is used to get an accurate result of Forest and deforested area from the satellite earth images. It is used to calculate forest density using vegetation. Then, the changes in area and forest density during a particular period can be distinguished.



[1] R. Premalal and D. S. Head, “Cloud Filtering Methodology for the Use of Optical Satellite Images in Sustainable Management of Tea Plantations.”
[2] P. G. Aroos, Mohamed, “Removal of Cloud Cover in Image Data Processing,” ACRS, 2008.
[3] N. S. Kumar, M. Prabu, and S. Assistant, “DEFORESTATION IDENTIFICATION MODEL USING,” pp. 2–7.
[4] A. K. Sah, B. P. Sah, K. Honji, N. Kubo, and S. Senthil, “Semi-Automated Cloud/Shadow Removal and Land Cover Change Detection Using Satellite Imagery,” ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XXXIX-B7, no. September, pp. 335–340, 2012.
[5] M. P. N. Suresh Kumar, S. Margret Anouncia, “Fuzzy-Based Satellite Image classification to find Greenery and Used Land,” Int. J. Tomogr. Simul., vol. 25, no. 1, pp. 1–2, 2014.
[6] S. Area, V. District, and T. Nadu, “Application of Satellite Remote Sensing to find Soil Fertilization by using Soil Colour,” Int. J. Online Eng., vol. 9, no. 2, pp. 44–49, 2013.
[7] M. S. Jamalabad and A. A. Abkar, “Forest Canopy Density Monitoring , Using Satellite Images - Semantic Scholar,” Proc. XXth ISPRS Congr., 2004.
[8] E. M. Barnes et al., “Remote- and Ground-Based Sensor Techniques to Map Soil Properties,” Photogramm. Eng. Remote Sens., vol. 69, no. 6, pp. 619–630, 2003.
[9] M. Prabu and S. M. Anouncia, “Prediction of Land Cover Changes in Vellore District of Tamil Nadu by Using Satellite Image Processing,” in Knowledge Computing and its Applications, Springer, 2018, pp. 87–100.
[10] E. H. Helmer and B. Ruefenacht, “Cloud-free satellite image mosaics with regression trees and histogram matching,” Photogramm. Eng. Remote Sensing, vol. 71, no. 9, pp. 1079–1089, 2005.
[11] N. -, A. Nasr, M. ElSaban, and H. Onsi, “Spatial Cloud Detection and Retrieval System for Satellite Images,” Int. J. Adv. Comput. Sci. Appl., vol. 3, no. 12, pp. 212–217, 2012.
[12] M. Prabu and S. Margret Anouncia, “NDVI generation of chlorophyll from OCM data for the Indian ocean region using multispectral images,” Res. J. Pharm. Biol. Chem. Sci., vol. 7, no. 5, 2016.
[13] M. G. V. Kannan, “Paddy Yield Estimation Using Remote Sensing and Geographical Information System,” J. Mod. Biotechnol., vol. 1, no. 1, pp. 26–30, 2012.
[14] S. M. Anouncia and M. Prabu, “Distributed Computing Model of Multispectral Time Series Data Analysis for Chlorophyll Concentration Determination Using Ocean Color Monitor-2 Data,” J. Test. Eval., vol. 47, no. 6, 2019.
[15] S. Biday and U. Bhosle, “Relative radiometric correction of cloudy multitemporal satellite imagery,” World Acad. Sci. Eng. Technol., vol. 39, pp. 254–258, 2009.
[16] P. M. Dare, “Shadow analysis in high-resolution satellite imagery of urban areas,” Photogramm. Eng. Remote Sensing, vol. 71, no. 2, pp. 169–177, 2005.