ANALYSIS OF DEFORESTATION MULTISPECTRAL SATELLITE DATA USING REMOTE SENSING TECHNIQUES
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Abstract
The coverage of global forest cover is most essential for soil health, climate, water cycle and air quality. The condition can be improved by reforestation and afforestation to some extent but cannot be restored to full range. The satellite images used for this analysis of forest change detection are of planet scope having 3 m resolution. The target of this research is to detect the forest cover change using remote sensing and GIS. For the identification of forest covers various techniques like segmentation and classification are used. The best classification is by Normalized Difference Vegetation Index (NDVI) which identifies the health of vegetation along with the changes in the different parameters. After using NDVI some values generated will detect the areas of forest changes with the amount of change. And with the help of this research it is observed that out of total forest area i.e. 76740.32 hectares, the negative forest change is reduced to 29.44% due to degradation of forests maybe due to fires, deforestation, leaf shedding etc.
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References
- Campbell, J. B. and Wynne, R. H. 2011. Introduction to remote sensing. Guilford Press.
- Cuaresma, J. C., Danylo, O., Fritz, S., McCallum, I., Obersteiner, M., See, L., and Walsh, B. 2017. Economic development and forest cover: evidence from satellite data. Scientific reports 7, 1, 1–8. DOI: https://doi.org/10.1038/srep40678
- Gandhi, G. M., Parthiban, S., Thummalu, N., and Christy, A. 2015. Ndvi: Vegetation change detection using remote sensing and gis–a case study of vellore district. Procedia computer science 57, 1199–1210. DOI: https://doi.org/10.1016/j.procs.2015.07.415
- Lechner, A. M., Foody, G. M., and Boyd, D. S. 2020. Applications in remote sensing to forest ecology and management. One Earth 2, 5, 405–412. DOI: https://doi.org/10.1016/j.oneear.2020.05.001
- Lei, Z., Fang, T., and Li, D. 2011. Land cover classification for remote sensing imagery using conditional texton forest with historical land cover map. IEEE Geoscience and Remote Sensing Letters 8, 4, 720–724. DOI: https://doi.org/10.1109/LGRS.2010.2103045
- Lewis, S. L., Edwards, D. P., and Galbraith, D. 2015. Increasing human dominance of tropical forests. Science 349, 6250, 827–832. DOI: https://doi.org/10.1126/science.aaa9932
- Nanare, I. K. H., Bhoyar, D. B., and Balamwar, S. V. 2021. Remote sensing satellite image analysis for deforestation in yavatmal district, maharashtra, india. In 2021 3rd International Conference on Signal Processing and Communication (ICPSC). IEEE, 684–688. DOI: https://doi.org/10.1109/ICSPC51351.2021.9451744
- Nath, B. and Acharjee, S. 2013. Forest cover change detection using normalized difference vegetation index (ndvi): A study of reingkhyongkine lake’s adjoining areas. Indian Cartogr 33, 2, 348–403.
- Niranjane, V. B. and Bhoyar, D. B. 2011. Performance analysis of different channel estimation techniques. In 2011 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 74–78. DOI: https://doi.org/10.1109/ICRTIT.2011.5972481
- Shimu, S. A., Aktar, M., Afjal, M. I., Nitu, A. M., Uddin, M. P., and Al Mamun, M. 2019. Ndvi based change detection in sundarban mangrove forest using remote sensing data. In 2019 4th International Conference on Electrical Information and Communication DOI: https://doi.org/10.1109/EICT48899.2019.9068819
- Technology (EICT). IEEE, 1–5.