Image Processing Based Vegetation Cover Monitoring and Its Categorization Using Differential Satellite Imageries for Urmodi River Watershed in Satara District, Maharashtra, India
Rapid monitoring of vegetation cover with precision has always been a challenge for maintaining accuracy over a large area. Remote Sensing (RS) based satellite imagery has significantly contributed in monitoring vegetation and land cover categorization. As the vegetation has a close relationship with detachment of soil and its sedimentation, regular monitoring of vegetation is essential especially in the catchment area of dams and reservoirs. In this study, vegetation maps were prepared through imaging processing of satellite imageries. With the help of Vegetation Index (VI) based maps, we were able to study the vegetation phenology in the watershed. The Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI) were obtained using the spectral bands of Landsat 8 and Sentinel 2 A satellite data. The classes were made in accordance to no vegetation cover (<0.1), low vegetation cover (0.1–0.3), moderate vegetation cover (0.3–0.4), high vegetation cover (>0.4). The area under each category was calculated with vector files. Further, the relationship between pixel values of Landsat 8 and Sentinel 2 was analyzed by downscaling the spatial resolution of Landsat 8 maps. The pixel value of two satellite based NDVI and SAVI shows same R\(^2\) value, that is 85.12 and EVI 83.15 respectively. Basically, the low vegetation cover depicted by the two imageries shows enormous difference which is quite huge for assessing the land/soil degradation. It was also revealed from the study that, Sentinel 2 imagery was very useful in computing EVI where the high density vegetation cover is present as compared to Landsat 8.
KeywordsLand degradation Vegetation Index (VI) NDVI SAVI EVI
We are very thankful to the European Space Agency (ESA) for provide Sentinel- 2 data through portal- Earth Explorer and also for providing Landsat 8 imagery. The first author is thankful to the School of Earth Sciences, Solapur University, Solapur for their financial support in the form of Departmental Research Fellowship (DRF).
- 2.Mróz, M., Sobieraj, A.: Comparison of several vegetation indices calculated on the basis of a seasonal SPOT XS time series, and their suitability for land cover and agricultural crop identification. Tech. Sci. 7, 39–66 (2004)Google Scholar
- 7.Rouse, J.W., Haas, R.H., Schell, J.A.: Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation (1974)Google Scholar
- 8.McFarland, T.M., van Riper, C.I.: Use of normalized difference vegetation index (NDVI) habitat models to predict breeding birds on the San Pedro River, Arizona, p. 42 (2013)Google Scholar
- 9.Shifaw, E., Sha, J., Li, X., Bao, Z., Ji, J., Chen, B.: Spatiotemporal analysis of vegetation cover (1984–2017) and modelling of its change drivers, the case of Pingtan Island. China. Model. Earth Syst. Environ. 0, 0 (2018)Google Scholar
- 14.Meneses-Tovar, C.L.: NDVI as indicator of degradation. Unasylva. 62, 39–46 (2011)Google Scholar
- 19.Arnoldus, H.M.J., Riquier, J.: World assessment of soil degradation - Phase I. In: FAO Soils Bulletin Assessing Soil Degradation. Food and Agriculture Organization of the United Nations, Rome (1977)Google Scholar
- 20.Roose, E.: Land use and soil degradation. In: FAO Soils Bulletin Assessing Soil Degradation. Food and Agriculture Organization of the United Nations, Rome (1977)Google Scholar