M V Priya R Kalpana S Pazhanivelan R Kumaraperumal K P Ragunath G. Vanitha Ashmitha Nihar P J Prajesh Vasumathi V


Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period.




Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), spatio-temporal, time series, vegetation indices

Abebe, G., Getachew, D. & Ewunetu, A. (2022). Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia. SN Applied Sciences 4, 30. https://doi.org/10.1007/s42452-021-04915-8
Arulbalaji, P. (2019). Analysis of land use/land cover changes using geospatial techniques in Salem district, Tamil Nadu, South India. SN Applied Sciences 1, 462. https://doi.org/10.1007/s42452-019-0485-5
Boegh, E., Soegaard, H., Broge, N., Hasager, C. B., Jensen, N. O., Schelde, K. & Thomsen, A. (2002). Airborne multi-spectral data for quantifying leaf area index, nitrogen concentration and photosynthetic efficiency in agriculture. Remote Sensing of Environment 81, 179-193. https://doi.org/10.1016/S0034-4257(01)00342-X
Butt, A., Shabbir, R., Ahmad, S. S. & Aziz, N. (2015). Land use change mapping and analysis using remote sensing and GIS: a case study of Simly watershed, Islamabad, Pakistan. The Egyptian Journal of Remote Sensing and Space Science 18, 251-259. https://doi.org/10.1016/j.ejrs.2015.07.003
Carlson, T. N. & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote sensing of Environment 62, 241-252. https://doi.org/10.1016/S0034-4257(97)00104-1
Davis, C. L., Hoffman, M. T. & Roberts. W. (2017). Long-term trends in vegetation phenology and productivity over Namaqualand using the GIMMS AVHRR NDVI3g data from 1982 to 2011. South African Journal of Botany 111, 76-85. https://doi.org/10.1016/j.sajb.2017.03.007
Dhanapriya, M., Kumaraperumal, R., Kannan, B. & Bhatt, H. P. (2018). Spatio-temporal analysis of vegetation dynamics for Saurashtra region of Gujarat. AgricINTERNATIONAL 5, 43.
Gao, X., Huete, A. R., Ni, W., & Miura, T. (2000). Optical – biophysical relationships of vegetation spectra without background contamination. Remote sensing of Environment 74, 609-620. https://doi.org/10.1016/S0034-4257(00)00150-4
Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X. & Ferreira, L. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of Environment 83, 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
Hassani, K., Gholizadeh, H., Taghvaeian, S., Natalie, V., Carpenter, J. & Jacob, J. (2023). Assessing the impact of spatial resolution of UAS-based remote sensing and spectral resolution of proximal sensing on crop nitrogen retrieval accuracy. International Journal of Remote Sensing 44(14), 4441-4464. https://doi.org/10.1080/01431161.20 23.2237162
Hussein, S. O., Kovacs, F. & Tobak, Z. (2017). Spatiotemporal assessment of vegetation indices and land cover for Erbil city and its surrounding using MODIS imageries. Journal of Environmental Geography, 10(1-2), 31-39. https://doi.org/10.1515/jengeo-2017-0004
Kavitha, S. & Ravichandran, K. (2020). A study on rainfall and temperature of Salem district Tamil Nadu, India. Journal of Emerging Technologies and Innovative Research 7, 3644-3655.
Matsushita, B., Yang, W., Chen, J., Onda, Y. & Qiu, G. (2007). Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest. Sensors 7, 2636-2651. https://doi.org/10.3390/s7112636
Mohamed, M. A., Anders, J. & Schneider, C. (2020). Monitoring of changes in Land Use/Land cover in Syria from 2010 to 2018 using multitemporal Landsat imagery and GIS. Land 9, 226. https://doi.org/10.3390/land9070226
Mokarram, M. & Sathyamoorthy, D. (2015). Modeling the relationship between elevation, aspect and spatial distribution of vegetation in the Darab Mountain, Iran using remote sensing data. Modeling Earth Systems and Environment 1, 30. https://doi.org/10.1007/s40808-015-0038-x
Moreira, A., Fontana, D. C. & Kuplich T. M. (2019). Wavelet approach applied to EVI/MODIS time series and meteorological data. ISPRS Journal of Photogrammetry and Remote Sensing 147, 335-344. https://doi.org/10.1016/j.isprsjprs.2018.11.024
Nomura, R. & Oki, K. (2021). Downscaling of MODIS NDVI by Using a Convolutional Neural Network-Based Model with Higher Resolution SAR Data. Remote Sensing 13, 732. https://doi.org/10.3390/rs13040732
Prajesh, P. J., Kannan, B., Pazhanivelan, S., Kumaraperumal, R. & Ragunath, K. P. (2019). Analysis of Seasonal Vegetation Dynamics Using MODIS Derived NDVI and NDWI Data: A Case Study of Tamil Nadu. Madras Agricultural Journal 106, 4-6. http://dx.doi.org/10.29321/MAJ.2019.000275
Traore, S. S., Landmann, T., Forkuo, E. K. & Traore, P. C. S. (2014). Assessing long-term trends in vegetation productivity change over the Bani river basin in Mali (West Africa). Journal of Geography and Earth Sciences 2, 21-34. http://oar.icrisat.org/id/eprint/8571
Vaani, N. & Porchelvan, P. (2018). Monitoring of agricultural drought using fortnightly variation of vegetation condition index (VCI) for the state of Tamil Nadu, India. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 159-164.https://ui.adsabs.harvard.edu/abs/2018ISPAr4249..159V
Vasumathi, V., Kalpana, R., Pazhanivelan, S., Kumaraperumal, R. & Priya, M. V. (2022). Identification of 'Start of Season' in Major Rainfed Crops of Tamil Nadu, India Using Remote Sensing Technology. International Journal of Environment and Climate Change 12, 327-334.10.9734/IJECC/2022/v12i1130978
Wardlow, B. D., Egbert, S. L. & Kastens, J. H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment 108, 290-310. https://doi.org/10.1016/j.rse.2006.11.021
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Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu. (2023). Journal of Applied and Natural Science, 15(3), 1170-1177. https://doi.org/10.31018/jans.v15i3.4803
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Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu. (2023). Journal of Applied and Natural Science, 15(3), 1170-1177. https://doi.org/10.31018/jans.v15i3.4803