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<p>The El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are the crucial phenomena that occasionally cause impactful consequences on the land surface. The influence of ENSO and IOD phases on vegetation condition variability has been evaluated in this study over southern Indian region cover Kerala and Tamilnadu states by using the Normalized Difference vegetation Index (NDVI). The study carried out using NOAA Sea Surface Temperature data (ERSST.v5) along with Landsat 4–5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) datasets to derive the NDVI images. The intensive physical occurrences of ENSO discerned 2017 as El Nino year with a Sea Surface Temperature (SST) anomaly of 2.13&#xa0;°C and 2015 as La Nina year with an SST anomaly of -1.82&#xa0;°C. The positive phase (pIOD) and negative phase (nIOD) stipulated as the year 2010 with an SST anomaly of 1.77&#xa0;°C, the year 2007 with an SST anomaly of −&#xa0;1.87&#xa0;°C, respectively. The NDVI results show that the study region experienced extreme drought during El Nino and nIOD, where it mostly discerned with less range of NDVI (~ 0.5 to − 0.2). The computed correlation analysis proclaimed the least dependency with a satisfying significance <i>p</i>-value ranging from 0.005 to 0.244 between SST and NDVI anomalies, during both the cases of ENSO and IOD. The change detection signified that 17.13% (7341.83&#xa0;km<sup>2</sup>) of dense vegetation coverage diminished with the concern of ENSO and 3.42% (1467.31&#xa0;km<sup>2</sup>) of dense vegetation expanded from nIOD to pIOD. It reveals that El Nino and nIOD lead to stronger impact on vegetation in this specific study region especially in southern Tamil Nadu compared to southern Kerala and the impacts of these climatic teleconnections differ with various geographical locations. The far off process also affects the Indian vegetation condition, which is proven as a true concept over the southern Indian region with the variations in NDVI parameter and its seasonality as the novelty of this study.</p>

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Role of El Nino-Southern Oscillation (ENSO) & Indian Ocean Dipole (IOD) Events on the Spatiotemporal Variability of NDVI in Southern Indian Region

  • 期刊论文

  • 发表日期：

    2025年6月14日

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  • Aarthi Deivanayagam,
  • Ranjit Kumar Sarangi

摘要

The El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are the crucial phenomena that occasionally cause impactful consequences on the land surface. The influence of ENSO and IOD phases on vegetation condition variability has been evaluated in this study over southern Indian region cover Kerala and Tamilnadu states by using the Normalized Difference vegetation Index (NDVI). The study carried out using NOAA Sea Surface Temperature data (ERSST.v5) along with Landsat 4–5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) datasets to derive the NDVI images. The intensive physical occurrences of ENSO discerned 2017 as El Nino year with a Sea Surface Temperature (SST) anomaly of 2.13 °C and 2015 as La Nina year with an SST anomaly of -1.82 °C. The positive phase (pIOD) and negative phase (nIOD) stipulated as the year 2010 with an SST anomaly of 1.77 °C, the year 2007 with an SST anomaly of − 1.87 °C, respectively. The NDVI results show that the study region experienced extreme drought during El Nino and nIOD, where it mostly discerned with less range of NDVI (~ 0.5 to − 0.2). The computed correlation analysis proclaimed the least dependency with a satisfying significance p-value ranging from 0.005 to 0.244 between SST and NDVI anomalies, during both the cases of ENSO and IOD. The change detection signified that 17.13% (7341.83 km2) of dense vegetation coverage diminished with the concern of ENSO and 3.42% (1467.31 km2) of dense vegetation expanded from nIOD to pIOD. It reveals that El Nino and nIOD lead to stronger impact on vegetation in this specific study region especially in southern Tamil Nadu compared to southern Kerala and the impacts of these climatic teleconnections differ with various geographical locations. The far off process also affects the Indian vegetation condition, which is proven as a true concept over the southern Indian region with the variations in NDVI parameter and its seasonality as the novelty of this study.