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The Normalized Difference Vegetation index (NDVI) is one of the most popular indexes and frequently analyzed for agricultural and vegetation assessment. To simplify the complexities of multispectral imageries, NDVI is the earliest remote sensing analytical measure. The widespread usage of NDVI has led to its popularity as it can be calculated with any of the multispectral sensors with visible and Near-IR bands. Studies reveal that NDVI is an effective tool for extracting the vegetation health status and for the quantified vegetation properties. It is widely used especially for Unmanned Aerial Systems (UAS) applications that inherits multiple risks of misusing it by the end users who are naïve to remote sensing knowledge. Summarizing this article by evaluating the progress of NDVI acquisition of three consecutive years 2020, 2021, 2022 for the Summer (March, April, May), Rainy (June, July, August, September), and Winter (October, November, December) Seasons, for the Varanasi District of India. A tabular and graphical representation of LULC classes like water bodies, urban, and greencover has also been plotted temporarily for each season for the respective years. NDVI plays a vital role in expressing the vegetation status which is highly effective and important for the UAS user community.

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A Temporal and Spatial Analysis of Seasonal Normalized Difference Vegetation Index of Varanasi District (India) Using Google Earth Engine

  • 会议论文

  • 发表日期：

    2026年

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  • Annu Kumari,
  • Subbiah Karthikeyan

摘要

The Normalized Difference Vegetation index (NDVI) is one of the most popular indexes and frequently analyzed for agricultural and vegetation assessment. To simplify the complexities of multispectral imageries, NDVI is the earliest remote sensing analytical measure. The widespread usage of NDVI has led to its popularity as it can be calculated with any of the multispectral sensors with visible and Near-IR bands. Studies reveal that NDVI is an effective tool for extracting the vegetation health status and for the quantified vegetation properties. It is widely used especially for Unmanned Aerial Systems (UAS) applications that inherits multiple risks of misusing it by the end users who are naïve to remote sensing knowledge. Summarizing this article by evaluating the progress of NDVI acquisition of three consecutive years 2020, 2021, 2022 for the Summer (March, April, May), Rainy (June, July, August, September), and Winter (October, November, December) Seasons, for the Varanasi District of India. A tabular and graphical representation of LULC classes like water bodies, urban, and greencover has also been plotted temporarily for each season for the respective years. NDVI plays a vital role in expressing the vegetation status which is highly effective and important for the UAS user community.