基于前哨时间序列NDVI的小麦作物分类——以萨哈兰普尔地区为例

2021 International Conference on Computing, Communication and Green Engineering (CCGE) Pub Date : 2021-09-23 DOI:10.1109/CCGE50943.2021.9776445

Saurabh Pargaien, R. Prakash, V. P. Dubey

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引用次数: 1

摘要

NDVI是一种广泛使用的数字指标，它利用了EM光谱的近红外和红色波段。利用遥感图像分析和评价存在活的绿色植被的地面位置。本文分析了NDVI在印度北方邦萨哈兰普尔地区选定地区小麦作物分布图中的应用。哨兵2B的图像于2018年11月16日至2019年4月15日收集。分析了16幅不同日期的卫星图像，计算了同一地区的NDVI值。土地覆盖分类采用了多种指标。通过计算NDVI可以识别植被、稀疏植被和茂密植被。本文对萨哈兰普尔地区16幅不同的哨兵图像进行处理，以NDVI值为基础识别小麦作物。该图显示3月份NDVI值较高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Wheat Crop Classification based on NDVI using Sentinel Time Series: A Case Study Saharanpur Region

The NDVI is an extensively used numerical indicator that utilizes the NIR and red band of the EM spectrum. It is waged to analyse and evaluate the ground locations for the presence of live green vegetation by using remote sensing images. This paper provides an analysis of the utility of NDVI in mapping the wheat crop of selected area of Saharanpur region, Uttar Pradesh, India. Images of Sentinel 2B were collected from November 16, 2018 to April 15, 2019. A total of 16 different date satellite images were analysed to calculate NDVI values of same region. For land cover classification various indices are used. Vegetation, sparse vegetation and dense vegetation can be identified by computing NDVI. In this article 16 different sentinel images of Saharanpur region are processed to identify the wheat crop on the basis of NDVI values. the graph shows high NDVI values in the month of march.

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2021 International Conference on Computing, Communication and Green Engineering (CCGE)

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