Assessing LAI using Multispectral Drone data

Ankita Tiwari; Prachi Singh; Ayushi Gupta; Prashant K. Srivastava

Ankita Tiwari Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi-221005, India
Prachi Singh Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi-221005, India
Ayushi Gupta Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi-221005, India
Prashant K. Srivastava Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi-221005, India

Abstract

The majority of vegetation indices (VI) integrate data from four spectral bands: red, blue, green, and near infrared. These indicators were created to mitigate the influence of exogenous variables. We collected spectral data and canopy parameters such as the leaf area index (LAI) and the proportion of photo synthetically active sunlight absorbed (P). To discuss the potential and limitations of various vegetation indices (WAVI), the difference indices normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), Enhanced Vegetation Index (EVI), Green and Red Ratio Vegetation Index (GRVI), Green Normalized Difference Vegetation Index (GNDVI), and Water Adjusted Vegetation Index (WAVI) are used. The paper discussion is based on a sensitivity analysis that takes into account the influence of canopy geometry (LAI and leaf inclination) as well as the soil backdrop. The calculations in this paper are based on the calculation of the correlation betweenUAV-bands on the data obtained from the plot and on the whisker plot representing a variant of the bands on the agricultural farm.

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