Evaluation of the Influence of Field Conditions on Aerial Multispectral Images and Vegetation Indices
Open Access
- 25 September 2022
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 14 (19), 4792
- https://doi.org/10.3390/rs14194792
Abstract
Remote sensing is a method used for monitoring and measuring agricultural crop fields. Unmanned aerial vehicles (UAV) are used to effectively monitor crops via different camera technologies. Even though aerial imaging can be considered a rather straightforward process, more focus should be given to data quality and processing. This research focuses on evaluating the influences of field conditions on raw data quality and commonly used vegetation indices. The aerial images were taken with a custom-built UAV by using a multispectral camera at four different times of the day and during multiple times of the season. Measurements were carried out in the summer seasons of 2019 and 2020. The imaging data were processed with different software to calculate vegetation indices for 10 reference areas inside the fields. The results clearly show that NDVI (normalized difference vegetation index) was the least affected vegetation index by the field conditions. The coefficient of variation (CV) was determined to evaluate the variations in vegetation index values within a day. Vegetation index TVI (transformed vegetation index) and NDVI had coefficient of variation values under 5%, whereas with GNDVI (green normalized difference vegetation index), the value was under 10%. Overall, the vegetation indices that include near-infrared (NIR) bands are less affected by field condition changes.This publication has 39 references indexed in Scilit:
- Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validationAgricultural and Forest Meteorology, 2008
- The MERIS terrestrial chlorophyll indexInternational Journal of Remote Sensing, 2004
- Relating soil surface moisture to reflectanceRemote Sensing of Environment, 2002
- On the relation between NDVI, fractional vegetation cover, and leaf area indexRemote Sensing of Environment, 1997
- Optimization of soil-adjusted vegetation indicesRemote Sensing of Environment, 1996
- Development of vegetation and soil indices for MODIS-EOSRemote Sensing of Environment, 1994
- A modified soil adjusted vegetation indexRemote Sensing of Environment, 1994
- Atmospherically resistant vegetation index (ARVI) for EOS-MODISIEEE Transactions on Geoscience and Remote Sensing, 1992
- Red and photographic infrared linear combinations for monitoring vegetationRemote Sensing of Environment, 1979
- A decimal code for the growth stages of cerealsWeed Research, 1974