期刊
REMOTE SENSING
卷 14, 期 19, 页码 -出版社
MDPI
DOI: 10.3390/rs14194792
关键词
field conditions; aerial imaging; multispectral images; drone; vegetation index
类别
资金
- University of Helsinki - Maatalouskoneiden tutkimussaatio (Agricultural Machinery Research Foundation)
Remote sensing is a method for monitoring and measuring agricultural crop fields using unmanned aerial vehicles equipped with different camera technologies. This research evaluates the effects of field conditions on data quality and commonly used vegetation indices, finding that vegetation indices with near-infrared bands are less affected by field condition changes.
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.
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