This study uses a small Unmanned Aircraft System (sUAS) equipped with a multispectral sensor to assess various Vegetation Indices (VIs) for their potential to monitor iron chlorosis levels in a grain sorghum crop. Iron chlorosis is a nutritional disorder that affects numerous varieties of crops and plants that are grown on high-pH, calcareous soils and greatly affects crop yield. The objective of this project is to find the best Vegetation Index (VI) to detect and monitor iron chlorosis.
A series of flights were completed over the course of the growing season and processed using Structure-from-Motion (SfM) photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red-edge, and near-infrared wavelengths. A series of ground data collection methods were used to analyze stress and chlorophyll levels and grain yield, correlating them to sUAS-acquired four-band multispectral imagery covering the area of interest for ground control and precise crop examination.
25 Vegetation Indices (VIs) were calculated using the collected reflectance maps and soil-removed reflectance maps (a supervised classification was used to remove soil via a binary classification). The separability for each VI was then calculated using a two-class distance measure, determining which contained the largest separation between the pixels representing iron chlorosis and healthy vegetation. The field-acquired levels of iron chlorosis were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of chlorosis (low, moderate and severe). It was concluded that the MERIS Terrestrial Chlorophyll (MTCI), Normalized Difference Red Edge (NDRE), and Normalized Green (NG) indices achieved the highest amount of separation between the iron chlorotic and healthy plant populations, with the NG being the most popular for both soil-included and soil-removed VIs, with soil-removed VIs reaching higher levels of separability.
|Advisor:||Starek, Michael J.|
|Commitee:||Brewer, Michael J., Jeffress, Gary|
|School:||Texas A&M University - Corpus Christi|
|Department:||Geospatial Surveying Engineering|
|School Location:||United States -- Texas|
|Source:||MAI 58/01M(E), Masters Abstracts International|
|Keywords:||Grain sorghum, Iron chlorosis, Multispectral imaging, Structure-from-motion, VIs, sUAS|
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