A categorical, improper probability method for combining NDVI and LiDAR elevation information for potential cotton precision agricultural applications

Authors:
Jeffrey L. Willers;Jixiang Wu;Charles O'Hara;Johnie N. Jenkins
Affiliations:
Genetics and Precision Agriculture Research Unit, USDA, ARS, Mississippi State, MS, United States;Plant Science Department, South Dakota State University, Brookings, SD, United States;Geosystems Research Institute, Mississippi State University and CEO, Spatial Information Solutions, Starkville, MS, United States;Genetics and Precision Agriculture Research Unit, USDA, ARS, Mississippi State, MS, United States
Venue:
Computers and Electronics in Agriculture
Year:
2012

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Abstract

An algorithm is presented to fuse the Normalized Difference Vegetation Index (NDVI) with Light Detection and Ranging (LiDAR) elevation data to produce a map potentially useful for site-specific management practices in cotton. A bi-variate Gaussian probability density distribution is modified to predict an improper probability distribution that also incorporates categorical variables associated with quadrant direction from the population means for the NDVI and elevation data layers. Water availability, influenced by slope and relative changes in elevation (as captured by the elevation data layer), affects crop phenology (as captured by the NDVI data layer). Thus, this fusion procedure results in a map potentially describing the joint effects of NDVI and elevation on cotton growth in a spatial and temporal way.