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

Computers and Electronics in Agriculture

Volumn 82, Issue , 2012, Pages 15-22

  Willers, Jeffrey L ^a   Wu, Jixiang ^b   O'Hara, Charles ^c   Jenkins, Johnie N ^a  

^a AGRICULTURAL RESEARCH SERVICE   (United States)

^b SOUTH DAKOTA STATE UNIVERSITY   (United States)

^c MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Algorithms; Categorical variables; Crop management; Data fusion; Image processing; Improper probability distributions

Indexed keywords

CATEGORICAL VARIABLES; COTTON GROWTH; CROP MANAGEMENT; CROP PHENOLOGY; ELEVATION DATA; GAUSSIAN PROBABILITY DENSITY DISTRIBUTION; JOINT EFFECT; LIGHT DETECTION AND RANGING; NDVI DATA; NORMALIZED DIFFERENCE VEGETATION INDEX; PROBABILITY METHODS; SITE SPECIFIC MANAGEMENT; WATER AVAILABILITY;

ALGORITHMS; COTTON; CROPS; DATA FUSION; FORESTRY; IMAGE PROCESSING; OPTICAL RADAR; PHENOLS; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS;

POPULATION STATISTICS;

AGRICULTURAL TECHNOLOGY; ALGORITHM; COTTON; DATA SET; ELEVATION; GAUSSIAN METHOD; GROWTH RATE; IMAGE PROCESSING; LIDAR; MAP; NDVI; PHENOLOGY; PRECISION AGRICULTURE; PROBABILITY; SPATIOTEMPORAL ANALYSIS; WATER AVAILABILITY;

GOSSYPIUM HIRSUTUM;

EID: 84862808098     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2011.11.010     Document Type: Article

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