Description Usage Arguments Details Value Author(s) References See Also Examples

Calculate normalised ratio index (nri) for a single given band combination or for all possible band combinations. Calculating nri is a frequently used method to standardize reflectance values and to find relationships between properties of the objects and their spectral data.

1 |

`x` |
List of class |

`b1` |
Band 1 given as band number or wavelength. |

`b2` |
Band 2 given as band number or wavelength. |

`recursive` |
If TRUE indices for all possible band combinations are calculated. If FALSE, only a single nri for the given bands in |

`bywavelength` |
Flag to determine if b1 and b2 are band number (bywavelength = FALSE) or wavelength (bywavelength = TRUE) values. |

`...` |
Further arguments passed to generic functions. Currently ignored. |

Function performs the following calculation:

*nri_{B1,~B2}=\frac{R_{B1}-R_{B2}}{R_{B1}-R_{B2}};*

with *R* being reflectance values at wavelength *B1* and *B2*, respectively.

If recursive = TRUE, all possible band combinations are calculated.

If recursive = FALSE, a data frame with index values is returned. Otherwise result is an object of class `Nri`

. See `glm.nri`

for applying a generalised linear model to an array of normalised ratio indices.

Lukas Lehnert

Sims, D.A.; Gamon, J.A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment: 81/2, 337 - 354.

Thenkabail, P.S.; Smith, R.B.; Pauw, E.D. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment: 71/2, 158 - 182.

1 2 3 4 5 6 7 8 9 10 | ```
data(spectral_data)
## Calculate NDVI
ndvi <- nri(spectral_data, b1=800, b2=680)
## Calculate all possible combinations for WorldView-2-8
spec_WV <- spectralResampling(spectral_data, "WorldView2-8",
response_function = FALSE)
nri_WV <- nri(spec_WV, recursive = TRUE)
nri_WV
``` |

hsdar documentation built on Dec. 14, 2017, 5:16 p.m.

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