vegetation_indices {vegIndexCalc}R Documentation

Calculate Different Vegetation Indices (VIs)

Description

The vegetation_indices() function supports the calculation of a wide range of vegetation indices (VIs) as listed below:

Data Requirements: The input data must be a CSV file containing the following columns:

Usage

vegetation_indices(data)

Arguments

data

A CSV file containing the required columns specified in the Data Requirements section

Value

The function returns a data frame with the following columns:

References

Deb, D., Deb, S., Chakraborty, D., Singh, J.P., Singh, A.K., Dutta, P. and Choudhury, A., 2020. Aboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: A comparison of support vector machine and traditional regression models. Geocarto International, pp.1-14

Deb, D., J. P. Singh, S. Deb, D. Datta, A. Ghosh and R. S. Chaurasia, 2017, An alternative approach for estimating above ground biomass using Resourcesat-2 satellite data and artificial neural network in Bundelkhand region of India, Environmental Monitoring and Assessment, 189: 576

Wang, F.M., Huang, J.F., Tang, Y.L. and Wang, X.Z., 2007. New vegetation index and its application in estimating leaf area index of rice. Rice Science, 14(3), pp.195-203.

Wilson, N.R. and Norman, L.M., 2018. Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI). International Journal of Remote Sensing, 39(10), pp.3243-3274.

Mokarram, M., Hojjati, M., Roshan, G. and Negahban, S., 2015. Modeling the behavior of Vegetation Indices in the salt dome of Korsia in North-East of Darab, Fars, Iran. Modeling Earth Systems and Environment, 1, pp.1-9.

Cao, J. and Yang, H., 2023. A dynamic normalized difference index for estimating soil organic matter concentration using visible and near-infrared spectroscopy. Ecological Indicators, 147, p.110037.

Sharma, B., Ritchie, G.L. and Rajan, N., 2015. Near‐remote green: Red perpendicular vegetation index ground cover fraction estimation in cotton. Crop Science, 55(5), pp.2252-2261.

Examples

# Sample data
sample_data <- data.frame(
  SERIAL_NO = 1:5,
  RED = c(0.13405, 0.134596, 0.141501, 0.143142, 0.147875),
  GREEN = c(0.091269, 0.090875, 0.106088, 0.098594, 0.099853),
  NIR = c(0.208945, 0.20439, 0.260778, 0.2183, 0.20648),
  BLUE = c(0.313418, 0.306585, 0.391168, 0.32745, 0.30972),
  L = c(0.133986, 0.125849, 0.091812, 0.130619, 0.109383),
  SWIR2 = c(28.07649, 26.57071, 26.1806, 25.61612, 24.4443)
)

# View the sample data
print(sample_data, row.names = FALSE)

# Calculate vegetation indices using the sample data
result <- vegetation_indices(sample_data)

# View the result
print(result, row.names = FALSE)

[Package vegIndexCalc version 0.1.0 Index]