geospatialsuite (version 0.1.1)

calculate_ndvi_enhanced: Calculate NDVI with time series options

Description

NDVI calculation specifically designed for time series analysis with date matching, quality filtering, temporal smoothing, and multi-temporal support. Use this for time series analysis, use calculate_vegetation_index() for single dates.

Usage

calculate_ndvi_enhanced(
  red_data,
  nir_data,
  clamp_range = c(-0.2, 1),
  match_by_date = FALSE,
  quality_filter = FALSE,
  temporal_smoothing = FALSE,
  verbose = FALSE,
  date_patterns = NULL
)

Value

SpatRaster with NDVI layers (single or multi-layer for time series)

Arguments

red_data

Red band data (files, directory, or raster objects)

nir_data

NIR band data (files, directory, or raster objects)

clamp_range

Range to clamp NDVI values (default: c(-0.2, 1))

match_by_date

Logical: match rasters by date using filenames

quality_filter

Apply quality filtering (remove outliers)

temporal_smoothing

Apply temporal smoothing for time series

verbose

Print progress messages

date_patterns

Custom date patterns for matching

Details

When to Use Enhanced vs Basic NDVI:

Use calculate_ndvi_enhanced() for:

• Time series analysis: Multiple dates, trend analysis

• Quality control: Remove outliers, temporal smoothing

• Date matching: Automatic pairing of red/NIR by date

• Multi-temporal studies: Seasonal analysis, change detection

Use calculate_vegetation_index(index_type="NDVI") for:

• Single date analysis: One-time calculation

• Different indices: Want to calculate EVI, SAVI, etc. too

• Quick calculations: Simple, fast NDVI

• Mixed workflows: Part of larger vegetation index analysis

Examples

if (FALSE) {
# These examples require external data files not included with the package
# Time series NDVI with date matching
ndvi_series <- calculate_ndvi_enhanced(
  red_data = "/path/to/red/time_series/",
  nir_data = "/path/to/nir/time_series/",
  match_by_date = TRUE,
  quality_filter = TRUE,
  temporal_smoothing = TRUE
)

# Simple NDVI (single date with quality control)
ndvi_clean <- calculate_ndvi_enhanced(
  red_data = red_raster,
  nir_data = nir_raster,
  quality_filter = TRUE
)
}