calculate_ndvi_enhanced: Calculate NDVI with time series options

In geospatialsuite: Comprehensive Geospatiotemporal Analysis and Multimodal Integration Toolkit

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
)

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

Value

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

Examples

## Not run: 
# 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
)

## End(Not run)

geospatialsuite documentation built on Nov. 6, 2025, 1:06 a.m.