geospatialsuite: Comprehensive Geospatiotemporal Analysis and Multimodal Integration Toolkit

geospatialsuite provides universal functions for geospatial analysis and reliable visualization that work with any region for multimodal data. Features include 60+ vegetation indices, efficient terra-based visualization, universal spatial mapping, dynamic crop analysis, water quality assessment, and publication-quality mapping with support for any geographic region and robust error handling.

Details

Key Features:

Universal Spatial Analysis:

Universal region support (states, countries, CONUS, custom boundaries)
Universal spatial join (works with ANY raster-vector combination)
Multi-dataset integration and temporal analysis
Spatial interpolation and terrain analysis

Advanced Vegetation Analysis:

60+ vegetation indices including NDVI, EVI, SAVI, ARVI, PRI, SIPI, etc.
Specialized crop analysis with stress detection and yield assessment
Auto band detection from multi-band satellite imagery
Quality filtering and temporal smoothing for time series

Reliable Visualization:

Universal mapping with auto-detection (quick_map() function)
Terra-based plotting using reliable terra::plot() and terra::plotRGB()
Interactive maps with leaflet integration (optional)
RGB composites with stretching algorithms
Comparison maps for before/after analysis

Agricultural Applications:

Dynamic CDL crop analysis (all crop codes and categories)
NDVI time series with classification
Crop-specific analysis (corn, soybeans, wheat, etc.)
Water quality assessment with threshold analysis

Performance & Reliability Features:

Standard terra plotting - no complex dependencies required
Robust error handling throughout all functions
Simplified visualization for maps
Smart fallback systems when optional packages unavailable

Quick Start Examples:

# One-line mapping (auto-detects everything!)
quick_map("mydata.shp")

# Auto-geocode data without coordinates
census_data <- data.frame(
  state = c("Ohio", "Pennsylvania", "Michigan"),
  median_income = c(58642, 61744, 59584)
)
spatial_data <- auto_geocode_data(census_data)
quick_map(spatial_data, variable = "median_income")

# Works with HUC codes too (any format: HUC_8, HUC-8, huc8)
watershed_data <- data.frame(
  HUC_8 = c("04100009", "04100012"),
  water_quality = c(72, 65)
)
huc_spatial <- auto_geocode_data(watershed_data)
quick_map(huc_spatial)

# Calculate multiple vegetation indices
indices <- calculate_multiple_indices(
  red = red_band, nir = nir_band,
  indices = c("NDVI", "EVI", "SAVI", "PRI")
)

# Comprehensive crop analysis
crop_analysis <- analyze_crop_vegetation(
  spectral_data = sentinel_data,
  crop_type = "corn",
  analysis_type = "comprehensive"
)

# Enhanced NDVI calculation
ndvi_enhanced <- calculate_ndvi_enhanced(
  red_data = red_raster,
  nir_data = nir_raster,
  quality_filter = TRUE
)

# Fast, reliable RGB plotting
plot_rgb_raster(satellite_data, r = 4, g = 3, b = 2,
               stretch = "hist", title = "False Color")

Recommended Optional Packages:

For enhanced features, consider installing these optional packages:

# For interactive mapping
install.packages("leaflet")

# For enhanced colors
install.packages(c("viridis", "RColorBrewer"))

# For advanced remote sensing (optional)
install.packages("RStoolbox")

# For multi-panel plots (optional)
install.packages("patchwork")

Core Dependencies Only:

The package works reliably with just the core dependencies:

terra (raster operations and plotting)
sf (vector operations)
ggplot2 (static mapping)
dplyr (data manipulation)

No complex visualization dependencies required!

Author

Olatunde D. Akanbi olatunde.akanbi@case.edu

Erika I. Barcelos erika.barcelos@case.edu

Roger H. French roger.french@case.edu