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Getting Started with geospatialsuite

geospatialsuite Development Team

Introduction

geospatialsuite is a comprehensive R package for geospatial analysis and visualization. It provides universal functions that work with any region, robust error handling, and simplified workflows for complex spatial analysis tasks.

Key Features

60+ vegetation indices with automatic band detection
Universal spatial mapping with one-line functionality
Reliable terra-based visualization without complex dependencies
Agricultural applications including CDL crop analysis
Water quality assessment with multiple indices
Robust error handling throughout all functions
Built-in sample data for learning and testing
Flexible data loading from files, directories, or objects

Installation

# Install from CRAN
install.packages("geospatialsuite")

# Load the package
library(geospatialsuite)
library(terra)

Quick Start with Built-in Sample Data

geospatialsuite includes built-in sample data so you can start immediately:

library(geospatialsuite)
library(terra)

# List available sample datasets
list_sample_datasets()
#>                 filename       type size_kb
#> 1         sample_red.rds SpatRaster       2
#> 2         sample_nir.rds SpatRaster       2
#> 3        sample_blue.rds SpatRaster       2
#> 4       sample_green.rds SpatRaster       2
#> 5       sample_swir1.rds SpatRaster       2
#> 6   sample_multiband.rds SpatRaster       8
#> 7      sample_points.rds         sf       3
#> 8    sample_boundary.rds         sf       2
#> 9 sample_coordinates.csv data.frame       1
#>                                          description
#> 1   Red band reflectance (10x10 pixels, Ohio region)
#> 2   NIR band reflectance (10x10 pixels, Ohio region)
#> 3  Blue band reflectance (10x10 pixels, Ohio region)
#> 4 Green band reflectance (10x10 pixels, Ohio region)
#> 5 SWIR1 band reflectance (10x10 pixels, Ohio region)
#> 6   Multi-band raster (Blue, Green, Red, NIR, SWIR1)
#> 7 Sample field locations (20 points with attributes)
#> 8                 Sample study area boundary polygon
#> 9    Sample coordinates with elevation and soil data
#>                                          use_case available
#> 1 Vegetation index calculation (NDVI, SAVI, etc.)      TRUE
#> 2 Vegetation index calculation (NDVI, SAVI, etc.)      TRUE
#> 3     Enhanced vegetation index (EVI) calculation      TRUE
#> 4           Water index calculation (NDWI, GNDVI)      TRUE
#> 5        Water and moisture indices (NDMI, MNDWI)      TRUE
#> 6    Auto band detection, multi-index calculation      TRUE
#> 7     Spatial join examples, extraction workflows      TRUE
#> 8    Region boundary examples, masking operations      TRUE
#> 9         Geocoding, spatial integration examples      TRUE
#>                                access_method
#> 1         load_sample_data('sample_red.rds')
#> 2         load_sample_data('sample_nir.rds')
#> 3        load_sample_data('sample_blue.rds')
#> 4       load_sample_data('sample_green.rds')
#> 5       load_sample_data('sample_swir1.rds')
#> 6   load_sample_data('sample_multiband.rds')
#> 7      load_sample_data('sample_points.rds')
#> 8    load_sample_data('sample_boundary.rds')
#> 9 load_sample_data('sample_coordinates.csv')

# Load sample raster data
red <- load_sample_data("sample_red.rds")
nir <- load_sample_data("sample_nir.rds")

# Calculate NDVI
ndvi <- calculate_vegetation_index(red = red, nir = nir, index_type = "NDVI")

# Visualize
plot(ndvi, main = "NDVI from Sample Data", col = terrain.colors(100))

Core Workflows

1. Calculate Vegetation Indices

# Load sample spectral bands
red <- load_sample_data("sample_red.rds")
nir <- load_sample_data("sample_nir.rds")
blue <- load_sample_data("sample_blue.rds")

# Calculate NDVI
ndvi <- calculate_vegetation_index(red = red, nir = nir, index_type = "NDVI")
plot(ndvi, main = "NDVI")


# Calculate EVI (requires blue band)
evi <- calculate_vegetation_index(red = red, nir = nir, blue = blue, index_type = "EVI")
plot(evi, main = "EVI")


# Calculate multiple indices at once
indices <- calculate_multiple_indices(
  red = red,
  nir = nir,
  blue = blue,
  indices = c("NDVI", "EVI", "SAVI"),
  output_stack = TRUE
)

# Plot all indices
plot(indices)

2. Working with Multi-band Data

# Load multi-band sample raster
multiband <- load_sample_data("sample_multiband.rds")

# Check band names
names(multiband)
#> [1] "blue"  "green" "red"   "nir"   "swir1"

# Auto-detect bands and calculate index
ndvi_auto <- calculate_vegetation_index(
  spectral_data = multiband,
  index_type = "NDVI",
  auto_detect_bands = TRUE
)

3. Spatial Operations

# Load sample vector data
points <- load_sample_data("sample_points.rds")
boundary <- load_sample_data("sample_boundary.rds")

# Calculate NDVI
red <- load_sample_data("sample_red.rds")
nir <- load_sample_data("sample_nir.rds")
ndvi <- calculate_vegetation_index(red = red, nir = nir, index_type = "NDVI")

# Extract raster values to points
points_with_values <- universal_spatial_join(
  source_data = points,
  target_data = ndvi,
  method = "extract"
)

# Check result
head(points_with_values)
#> Simple feature collection with 6 features and 7 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -83.94662 ymin: 39.00987 xmax: -83.91607 ymax: 39.09223
#> Geodetic CRS:  WGS 84
#>   site_id  ndvi   evi crop_type elevation_m soil_moisture
#> 1 SITE_01 0.593 0.274  soybeans         269          0.27
#> 2 SITE_02 0.660 0.485     wheat         283          0.28
#> 3 SITE_03 0.646 0.579      corn         211          0.21
#> 4 SITE_04 0.388 0.483   pasture         256          0.31
#> 5 SITE_05 0.776 0.588      corn         219          0.35
#> 6 SITE_06 0.645 0.539  soybeans         205          0.30
#>                     geometry extracted_NDVI
#> 1 POINT (-83.94662 39.04604)      0.7709993
#> 2 POINT (-83.94162 39.00987)      0.7667765
#> 3 POINT (-83.94104 39.02066)      0.7647660
#> 4  POINT (-83.9309 39.09223)      0.7207893
#> 5 POINT (-83.93327 39.03194)      0.6405917
#> 6 POINT (-83.91607 39.02653)      0.7215534

4. Data Visualization

# Load sample data
points <- load_sample_data("sample_points.rds")

# Quick map (auto-detects everything)
quick_map(points, variable = "ndvi", title = "NDVI at Sample Sites")

Available Sample Datasets

# List all available datasets
datasets <- list_sample_datasets()
print(datasets[, c("filename", "type", "description")])
#>                 filename       type
#> 1         sample_red.rds SpatRaster
#> 2         sample_nir.rds SpatRaster
#> 3        sample_blue.rds SpatRaster
#> 4       sample_green.rds SpatRaster
#> 5       sample_swir1.rds SpatRaster
#> 6   sample_multiband.rds SpatRaster
#> 7      sample_points.rds         sf
#> 8    sample_boundary.rds         sf
#> 9 sample_coordinates.csv data.frame
#>                                          description
#> 1   Red band reflectance (10x10 pixels, Ohio region)
#> 2   NIR band reflectance (10x10 pixels, Ohio region)
#> 3  Blue band reflectance (10x10 pixels, Ohio region)
#> 4 Green band reflectance (10x10 pixels, Ohio region)
#> 5 SWIR1 band reflectance (10x10 pixels, Ohio region)
#> 6   Multi-band raster (Blue, Green, Red, NIR, SWIR1)
#> 7 Sample field locations (20 points with attributes)
#> 8                 Sample study area boundary polygon
#> 9    Sample coordinates with elevation and soil data

Sample datasets include:

sample_red.rds - Red band SpatRaster (10×10 pixels)
sample_nir.rds - NIR band SpatRaster
sample_blue.rds - Blue band SpatRaster
sample_green.rds - Green band SpatRaster
sample_swir1.rds - SWIR1 band SpatRaster
sample_multiband.rds - Multi-band stack (5 bands)
sample_points.rds - Sample field locations (sf object)
sample_boundary.rds - Study area polygon (sf object)
sample_coordinates.csv - Tabular data with coordinates

Working with Your Own Data

Loading Raster Files with geospatialsuite

Single GeoTIFF File

# Use geospatialsuite's load_raster_data() function
# It provides robust error handling and validation

# Load a single .tif file
my_raster <- load_raster_data("path/to/your/ndvi.tif")

# Result is a list, extract the raster
ndvi_raster <- my_raster[[1]]

# Now use with geospatialsuite functions
# The raster is ready for analysis
summary(ndvi_raster)

Multiple GeoTIFF Files

# Load multiple Landsat bands with geospatialsuite
# Handles validation automatically

landsat_files <- c(
  "LC08_B4_red.tif",
  "LC08_B5_nir.tif",
  "LC08_B3_green.tif"
)

# geospatialsuite loads them with error checking
bands <- load_raster_data(landsat_files, verbose = TRUE)

# Extract individual bands
red_band <- bands[[1]]
nir_band <- bands[[2]]
green_band <- bands[[3]]

# Calculate indices using geospatialsuite
ndvi <- calculate_vegetation_index(
  red = red_band,
  nir = nir_band,
  index_type = "NDVI"
)

gndvi <- calculate_vegetation_index(
  green = green_band,
  nir = nir_band,
  index_type = "GNDVI"
)

Load from Directory

# geospatialsuite can load all rasters from a directory
# Perfect for batch processing

# Load all .tif files from Landsat directory
all_bands <- load_raster_data(
  "/path/to/landsat/imagery/",
  pattern = "\\.(tif|tiff)$",
  verbose = TRUE
)

# geospatialsuite finds, loads, and validates all files
# Returns a list of SpatRaster objects ready to use
cat("Loaded", length(all_bands), "raster files\n")

Real-World Landsat Workflow

# Complete workflow using geospatialsuite functions

library(geospatialsuite)

# 1. Load Landsat bands using geospatialsuite
landsat_bands <- load_raster_data(
  "landsat/LC08_L2SP_021033_20240715/",
  pattern = "SR_B[2-5].TIF$",
  verbose = TRUE
)

# geospatialsuite loaded them with validation
# Extract bands (scaled values 0-1 after Collection 2 scaling)
blue <- landsat_bands[[1]]   # After scaling
green <- landsat_bands[[2]]
red <- landsat_bands[[3]]
nir <- landsat_bands[[4]]

# 2. Calculate vegetation indices using geospatialsuite
# The package has 60+ pre-programmed indices
veg_indices <- calculate_multiple_indices(
  red = red,
  nir = nir,
  blue = blue,
  green = green,
  indices = c("NDVI", "EVI", "SAVI", "GNDVI"),
  output_stack = TRUE
)

# 3. Visualize using geospatialsuite
quick_map(veg_indices$NDVI, title = "Landsat 8 NDVI - July 15, 2024")

Working with Vector Data

Loading Shapefiles

# Load shapefile with sf (standard approach)
library(sf)
field_boundaries <- sf::st_read("data/farm_fields.shp")

# Then use with geospatialsuite's spatial functions
# Calculate NDVI first
ndvi <- calculate_vegetation_index(red = red, nir = nir, index_type = "NDVI")

# Extract NDVI to field boundaries using geospatialsuite
fields_with_ndvi <- universal_spatial_join(
  source_data = field_boundaries,
  target_data = ndvi,
  method = "extract"
)

# geospatialsuite handles CRS mismatches automatically
# Returns field boundaries with extracted NDVI statistics
head(fields_with_ndvi)

Working with GeoPackages

# Load GeoPackage (modern format, better than shapefile)
farm_data <- sf::st_read("farm_management.gpkg", layer = "fields")
sample_points <- sf::st_read("farm_management.gpkg", layer = "samples")

# Use geospatialsuite's spatial join
samples_with_indices <- universal_spatial_join(
  source_data = sample_points,
  target_data = veg_indices,
  method = "extract",
  buffer_distance = 30  # 30m buffer
)

# geospatialsuite extracted all indices in the stack
# Each index becomes a column in the result
names(samples_with_indices)

Multi-band Raster with Auto-Detection

# geospatialsuite's auto-detection feature

# Load a stacked multi-band GeoTIFF
multiband_raster <- load_raster_data("sentinel2_stack.tif")[[1]]

# Name the bands (Sentinel-2 example)
names(multiband_raster) <- c("blue", "green", "red", "nir", "swir1")

# Use geospatialsuite's auto-detection
# It finds the right bands automatically!
indices <- calculate_multiple_indices(
  spectral_data = multiband_raster,
  indices = c("NDVI", "EVI", "MNDWI"),
  auto_detect_bands = TRUE,  # This is geospatialsuite's feature!
  output_stack = TRUE
)

# No need to specify which band is which
# geospatialsuite figured it out!

Complete Real-World Example

# End-to-end agricultural monitoring with geospatialsuite

library(geospatialsuite)
library(sf)

# 1. Load satellite imagery using geospatialsuite
spectral_bands <- load_raster_data(
  "/path/to/satellite/bands/",
  pattern = "B[0-9].tif$",
  verbose = TRUE
)

# 2. Extract bands
red <- spectral_bands[[3]]
nir <- spectral_bands[[4]]
green <- spectral_bands[[2]]

# 3. Calculate indices using geospatialsuite
crop_health <- calculate_multiple_indices(
  red = red,
  nir = nir,
  green = green,
  indices = c("NDVI", "GNDVI", "SAVI"),
  output_stack = TRUE
)

# 4. Load field data
fields <- sf::st_read("farm_data/fields.shp")

# 5. Extract to fields using geospatialsuite
fields_analysis <- universal_spatial_join(
  source_data = fields,
  target_data = crop_health,
  method = "extract"
)

# 6. Visualize using geospatialsuite
quick_map(fields_analysis, 
          variable = "NDVI",
          title = "Field Health Assessment")

# geospatialsuite handled:
# - Loading multiple files
# - Calculating indices
# - Spatial extraction with CRS handling
# - Visualization

Listing Available Indices

# See all vegetation indices geospatialsuite provides
veg_indices <- list_vegetation_indices()
head(veg_indices[, c("Index", "Category", "Description")])
#>   Index Category                              Description
#> 1  NDVI    basic   Normalized Difference Vegetation Index
#> 2  SAVI    basic           Soil Adjusted Vegetation Index
#> 3 MSAVI    basic  Modified Soil Adjusted Vegetation Index
#> 4 OSAVI    basic Optimized Soil Adjusted Vegetation Index
#> 5   EVI    basic                Enhanced Vegetation Index
#> 6  EVI2    basic       Two-band Enhanced Vegetation Index

# See water indices
water_indices <- list_water_indices()
head(water_indices)
#>   Index                Type Required_Bands Primary_Application
#> 1  NDWI     Water Detection     Green, NIR     water_detection
#> 2 MNDWI     Water Detection   Green, SWIR1     water_detection
#> 3  NDMI Vegetation Moisture     NIR, SWIR1 moisture_monitoring
#> 4   MSI     Moisture Stress     NIR, SWIR1  drought_assessment
#> 5  NDII Vegetation Moisture     NIR, SWIR1 moisture_monitoring
#> 6    WI       Water Content     NIR, SWIR1 moisture_monitoring
#>                                                                           Description
#> 1 Normalized Difference Water Index (McFeeters 1996) - Original water detection index
#> 2 Modified NDWI (Xu 2006) - Enhanced water detection, reduces built-up area confusion
#> 3          Normalized Difference Moisture Index (Gao 1996) - Vegetation water content
#> 4        Moisture Stress Index - Plant water stress detection (lower = more moisture)
#> 5     Normalized Difference Infrared Index - Alternative name for vegetation moisture
#> 6                             Water Index - Simple ratio for water content assessment
#>   Value_Range  Water_Threshold          Reference
#> 1     [-1, 1]            > 0.3   McFeeters (1996)
#> 2     [-1, 1]            > 0.5          Xu (2006)
#> 3     [-1, 1] N/A (vegetation)         Gao (1996)
#> 4    [0, 10+]            < 1.0            Various
#> 5     [-1, 1] N/A (vegetation) Hunt & Rock (1989)
#> 6    [0, 10+]            > 1.0            Various

Getting Help

# Package documentation
help(package = "geospatialsuite")

# Function help
?calculate_vegetation_index
?load_raster_data
?universal_spatial_join
?quick_map

# Test package installation
test_geospatialsuite_package_simple()

#> $test_results
#> $test_results$basic_ndvi_test
#> [1] TRUE
#> 
#> $test_results$water_index_test
#> [1] TRUE
#> 
#> $test_results$basic_visualization_test
#> [1] TRUE
#> 
#> $test_results$multiple_indices_simple_test
#> [1] TRUE
#> 
#> $test_results$enhanced_ndvi_simple_test
#> [1] TRUE
#> 
#> $test_results$dependencies_test
#> [1] TRUE
#> 
#> $test_results$spatial_operations_test
#> [1] TRUE
#> 
#> $test_results$data_loading_test
#> [1] TRUE
#> 
#> 
#> $summary
#> $summary$total_tests
#> [1] 8
#> 
#> $summary$passed_tests
#> [1] 8
#> 
#> $summary$failed_tests
#> [1] 0
#> 
#> $summary$success_rate
#> [1] 100
#> 
#> $summary$duration_seconds
#> [1] 0.33
#> 
#> $summary$version
#> [1] "0.1.0"
#> 
#> 
#> $test_output_dir
#> [1] "/tmp/Rtmp7PBFAz"
#> 
#> $timestamp
#> [1] "2026-05-07 23:48:41 EDT"
#> 
#> $test_approach
#> [1] "simplified_robust"
#> 
#> $core_message
#> [1] "Focused on essential functionality with minimal complexity"

Summary

geospatialsuite provides:

Data Loading:

load_raster_data() - Load .tif files with validation
load_sample_data() - Access built-in samples

Analysis:

calculate_vegetation_index() - 60+ indices
calculate_multiple_indices() - Batch processing
Auto band detection feature

Spatial Operations:

universal_spatial_join() - Extract values, automatic CRS handling

Visualization:

quick_map() - One-line mapping
create_spatial_map() - Custom maps

All with robust error handling and simplified workflows!

For more detailed tutorials, see the other vignettes:

Vegetation Indices Analysis
Agricultural Applications
Spatial Analysis and Integration
Water Quality Assessment
Complete Workflows and Case Studies

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.