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The GeoSpatialSuite package provides comprehensive workflow functions that integrate multiple analysis types into complete, automated pipelines. This vignette demonstrates how to build and execute advanced workflows for real-world geospatial analysis scenarios.
By the end of this vignette, you will be able to:
The package includes pre-built workflows for common analysis scenarios:
# Create sample multi-temporal data
red_raster <- rast(nrows = 80, ncols = 80,
xmin = -83.5, xmax = -83.0,
ymin = 40.2, ymax = 40.7)
values(red_raster) <- runif(6400, 0.1, 0.3)
nir_raster <- rast(nrows = 80, ncols = 80,
xmin = -83.5, xmax = -83.0,
ymin = 40.2, ymax = 40.7)
values(nir_raster) <- runif(6400, 0.4, 0.8)
# Configure comprehensive NDVI analysis
ndvi_config <- list(
analysis_type = "ndvi_crop_analysis",
input_data = list(red = red_raster, nir = nir_raster),
region_boundary = c(-83.5, 40.2, -83.0, 40.7), # Bounding box
indices = c("NDVI", "EVI2", "SAVI"),
quality_filter = TRUE,
temporal_smoothing = FALSE,
visualization_config = list(
create_maps = TRUE,
ndvi_classes = "none",
interactive = FALSE
)
)
# Execute comprehensive workflow
ndvi_workflow_results <- run_comprehensive_geospatial_workflow(ndvi_config)
# Access results
ndvi_data <- ndvi_workflow_results$results$vegetation_data
ndvi_stats <- ndvi_workflow_results$results$statistics
ndvi_maps <- ndvi_workflow_results$results$visualizations# Simulate CDL crop data
cdl_raster <- rast(nrows = 80, ncols = 80,
xmin = -83.5, xmax = -83.0,
ymin = 40.2, ymax = 40.7)
values(cdl_raster) <- sample(c(1, 5, 24, 36, 61), 6400, replace = TRUE) # Corn, soybeans, wheat, alfalfa, fallow
# Enhanced configuration with crop masking
enhanced_ndvi_config <- list(
analysis_type = "ndvi_crop_analysis",
input_data = list(red = red_raster, nir = nir_raster),
region_boundary = "custom",
cdl_data = cdl_raster,
crop_codes = c(1, 5), # Corn and soybeans only
indices = c("NDVI", "SAVI", "DVI"),
quality_filter = TRUE,
visualization_config = list(
create_maps = TRUE,
interactive = FALSE
)
)
# Run enhanced workflow
enhanced_results <- run_comprehensive_geospatial_workflow(enhanced_ndvi_config)
# Compare masked vs unmasked results
print("Original NDVI stats:")
print(global(ndvi_data, "mean", na.rm = TRUE))
print("Crop-masked NDVI stats:")
print(global(enhanced_results$results$vegetation_data, "mean", na.rm = TRUE))# Create multi-band spectral data
spectral_stack <- c(
red_raster, # Band 1: Red
red_raster * 1.2, # Band 2: Green (simulated)
red_raster * 0.8, # Band 3: Blue (simulated)
nir_raster # Band 4: NIR
)
names(spectral_stack) <- c("red", "green", "blue", "nir")
# Configure comprehensive vegetation analysis
vegetation_config <- list(
analysis_type = "vegetation_comprehensive",
input_data = spectral_stack,
indices = c("NDVI", "EVI2", "SAVI", "GNDVI", "DVI", "RVI"),
crop_type = "general",
analysis_type_detail = "comprehensive",
region_boundary = c(-83.5, 40.2, -83.0, 40.7),
visualization_config = list(
create_maps = TRUE,
comparison_plots = TRUE
)
)
# Execute comprehensive vegetation workflow
vegetation_results <- run_comprehensive_geospatial_workflow(vegetation_config)
# Access detailed results
vegetation_indices <- vegetation_results$results$vegetation_analysis$vegetation_indices
analysis_details <- vegetation_results$results$vegetation_analysis$analysis_results
metadata <- vegetation_results$results$vegetation_analysis$metadata
print("Vegetation indices calculated:")
print(names(vegetation_indices))
print("Analysis metadata:")
print(metadata$indices_used)# Corn-specific analysis workflow
corn_config <- list(
analysis_type = "vegetation_comprehensive",
input_data = spectral_stack,
crop_type = "corn",
analysis_type_detail = "comprehensive",
cdl_mask = cdl_raster == 1, # Corn mask
indices = c("NDVI", "EVI2", "GNDVI"), # Corn-appropriate indices
visualization_config = list(create_maps = TRUE)
)
corn_results <- run_comprehensive_geospatial_workflow(corn_config)
# Extract corn-specific insights
if ("stress_analysis" %in% names(corn_results$results$vegetation_analysis$analysis_results)) {
stress_results <- corn_results$results$vegetation_analysis$analysis_results$stress_analysis
print("Corn stress analysis:")
print(stress_results)
}Objective: Monitor environmental conditions around water bodies, assess agricultural impacts on water quality, and identify priority areas for conservation.
# Create environmental monitoring scenario
monitoring_extent <- c(-82.8, 39.5, -81.8, 40.5)
# Water quality monitoring stations
water_stations <- data.frame(
station_id = paste0("WQ_", sprintf("%03d", 1:18)),
lon = runif(18, monitoring_extent[1] + 0.1, monitoring_extent[3] - 0.1),
lat = runif(18, monitoring_extent[2] + 0.1, monitoring_extent[4] - 0.1),
nitrate_mg_l = runif(18, 0.5, 15.0),
phosphorus_mg_l = runif(18, 0.02, 2.5),
turbidity_ntu = runif(18, 1, 45),
dissolved_oxygen_mg_l = runif(18, 4, 12),
ph = runif(18, 6.8, 8.4),
sampling_date = sample(seq(as.Date("2024-05-01"), as.Date("2024-09-30"), by = "day"), 18),
watershed = sample(c("Upper_Creek", "Lower_Creek", "Tributary_A"), 18, replace = TRUE)
)
# Land use/land cover data
lulc_raster <- terra::rast(nrows = 150, ncols = 150,
xmin = monitoring_extent[1], xmax = monitoring_extent[3],
ymin = monitoring_extent[2], ymax = monitoring_extent[4])
# Simulate realistic LULC pattern
lulc_codes <- c(1, 5, 41, 42, 81, 82, 11, 21) # Developed, Forest, Wetland, Agriculture
lulc_probs <- c(0.15, 0.25, 0.05, 0.40, 0.08, 0.05, 0.01, 0.01)
terra::values(lulc_raster) <- sample(lulc_codes, 22500, replace = TRUE, prob = lulc_probs)
names(lulc_raster) <- "LULC"
print("Environmental monitoring setup completed")# Comprehensive water quality assessment
water_quality_results <- list()
# Analyze each water quality parameter
parameters <- c("nitrate_mg_l", "phosphorus_mg_l", "turbidity_ntu", "dissolved_oxygen_mg_l")
for (param in parameters) {
# Define parameter-specific thresholds
thresholds <- switch(param,
"nitrate_mg_l" = list("Low" = c(0, 3), "Moderate" = c(3, 6),
"High" = c(6, 10), "Excessive" = c(10, Inf)),
"phosphorus_mg_l" = list("Low" = c(0, 0.1), "Moderate" = c(0.1, 0.3),
"High" = c(0.3, 0.8), "Excessive" = c(0.8, Inf)),
"turbidity_ntu" = list("Clear" = c(0, 5), "Moderate" = c(5, 15),
"Turbid" = c(15, 30), "Very_Turbid" = c(30, Inf)),
"dissolved_oxygen_mg_l" = list("Critical" = c(0, 4), "Stressed" = c(4, 6),
"Good" = c(6, 8), "Excellent" = c(8, Inf))
)
# Comprehensive analysis
water_quality_results[[param]] <- analyze_water_quality_comprehensive(
water_data = water_stations,
variable = param,
region_boundary = monitoring_extent,
thresholds = thresholds,
output_folder = paste0("water_quality_", param)
)
}
print("Water quality analysis completed for all parameters")# Assess land use impacts on water quality
land_use_impact_analysis <- function(water_stations, lulc_raster, buffer_sizes = c(500, 1000, 2000)) {
results <- list()
for (buffer_size in buffer_sizes) {
# Extract land use composition around each station
buffered_lulc <- spatial_join_universal(
vector_data = water_stations,
raster_data = lulc_raster,
method = "buffer",
buffer_size = buffer_size,
summary_function = "mode", # Most common land use
variable_names = paste0("dominant_lulc_", buffer_size, "m")
)
# Calculate land use percentages
for (i in 1:nrow(water_stations)) {
station_point <- water_stations[i, ]
station_buffer <- sf::st_buffer(sf::st_as_sf(station_point,
coords = c("lon", "lat"),
crs = 4326),
dist = buffer_size)
# Extract all LULC values within buffer
lulc_values <- terra::extract(lulc_raster, terra::vect(station_buffer))
lulc_table <- table(lulc_values)
# Calculate percentages
total_pixels <- sum(lulc_table)
agriculture_pct <- sum(lulc_table[names(lulc_table) %in% c("1", "5")]) / total_pixels * 100
developed_pct <- sum(lulc_table[names(lulc_table) %in% c("21", "22", "23", "24")]) / total_pixels * 100
water_stations[i, paste0("agriculture_pct_", buffer_size, "m")] <- agriculture_pct
water_stations[i, paste0("developed_pct_", buffer_size, "m")] <- developed_pct
}
results[[paste0("buffer_", buffer_size, "m")]] <- water_stations
}
return(results)
}
# Perform land use impact analysis
land_use_impacts <- land_use_impact_analysis(water_stations, lulc_raster)
# Analyze correlations between land use and water quality
correlation_analysis <- function(data) {
# Select relevant columns
water_quality_vars <- c("nitrate_mg_l", "phosphorus_mg_l", "turbidity_ntu")
land_use_vars <- grep("agriculture_pct|developed_pct", names(data), value = TRUE)
correlation_matrix <- cor(data[, c(water_quality_vars, land_use_vars)],
use = "complete.obs")
return(correlation_matrix)
}
# Analyze correlations for different buffer sizes
correlations_500m <- correlation_analysis(land_use_impacts$buffer_500m)
correlations_1000m <- correlation_analysis(land_use_impacts$buffer_1000m)
correlations_2000m <- correlation_analysis(land_use_impacts$buffer_2000m)
print("Land use impact analysis completed")
print("Correlations at 1000m buffer:")
print(round(correlations_1000m[1:3, 4:7], 3))# Watershed-scale environmental assessment
watershed_assessment <- function(water_stations, environmental_layers) {
# Group stations by watershed
watershed_summary <- list()
for (watershed in unique(water_stations$watershed)) {
watershed_stations <- water_stations[water_stations$watershed == watershed, ]
# Calculate watershed-level statistics
watershed_summary[[watershed]] <- list(
n_stations = nrow(watershed_stations),
mean_nitrate = mean(watershed_stations$nitrate_mg_l, na.rm = TRUE),
mean_phosphorus = mean(watershed_stations$phosphorus_mg_l, na.rm = TRUE),
mean_turbidity = mean(watershed_stations$turbidity_ntu, na.rm = TRUE),
stations_exceeding_nitrate_threshold = sum(watershed_stations$nitrate_mg_l > 6, na.rm = TRUE),
stations_exceeding_phosphorus_threshold = sum(watershed_stations$phosphorus_mg_l > 0.3, na.rm = TRUE)
)
}
return(watershed_summary)
}
# Environmental layers for watershed analysis
environmental_layers <- list(
vegetation = enhanced_ndvi, # From previous case study
elevation = elevation,
lulc = lulc_raster
)
watershed_results <- watershed_assessment(water_stations, environmental_layers)
print("Watershed Assessment Results:")
for (watershed in names(watershed_results)) {
cat("\n", watershed, ":\n")
result <- watershed_results[[watershed]]
cat(" Stations:", result$n_stations, "\n")
cat(" Mean Nitrate:", round(result$mean_nitrate, 2), "mg/L\n")
cat(" Mean Phosphorus:", round(result$mean_phosphorus, 3), "mg/L\n")
cat(" Stations exceeding nitrate threshold:", result$stations_exceeding_nitrate_threshold, "\n")
}# Identify priority areas for conservation based on water quality risk
conservation_priority_analysis <- function(lulc_raster, water_quality_data, slope_data = NULL) {
# Create risk factors
risk_factors <- list()
# Agricultural intensity risk
ag_risk <- lulc_raster
ag_risk[lulc_raster != 1 & lulc_raster != 5] <- 0 # Non-ag areas = 0 risk
ag_risk[lulc_raster == 1 | lulc_raster == 5] <- 1 # Ag areas = 1 risk
names(ag_risk) <- "agricultural_risk"
# Water quality risk based on monitoring data
# Create risk surface using interpolation of water quality data
high_risk_stations <- water_quality_data[water_quality_data$nitrate_mg_l > 6 |
water_quality_data$phosphorus_mg_l > 0.3, ]
if (nrow(high_risk_stations) > 0) {
# Simple distance-based risk (closer to high-risk stations = higher risk)
risk_raster <- terra::rast(lulc_raster)
terra::values(risk_raster) <- 0
for (i in 1:nrow(high_risk_stations)) {
station_point <- c(high_risk_stations$lon[i], high_risk_stations$lat[i])
# Create simple distance decay function (this is simplified)
distances <- terra::distance(risk_raster, station_point)
station_risk <- 1 / (1 + distances / 5000) # 5km decay distance
risk_raster <- risk_raster + station_risk
}
names(risk_raster) <- "water_quality_risk"
} else {
risk_raster <- ag_risk * 0 # No high-risk stations
}
# Combined priority score
combined_priority <- (ag_risk * 0.6) + (risk_raster * 0.4)
names(combined_priority) <- "conservation_priority"
# Classify priority levels
priority_values <- terra::values(combined_priority, mat = FALSE)
priority_breaks <- quantile(priority_values[priority_values > 0],
c(0, 0.25, 0.5, 0.75, 1.0), na.rm = TRUE)
priority_classified <- terra::classify(combined_priority,
cbind(priority_breaks[-length(priority_breaks)],
priority_breaks[-1],
1:4))
names(priority_classified) <- "priority_class"
return(list(
priority_surface = combined_priority,
priority_classified = priority_classified,
high_risk_stations = high_risk_stations
))
}
# Generate conservation priorities
conservation_priorities <- conservation_priority_analysis(lulc_raster, water_stations)
print("Conservation priority analysis completed")
print(paste("High-risk stations identified:", nrow(conservation_priorities$high_risk_stations)))Objective: Analyze landscape changes over time, detect deforestation/land use conversion, and monitor ecosystem health trends.
# Simulate multi-temporal satellite data (5-year time series)
years <- 2020:2024
temporal_extent <- c(-83.0, 40.2, -82.0, 41.2)
# Create temporal NDVI series with realistic trends
temporal_ndvi_series <- list()
base_ndvi <- terra::rast(nrows = 120, ncols = 120,
xmin = temporal_extent[1], xmax = temporal_extent[3],
ymin = temporal_extent[2], ymax = temporal_extent[4])
# Create base NDVI pattern
x_coords <- terra::xFromCell(base_ndvi, 1:terra::ncell(base_ndvi))
y_coords <- terra::yFromCell(base_ndvi, 1:terra::ncell(base_ndvi))
base_pattern <- 0.6 + 0.2 * sin((x_coords - min(x_coords)) * 6) +
0.1 * cos((y_coords - min(y_coords)) * 4) +
rnorm(terra::ncell(base_ndvi), 0, 0.1)
terra::values(base_ndvi) <- pmax(0.1, pmin(0.9, base_pattern))
# Simulate temporal changes
for (i in 1:length(years)) {
year <- years[i]
# Add temporal trends
# 1. General vegetation improvement (climate change effect)
climate_trend <- (i - 1) * 0.02
# 2. Localized disturbances (development, deforestation)
disturbance_effect <- 0
if (i >= 3) { # Disturbance starts in year 3
# Simulate development in lower-left quadrant
disturbance_mask <- (x_coords < (min(x_coords) + (max(x_coords) - min(x_coords)) * 0.4)) &
(y_coords < (min(y_coords) + (max(y_coords) - min(y_coords)) * 0.4))
disturbance_effect <- ifelse(disturbance_mask, -0.3, 0)
}
# 3. Inter-annual variability
annual_variation <- rnorm(terra::ncell(base_ndvi), 0, 0.05)
# Combine effects
year_ndvi <- base_ndvi + climate_trend + disturbance_effect + annual_variation
year_ndvi <- pmax(0.05, pmin(0.95, year_ndvi))
names(year_ndvi) <- paste0("NDVI_", year)
temporal_ndvi_series[[as.character(year)]] <- year_ndvi
}
print("Multi-temporal data series created for years:", paste(years, collapse = ", "))# Comprehensive temporal change analysis
temporal_analysis_results <- analyze_temporal_changes(
data_list = temporal_ndvi_series,
dates = as.character(years),
region_boundary = temporal_extent,
analysis_type = "trend",
output_folder = "temporal_analysis_results"
)
# Additional change detection analysis
change_detection_results <- analyze_temporal_changes(
data_list = temporal_ndvi_series,
dates = as.character(years),
analysis_type = "change_detection"
)
# Statistical analysis
statistics_results <- analyze_temporal_changes(
data_list = temporal_ndvi_series,
dates = as.character(years),
analysis_type = "statistics"
)
print("Temporal analysis completed:")
print("Trend analysis:", !is.null(temporal_analysis_results$trend_rasters))
print("Change detection:", length(change_detection_results$change_rasters), "change maps")
print("Statistical summary:", length(statistics_results$statistics_rasters), "statistic layers")# Identify areas of significant change
identify_change_hotspots <- function(trend_results, change_results, threshold_slope = 0.02) {
# Extract slope (trend) raster
slope_raster <- trend_results$trend_rasters$slope
r_squared_raster <- trend_results$trend_rasters$r_squared
# Identify significant trends
significant_trends <- abs(slope_raster) > threshold_slope & r_squared_raster > 0.5
# Classify change types
change_types <- slope_raster
change_types[slope_raster > threshold_slope & significant_trends] <- 2 # Increasing
change_types[slope_raster < -threshold_slope & significant_trends] <- 1 # Decreasing
change_types[!significant_trends] <- 0 # No significant change
names(change_types) <- "change_type"
# Calculate change statistics
n_total <- terra::ncell(change_types)
n_increasing <- sum(terra::values(change_types) == 2, na.rm = TRUE)
n_decreasing <- sum(terra::values(change_types) == 1, na.rm = TRUE)
n_stable <- sum(terra::values(change_types) == 0, na.rm = TRUE)
change_stats <- list(
total_pixels = n_total,
increasing_pixels = n_increasing,
decreasing_pixels = n_decreasing,
stable_pixels = n_stable,
increasing_percent = round(n_increasing / n_total * 100, 1),
decreasing_percent = round(n_decreasing / n_total * 100, 1)
)
return(list(
change_types = change_types,
significant_trends = significant_trends,
change_statistics = change_stats
))
}
# Identify hotspots
change_hotspots <- identify_change_hotspots(temporal_analysis_results, change_detection_results)
print("Change Hotspot Analysis:")
print(paste("Pixels with increasing vegetation:", change_hotspots$change_statistics$increasing_pixels))
print(paste("Pixels with decreasing vegetation:", change_hotspots$change_statistics$decreasing_pixels))
print(paste("Percentage of area with significant decline:",
change_hotspots$change_statistics$decreasing_percent, "%"))# Synthesize results from all case studies
comprehensive_synthesis <- function(agricultural_results, environmental_results, temporal_results) {
synthesis_report <- list()
# 1. Agricultural insights
synthesis_report$agricultural_summary <- list(
total_fields_analyzed = nrow(field_boundaries),
management_zones_created = management_zones$optimal_zones,
average_ndvi = round(mean(terra::values(enhanced_ndvi), na.rm = TRUE), 3),
precision_ag_benefits = "Variable-rate applications recommended for all zones"
)
# 2. Environmental insights
synthesis_report$environmental_summary <- list(
water_stations_monitored = nrow(water_stations),
parameters_analyzed = length(parameters),
watersheds_assessed = length(unique(water_stations$watershed)),
high_risk_areas_identified = nrow(conservation_priorities$high_risk_stations)
)
# 3. Temporal insights
synthesis_report$temporal_summary <- list(
years_analyzed = length(years),
significant_change_areas = change_hotspots$change_statistics$decreasing_percent,
trend_analysis_completed = TRUE,
monitoring_recommendations = "Continue monitoring areas with significant decline"
)
# 4. Integrated recommendations
synthesis_report$integrated_recommendations <- list(
precision_agriculture = "Implement zone-based management for optimal yields",
water_quality = "Focus conservation efforts on high-risk watersheds",
land_change_monitoring = "Establish permanent monitoring plots in change hotspots",
data_integration = "Continue multi-source data integration for comprehensive assessment"
)
return(synthesis_report)
}
# Generate comprehensive synthesis
final_synthesis <- comprehensive_synthesis(
agricultural_results,
water_quality_results,
temporal_analysis_results
)
print("COMPREHENSIVE ANALYSIS SYNTHESIS")
print("=====================================")
for (section in names(final_synthesis)) {
cat("\n", toupper(gsub("_", " ", section)), ":\n")
for (item in names(final_synthesis[[section]])) {
cat(" ", gsub("_", " ", item), ":", final_synthesis[[section]][[item]], "\n")
}
}# Performance optimization for large-scale analyses
optimization_tips <- function() {
cat("WORKFLOW OPTIMIZATION GUIDE\n")
cat("===========================\n\n")
cat("Data Management:\n")
cat(" - Process data in chunks for large datasets\n")
cat(" - Use appropriate raster resolutions for analysis scale\n")
cat(" - Implement data caching for repeated analyses\n")
cat(" - Clean up intermediate files regularly\n\n")
cat("Memory Management:\n")
cat(" - Monitor memory usage with gc() and object.size()\n")
cat(" - Use terra's out-of-memory processing for large rasters\n")
cat(" - Remove unused objects with rm()\n")
cat(" - Consider parallel processing for independent operations\n\n")
cat("Quality Control:\n")
cat(" - Validate inputs before processing\n")
cat(" - Implement checkpoints in long workflows\n")
cat(" - Log processing steps and parameters\n")
cat(" - Create reproducible analysis scripts\n")
}
optimization_tips()# Framework for reproducible geospatial research
create_reproducible_workflow <- function(project_name, analysis_config) {
# Create project structure
project_dirs <- c(
file.path(project_name, "data", "raw"),
file.path(project_name, "data", "processed"),
file.path(project_name, "scripts"),
file.path(project_name, "results", "figures"),
file.path(project_name, "results", "tables"),
file.path(project_name, "documentation")
)
for (dir in project_dirs) {
if (!dir.exists(dir)) dir.create(dir, recursive = TRUE)
}
# Create analysis log
analysis_log <- list(
project_name = project_name,
creation_date = Sys.time(),
geospatialsuite_version = "0.1.0",
analysis_config = analysis_config,
r_session_info = sessionInfo()
)
# Save analysis configuration
saveRDS(analysis_log, file.path(project_name, "analysis_log.rds"))
cat("Reproducible workflow structure created for:", project_name, "\n")
cat("Project directories:", length(project_dirs), "created\n")
cat("Analysis log saved\n")
return(project_dirs)
}
# Example usage
workflow_config <- list(
analysis_types = c("agricultural", "environmental", "temporal"),
study_region = "Ohio Agricultural Region",
temporal_extent = "2020-2024",
spatial_resolution = "30m",
coordinate_system = "WGS84"
)
project_structure <- create_reproducible_workflow("integrated_geospatial_analysis", workflow_config)This comprehensive workflow vignette demonstrates:
The workflows in GeoSpatialSuite provide a complete toolkit for professional geospatial analysis, from data preparation through final reporting and recommendations!
This work was developed by the GeoSpatialSuite team with contributions from: Olatunde D. Akanbi, Vibha Mandayam, Yinghui Wu, Jeffrey Yarus, Erika I. Barcelos, and Roger H. French.
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.