ProcessSpace

Adam Cummings (USFS), adam.cummings@usda.gov

A package to process Lidar to visualize stream channels flow paths.

Description

This process space tool automates a detailed catchment-level assessment of streams and channels using LiDAR-derived DEMs to enable visualization of flow path anomalies and restoration opportunities not easily observed in the field or using conventional maps. From steep tributaries to the valley bottom, flow paths can be assessed to determine if they were degraded, ditched, or if flow paths had been redirected or constricted due to current or historical roads or other disturbances. This information can be used to restore natural flow paths with high precision interventions to restore stream function, reduce need for road and culvert repair, and restore downstream valley-bottom habitats.

Outputs include topographical cross-sections, 1-ft and 2-ft process space polygons that estimate increases to baseline surface water levels at time of LiDAR and color-coded detrended elevation models all of which can be visualized three-dimensionally in Google Earth using the kmz file output.

Installation

Install the package as follows:

install.packages('devtools')
library(devtools)
install_github('adamkc/ProcessSpace')
library(ProcessSpace)

This package requires a few spatial packages that themselves have many requirements. This installation process doesn’t always go smoothly/perfectly. It may try to update 30-50 packages and throw an error along the way.. ProcessSpace requires the following packages (with versions updated since 2020, roughly.) If you hit issues, consider just installing these packages first, slowly, one at a time, and then retrying installing ProcessSpace.

  install.packages("dplyr")
  install.packages("tidyr")
  install.packages("purrr")
  install.packages("ggplot2")
  install.packages("cowplot")
  install.packages("magrittr")
  install.packages("ggmap")
  install.packages("sf")
  install.packages("plotKML")
  install.packages("raster")
  install.packages("concaveman")
  install.packages("crayon")
  install.packages("gstat")
  install.packages("smoothr")
  install.packages("units")
  install.packages("zip")

Inputs

1. Digital Terrain Model
2. Target Stream Reach

Outputs

1. Algorithmic cross sections
2. Process Space delineation
3. Report export
4. Detrended elevations
5. A mindset that extends beyond the meadow surface

Example Code:

The example below will produce a KMZ file to load into Google Earth.

library(ProcessSpace)
library(ggmap) # necessary to load google credentials for sat imagery. Not required.

#Filepath to raster DEM (change to your own file):
rasterLocation <- system.file("external/raster.tif", package="ProcessSpace")
#Filepath to stream shapefile (change to your own file):
streamLocation <- system.file("external/streams.shp", package="ProcessSpace")
streams <- sf::read_sf(streamLocation)
#Pick a target stream for analysis: (`LINKNO` is the FID in this shapefile)
targetStream <- streams %>% dplyr::filter(LINKNO %in% c(12,20))


targetStream %>%
  generateCrossSections(xSectionDensity = units::as_units(100,"m"), # How often cross sections are taken.
                        googleZoom=16,                              # Pick appropiate Google zoom level.
                        xSectionLength = units::as_units(100,"m"),  # How far from channel to extend cross sections.
                        cut1Dir = "W") %>%                          # cut1Dir: cardinal direction of upstream point of targetstream
  allAtOnce(outputFilename = "exampleOutput.pdf",
            rasterDir = rasterLocation,
            streamDir = streamLocation,
            returnObject = FALSE,
            doExportSpatial = TRUE)