In impact-initiatives-geospatial/surveyGEER: Bringing relevant GEE Data Into Your Assessment

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval=F
)

Intro

This notebook will demonstrate how several non-climate indicators are created. We will load the images, map them, and then combine them all into one multi-band image for extraction purposes. At the end I will wrap the process into one function which will be available in the surveyGEER library

Non- climate related indicators

• Access to cities [@Weiss2018]
• Access to healthcare facilities [@Weiss2020]
• Access to healthcare facilities (walking) [@Weiss2020]
• SRTM Landforms [@theobald2015_srtm_landforms]
• ALOS Landforms [@theobald2015_srtm_landforms]

Load Images

• load libraries
• load images

library(surveyGEER)
library(rgee)
library(sf)
ee_Initialize()

city_access <- ee$Image("Oxford/MAP/accessibility_to_cities_2015_v1_0")
healthcare_access <- ee$Image("Oxford/MAP/accessibility_to_healthcare_2019")
srtm_landforms = ee$Image("CSP/ERGo/1_0/Global/SRTM_landforms")
alos_landforms = ee$Image("CSP/ERGo/1_0/Global/ALOS_landforms")
srtm_dem <-  ee$Image("CGIAR/SRTM90_V4")

# lets use the bounding box of somalia to center our map
ee_bbox <- sf::st_bbox(som_boundary) |> 
  sf::st_as_sfc() |> 
  sf_as_ee()

Maps

Below we set the visualization parameters for each map and save each map to an object.

somalia_city_access_viz <- list(min=0,
                   max= 1000,
                   palette=list("#green","yellow","red")
)
somalia_health_access_viz <- list(min=0,
                   max= 1000,
                   palette=list("gred","yellow","red"), 
                   bands=list("accessibility")
)
somalia_health_access_walking_viz <- list(min=0,
                   max= 1000,
                   palette=list("green","yellow","red"), 
                   bands=list("accessibility_walking_only")
)
srtm_viz <- get_metadata_palette_viz(srtm_landforms,bands = "constant")


m1_city_access2015 <- Map$addLayer(
  city_access,
  visParams = somalia_city_access_viz
  )

m2_health_access2019 <- Map$addLayer(
  healthcare_access,
  visParams = somalia_health_access_viz
  )

m3_health_access_walking_2019 <- Map$addLayer(
  healthcare_access,
  visParams = somalia_health_access_walking_viz
  )

m4_srtm_landform<- Map$addLayer(srtm_landforms,visParams = srtm_viz,"srtm_landform")

We can then map all the layers at once

Map$centerObject(ee_bbox,6)
m1_city_access2015+
  m2_health_access2019+
  m3_health_access_walking_2019+
  m4_srtm_landform

Joining Bands

If we want to extract this values to points or zones (polygons) later it will be easiest to just combine them into one multi-band image that we can just perform one ee_extract or ee_extract_tidy on.

city_access$
  select("accessibility")$
  rename("city_accessibility2015")$
  addBands(
    healthcare_access$select(list("accessibility","accessibility_walking_only"))$
      rename(list("healthcare_accessibility2019","healthcare_accessbility_walking_only2019"))
    )$
  addBands(srtm_landforms$rename("srtm_landforms"))$
  addBands(alos_landforms$rename("alos_landforms"))$
  addBands(srtm_dem$rename("srtm4_dem"))$
  bandNames()$getInfo()

Putting it all together!

Below we put the above into one generic function. This function has also been added to the surveyGEER library for portability.

#' get_oxford_access_indicators
#'
#' @return multi-band image with
#' @export
#'
#' @examples
get_oxford_access_indicators <- function(return_tidyee=F){
  city_access <- ee$Image("Oxford/MAP/accessibility_to_cities_2015_v1_0")
  healthcare_access <- ee$Image("Oxford/MAP/accessibility_to_healthcare_2019")
  city_access <- city_access$set("system:time_start",ee$Date("2015-01-01"))
  healthcare_access <- healthcare_access$set("system:time_start",ee$Date("2019-01-01"))


  res <- city_access$
  select("accessibility")$
  rename("city_accessibility2015")$
  addBands(
    healthcare_access$select(list("accessibility","accessibility_walking_only"))$
      rename(list("healthcare_accessibility2019","healthcare_accessbility_walking_only2019"))
    )
  if(return_tidyee){
    res <- as_tidyee(res)
  }
  return(res)
}

#' get_geomorph_landforms
#'
#' @return image or tidyee with image
#' @export
#'
#' @examples
get_geomorph_landform_indicators <-  function(return_tidyee=F){
  srtm_landforms = ee$Image("CSP/ERGo/1_0/Global/SRTM_landforms")
  alos_landforms = ee$Image("CSP/ERGo/1_0/Global/ALOS_landforms")
  # ee_get_date_img(srtm_landforms)
  res <- srtm_landforms$
    rename("srtm_landforms")$
    set("system:time_start",ee$Date("2022-01-01"))$
    addBands(alos_landforms$rename("alos_landforms"))
  if(return_tidyee){
    res <- as_tidyee(res)
  }

  return(res)

}


#' get_non_climate_indicators
#'
#' @return  ee$Image with one band for each indicator
#' @export
#'
#' @examples \dontrun{
#' library(surveyGEER)
#' non_climate_indicators<-get_non_climate_indicators()
#' }
#' 
get_non_climate_indicators <-  function(){
  city_access <- ee$Image("Oxford/MAP/accessibility_to_cities_2015_v1_0")
  healthcare_access <- ee$Image("Oxford/MAP/accessibility_to_healthcare_2019")
  srtm_landforms = ee$Image("CSP/ERGo/1_0/Global/SRTM_landforms")
  alos_landforms = ee$Image("CSP/ERGo/1_0/Global/ALOS_landforms")


  city_access$
  select("accessibility")$
  rename("city_accessibility2015")$
  addBands(
    healthcare_access$select(list("accessibility","accessibility_walking_only"))$
      rename(list("healthcare_accessibility2019","healthcare_accessbility_walking_only2019"))
    )$
  addBands(srtm_landforms$rename("srtm_landforms"))$
  addBands(alos_landforms$rename("alos_landforms"))
}

References

impact-initiatives-geospatial/surveyGEER documentation built on Feb. 4, 2023, 12:13 p.m.