Black Marble Data and Statistics • blackmarbler

BlackMarbleR is a R package that provides a simple way to use nighttime lights data from NASA’s Black Marble. Black Marble is a NASA Earth Science Data Systems (ESDS) project that provides a product suite of daily, monthly and yearly global nighttime lights. This package automates the process of downloading all relevant tiles from the NASA LAADS DAAC to cover a region of interest, converting and mosaicing the raw files (in HDF5 format) to georeferenced rasters.

Installation
Bearer token
Usage
Functions and arguments
Black Marble Resources

Installation

The package can be installed via CRAN.

install.packages("blackmarbler")

To install the development version from Github:

# install.packages("devtools")
devtools::install_github("worldbank/blackmarbler")

Bearer Token

To obtain a bearer token, you’ll need to have a registered NASA Earth Data account. On the webpage, click “login” and create a username/password if needed.

After an account is created, the NASA Bearer Token can be retrieved either using the get_nasa_token function or manually (see below).

Programmatically retrieve token

The NASA Bearer Token can also be programmatically retrieved using the get_nasa_token() function. After making an account, the get_nasa_token() function uses your username and password to retrieve the Bearer token.

bearer <- get_nasa_token(username = "USERNAME-HERE", 
                         password = "PASSWORD-HERE")

Manually retrieve token

The function requires using a Earthdata Download Bearer Token; to obtain a token, follow the below steps:

Go to the NASA LAADS Archive
Click “Login” (button on top right), and click “Earthdata Login”; create an account if needed.
Enter your username and password.
Click “Login”, then “Generate Token” on the dropdown.
Click the “Generate Token” tab, then click the green button to generate a token
Click the blue “Show token” button; this is your bearer token. It will be a long string of text (over 500 characters).

NASA LAADS Bearer Token

Usage

Setup

Before downloading and extracting Black Marble data, we first load packages, define the NASA bearer token, and define a region of interest.

#### Setup
# Load packages
library(blackmarbler)
library(geodata)
library(sf)
library(terra)
library(ggplot2)
library(tidyterra)
library(lubridate)

#### Define NASA bearer token
bearer <- "BEARER-TOKEN-HERE"

### ROI
# Define region of interest (roi). The roi must be (1) an sf polygon and (2)
# in the WGS84 (epsg:4326) coordinate reference system. Here, we use the
# getData function to load a polygon of Ghana
roi_sf <- gadm(country = "GHA", level=1, path = tempdir())

Make raster of nighttime lights

The below example shows making daily, monthly, and annual rasters of nighttime lights for Ghana.

### Daily data: raster for February 5, 2021
r_20210205 <- bm_raster(roi_sf = roi_sf,
                        product_id = "VNP46A2",
                        date = "2021-02-05",
                        bearer = bearer)

### Monthly data: raster for October 2021
r_202110 <- bm_raster(roi_sf = roi_sf,
                      product_id = "VNP46A3",
                      date = "2021-10-01", # The day is ignored
                      bearer = bearer)

### Annual data: raster for 2021
r_2021 <- bm_raster(roi_sf = roi_sf,
                    product_id = "VNP46A4",
                    date = 2021,
                    bearer = bearer)

Make raster of nighttime lights across multiple time periods

To extract data for multiple time periods, add multiple time periods to date. The function will return a SpatRaster object with multiple bands, where each band corresponds to a different date. The below code provides examples getting data across multiple days, months, and years.

#### Daily data in March 2021
r_daily <- bm_raster(roi_sf = roi_sf,
                     product_id = "VNP46A2",
                     date = seq.Date(from = ymd("2021-03-01"), to = ymd("2021-03-31"), by = "day"),
                     bearer = bearer)

#### Monthly aggregated data in 2021 and 2022
r_monthly <- bm_raster(roi_sf = roi_sf,
                       product_id = "VNP46A3",
                       date = seq.Date(from = ymd("2021-01-01"), to = ymd("2022-12-01"), by = "month"),
                       bearer = bearer)

#### Yearly aggregated data in 2012 and 2021
r_annual <- bm_raster(roi_sf = roi_sf,
                      product_id = "VNP46A4",
                      date = 2012:2021,
                      bearer = bearer)

Map of nighttime lights

Using one of the rasters, we can make a map of nighttime lights

#### Make raster
r <- bm_raster(roi_sf = roi_sf,
               product_id = "VNP46A3",
               date = "2021-10-01",
               bearer = bearer)

#### Prep data
r <- r |> terra::mask(roi_sf)

## Distribution is skewed, so log
r[] <- log(r[] + 1)

##### Map
ggplot() +
  geom_spatraster(data = r) +
  scale_fill_gradient2(low = "black",
                       mid = "yellow",
                       high = "red",
                       midpoint = 4.5,
                       na.value = "transparent") +
  labs(title = "Nighttime Lights: October 2021") +
  coord_sf() +
  theme_void() +
  theme(plot.title = element_text(face = "bold", hjust = 0.5),
  legend.position = "none")

Nighttime Lights Map

Trends over time

We can use the bm_extract function to observe changes in nighttime lights over time. The bm_extract function leverages the exactextractr package to aggregate nighttime lights data to polygons. Below we show trends in annual nighttime lights data across Ghana’s first administrative divisions.

#### Extract annual data
ntl_df <- bm_extract(roi_sf = roi_sf,
                     product_id = "VNP46A4",
                     date = 2012:2022,
                     bearer = bearer)

#### Trends over time
ntl_df |>
  ggplot() +
  geom_col(aes(x = date,
  y = ntl_mean),
  fill = "darkorange") +
  facet_wrap(~NAME_1) +
  labs(x = NULL,
       y = "NTL Luminosity",
       title = "Ghana Admin Level 1: Annual Average Nighttime Lights") +
  scale_x_continuous(labels = seq(2012, 2022, 4),
                     breaks = seq(2012, 2022, 4)) +
  theme_minimal() +
  theme(strip.text = element_text(face = "bold"))

Nighttime Lights Trends

Workflow to update data

Some users may want to monitor near-real-time changes in nighttime lights. For example, daily Black Marble nighttime lights data is updated regularly, where data is available roughly on a week delay; same use cases may require examining trends in daily nighttime lights data as new data becomes available. Below shows example code that could be regularly run to produce an updated daily dataset of nighttime lights.

The below code produces a dataframe of nighttime lights for each date, where average nighttime lights for Ghana’s 1st administrative division is produced. The code will check whether data has already been downloaded/extracted for a specific date, and only download/extract new data.

# Create directories to store data
dir.create(file.path(getwd(), "bm_files"))
dir.create(file.path(getwd(), "bm_files", "daily"))

# Extract daily-level nighttime lights data for Ghana's first administrative divisions.
# Save a separate dataset for each date in the `"~/Desktop/bm_files/daily"` directory.
# The code extracts data from January 1, 2023 to today. Given that daily nighttime lights
# data is produced on roughly a week delay, the function will only extract data that exists;
# it will skip extracting data for dates where data has not yet been produced by NASA Black Marble.
bm_extract(roi_sf = roi_sf,
           product_id = "VNP46A2",
           date = seq.Date(from = ymd("2023-01-01"), to = Sys.Date(), by = 1),
           bearer = bearer,
           output_location_type = "file",
           file_dir = file.path(getwd(), "bm_files", "daily"))

# Append daily-level datasets into one file
file.path(getwd(), "bm_files", "daily") |>
  list.files(pattern = "*.Rds",
  full.names = T) |>
  map_df(readRDS) |>
  saveRDS(file.path(getwd(), "bm_files", "ntl_daily.Rds"))

Functions and arguments

Functions

The package provides two functions.

bm_raster produces a raster of Black Marble nighttime lights.
bm_extract produces a dataframe of aggregated nighttime lights to a region of interest (e.g., average nighttime lights within US States).

Both functions take the following arguments:

Required arguments

roi_sf: Region of interest; sf polygon. Must be in the WGS 84 (epsg:4326) coordinate reference system. For bm_extract, aggregates nighttime lights within each polygon of roi_sf.
product_id: One of the following:
- "VNP46A1": Daily (raw)
- "VNP46A2": Daily (corrected)
- "VNP46A3": Monthly
- "VNP46A4": Annual
date: Date of raster data. Entering one date will produce a SpatRaster object. Entering multiple dates will produce a SpatRaster object with multiple bands; one band per date.
- For product_ids "VNP46A1" and "VNP46A2", a date (eg, "2021-10-03").
- For product_id "VNP46A3", a date or year-month (e.g., "2021-10-01", where the day will be ignored, or "2021-10").
- For product_id "VNP46A4", year or date (e.g., "2021-10-01", where the month and day will be ignored, or 2021).
bearer: NASA bearer token. For instructions on how to create a token, see here.

Optional arguments

variable: Variable to used to create raster (default: NULL). For information on all variable choices, see here; for VNP46A1, see Table 3; for VNP46A2 see Table 6; for VNP46A3 and VNP46A4, see Table 9. If NULL, uses the following default variables:
- For product_id "VNP46A1", uses DNB_At_Sensor_Radiance_500m.
- For product_id "VNP46A2", uses Gap_Filled_DNB_BRDF-Corrected_NTL.
- For product_ids "VNP46A3" and "VNP46A4", uses NearNadir_Composite_Snow_Free.
quality_flag_rm: Quality flag values to use to set values to NA. Each pixel has a quality flag value, where low quality values can be removed. Values are set to NA for each value in ther quality_flag_rm vector. (Default: NULL).
- For VNP46A1 and VNP46A2 (daily data):
  - 0: High-quality, Persistent nighttime lights
  - 1: High-quality, Ephemeral nighttime Lights
  - 2: Poor-quality, Outlier, potential cloud contamination, or other issues
- For VNP46A3 and VNP46A4 (monthly and annual data):
  - 0: Good-quality, The number of observations used for the composite is larger than 3
  - 1: Poor-quality, The number of observations used for the composite is less than or equal to 3
  - 2: Gap filled NTL based on historical data
check_all_tiles_exist: Check whether all Black Marble nighttime light tiles exist for the region of interest. Sometimes not all tiles are available, so the full region of interest may not be covered. If TRUE, skips cases where not all tiles are available. (Default: TRUE).
interpol_na: When data for more than one date is downloaded, whether to interpolate NA values in rasters using the terra::approximate function. Additional arguments for the terra::approximate function can also be passed into bm_raster/bm_extract (eg, method, rule, f, ties, z, NA_rule). (Default: FALSE).
h5_dir: Black Marble data are originally downloaded as h5 files. If h5_dir = NULL, the function downloads to a temporary directory then deletes the directory. If h5_dir is set to a path, h5 files are saved to that directory and not deleted. The function will then check if the needed h5 file already exists in the directory; if it exists, the function will not re-download the h5 file.
output_location_type: Where output should be stored (default: r_memory). Either:
- r_memory where the function will return an output in R
- file where the function will export the data as a file. For bm_raster, a .tif file will be saved; for bm_extract, a .Rds file will be saved. A file is saved for each date. Consequently, if date = c(2018, 2019, 2020), three datasets will be saved: one for each year. Saving a dataset for each date can facilitate re-running the function later and only downloading data for dates where data have not been downloaded.

If output_location_type = "file", the following arguments can be used:

file_dir: The directory where data should be exported (default: NULL, so the working directory will be used)
file_prefix: Prefix to add to the file to be saved. The file will be saved as the following: [file_prefix][product_id]_t[date].[tif/Rds]
file_skip_if_exists: Whether the function should first check wither the file already exists, and to skip downloading or extracting data if the data for that date if the file already exists (default: TRUE). If the function is first run with date = c(2018, 2019, 2020), then is later run with date = c(2018, 2019, 2020, 2021), the function will only download/extract data for 2021. Skipping existing files can facilitate re-running the function at a later date to download only more recent data.
file_return_null: Whether to return NULL instead of a output to R (SpatRaster or dataframe). When output_location_type = 'file', the function will export data to the file_dir directory. When file_return_null = FALSE, the function will also return the queried data—so the data is available in R memory. Setting file_return_null = TRUE, data will be saved to file_dir but no data will be returned by the function to R memory (default: FALSE).
…: Additional arguments for terra::approximate, if interpol_na = TRUE

Argument for `bm_extract` only

aggregation_fun: A vector of functions to aggregate data (default: "mean"). The exact_extract function from the exactextractr package is used for aggregations; this parameter is passed to fun argument in exactextractr::exact_extract.
add_n_pixels: Whether to add a variable indicating the number of nighttime light pixels used to compute nighttime lights statistics (eg, number of pixels used to compute average of nighttime lights). When TRUE, it adds three values: n_non_na_pixels (the number of non-NA pixels used for computing nighttime light statistics); n_pixels (the total number of pixels); and prop_non_na_pixels the proportion of the two. (Default: TRUE).

Black Marble Resources

For more information on NASA Black Marble, see:

BlackMarbleR