MODIS fire – r.iresmi.net

A photo of a forest fire — Eagle Creek Fire – CC-BY-NC-SA by Curtis Gregory Perry

Day 15 of 30DayMapChallenge: « Fire » (previously).

An animation of global fires in 2024 using MODIS data.

library(dggridR)
library(dplyr)
library(readr)
library(ggplot2)
library(purrr)
library(sf)
library(rnaturalearth)
library(glue)
library(classInt)

Data

See the docs. You can use a client like Filezilla to download the data.

SFTP: fuoco.geog.umd.edu
Login / Password (as of time of writing): fire / burnt

Get all year 2024 files available in /data/MODIS/C61/MCD14ML.

We’ll use binning on a 250 km discrete global grid.

# countries background
world <- ne_countries(scale = 10) |> 
  st_make_valid() |> 
  st_wrap_dateline()

# build the grid
dggs <- dgconstruct(spacing = 250) 

hex <- dggs |> 
  dgshptogrid(world, cellsize = 0.5) |> 
  st_make_valid() |>
  st_wrap_dateline() |> 
  st_filter(world) |> 
  select(seqnum)

# read all MODIS files and find their grid cell ID
modis <- dir("~/data/modis/", full.names = TRUE) |> 
  read_fwf(
    skip = 1,
    col_types = cols("YYYYMMDD" = col_date(format = "%Y%m%d")),
    fwf_positions(
      c(1, 10, 15, 17, 26, 36, 42, 48, 53, 61, 65, 68),
      c(9, 14, 16, 25, 35, 41, 47, 52, 60, 64, 67, 69),
      c("YYYYMMDD", "HHMM", "sat", "lat", "lon", "T21", "T31", "sample", "FRP", 
        "conf", "type", "dn")),
    num_threads = 10) |> 
  mutate(seqnum = dgGEO_to_SEQNUM(dggs, lon, lat)$seqnum)

Map

We generate one PNG file per day and create the video with a call to a system-installed ffmpeg.

# compute the number of fires for each cell and each day
modis_cells <- modis |> 
  count(seqnum, YYYYMMDD) |> 
  left_join(hex,
            join_by(seqnum)) |> 
  st_sf()

# prepare the breaks, to keep them identical on each image
breaks <- classIntervals(modis_cells$n, n = 4, style = "kmeans")

# create a PNG map for one day
create_map <- function(d) {
  p <- modis_cells |> 
    filter(YYYYMMDD == d) |> 
    ggplot() +
    geom_sf(data = world, fill = "#1a3853", color = "#002240") +
    geom_sf(aes(fill = n, color = n)) +
    scale_fill_viridis_c(aesthetics =  c("colour", "fill"),
                         breaks = round(breaks$brks),
                         transform = "log",
                         name = "Fires\nper cell\n(log\nscale)",
                         option = "B",
                         limits = c(1, max(modis_cells$seqnum))) +
    coord_sf(crs = "EPSG:8857") +
    labs(title = "MODIS fire detection",
         subtitle = d,
         caption = glue("MODIS - Global Monthly Fire Location \\
                        Product (MCD14ML)
                        https://r.iresmi.net/ - {Sys.Date()}")) +
    theme_void() +
    theme(text = element_text(family = "Ubuntu",
                              color = "white"),
          plot.margin = margin(2, 2, 2, 5, unit = "mm"),
          plot.caption = element_text(size = 7,
                                      color = "#777"))
  ggsave(glue("img/fire_{d}.png"), p, bg = "#002240", width = 9, height = 5)
}

# Iterate
modis |> 
  distinct(YYYYMMDD) |> 
  pull(YYYYMMDD) |> 
  walk(create_map, .progress = TRUE)

# generate the video
system(glue('ffmpeg -framerate 24 -pattern_type glob -i "img/fire*.png" \\
            -c:v libx264 -pix_fmt yuv420p modis.mp4'))

Figure 1: Animation of global fires in 2024 using MODIS data

--- title: "MODIS fire" description: "Day 15 of 30DayMapChallenge. Fire" author: "Michaël" date: "2025-11-15T21:00:00" date-modified: last-modified categories: - R - 30DayMapChallenge - spatial - datavisualization draft: false freeze: true editor_options: chunk_output_type: console chunk_output_type: console execute: eval: false message: false --- [![Eagle Creek Fire -- CC-BY-NC-SA by Curtis Gregory Perry](images/36906522722_938f6ed36a_c.jpg){fig-alt="A photo of a forest fire"}](https://www.flickr.com/photos/curtisperry/36906522722/) Day 15 of [30DayMapChallenge](https://30daymapchallenge.com/): « Fire » ([previously](/#category=30DayMapChallenge)). An animation of global fires in 2024 using [MODIS](https://modis.gsfc.nasa.gov/) data. ```{r} #| label: setup library(dggridR) library(dplyr) library(readr) library(ggplot2) library(purrr) library(sf) library(rnaturalearth) library(glue) library(classInt) ``` ## Data See the [docs](https://firms.modaps.eosdis.nasa.gov/download/). You can use a client like Filezilla to download the data. - SFTP: `fuoco.geog.umd.edu` - Login / Password (as of time of writing): `fire` / `burnt` Get all year 2024 files available in */data/MODIS/C61/MCD14ML*. We'll use binning on a 250 km [discrete global grid](https://en.wikipedia.org/wiki/Discrete_global_grid). ```{r} #| label: data #| cache: true # countries background world <- ne_countries(scale = 10) |> st_make_valid() |> st_wrap_dateline() # build the grid dggs <- dgconstruct(spacing = 250) hex <- dggs |> dgshptogrid(world, cellsize = 0.5) |> st_make_valid() |> st_wrap_dateline() |> st_filter(world) |> select(seqnum) # read all MODIS files and find their grid cell ID modis <- dir("~/data/modis/", full.names = TRUE) |> read_fwf( skip = 1, col_types = cols("YYYYMMDD" = col_date(format = "%Y%m%d")), fwf_positions( c(1, 10, 15, 17, 26, 36, 42, 48, 53, 61, 65, 68), c(9, 14, 16, 25, 35, 41, 47, 52, 60, 64, 67, 69), c("YYYYMMDD", "HHMM", "sat", "lat", "lon", "T21", "T31", "sample", "FRP", "conf", "type", "dn")), num_threads = 10) |> mutate(seqnum = dgGEO_to_SEQNUM(dggs, lon, lat)$seqnum) ``` ## Map We generate one PNG file per day and create the video with a call to a system-installed ffmpeg. ```{r} #| label: map # compute the number of fires for each cell and each day modis_cells <- modis |> count(seqnum, YYYYMMDD) |> left_join(hex, join_by(seqnum)) |> st_sf() # prepare the breaks, to keep them identical on each image breaks <- classIntervals(modis_cells$n, n = 4, style = "kmeans") # create a PNG map for one day create_map <- function(d) { p <- modis_cells |> filter(YYYYMMDD == d) |> ggplot() + geom_sf(data = world, fill = "#1a3853", color = "#002240") + geom_sf(aes(fill = n, color = n)) + scale_fill_viridis_c(aesthetics = c("colour", "fill"), breaks = round(breaks$brks), transform = "log", name = "Fires\nper cell\n(log\nscale)", option = "B", limits = c(1, max(modis_cells$seqnum))) + coord_sf(crs = "EPSG:8857") + labs(title = "MODIS fire detection", subtitle = d, caption = glue("MODIS - Global Monthly Fire Location \\ Product (MCD14ML) https://r.iresmi.net/ - {Sys.Date()}")) + theme_void() + theme(text = element_text(family = "Ubuntu", color = "white"), plot.margin = margin(2, 2, 2, 5, unit = "mm"), plot.caption = element_text(size = 7, color = "#777")) ggsave(glue("img/fire_{d}.png"), p, bg = "#002240", width = 9, height = 5) } # Iterate modis |> distinct(YYYYMMDD) |> pull(YYYYMMDD) |> walk(create_map, .progress = TRUE) # generate the video system(glue('ffmpeg -framerate 24 -pattern_type glob -i "img/fire*.png" \\ -c:v libx264 -pix_fmt yuv420p modis.mp4')) ``` ::: {#fig-modis} {{< video modis.mp4 >}} Animation of global fires in 2024 using MODIS data :::