Pulling Sentinal 2 data

1 Set Java Options

# Run these Java options before anything else.
options(java.parameters = "-Xmx64G")
options(timeout = max(600, getOption("timeout")))

2 R libraries and global setting.

#library(Rcpp)
library(sf)
library(gdalcubes)
library(rstac)
library(gdalUtils)
library(terra)
library(rgdal)
library(reshape2)
library(osmdata)
library(terra)
library(dplyr)
#library(glue)
library(stars)
library(ggplot2)
library(colorspace)
library(geos)
#library(glue)
library(osmdata)
library(ggthemes)
library(tidyr)
gdalcubes_options(parallel = 8)

sf::sf_extSoftVersion()
          GEOS           GDAL         proj.4 GDAL_with_GEOS     USE_PROJ_H 
      "3.11.0"        "3.5.3"        "9.1.0"         "true"         "true" 
          PROJ 
       "9.1.0" 
gdalcubes_gdal_has_geos()
[1] TRUE

3 Start timer

start <- Sys.time()

4 Set color palette

library(ggtern)
our_yellow <- rgb2hex(r = 253, g = 201, b = 51)
our_green <- rgb2hex(r = 10, g = 84, b = 62)
our_grey <- rgb2hex(r = 92, g = 96, b = 95)
our_white <- rgb2hex(r = 255, g = 255, b = 255)

5 Load area of interest

# Read the shapefile into an sf object
aoi_total <- st_read("/Users/ty/Documents/Github/Southern_California_Edison_Fire_Risk/SCE_Fire_Zone_V2/SCE_Fire_Zone_V2.shp") %>% 
  st_as_sf()
Reading layer `SCE_Fire_Zone_V2' from data source 
  `/Users/ty/Documents/Github/Southern_California_Edison_Fire_Risk/SCE_Fire_Zone_V2/SCE_Fire_Zone_V2.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 12 features and 5 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 176062.4 ymin: 3674043 xmax: 764123.1 ymax: 4254012
Projected CRS: NAD83 / UTM zone 11N
# Plot the entire spatial dataset
plot(aoi_total)

# Filter the dataset to obtain the geometry with OBJECTID 5
aoi <- aoi_total %>%
  filter(OBJECTID == 5)

# Obtain and plot the bounding box of the filtered geometry
shape_bbox <- st_bbox(aoi)
plot(aoi)

# Transform the filtered geometry to EPSG:4326 and store its bounding box
aoi %>% st_transform("EPSG:4326") %>%
  st_bbox() -> bbox_4326

# Transform the filtered geometry to EPSG:32618 and store its bounding box
aoi %>% st_transform("EPSG:32618") %>%
  st_bbox() -> bbox_32618

6 Arrange STAC collection

In this code chunk, the primary goal is to search for and obtain satellite imagery data. The data source being tapped into is a SpatioTemporal Asset Catalog (STAC) provided by an online service (earth-search by Element84). Here’s a breakdown:

A connection is established with the STAC service, searching specifically within the “sentinel-s2-l2a-cogs” collection. -The search is spatially constrained to a bounding box (bbox_4326) and temporally limited to a range of one day, between May 15 and May 16, 2021. -Once the search is conducted, the desired assets or spectral bands from the returned satellite images are defined, ranging from Band 1 (B01) to Band 12 (B12) and including the Scene Classification Layer (SCL). -These bands are then organized into an image collection for further processing or analysis.

# Initialize STAC connection
s = stac("https://earth-search.aws.element84.com/v0")

# Search for Sentinel-2 images within specified bounding box and date range
items = s %>%
    stac_search(collections = "sentinel-s2-l2a-cogs",
                bbox = c(bbox_4326["xmin"], 
                         bbox_4326["ymin"],
                         bbox_4326["xmax"], 
                         bbox_4326["ymax"]), 
                datetime = "2021-05-15/2021-05-16") %>%
    post_request() %>%
    items_fetch(progress = FALSE)

# Print number of found items
length(items$features)
[1] 12
# Prepare the assets for analysis
library(gdalcubes)
assets = c("B01", "B02", "B03", "B04", "B05", "B06", 
           "B07", 
           "B08", "B8A", "B09", "B11", "B12", "SCL")
s2_collection = stac_image_collection(items$features, asset_names = assets)

# Display the image collection
s2_collection
Image collection object, referencing 12 images with 13 bands
Images:
                      name      left      top   bottom     right
1 S2B_11SNS_20210515_1_L2A -117.0002 33.43957 32.44372 -115.8191
2 S2B_11SPS_20210515_1_L2A -115.9361 33.43490 32.42937 -114.7436
3 S2B_11SQS_20210515_0_L2A -114.8732 33.42092 32.41918 -113.9566
4 S2B_12STB_20210515_0_L2A -114.2244 33.40433 32.61015 -113.9559
5 S2B_11SNT_20210515_0_L2A -117.0002 34.34164 33.34577 -115.8066
6 S2B_11SPT_20210515_0_L2A -115.9253 34.33683 33.33091 -114.7198
             datetime        srs
1 2021-05-15T18:35:13 EPSG:32611
2 2021-05-15T18:35:10 EPSG:32611
3 2021-05-15T18:35:06 EPSG:32611
4 2021-05-15T18:35:01 EPSG:32612
5 2021-05-15T18:34:59 EPSG:32611
6 2021-05-15T18:34:55 EPSG:32611
[ omitted 6 images ] 

Bands:
   name offset scale unit nodata image_count
1   B01      0     1                      12
2   B02      0     1                      12
3   B03      0     1                      12
4   B04      0     1                      12
5   B05      0     1                      12
6   B06      0     1                      12
7   B07      0     1                      12
8   B08      0     1                      12
9   B09      0     1                      12
10  B11      0     1                      12
11  B12      0     1                      12
12  B8A      0     1                      12
13  SCL      0     1                      12

7 Define view window

In this code chunk, a ‘view’ on the previously obtained satellite image collection is being defined. Think of this as setting up a specific lens or perspective to look at the satellite data:

-The view is set to the coordinate reference system EPSG:32618. -Spatial resolution is defined as 100x100 meters. -Temporal resolution is defined monthly (P1M), even though the actual range is only one day. -When there are multiple values in a grid cell or timeframe, they are aggregated using the median value. -If any resampling is needed, the nearest neighbor method is used (near). -The spatial and temporal extents are constrained to specific values. -By defining this view, it allows for consistent analysis and visualization of the image collection within the specified spatial and temporal resolutions and extents.

# Define a specific view on the satellite image collection
v = cube_view(
    srs = "EPSG:32618", 
    dx = 100, 
    dy = 100, 
    dt = "P1M", 
    aggregation = "median", 
    resampling = "near",
    extent = list(
        t0 = "2021-05-15", 
        t1 = "2021-05-16",
        left = bbox_32618["xmin"], 
        right = bbox_32618["xmax"],
        top = bbox_32618["ymax"], 
        bottom = bbox_32618["ymin"]
    )
)

# Display the defined view
v
A data cube view object

Dimensions:
                low              high count pixel_size
t        2021-05-01        2021-05-31     1        P1M
y  4471226.41402451  4741326.41402451  2701        100
x -3463720.00044994 -3191420.00044994  2723        100

SRS: "EPSG:32618"
Temporal aggregation method: "median"
Spatial resampling method: "near"

8 Pull data

In this chunk, the primary aim is to transform and prepare satellite imagery data for analysis:

-The current time is stored in variable a for tracking the time taken by the process. -The previously defined ‘view’ on the satellite imagery, v, is used to create a raster cube, a multi-dimensional array containing the satellite data. This raster cube contains spatial, spectral, and temporal data. -The desired spectral bands are selected. -The data is limited to a specific area of interest, aoi. -The band names are renamed to their respective wavelengths in nanometers for clarity. -A subset of the data, comprising 50,000 random samples, is selected. -Unwanted columns are removed, and the dataset is transformed into a long format, where each row represents a particular date and wavelength combination. -The entire process duration is computed by taking the difference between the end time (b) and the start time (a). -The transformed dataset y is then displayed.

# Record start time
a <- Sys.time()

# Transform the satellite image collection into a raster cube
x <- s2_collection %>%
    raster_cube(v) %>%
    select_bands(c("B01", "B02", "B03", "B04", 
                   "B05", "B06", "B07", "B08", 
                   "B8A", "B09", "B11", "B12")) %>%
    extract_geom(aoi) %>%
    rename(
        "time" = "time",
        "443" = "B01",
        "490" = "B02",
        "560" = "B03",
        "665" = "B04",
        "705" = "B05",
        "740" = "B06",
        "783" = "B07",
        "842" = "B08",
        "865" = "B8A",
        "940" = "B09",
        "1610" = "B11",
        "2190" = "B12"
    )

# Sample, transform and prepare data for analysis
y <- x %>%
    slice_sample(n = 50000) %>%
    select(-FID) %>%
    pivot_longer(!time, names_to = "wavelength_nm", values_to = "reflectance") %>%
    mutate(wavelength_nm = as.numeric(wavelength_nm))

# Record end time and compute duration
b <- Sys.time()
processing_time <- difftime(b, a)

# Display the processing time and transformed dataset
processing_time
Time difference of 3.847375 mins
y
# A tibble: 600,000 × 3
   time       wavelength_nm reflectance
   <chr>              <dbl>       <dbl>
 1 2021-05-01           443        1400
 2 2021-05-01           490        1583
 3 2021-05-01           560        2124
 4 2021-05-01           665        2800
 5 2021-05-01           705        3044
 6 2021-05-01           740        3117
 7 2021-05-01           783        3209
 8 2021-05-01           842        3314
 9 2021-05-01           865        3245
10 2021-05-01           940        3631
# ℹ 599,990 more rows

9 Base plot

# Set custom colors for the plot
our_green <- "#4CAF50"
our_white <- "#FFFFFF"
our_yellow <- "#FFEB3B"

# Create a 2D density plot
day_density <- ggplot(data = y, aes(x = wavelength_nm, y = reflectance, group = time)) + 
  stat_smooth(color = our_green, fill = "lightgrey") +
  geom_density2d(colour = "black", bins = 10, alpha = 0.1) +
  stat_density2d(aes(alpha = ..level.., fill = ..level..), 
                 linewidth = 2, bins = 10, geom = "polygon") + 
  scale_fill_gradient(low = our_white, high = our_yellow) +
  scale_alpha(range = c(0.00, 0.8), guide = FALSE) +
  theme_tufte() +
  xlab("wavelength") +
  ylab("reflectance") +
  ylim(0, 16000) +
  theme(
    aspect.ratio = 5/14, 
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, 
                               colour = c("darkblue", "blue", "green", "red", 
                                          "darkred", "darkred", "darkred", "darkred", 
                                          "darkred", "black", "black", "black", "black")),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    plot.margin = margin(t = 30, r = 10, b = 40, l = 18)
  ) +
  scale_x_continuous(breaks = c(443, 490, 560, 665, 705, 740, 783, 842, 865, 940, 1610, 2190))

# Display the plot
day_density

10 Inlay 1 - geographic zone

guide_map <- ggplot(data= aoi_total) +
  geom_sf(fill=our_yellow, color=our_white) +
  geom_sf(data= aoi, fill=our_green, color=our_white) +
  theme_tufte()+
  ggtitle("Zone 5")+
  theme(axis.text.x=element_blank(), #remove x axis labels
        axis.ticks.x=element_blank(), #remove x axis ticks
        axis.text.y=element_blank(),  #remove y axis labels
        axis.ticks.y=element_blank()  #remove y axis ticks, bg=none
        )+ theme(plot.title = element_text(hjust=0.8, vjust = -2))
guide_map

11 Inlay 2 - date text

library(geosphere)
aoi_total |> st_centroid()  |> st_transform(crs="+proj=longlat") |> st_coordinates() |> colMeans() -> lat_long

daylength_line <- daylength(lat = lat_long[2], 1:365)

daylengths <- data.frame(time= 1:365, daylength = daylength_line)

library(lubridate)

# Create a template date object
date <- as.POSIXlt("2021-05-15")

doy <- format(date, format = "%j") |> as.numeric()

display_date <- format(date, format="%e %B %Y   ")

12 Inlay 3 - daylength

date_inlay <- ggplot(data=daylengths) + 
 
  ggtitle("Daylength")+
  geom_ribbon(aes(x=time, ymin=daylength, ymax=15), fill=our_grey, alpha=0.5) +
  geom_ribbon(aes(x=time, ymax=daylength, ymin=9), fill=our_yellow, alpha=1) +
  geom_hline(yintercept=12, color=our_white) +
 geom_vline(xintercept=doy, color=our_green, size=1) +
  theme_tufte() +
  ylim(9,15) +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) + theme(plot.title = element_text(hjust=0.5, vjust = 0))
date_inlay

13 Ensemble map assembly

library(cowplot)
library(magick)
map_overlay <- ggdraw(day_density) + 
  draw_plot(guide_map, x = 1.08, y = 1, hjust = 1, vjust = 1, width = 0.3, height = 0.3)+
  draw_plot(date_inlay, x = 1, y = 0.35, hjust = 1, vjust = 1, width = 0.1, height = 0.25)+
  geom_text(aes(x=1, y=0.08, label=display_date, hjust = 1), color=our_grey, cex=3, fontface='bold') +
 # draw_image("Ty_powerline_plots/Southern_California_Edison_Logo.png", x = -0.24, y = 0.38, scale=.3)+
 # draw_image("Ty_powerline_plots/earthlab_logo.png", x = -0.38, y = 0.38, scale=.25)+
  geom_text(aes(x=0.4, y=.9, label="Spectral library - Monthly average"), color=our_green, hjust = 0, cex=8, fontface='bold') +
  geom_text(aes(x=0.01, y=.04, 
    label="Created by ESIIL (T. Tuff) for Fall Hackathon -- October 2023. Sentinel 2 Data from 'https://earth-search.aws.element84.com/v0'"), color=our_grey, hjust  = 0, cex=3) +
geom_text(aes(x=0.4, y=.1, label="wavelength (nm)"), color=our_grey, hjust = 0, cex=4, fontface='bold') +
geom_text(aes(x=0.01, y=.5,angle = 90, label="reflectance"), color=our_grey, hjust = 0, cex=4, fontface='bold')
map_overlay

14 Save map

ggsave(map_overlay, file="day_density_15_May_2021_zone_5.png", bg="white", dpi = 600, width = 12,
  height = 5)

15 End timer

end <- Sys.time()
difftime(end,start)
Time difference of 4.4868 mins