vignettes/IceSat-2_Mission_Orbits_HTML.Rmd
IceSat-2_Mission_Orbits_HTML.RmdThis first vignette demonstrates how to download and process time specific orbits. We’ll use one of the Reference Ground Track (RGT) cycles and merge it with other data sources with the purpose to visualize specific areas.
We’ll load one of the latest which is “RGT_cycle_14” (from December 22, 2021 to March 23, 2022). The documentation of the “RGT_cycle_14” data includes more details on how a user can come to the same data format for any of the RGT Cycles.
pkgs = c('IceSat2R', 'magrittr', 'mapview', 'sf', 'rnaturalearth',
'data.table', 'DT', 'stargazer')
load_pkgs = lapply(pkgs, require, character.only = TRUE) # load required R packages
sf::sf_use_s2(use_s2 = FALSE) # disable 's2' in this vignette
mapview::mapviewOptions(leafletHeight = '600px',
leafletWidth = '700px') # applies to all leaflet maps
#.............................
# load the 'RGT_cycle_14' data
#.............................
data(RGT_cycle_14)
res_rgt_many = sf::st_as_sf(x = RGT_cycle_14, coords = c('longitude', 'latitude'), crs = 4326)
res_rgt_many## Simple feature collection with 131765 features and 6 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -179.9986 ymin: -87.66742 xmax: 179.9984 ymax: 87.3305
## Geodetic CRS: WGS 84
## First 10 features:
## day_of_year Date hour minute second RGT geometry
## 1 356 2021-12-22 7 57 49 1 POINT (-0.1318472 0.02795893)
## 2 356 2021-12-22 7 58 49 1 POINT (-0.5162124 3.868758)
## 3 356 2021-12-22 7 59 49 1 POINT (-0.901809 7.709809)
## 4 356 2021-12-22 8 0 49 1 POINT (-1.289879 11.55065)
## 5 356 2021-12-22 8 1 49 1 POINT (-1.681755 15.39082)
## 6 356 2021-12-22 8 2 49 1 POINT (-2.078916 19.2299)
## 7 356 2021-12-22 8 3 49 1 POINT (-2.483051 23.06748)
## 8 356 2021-12-22 8 4 49 1 POINT (-2.896146 26.90316)
## 9 356 2021-12-22 8 5 49 1 POINT (-3.3206 30.73662)
## 10 356 2021-12-22 8 6 49 1 POINT (-3.759374 34.56754)We’ll proceed to merge the orbit geometry points with the countries data of the rnaturalearth R package (1:110 million scales) and for this purpose, we keep only the “sovereignt” and “sov_a3” columns,
cntr = rnaturalearth::ne_countries(scale = 110, type = 'countries', returnclass = 'sf')
cntr = cntr[, c('sovereignt', 'sov_a3')]
cntr## Simple feature collection with 177 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## CRS: +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## First 10 features:
## sovereignt sov_a3 geometry
## 0 Afghanistan AFG MULTIPOLYGON (((61.21082 35...
## 1 Angola AGO MULTIPOLYGON (((16.32653 -5...
## 2 Albania ALB MULTIPOLYGON (((20.59025 41...
## 3 United Arab Emirates ARE MULTIPOLYGON (((51.57952 24...
## 4 Argentina ARG MULTIPOLYGON (((-65.5 -55.2...
## 5 Armenia ARM MULTIPOLYGON (((43.58275 41...
## 6 Antarctica ATA MULTIPOLYGON (((-59.57209 -...
## 7 France FR1 MULTIPOLYGON (((68.935 -48....
## 8 Australia AU1 MULTIPOLYGON (((145.398 -40...
## 9 Austria AUT MULTIPOLYGON (((16.97967 48...We then merge the orbit points with the country geometries and specify also “left = TRUE” to keep also observations that do not intersect with the rnaturalearth countries data,
dat_both = suppressMessages(sf::st_join(x = res_rgt_many,
y = cntr,
join = sf::st_intersects,
left = TRUE))
dat_both## Simple feature collection with 131765 features and 8 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -179.9986 ymin: -87.66742 xmax: 179.9984 ymax: 87.3305
## Geodetic CRS: WGS 84
## First 10 features:
## day_of_year Date hour minute second RGT sovereignt sov_a3
## 1 356 2021-12-22 7 57 49 1 <NA> <NA>
## 2 356 2021-12-22 7 58 49 1 <NA> <NA>
## 3 356 2021-12-22 7 59 49 1 Ghana GHA
## 4 356 2021-12-22 8 0 49 1 Burkina Faso BFA
## 5 356 2021-12-22 8 1 49 1 Mali MLI
## 6 356 2021-12-22 8 2 49 1 Mali MLI
## 7 356 2021-12-22 8 3 49 1 Mali MLI
## 8 356 2021-12-22 8 4 49 1 Algeria DZA
## 9 356 2021-12-22 8 5 49 1 Algeria DZA
## 10 356 2021-12-22 8 6 49 1 Morocco MAR
## geometry
## 1 POINT (-0.1318472 0.02795893)
## 2 POINT (-0.5162124 3.868758)
## 3 POINT (-0.901809 7.709809)
## 4 POINT (-1.289879 11.55065)
## 5 POINT (-1.681755 15.39082)
## 6 POINT (-2.078916 19.2299)
## 7 POINT (-2.483051 23.06748)
## 8 POINT (-2.896146 26.90316)
## 9 POINT (-3.3206 30.73662)
## 10 POINT (-3.759374 34.56754)The unique number of RGT’s for “RGT_cycle_14” are
## [1] 1387We observe that from December 22, 2021 to March 23, 2022,
df_tbl = data.frame(table(dat_both$sovereignt), stringsAsFactors = F)
colnames(df_tbl) = c('country', 'Num_IceSat2_points')
df_subs = dat_both[, c('RGT', 'sovereignt')]
df_subs$geometry = NULL
df_subs = data.table::data.table(df_subs, stringsAsFactors = F)
colnames(df_subs) = c('RGT', 'country')
df_subs = split(df_subs, by = 'country')
df_subs = lapply(df_subs, function(x) {
unq_rgt = sort(unique(x$RGT))
items = ifelse(length(unq_rgt) < 5, length(unq_rgt), 5)
concat = paste(unq_rgt[1:items], collapse = '-')
iter_dat = data.table::setDT(list(country = unique(x$country),
Num_RGTs = length(unq_rgt),
first_5_RGTs = concat))
iter_dat
})
df_subs = data.table::rbindlist(df_subs)
df_tbl = merge(df_tbl, df_subs, by = 'country')
df_tbl = df_tbl[order(df_tbl$Num_IceSat2_points, decreasing = T), ]
DT_dtbl = DT::datatable(df_tbl, rownames = FALSE)all RGT’s (1387 in number) intersect with “Antarctica” and almost all with “Russia”.
The onshore and offshore number of ICESat-2 points and percentages for the “RGT_cycle_14” equal to
num_sea = sum(is.na(dat_both$sovereignt))
num_land = sum(!is.na(dat_both$sovereignt))
perc_sea = round(num_sea / nrow(dat_both), digits = 4) * 100.0
perc_land = round(num_land / nrow(dat_both), digits = 4) * 100.0
dtbl_land_sea = data.frame(list(percentage = c(perc_sea, perc_land),
Num_Icesat2_points = c(num_sea, num_land)))
row.names(dtbl_land_sea) = c('sea', 'land')
stargazer::stargazer(dtbl_land_sea,
type = 'html',
summary = FALSE,
rownames = TRUE,
header = FALSE,
table.placement = 'h',
title = 'Land and Sea Proportions')| percentage | Num_Icesat2_points | |
| sea | 67.070 | 88,369 |
| land | 32.930 | 43,396 |
We can also observe the ICESat-2 “RGT_cycle_14” coverage based on the 1 to 10 million large scale Natural Earth Glaciated Areas data,
data(ne_10m_glaciated_areas)We’ll restrict the processing to the major polar glaciers (that have a name included),
ne_obj_subs = subset(ne_10m_glaciated_areas, !is.na(name))
ne_obj_subs = sf::st_make_valid(x = ne_obj_subs) # check validity of geometries
ne_obj_subs## Simple feature collection with 68 features and 5 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: -180 ymin: -89.99993 xmax: 180 ymax: 82.96573
## Geodetic CRS: WGS 84
## First 10 features:
## recnum scalerank featurecla name min_zoom
## 143 143 3 Glaciated areas Mount Brown Icefield 2.1
## 148 148 5 Glaciated areas Braithwaite Icefield 5.0
## 152 152 3 Glaciated areas Hooker Icefield 2.1
## 206 206 5 Glaciated areas Homathko Icefield 5.0
## 214 214 6 Glaciated areas Clachnacudainn Icefield 5.7
## 215 215 6 Glaciated areas Albert Icefield 5.7
## 228 228 3 Glaciated areas Plateau Icefield 2.1
## 230 230 5 Glaciated areas Pemberton Icefield 5.0
## 256 256 3 Glaciated areas Cambria Icefiled 2.1
## 273 0 3 Glaciated areas Lyell Icefield 2.1
## geometry
## 143 MULTIPOLYGON (((-118.4066 5...
## 148 MULTIPOLYGON (((-119.9303 5...
## 152 MULTIPOLYGON (((-117.8572 5...
## 206 MULTIPOLYGON (((-124.6489 5...
## 214 MULTIPOLYGON (((-118.0284 5...
## 215 MULTIPOLYGON (((-117.6752 5...
## 228 MULTIPOLYGON (((-123.8453 5...
## 230 MULTIPOLYGON (((-123.3869 5...
## 256 MULTIPOLYGON (((-129.661 56...
## 273 MULTIPOLYGON (((-117.2649 5...and we’ll visualize the subset using the mapview package,
mpv = mapview::mapview(ne_obj_subs,
color = 'cyan',
col.regions = 'blue',
alpha.regions = 0.5,
legend = FALSE)
mpv