Micha Silver and Alessandro Oggioni 1/02/2022
- Install and load packages
- Query DEIMS SDR
- Spatial queries
- Dependency on quality of data in DEIMS SDR
- Datasets from Open DataScience Europe
- Datasets from MODIS
This code demonstrates the use of the new ReLTER package. For more
details, see Allesando Oggioni’s github
page
Alessandro Oggioni, Micha Silver, Luigi Ranghetti & Paolo Tagliolato. (2021). oggioniale/ReLTER: ReLTER v1.0.0 (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5576813
Begin by installing packages and loading them.
# These packages are required
pkg_list <- c("remotes", "tmap", "tmaptools", "sf", "terra", "OpenStreetMap", "raster")
# Check if already installed, install if not
installed_packages <- pkg_list %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
install.packages(pkg_list[!installed_packages])
}
# Load Packages
lapply(pkg_list, function(p) {
require(p, character.only = TRUE, quietly = TRUE)
})
# Now install `ReLTER` from github and load
# remotes::install_github('oggioniale/ReLTER')
# If you want the development version, with latest functions:
remotes::install_github("oggioniale/ReLTER@dev")
library(ReLTER)
# Choose where to save outputs
Output_dir = "./Output"
if (!dir.exists(Output_dir)) {
dir.create(Output_dir)
}The ReLTER package interfaces with the DEIMS SDR
database.
We can query the database in many ways: First retrieve the full URL to a
few eLTER sites. Sites can be selected by country name, site name or
both. Note that partial matching is also supported. So
country_name = "Austri" will find sites in Austria, but not Australia.
eisen <- get_ilter_generalinfo(country = "Austria", site_name = "LTSER Platform Eisen")
eisen_deimsid <- eisen$uri
# Using abbreviated 'United K' to differentiate from United States
cairngorms <- get_ilter_generalinfo(country = "United K", site_name = "Cairngorms National")
cairngorms_deimsid <- cairngorms$uriIn ReLTER there are functions to grab metadata for the sites. Metadata
is available for a few categories.
These categories are available:
- ‘Affiliations’
- ‘Boundaries’ (spatial layer)
- ‘Contacts’
- ‘EnvCharacts’ (environmental characteristics)
- ‘General’,
- ‘Infrastructure’
- ‘Parameters’ (which parameters are collected)
- ‘RelateRes’ (related research)
- ‘ResearchTop’ (research topics)
Here are a few basic examples:
response <- get_site_info(eisen_deimsid, category = "ResearchTop")
response$researchTopics## [[1]]
## researchTopicsLabel researchTopicsUri
## 1 agriculture http://vocabs.lter-europe.net/EnvThes/21605
## 2 air chemistry http://vocabs.lter-europe.net/EnvThes/21657
## 3 animal ecology http://vocabs.lter-europe.net/EnvThes/71
## 4 aquaculture http://vocabs.lter-europe.net/EnvThes/30003
## 5 biodiversity http://vocabs.lter-europe.net/EnvThes/21673
## 6 biogeochemistry http://vocabs.lter-europe.net/EnvThes/21609
## 7 biology http://vocabs.lter-europe.net/EnvThes/21611
## 8 climate change http://vocabs.lter-europe.net/EnvThes/21754
## 9 climate monitoring http://vocabs.lter-europe.net/EnvThes/21757
## 10 community dynamics http://vocabs.lter-europe.net/EnvThes/21680
## 11 conservation http://vocabs.lter-europe.net/EnvThes/21663
## 12 ecophysiology http://vocabs.lter-europe.net/EnvThes/21652
## 13 ecosystem ecology http://vocabs.lter-europe.net/EnvThes/21689
## 14 ecosystem function http://vocabs.lter-europe.net/EnvThes/20519
## 15 ecosystem service http://vocabs.lter-europe.net/EnvThes/20520
## 16 forest ecology http://vocabs.lter-europe.net/EnvThes/21693
## 17 geology http://vocabs.lter-europe.net/EnvThes/21740
## 18 hydrology http://vocabs.lter-europe.net/EnvThes/21747
## 19 lake ecology http://vocabs.lter-europe.net/EnvThes/69
## 20 land use history http://vocabs.lter-europe.net/EnvThes/21744
## 21 landscape ecology http://vocabs.lter-europe.net/EnvThes/21703
## 22 limnology http://vocabs.lter-europe.net/EnvThes/21749
## 23 long term ecological research http://vocabs.lter-europe.net/EnvThes/21751
## 24 microbial ecology http://vocabs.lter-europe.net/EnvThes/77
## 25 natural science http://vocabs.lter-europe.net/EnvThes/30031
## 26 physiology http://vocabs.lter-europe.net/EnvThes/21651
## 27 plant ecology http://vocabs.lter-europe.net/EnvThes/21710
## 28 plant physiology http://vocabs.lter-europe.net/EnvThes/21653
## 29 silviculture http://vocabs.lter-europe.net/EnvThes/21608
## 30 social sciences http://vocabs.lter-europe.net/EnvThes/30006
## 31 sociology http://vocabs.lter-europe.net/EnvThes/30012
## 32 soil chemistry http://vocabs.lter-europe.net/EnvThes/21660
## 33 species diversity http://vocabs.lter-europe.net/EnvThes/21679
## 34 stream ecology http://vocabs.lter-europe.net/EnvThes/72
## 35 terrestrial ecology http://vocabs.lter-europe.net/EnvThes/79
## 36 trophic dynamics http://vocabs.lter-europe.net/EnvThes/21682
## 37 vegetation dynamics http://vocabs.lter-europe.net/EnvThes/21711
response <- get_site_info(cairngorms_deimsid, category = "Affiliations")
response$affiliation.projects## [[1]]
## label
## 1 Natura 2000
## 2 Ramsar Convention on Wetlands
## 3 Teabag
## 4 Water Framework Directive (2000/60/EC)
## 5 eLTER (H2020)
## 6 eLTER catalogue
## uri
## 1 <NA>
## 2 https://www.ramsar.org
## 3 <NA>
## 4 https://ec.europa.eu/environment/water/water-framework/index_en.html
## 5 https://cordis.europa.eu/project/id/654359
## 6 <NA>
Now use the DEIMS ID, acquired above, to get the boundary of a site, by
setting category = "Boundaries".
# Acquire boundary for site
eisen_boundary <- get_site_info(eisen_deimsid, "Boundaries")
# Prepare OSM background tile and plot
osm <- read_osm(eisen_boundary, ext = 1.2)
tmap_mode("plot")## tmap mode set to plotting
# For interactive maps use: tmap_mode('view') Then these basemaps are
# available: tm_basemap('Stamen.TerrainBackground') +
# tm_basemap('OpenStreetMap') +
tm_shape(osm) + tm_rgb() + tm_shape(eisen_boundary) + tm_polygons(col = "skyblue",
alpha = 0.25, border.col = "blue")
The code below saves the boundary to a file for use in other GIS software.
# Edit here to choose your output directory
boundary_file <- file.path(Output_dir, "eisen_boundary.gpkg")
# Remove country column since it is a list (Some sites extend across country
# boundaries)
eisen_boundary <- subset(eisen_boundary, select = -country)
st_write(eisen_boundary, dsn = boundary_file, append = FALSE)## Deleting layer `eisen_boundary' using driver `GPKG'
## Writing layer `eisen_boundary' to data source
## `./Output/eisen_boundary.gpkg' using driver `GPKG'
## Writing 1 features with 7 fields and geometry type Polygon.
ReLTER relies on the DEIMS SDR database for all site queries.
Therefore, any errors or missing data will obviously be echoed in the
ReLTER results. These errors include:
- Missing information
- Duplicate names
- Missing boundary shapefile
Here are a few examples:
eisen_contact <- get_site_info(eisen_deimsid, "Contact")
names(eisen_contact)
## [1] "title" "uri" "geoCoord" "country" "geoElev.avg"
## [6] "geoElev.min" "geoElev.max" "geoElev.unit"
# No contact information :-(
kiskun <- get_ilter_generalinfo(country_name = "Hungary", site_name = "KISKUN LTER")
kiskun_deimsid <- kiskun$uri
length(kiskun_deimsid)
## [1] 8
# Multiple sites with similar name :-( Which to choose? View the list...
kiskun$title
## [1] "Kiskun Forest Reserve Sites, KISKUN LTER - Hungary"
## [2] "VULCAN Kiskunsag, KISKUN LTER - Hungary"
## [3] "Kiskun Restoration Experiments, KISKUN LTER - Hungary"
## [4] "Kiskun Site Network (Jedlik), KISKUN LTER - Hungary"
## [5] "KISKUN LTER - Hungary"
## [6] "LTER Fulophaza Site, KISKUN LTER - Hungary"
## [7] "Bugac-Bocsa-Orgovany Site, KISKUN LTER - Hungary"
## [8] "Orgovany Site, KISKUN LTER - Hungary"
kiskun_deimsid <- kiskun$uri[5]
length(kiskun_deimsid)
## [1] 1
kiskun_boundary <- get_site_info(kiskun_deimsid, "Boundaries")
##
## ----
## This site does not have boundaries uploaded to DEIMS-SDR.
## Please verify in the site page: https://deims.org/124f227a-787d-4378-bc29-aa94f29e1732
## ----
# Oops, no boundary for this site!The recent efforts by the Geoharmonizer program have resulted in a consolidated set of freely available raster data (gridded) datasets. All rasters are formatted as Cloud Optimzed Geotiff (COG). These can be viewed on the ODS web portal.
The code below demonstrates how to access various data from ODS from within R, and to clip to site boundaries. The datasets currently implemented are:
- “landcover” (Landcover at 30 meter resolution from Landsat)
- “clc2018” (Corine landcover from 2018)
- “osm_buildings” (Open Street Maps buildings)
- “natura2000”
- “ndvi_spring”
- “ndvi_summer”
- “ndvi_autumn”
- “ndvi_winter”
# Use boundary and OSM tile from above
eisen_landcover <- get_site_ODS(eisen_deimsid, "landcover")
tm_shape(osm) + tm_rgb() + tm_shape(eisen_landcover) + tm_raster(style = "pretty",
palette = "RdYlBu", alpha = 0.75)## stars object downsampled to 1089 by 918 cells. See tm_shape manual (argument raster.downsample)
eisen_corine <- get_site_ODS(eisen_deimsid, "clc2018")
tm_shape(osm) + tm_rgb() + tm_shape(eisen_corine) + tm_raster(style = "pretty", palette = "Spectral",
alpha = 0.75)## stars object downsampled to 1089 by 918 cells. See tm_shape manual (argument raster.downsample)
# Takes several minutes
eisen_ndvi <- get_site_ODS(eisen_deimsid, "ndvi_spring")
tm_shape(osm) + tm_rgb() + tm_shape(eisen_ndvi) + tm_raster(style = "pretty", palette = "RdYlGn",
alpha = 0.75)## stars object downsampled to 1089 by 918 cells. See tm_shape manual (argument raster.downsample)
## Variable(s) "NA" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
ODS data layers are at 30 meter resolution, suitable for small sites. This code examines the Tereno site at Harsleben.
# Acquire Tereno DEIMS ID and boundary
tereno <- get_ilter_generalinfo(country_name = "Germany", site_name = "Tereno - Harsleben")
tereno_deimsid <- tereno$uri
tereno_boundary <- get_site_info(tereno_deimsid, "Boundaries")
# Prepare new OSM background and plot
osm <- read_osm(tereno_boundary, ext = 1.2)
tereno_ndvi <- get_site_ODS(tereno_deimsid, "ndvi_autumn")
tm_shape(osm) + tm_rgb() + tm_shape(tereno_ndvi) + tm_raster(style = "pretty", palette = "RdYlGn",
alpha = 0.75)
Again compare Landsat based landcover (30 m.) with Corine 2018 (100 m.)
tereno_landcover <- get_site_ODS(tereno_deimsid, "landcover")
tm_shape(osm) + tm_rgb() + tm_shape(tereno_landcover) + tm_raster(style = "pretty",
palette = "Spectral", alpha = 0.75)
tereno_corine <- get_site_ODS(tereno_deimsid, "clc2018")
tm_shape(osm) + tm_rgb() + tm_shape(tereno_corine) + tm_raster(style = "pretty",
palette = "Spectral", alpha = 0.75)
# Edit here to choose your output directory
landcover_file <- file.path(Output_dir, "tereno_landcover.tif")
writeRaster(tereno_landcover, landcover_file, overwrite = TRUE)This code chunk shows how to acquire OpenStreetMap building footprints
from the ODS datasets. OSM building footprints are available by setting
the dataset option to “osm_buildings”. Here is an example for the
Saldur river basin in Italy.
saldur <- get_ilter_generalinfo(country = "Italy", site_name = "Saldur River Catchment")
saldur_deimsid <- saldur$uri
saldurRiver_osmLandUse <- get_site_ODS(deimsid = saldur_deimsid, dataset = "osm_buildings")
saldur_boundary <- get_site_info(deimsid = saldur_deimsid, "Boundaries")
# Prepare OSM background tile and plot
osm <- read_osm(saldur_boundary, ext = 1.2)
# Hillshade of Saldur river bounding box
saldur_hs <- raster::raster("ReLTER_demo_files/saldur_hillshade.tif")
tm_shape(osm) + tm_rgb() + tm_compass(type = "arrow", position = c("right", "bottom"),
text.size = 1) + tm_scale_bar(position = c(0.6, "bottom"), text.size = 0.8) +
tm_credits("Data from lcv building Copernicus", position = c("left", "top"),
size = 1) + tm_layout(legend.position = c("left", "bottom")) + tm_shape(saldur_hs) +
tm_raster(palette = "-Greys", style = "cont", legend.show = FALSE, alpha = 0.4) +
tm_shape(saldur_boundary) + tm_polygons(col = "skyblue", alpha = 0.2, border.col = "gray") +
tm_shape(saldurRiver_osmLandUse) + tm_raster(style = "cont") + tm_layout(legend.outside = TRUE)
This capability of ReLTER relies on another package MODIStsp L.
Busetto, L. Ranghetti (2016) MODIStsp: An R package for automatic
preprocessing of MODIS Land Products time series, Computers &
Geosciences, Volume 97, Pages 40-48, ISSN 0098-3004,
https://doi.org/10.1016/j.cageo.2016.08.020. URL
https://github.com/ropensci/MODIStsp/.
get_site_MODIS(show_products = TRUE)## [1] "Surf_Ref_8Days_500m (M*D09A1)"
## [2] "Surf_Ref_Daily_005dg (M*D09CMG)"
## [3] "Surf_Ref_Daily_500m (M*D09GA)"
## [4] "Surf_Ref_Daily_250m (M*D09GQ)"
## [5] "Surf_Ref_8Days_250m (M*D09Q1)"
## [6] "Ocean_Ref_Daily_1Km (M*DOCGA)"
## [7] "Therm_Daily_1Km (M*DTBGA)"
## [8] "Snow_Cov_Daily_500m (M*D10A1)"
## [9] "Snow_Cov_8-Day_500m (M*D10_A2)"
## [10] "Snow_Cov_Day_0.05Deg (M*D10C1)"
## [11] "Snow_Cov_8-Day0.05Deg CMG (M*D10C2)"
## [12] "Snow_Cov_Month_0.05Deg CMG (M*D10CM)"
## [13] "Surf_Temp_Daily_005dg (M*D11C1)"
## [14] "Surf_Temp_Daily_1Km (M*D11A1)"
## [15] "Surf_Temp_8Days_1Km (M*D11A2)"
## [16] "Surf_Temp_Daily_GridSin (M*D11B1)"
## [17] "Surf_Temp_8Days_GridSin (M*D11B2)"
## [18] "Surf_Temp_Monthly_GridSin (M*D11B3)"
## [19] "Surf_Temp_8Days_005dg (M*D11C2)"
## [20] "Surf_Temp_Monthly_005dg (M*D11C3)"
## [21] "LST_3band_emissivity_Daily_1km (M*D21A1D)"
## [22] "LST_3band_emissivity_Daily_1km_night (M*D21A1N)"
## [23] "LST_3band_emissivity_8day_1km (M*D21A2)"
## [24] "BRDF_Albedo_ModelPar_Daily_500m (MCD43A1)"
## [25] "BRDF_Albedo_Quality_Daily_500m (MCD43A2)"
## [26] "Albedo_Daily_500m (MCD43A3)"
## [27] "BRDF_Adj_Refl_Daily_500m (MCD43A4)"
## [28] "BRDF_Albedo_ModelPar_Daily_005dg (MCD43C1)"
## [29] "BRDF_Albedo_Quality_Daily_005dg (MCD43C2)"
## [30] "Albedo_Daily_005dg (MCD43C3)"
## [31] "BRDF_Adj_Refl_16Day_005dg (MCD43C4)"
## [32] "AlbPar_1_B1_Daily_30ArcSec (MCD43D01)"
## [33] "AlbPar_2_B1_Daily_30ArcSec (MCD43D02)"
## [34] "AlbPar_3_B1_Daily_30ArcSec (MCD43D03)"
## [35] "AlbPar_1_B2_Daily_30ArcSec (MCD43D04)"
## [36] "AlbPar_2_B2_Daily_30ArcSec (MCD43D05)"
## [37] "AlbPar_3_B2_Daily_30ArcSec (MCD43D06)"
## [38] "AlbPar_1_B3_Daily_30ArcSec (MCD43D07)"
## [39] "AlbPar_2_B3_Daily_30ArcSec (MCD43D08)"
## [40] "AlbPar_3_B3_Daily_30ArcSec (MCD43D09)"
## [41] "AlbPar_1_B4_Daily_30ArcSec (MCD43D10)"
## [42] "AlbPar_2_B4_Daily_30ArcSec (MCD43D11)"
## [43] "AlbPar_3_B4_Daily_30ArcSec (MCD43D12)"
## [44] "AlbPar_1_B4_Daily_30ArcSec (MCD43D13)"
## [45] "AlbPar_2_B4_Daily_30ArcSec (MCD43D14)"
## [46] "AlbPar_3_B4_Daily_30ArcSec (MCD43D15)"
## [47] "AlbPar_1_B5_Daily_30ArcSec (MCD43D16)"
## [48] "AlbPar_2_B5_Daily_30ArcSec (MCD43D17)"
## [49] "AlbPar_3_B5_Daily_30ArcSec (MCD43D18)"
## [50] "AlbPar_1_B6_Daily_30ArcSec (MCD43D19)"
## [51] "AlbPar_2_B6_Daily_30ArcSec (MCD43D20)"
## [52] "AlbPar_3_B6_Daily_30ArcSec (MCD43D21)"
## [53] "AlbPar_1_Vis_Daily_30ArcSec (MCD43D22)"
## [54] "AlbPar_2_Vis_Daily_30ArcSec (MCD43D23)"
## [55] "AlbPar_3_Vis_Daily_30ArcSec (MCD43D24)"
## [56] "AlbPar_1_NIR_Daily_30ArcSec (MCD43D25)"
## [57] "AlbPar_2_NIR_Daily_30ArcSec (MCD43D26)"
## [58] "AlbPar_3_NIR_Daily_30ArcSec (MCD43D27)"
## [59] "AlbPar_1_SWIR_Daily_30ArcSec (MCD43D28)"
## [60] "AlbPar_2_SWIR_Daily_30ArcSec (MCD43D29)"
## [61] "AlbPar_3_SWIR_Daily_30ArcSec (MCD43D30)"
## [62] "BRDF_Albedo_Quality_Daily_30ArcSec (MCD43D31)"
## [63] "BRDF_Albedo_SolNoon_Daily_30ArcSec (MCD43D32)"
## [64] "Alb_ValObs_B1_Daily_30ArcSec (MCD43D33)"
## [65] "Alb_ValObs_B2_Daily_30ArcSec (MCD43D34)"
## [66] "Alb_ValObs_B3_Daily_30ArcSec (MCD43D35)"
## [67] "Alb_ValObs_B4_Daily_30ArcSec (MCD43D36)"
## [68] "Alb_ValObs_B5_Daily_30ArcSec (MCD43D37)"
## [69] "Alb_ValObs_B6_Daily_30ArcSec (MCD43D38)"
## [70] "Alb_ValObs_B7_Daily_30ArcSec (MCD43D39)"
## [71] "BRDF_Albedo_Snow_Daily_30ArcSec (MCD43D40)"
## [72] "BRDF_Alb_Unc_Daily_30ArcSec (MCD43D41)"
## [73] "BRDF_Alb_BSA_B1_Daily_30ArcSec (MCD43D42)"
## [74] "BRDF_Alb_BSA_B2_Daily_30ArcSec (MCD43D43)"
## [75] "BRDF_Alb_BSA_B3_Daily_30ArcSec (MCD43D44)"
## [76] "BRDF_Alb_BSA_B4_Daily_30ArcSec (MCD43D45)"
## [77] "BRDF_Alb_BSA_B5_Daily_30ArcSec (MCD43D46)"
## [78] "BRDF_Alb_BSA_B6_Daily_30ArcSec (MCD43D47)"
## [79] "BRDF_Alb_BSA_B7_Daily_30ArcSec (MCD43D48)"
## [80] "BRDF_Alb_BSA_Vis_Daily_30ArcSec (MCD43D49)"
## [81] "BRDF_Alb_BSA_NIR_Daily_30ArcSec (MCD43D50)"
## [82] "BRDF_Alb_BSA_SWIR_Daily_30ArcSec (MCD43D51)"
## [83] "BRDF_Alb_WSA_B1_Daily_30ArcSec (MCD43D52)"
## [84] "BRDF_Alb_WSA_B2_Daily_30ArcSec (MCD43D53)"
## [85] "BRDF_Alb_WSA_B3_Daily_30ArcSec (MCD43D54)"
## [86] "BRDF_Alb_WSA_B4_Daily_30ArcSec (MCD43D55)"
## [87] "BRDF_Alb_WSA_B5_Daily_30ArcSec (MCD43D56)"
## [88] "BRDF_Alb_WSA_B6_Daily_30ArcSec (MCD43D57)"
## [89] "BRDF_Alb_WSA_B7_Daily_30ArcSec (MCD43D58)"
## [90] "BRDF_Alb_WSA_Vis_Daily_30ArcSec (MCD43D59)"
## [91] "BRDF_Alb_WSA_NIR_Daily_30ArcSec (MCD43D60)"
## [92] "BRDF_Alb_WSA_SWIR_Daily_30ArcSec (MCD43D61)"
## [93] "BRDF_Albedo_NBAR_Band1_Daily_30ArcSec (MCD43D62)"
## [94] "BRDF_Albedo_NBAR_Band2_Daily_30ArcSec (MCD43D63)"
## [95] "BRDF_Albedo_NBAR_Band3_Daily_30ArcSec (MCD43D64)"
## [96] "BRDF_Albedo_NBAR_Band4_Daily_30ArcSec (MCD43D65)"
## [97] "BRDF_Albedo_NBAR_Band5_Daily_30ArcSec (MCD43D66)"
## [98] "BRDF_Albedo_NBAR_Band6_Daily_30ArcSec (MCD43D67)"
## [99] "BRDF_Albedo_NBAR_Band7_Daily_30ArcSec (MCD43D68)"
## [100] "Vegetation_Indexes_16Days_500m (M*D13A1)"
## [101] "Vegetation_Indexes_16Days_1Km (M*D13A2)"
## [102] "Vegetation_Indexes_Monthly_1Km (M*D13A3)"
## [103] "Vegetation_Indexes_16Days_005dg (M*D13C1)"
## [104] "Vegetation_Indexes_Monthly_005dg (M*D13C2)"
## [105] "Vegetation Indexes_16Days_250m (M*D13Q1)"
## [106] "LAI_8Days_500m (MCD15A2H)"
## [107] "LAI_4Days_500m (MCD15A3H)"
## [108] "LAI_8Days_500m (M*D15A2H)"
## [109] "Net_ET_8Day_500m (M*D16A2)"
## [110] "Net_ETgf_8Day_500m (M*D16A2GF)"
## [111] "Net_ETgf_Yearly_500m (M*D16A3GF)"
## [112] "Gross_PP_8Days_500m (M*D17A2H)"
## [113] "Gross_PP_GapFil_8Days_500m (M*D17A2HGF)"
## [114] "Net_PP_GapFil_Yearly_500m (M*D17A3HGF)"
## [115] "Veg_Cont_Fields_Yearly_250m (MOD44B)"
## [116] "Land_Wat_Mask_Yearly_250m (MOD44W)"
## [117] "Burned_Monthly_500m (MCD64A1)"
## [118] "ThermalAn_Fire_Daily_1Km (M*D14A1)"
## [119] "ThermalAn_Fire_8Days_1Km (M*D14A2)"
## [120] "LandCover_Type_Yearly_005dg (MCD12C1)"
## [121] "LandCover_Type_Yearly_500m (MCD12Q1)"
## [122] "LandCover_Dynamics_Yearly_500m (MCD12Q2)"
## [123] "Dwnwrd_Srw_Rad_3h_005dg (MCD18A1)"
## [124] "Dwnwrd_PAR_3h_005dg (MCD18A2)"
## [125] "MAIA_Land_Surf_BRF (MCD19A1)"
## [126] "MAIA_Land_AOT_daily (MCD19A2)"
## NULL
get_site_MODIS(show_bands = "Vegetation_Indexes_Monthly_1Km (M*D13A3)")## [1] "NDVI" "EVI" "VI_QA" "b1_Red" "b2_NIR" "b3_Blue"
## [7] "b7_SWIR" "View_Zen" "Sun_Zen" "Rel_Az" "Rely"
## NULL
get_site_MODIS(show_bands = "LST_3band_emissivity_8day_1km (M*D21A2)")## [1] "LST_Day_1KM" "QC_Day" "View_Angle_Day" "View_Time_Day"
## [5] "LST_Night_1KM" "QC_Night" "View_Angle_Night" "View_Time_Night"
## [9] "Emis_29" "Emis_31" "Emis_32"
## NULL
To acquire MODIS data you must be registered at: https://urs.earthdata.nasa.gov/ Then use your earthdata username and password in the code below
# Username and password saved in advance For example:
# creds <- list('username'='homer', password='Simpsons') saveRDS(creds,
# 'earthdata_credentials.rds)
# Then...
creds <- readRDS("~/work/EU_Projects/EO_demo/earthdata_credentials.rds")# Set which product and which bands to get
product = "Vegetation_Indexes_Monthly_1Km (M*D13A3)"
bands = "NDVI" # Also EVI is available
eisen_ndvi <- get_site_MODIS(eisen_deimsid, # Which site
earthdata_user = creds$username, # From above
earthdata_passwd = creds$password,
product = product,
bands = bands,
from_date = "2020.01.01",
to_date = "2020.12.31", # From-To dates
scale = TRUE, # Rescale COG back to real values
out_folder = Output_dir, # Where to save outputs
save_ts_dir = Output_dir
)
# Plot was saved to:
plot_file <- paste("time_series", paste(bands, collapse="_"), sep="_")
plot_path <- file.path(Output_dir, paste0(plot_file, ".png"))
knitr::include_graphics(plot_path)