Accessing NASA data
Ivan Lizarazo
12 de marzo de 2017
The nasadata R package
This package, written by Eduardo Flores, provides an attractive way to access some of NASA’s open-source API’s to build applications or models. This document is based on the package vignette.
The package is built around three API’s, but for the sake of clarity, the first two are grouped into one example.
EONET Webservice
The Earth Observatory Natural Event Tracker is a Webservice that feeds curated natural “events” that are tracked by a few sources.
An event is something like a storm, fire, drought, etc. So, let’s suppose we want to see some event that occured a few days ago.
Let’s start by installing the package.
#install.packages("devtools")
#devtools::install_github("Eflores89/nasadata")
#install.packages("nasadata")
#install.packages("DBI")
library(nasadata)Including Plots
Now, we can query the webservice to find all of the available ones like this:
categories <- eonet_categories()categories[,1:2]## id title
## 1 6 Drought
## 2 7 Dust and Haze
## 3 16 Earthquakes
## 4 9 Floods
## 5 14 Landslides
## 6 19 Manmade
## 7 15 Sea and Lake Ice
## 8 10 Severe Storms
## 9 17 Snow
## 10 18 Temperature Extremes
## 11 12 Volcanoes
## 12 13 Water Color
## 13 8 WildfiresThe sources reporting these events are very important, so we can see this with a similar function:
sources <- eonet_sources()
# there are a few columns, I'll only show two...
names(sources)## [1] "id" "title" "source" "link"# [1] "id" "title" "source" "link"
sources[,1:2]## id title
## 1 AU_BOM Australia Bureau of Meteorology
## 2 BCWILDFIRE British Columbia Wildfire Service
## 3 CALFIRE California Department of Forestry and Fire Protection
## 4 CEMS Copernicus Emergency Management Service
## 5 EO Earth Observatory
## 6 GDACS Global Disaster Alert and Coordination System
## 7 GLIDE GLobal IDEntifier Number (GLIDE)
## 8 InciWeb InciWeb
## 9 IDC International Charter on Space and Major Disasters
## 10 MRR LANCE Rapid Response
## 11 NASA_ESRS NASA Earth Science and Remote Sensing Unit
## 12 PDC Pacific Disaster Center
## 13 ReliefWeb ReliefWeb
## 14 SIVolcano Smithsonian Institution Global Volcanism Program
## 15 NATICE U.S. National Ice Center
## 16 UNISYS Unisys Weather
## 17 USGS_CMT USGS Emergency Operations Collection Management Tool
## 18 HDDS USGS Hazards Data Distribution System
## 19 DFES_WA Western Australia Department of Fire and Emergency ServicesNow that we got this, let’s see if we can get several individual events. The earth_event() function does this in an intuitive way. We are going to bring the latest five events reported by InciWeb:
five_events <- earth_event(status = "all",
sources = "InciWeb",
category_id = "all",
limit = 5,
LimitType = "limit")
class(five_events)## [1] "list"names(five_events)## [1] "Events" "Sources" "Categories" "Geography" "Meta"five_events$Events$event_id## [1] "EONET_2745" "EONET_2744" "EONET_2740" "EONET_2743" "EONET_2732"five_events$Events$event_title## [1] "283 Fire, OKLAHOMA"
## [2] "Starbuck Fire, OKLAHOMA"
## [3] "Perryton Fire , TEXAS"
## [4] "Northwest Oklahoma Complex Fire, OKLAHOMA"
## [5] "Rael Fire, NEW MEXICO"five_events$Sources$source_url## [1] "https://inciweb.nwcg.gov/incident/5136/"
## [2] "https://inciweb.nwcg.gov/incident/5135/"
## [3] "https://inciweb.nwcg.gov/incident/5133/"
## [4] "https://inciweb.nwcg.gov/incident/5138/"
## [5] "https://inciweb.nwcg.gov/incident/5132/"five_events$Geography## $EONET_2745
## date type coordinates
## 1 2017-03-07T01:30:00Z Point -99.83722, 36.71000
##
## $EONET_2744
## date type coordinates
## 1 2017-03-07T00:30:00Z Point -100.17528, 36.77444
##
## $EONET_2740
## date type coordinates
## 1 2017-03-06T16:00:00Z Point -100.53472, 36.09306
##
## $EONET_2743
## date type coordinates
## 1 2017-03-06T12:30:00Z Point -100.17528, 36.44111
##
## $EONET_2732
## date type coordinates
## 1 2017-03-02T11:30:00Z Point -108.3394, 33.8000Imagery and Assets API’s
The Earth Imagery API lets users access imagery that is being retrieved from Landsat 8 Satellites and stored in Google Earth Engine. From what we will see, the images are poor quality but one obvious solution here is to obtain before and after images and detect large variations from them.
The Assets API is basically a helper for the Imagery API: it gives us dates of available coordinates, so that we can query the latter and retrieve the image.
Let’s see if we can see one of the “five events” we recorded earlier. specifically the one identified as EONET_2732
First, we want to see if there is any images recorded in that time frame (the Landsat frecuency is roughly every 16 days).
# coordinates of event
coordenadas <- five_events$Geography
coordenadas$EONET_2732$coordinates[[1]]## [1] -108.3394 33.8000coord_long <- coordenadas$EONET_2732$coordinates[[1]][1]
coord_lat <- coordenadas$EONET_2732$coordinates[[1]][2]
coordenadas$EONET_2732$date## [1] "2017-03-02T11:30:00Z"In the following code the key must correspond to your NASA API key. It’s free and you can get it at api.nasa.gov
# images available
images <- earth_asset(key,
lon = coord_long,
lat = coord_lat,
start_date = "2016-03-01",
end_date = "2017-03-12")names(images)## [1] "date" "id" "type" "coordinates"images$date## [1] 2016-05-04T17:44:41 2016-03-01T17:45:21 2016-03-17T17:45:16
## [4] 2016-04-02T17:45:07 2016-04-18T17:45:01 2016-05-04T17:45:05
## [7] 2016-05-20T17:45:03 2016-06-05T17:45:08 2016-06-21T17:45:13
## [10] 2016-07-07T17:45:22 2016-07-23T17:45:27 2016-08-08T17:45:29
## [13] 2016-08-24T17:45:36 2016-09-09T17:45:41 2016-09-25T17:45:42
## [16] 2016-10-11T17:45:46 2016-10-27T17:45:49 2016-11-12T17:45:48
## [19] 2016-11-28T17:45:48 2016-12-14T17:45:44 2017-01-31T17:45:31
## [22] 2017-02-16T17:45:24 2017-03-04T17:45:17 2016-03-08T17:51:07
## [25] 2016-03-24T17:50:59 2016-04-09T17:50:52 2016-04-25T17:50:46
## [28] 2016-05-11T17:50:47 2016-05-27T17:50:53 2016-06-12T17:50:56
## [31] 2016-06-28T17:51:04 2016-07-14T17:51:12 2016-07-30T17:51:16
## [34] 2016-08-15T17:51:19 2016-08-31T17:51:26 2016-09-16T17:51:29
## [37] 2016-10-02T17:51:30 2016-10-18T17:51:35 2016-11-03T17:51:35
## [40] 2016-11-19T17:51:35 2016-12-05T17:51:34 2016-12-21T17:51:29
## [43] 2017-01-06T17:51:26 2017-01-22T17:51:22 2017-02-07T17:51:13
## [46] 2017-02-23T17:51:08 2016-03-08T17:51:31 2016-03-24T17:51:23
## [49] 2016-04-09T17:51:16 2016-04-25T17:51:10 2016-05-11T17:51:11
## [52] 2016-05-27T17:51:17 2016-06-12T17:51:20 2016-06-28T17:51:28
## [55] 2016-07-14T17:51:35 2016-07-30T17:51:39 2016-08-15T17:51:43
## [58] 2016-08-31T17:51:50 2016-09-16T17:51:53 2016-10-02T17:51:54
## [61] 2016-10-18T17:51:59 2016-11-03T17:51:59 2016-11-19T17:51:59
## [64] 2016-12-05T17:51:58 2016-12-21T17:51:53 2017-01-06T17:51:50
## [67] 2017-01-22T17:51:45 2017-02-07T17:51:37 2017-02-23T17:51:32
## 69 Levels: 2016-03-01T17:45:21 2016-03-08T17:51:07 ... 2017-03-04T17:45:17So, it seems we are lucky. The fire ocurred before the last picture taken. Thus, let’s see how we can download this last image.
img <- earth_image(key,
lon = coord_long,
lat = coord_lat,
date = "2017-03-04", plot = TRUE)
The above image has a poor spatial resolution. It’s very hard to recognize what is going on. Let’s try seeing an image before the event:
img <- earth_image(key,
lon = coord_long,
lat = coord_lat,
date = "2017-02-23", plot = TRUE)
What do you think? Let’s confirm what happened by seeing an older image:
img <- earth_image(key,
lon = coord_long,
lat = coord_lat,
date = "2017-01-06", plot = TRUE)
A bit of fun
Now, let’s see Barranquilla from the space:
coord_lat <- 10.90
coord_long <- -74.8
# images available
images <- earth_asset(key,
lon = coord_long,
lat = coord_lat,
start_date = "2016-03-01",
end_date = "2017-03-12")
images$date## [1] 2016-03-02T15:16:49 2016-03-18T15:16:44 2016-04-03T15:16:34
## [4] 2016-04-19T15:16:29 2016-05-05T15:16:32 2016-05-21T15:16:32
## [7] 2016-06-06T15:16:37 2016-06-22T15:16:41 2016-07-08T15:16:50
## [10] 2016-07-24T15:16:55 2016-08-09T15:16:57 2016-08-25T15:17:04
## [13] 2016-09-10T15:17:09 2016-09-26T15:17:10 2016-10-12T15:17:15
## [16] 2016-10-28T15:17:17 2016-11-13T15:17:16 2016-11-29T15:17:16
## [19] 2016-12-15T15:17:12 2016-12-31T15:17:09 2017-01-16T15:17:05
## [22] 2017-02-01T15:16:58 2017-02-17T15:16:51 2017-03-05T15:16:45
## [25] 2016-03-02T15:17:13 2016-03-18T15:17:08 2016-04-03T15:16:58
## [28] 2016-04-19T15:16:53 2016-05-05T15:16:56 2016-05-21T15:16:56
## [31] 2016-06-06T15:17:00 2016-06-22T15:17:05 2016-07-08T15:17:14
## [34] 2016-07-24T15:17:19 2016-08-09T15:17:21 2016-08-25T15:17:28
## [37] 2016-09-10T15:17:33 2016-09-26T15:17:33 2016-10-28T15:17:41
## [40] 2016-11-13T15:17:39 2016-11-29T15:17:40 2016-12-15T15:17:36
## [43] 2016-12-31T15:17:33 2017-01-16T15:17:29 2017-02-01T15:17:22
## [46] 2017-02-17T15:17:15 2017-03-05T15:17:09
## 47 Levels: 2016-03-02T15:16:49 2016-03-02T15:17:13 ... 2017-03-05T15:17:09img <- earth_image(key,
lon = coord_long,
lat = coord_lat,
date = "2017-03-05", plot = TRUE)
Let’s move a bit the location:
coord_lat <- 10.90
coord_long <- -74.9
mg <- earth_image(key,
lon = coord_long,
lat = coord_lat,
date = "2017-03-05", plot = TRUE)
No more for now. Thank you for your attention.