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README.md
In tidyrgee: 'tidyverse' Methods for 'Earth Engine'
tidyrgee
tidyrgee brings components of
dplyr’s syntax to remote sensing
analysis, using the rgee package.
rgee is an R-API for the Google Earth Engine
(GEE) which provides R support to the
methods/functions available in the JavaScript code editor and python
API. The rgee syntax was written to be very similar to the GEE
Javascript/python. However, this syntax can feel unnatural and difficult
at times especially to users with less experience in GEE. Simple
concepts that are easy express verbally can be cumbersome even to
advanced users (see Syntax Comparison). The tidyverse has provided
principals and
concepts that help
data scientists/R-users efficiently write and communicate there code in
a clear and concise manner. tidyrgee aims to bring these principals to
GEE-remote sensing analyses.
tidyrgee provides the convenience of pipe-able dplyr style methods such
as filter, group_by, summarise, select,mutate,etc. using
rlang’s style of non-standard
evaluation (NSE)
try it out!
Installation
You can install the development version of tidyrgee from
GitHub with:
# install.packages("devtools")
devtools::install_github("r-tidy-remote-sensing/tidyrgee")
It is important to note that to use tidyrgee you must be signed up for a
GEE developer account. Additionally you must install the rgee package
following there installation and setup instructions
here
Syntax Comparison
Below is a quick example demonstrating the simplified syntax. Note that
the rgee syntax is very similar to the syntax in the Javascript code
editor. In this example I want to simply calculate mean monthly NDVI
(per pixel) for every year from 2000-2015. This is clearly a fairly
simple analysis to verbalize/conceptualize. Yet, using using standard
GEE conventions, the process is not so simple. Aside, from many
peculiarities such as flattening a list and then calling and then
rebuilding the imageCollection at the end, I also have to write and
think about a double mapping statement using months and years (sort
of like a double for-loop). By comparison the tidyrgee syntax removes
the complexity and allows me to write the code in a more human
readable/interpretable format.
rgee (similar to Javascript)
tidyrgee
wzxhzdk:1
wzxhzdk:2
Example usage
Below are a couple examples showing some of the available functions.
To load images/imageCollections you follow the standard approach using
rgee:
- load libraries
- initialize the GEE session
- load
ee$ImageCollection/ ee$Image
library(tidyrgee)
library(rgee)
ee_Initialize(quiet = T)
modis_ic <- ee$ImageCollection("MODIS/006/MOD13Q1")
Once the above steps are performed you can convert the
ee$ImageCollection to a tidyee object with the function as_tidyee.
The tidyee object stores the original ee$ImageCollection as ee_ob
(for earth engine object) and produces as virtual table/data.frame
stored as vrt. This vrt not only facilitates the use of
dplyr/tidyverse methods, but also allows the user to better view the
data stored in the accompanying imageCollection. The ee_ob and vrt
inside the tidyee object are linked, any function applied to the tidyee
object will apply to them both so that they remain in sync.
modis_tidy <- as_tidyee(modis_ic)
the vrt comes with a few built in columns which you can use off the
bat for filtering and grouping, but you can also mutate additional
info for filtering and grouping (i.e using lubridate to create new
temporal groupings)
knitr::kable(modis_tidy$vrt |> head())
| id | time_start | system_index | date | month | year | doy | band_names |
|:-----------------------------|:-----------|:-------------|:-----------|------:|-----:|----:|:--------------------------------------------------------------------------------------------------------------------------------------------------------|
| MODIS/006/MOD13Q1/2000_02_18 | 2000-02-18 | 2000_02_18 | 2000-02-18 | 2 | 2000 | 49 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA |
| MODIS/006/MOD13Q1/2000_03_05 | 2000-03-05 | 2000_03_05 | 2000-03-05 | 3 | 2000 | 65 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA |
| MODIS/006/MOD13Q1/2000_03_21 | 2000-03-21 | 2000_03_21 | 2000-03-21 | 3 | 2000 | 81 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA |
| MODIS/006/MOD13Q1/2000_04_06 | 2000-04-06 | 2000_04_06 | 2000-04-06 | 4 | 2000 | 97 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA |
| MODIS/006/MOD13Q1/2000_04_22 | 2000-04-22 | 2000_04_22 | 2000-04-22 | 4 | 2000 | 113 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA |
| MODIS/006/MOD13Q1/2000_05_08 | 2000-05-08 | 2000_05_08 | 2000-05-08 | 5 | 2000 | 129 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA |
Next we demonstrate filtering by date, month, and year. The vrt and
ee_ob are always filtered together
- by date
modis_tidy |>
filter(date>="2021-06-01")
#> band names: [ NDVI, EVI, DetailedQA, sur_refl_b01, sur_refl_b02, sur_refl_b03, sur_refl_b07, ViewZenith, SolarZenith, RelativeAzimuth, DayOfYear, SummaryQA ]
#>
#> $ee_ob
#> EarthEngine Object: ImageCollection
#> $vrt
#> # A tibble: 28 x 9
#> id time_start syste~1 date month year doy band_~2
#> <chr> <dttm> <chr> <date> <dbl> <dbl> <dbl> <list>
#> 1 MODIS/006/M~ 2021-06-10 00:00:00 2021_0~ 2021-06-10 6 2021 161 <chr>
#> 2 MODIS/006/M~ 2021-06-26 00:00:00 2021_0~ 2021-06-26 6 2021 177 <chr>
#> 3 MODIS/006/M~ 2021-07-12 00:00:00 2021_0~ 2021-07-12 7 2021 193 <chr>
#> 4 MODIS/006/M~ 2021-07-28 00:00:00 2021_0~ 2021-07-28 7 2021 209 <chr>
#> 5 MODIS/006/M~ 2021-08-13 00:00:00 2021_0~ 2021-08-13 8 2021 225 <chr>
#> 6 MODIS/006/M~ 2021-08-29 00:00:00 2021_0~ 2021-08-29 8 2021 241 <chr>
#> 7 MODIS/006/M~ 2021-09-14 00:00:00 2021_0~ 2021-09-14 9 2021 257 <chr>
#> 8 MODIS/006/M~ 2021-09-30 00:00:00 2021_0~ 2021-09-30 9 2021 273 <chr>
#> 9 MODIS/006/M~ 2021-10-16 00:00:00 2021_1~ 2021-10-16 10 2021 289 <chr>
#> 10 MODIS/006/M~ 2021-11-01 00:00:00 2021_1~ 2021-11-01 11 2021 305 <chr>
#> # ... with 18 more rows, 1 more variable: tidyee_index <chr>, and abbreviated
#> # variable names 1: system_index, 2: band_names
#> # i Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
#>
#> attr(,"class")
#> [1] "tidyee"
- by year
modis_tidy |>
filter(year%in% 2010:2011)
#> band names: [ NDVI, EVI, DetailedQA, sur_refl_b01, sur_refl_b02, sur_refl_b03, sur_refl_b07, ViewZenith, SolarZenith, RelativeAzimuth, DayOfYear, SummaryQA ]
#>
#> $ee_ob
#> EarthEngine Object: ImageCollection
#> $vrt
#> # A tibble: 46 x 9
#> id time_start syste~1 date month year doy band_~2
#> <chr> <dttm> <chr> <date> <dbl> <dbl> <dbl> <list>
#> 1 MODIS/006/M~ 2010-01-01 00:00:00 2010_0~ 2010-01-01 1 2010 1 <chr>
#> 2 MODIS/006/M~ 2010-01-17 00:00:00 2010_0~ 2010-01-17 1 2010 17 <chr>
#> 3 MODIS/006/M~ 2010-02-02 00:00:00 2010_0~ 2010-02-02 2 2010 33 <chr>
#> 4 MODIS/006/M~ 2010-02-18 00:00:00 2010_0~ 2010-02-18 2 2010 49 <chr>
#> 5 MODIS/006/M~ 2010-03-06 00:00:00 2010_0~ 2010-03-06 3 2010 65 <chr>
#> 6 MODIS/006/M~ 2010-03-22 00:00:00 2010_0~ 2010-03-22 3 2010 81 <chr>
#> 7 MODIS/006/M~ 2010-04-07 00:00:00 2010_0~ 2010-04-07 4 2010 97 <chr>
#> 8 MODIS/006/M~ 2010-04-23 00:00:00 2010_0~ 2010-04-23 4 2010 113 <chr>
#> 9 MODIS/006/M~ 2010-05-09 00:00:00 2010_0~ 2010-05-09 5 2010 129 <chr>
#> 10 MODIS/006/M~ 2010-05-25 00:00:00 2010_0~ 2010-05-25 5 2010 145 <chr>
#> # ... with 36 more rows, 1 more variable: tidyee_index <chr>, and abbreviated
#> # variable names 1: system_index, 2: band_names
#> # i Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
#>
#> attr(,"class")
#> [1] "tidyee"
- by month
modis_tidy |>
filter(month%in% c(7,8))
#> band names: [ NDVI, EVI, DetailedQA, sur_refl_b01, sur_refl_b02, sur_refl_b03, sur_refl_b07, ViewZenith, SolarZenith, RelativeAzimuth, DayOfYear, SummaryQA ]
#>
#> $ee_ob
#> EarthEngine Object: ImageCollection
#> $vrt
#> # A tibble: 91 x 9
#> id time_start syste~1 date month year doy band_~2
#> <chr> <dttm> <chr> <date> <dbl> <dbl> <dbl> <list>
#> 1 MODIS/006/M~ 2000-07-11 00:00:00 2000_0~ 2000-07-11 7 2000 193 <chr>
#> 2 MODIS/006/M~ 2000-07-27 00:00:00 2000_0~ 2000-07-27 7 2000 209 <chr>
#> 3 MODIS/006/M~ 2000-08-12 00:00:00 2000_0~ 2000-08-12 8 2000 225 <chr>
#> 4 MODIS/006/M~ 2000-08-28 00:00:00 2000_0~ 2000-08-28 8 2000 241 <chr>
#> 5 MODIS/006/M~ 2001-07-12 00:00:00 2001_0~ 2001-07-12 7 2001 193 <chr>
#> 6 MODIS/006/M~ 2001-07-28 00:00:00 2001_0~ 2001-07-28 7 2001 209 <chr>
#> 7 MODIS/006/M~ 2001-08-13 00:00:00 2001_0~ 2001-08-13 8 2001 225 <chr>
#> 8 MODIS/006/M~ 2001-08-29 00:00:00 2001_0~ 2001-08-29 8 2001 241 <chr>
#> 9 MODIS/006/M~ 2002-07-12 00:00:00 2002_0~ 2002-07-12 7 2002 193 <chr>
#> 10 MODIS/006/M~ 2002-07-28 00:00:00 2002_0~ 2002-07-28 7 2002 209 <chr>
#> # ... with 81 more rows, 1 more variable: tidyee_index <chr>, and abbreviated
#> # variable names 1: system_index, 2: band_names
#> # i Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
#>
#> attr(,"class")
#> [1] "tidyee"
Putting a dplyr-like chain together:
In this next example we pipe together multiple functions (select,
filter, group_by, summarise) to
- select the
NDVI band from the ImageCollection
- filter the imageCollection to a desired date range
- group the filtered ImageCollection by month
- summarizing each pixel by year and month.
The result will be an ImageCollection with the one Image per month
(12 images) where each pixel in each image represents the average NDVI
value for that month calculated using monthly data from 2000 2015.
modis_tidy |>
select("NDVI") |>
filter(year %in% 2000:2015) |>
group_by(month) |>
summarise(stat= "mean")
#> band names: [ NDVI_mean ]
#>
#> $ee_ob
#> EarthEngine Object: ImageCollection
#> $vrt
#> # A tibble: 12 x 7
#> month dates_summ~1 numbe~2 time_start time_end date
#> <dbl> <list> <int> <dttm> <dttm> <date>
#> 1 1 <dttm [30]> 30 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01
#> 2 2 <dttm [31]> 31 2000-02-18 00:00:00 2000-02-18 00:00:00 2000-02-18
#> 3 3 <dttm [32]> 32 2000-03-05 00:00:00 2000-03-05 00:00:00 2000-03-05
#> 4 4 <dttm [32]> 32 2000-04-06 00:00:00 2000-04-06 00:00:00 2000-04-06
#> 5 5 <dttm [32]> 32 2000-05-08 00:00:00 2000-05-08 00:00:00 2000-05-08
#> 6 6 <dttm [32]> 32 2000-06-09 00:00:00 2000-06-09 00:00:00 2000-06-09
#> 7 7 <dttm [32]> 32 2000-07-11 00:00:00 2000-07-11 00:00:00 2000-07-11
#> 8 8 <dttm [32]> 32 2000-08-12 00:00:00 2000-08-12 00:00:00 2000-08-12
#> 9 9 <dttm [32]> 32 2000-09-13 00:00:00 2000-09-13 00:00:00 2000-09-13
#> 10 10 <dttm [20]> 20 2000-10-15 00:00:00 2000-10-15 00:00:00 2000-10-15
#> 11 11 <dttm [28]> 28 2000-11-16 00:00:00 2000-11-16 00:00:00 2000-11-16
#> 12 12 <dttm [32]> 32 2000-12-02 00:00:00 2000-12-02 00:00:00 2000-12-02
#> # ... with 1 more variable: band_names <list>, and abbreviated variable names
#> # 1: dates_summarised, 2: number_images
#> # i Use `colnames()` to see all variable names
#>
#> attr(,"class")
#> [1] "tidyee"
You can easily group_by more than 1 property to calculate different
summary stats. Below we
- filter to only data from 2021-2022
- group by year, month and calculate the median NDVI pixel value
As we are using the MODIS 16-day composite we summarising approximately
2 images per month to create median composite image fo reach month in
the specified years. The vrt holds a list-col containing all the
dates summarised per new composite image.
modis_tidy |>
select("NDVI") |>
filter(year %in% 2021:2022) |>
group_by(year,month) |>
summarise(stat= "median")
#> band names: [ NDVI_median ]
#>
#> $ee_ob
#> EarthEngine Object: ImageCollection
#> $vrt
#> # A tibble: 20 x 8
#> year month dates_summarised number~1 time_start time_end
#> <dbl> <dbl> <list> <int> <dttm> <dttm>
#> 1 2021 1 <dttm [2]> 2 2021-01-01 00:00:00 2021-01-01 00:00:00
#> 2 2021 2 <dttm [2]> 2 2021-02-02 00:00:00 2021-02-02 00:00:00
#> 3 2021 3 <dttm [2]> 2 2021-03-06 00:00:00 2021-03-06 00:00:00
#> 4 2021 4 <dttm [2]> 2 2021-04-07 00:00:00 2021-04-07 00:00:00
#> 5 2021 5 <dttm [2]> 2 2021-05-09 00:00:00 2021-05-09 00:00:00
#> 6 2021 6 <dttm [2]> 2 2021-06-10 00:00:00 2021-06-10 00:00:00
#> 7 2021 7 <dttm [2]> 2 2021-07-12 00:00:00 2021-07-12 00:00:00
#> 8 2021 8 <dttm [2]> 2 2021-08-13 00:00:00 2021-08-13 00:00:00
#> 9 2021 9 <dttm [2]> 2 2021-09-14 00:00:00 2021-09-14 00:00:00
#> 10 2021 10 <dttm [1]> 1 2021-10-16 00:00:00 2021-10-16 00:00:00
#> 11 2021 11 <dttm [2]> 2 2021-11-01 00:00:00 2021-11-01 00:00:00
#> 12 2021 12 <dttm [2]> 2 2021-12-03 00:00:00 2021-12-03 00:00:00
#> 13 2022 1 <dttm [2]> 2 2022-01-01 00:00:00 2022-01-01 00:00:00
#> 14 2022 2 <dttm [2]> 2 2022-02-02 00:00:00 2022-02-02 00:00:00
#> 15 2022 3 <dttm [2]> 2 2022-03-06 00:00:00 2022-03-06 00:00:00
#> 16 2022 4 <dttm [2]> 2 2022-04-07 00:00:00 2022-04-07 00:00:00
#> 17 2022 5 <dttm [2]> 2 2022-05-09 00:00:00 2022-05-09 00:00:00
#> 18 2022 6 <dttm [2]> 2 2022-06-10 00:00:00 2022-06-10 00:00:00
#> 19 2022 7 <dttm [2]> 2 2022-07-12 00:00:00 2022-07-12 00:00:00
#> 20 2022 8 <dttm [1]> 1 2022-08-13 00:00:00 2022-08-13 00:00:00
#> # ... with 2 more variables: date <date>, band_names <list>, and abbreviated
#> # variable name 1: number_images
#> # i Use `colnames()` to see all variable names
#>
#> attr(,"class")
#> [1] "tidyee"
To improve interoperability with rgee we have included the as_ee
function to return the tidyee object back to rgee classes when
necessary
modis_ic <- modis_tidy |> as_ee()
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tidyrgee
tidyrgee brings components of dplyr’s syntax to remote sensing analysis, using the rgee package.
rgee is an R-API for the Google Earth Engine
(GEE) which provides R support to the
methods/functions available in the JavaScript code editor and python
API. The rgee syntax was written to be very similar to the GEE
Javascript/python. However, this syntax can feel unnatural and difficult
at times especially to users with less experience in GEE. Simple
concepts that are easy express verbally can be cumbersome even to
advanced users (see Syntax Comparison). The tidyverse has provided
principals and
concepts that help
data scientists/R-users efficiently write and communicate there code in
a clear and concise manner. tidyrgee aims to bring these principals to
GEE-remote sensing analyses.
tidyrgee provides the convenience of pipe-able dplyr style methods such
as filter, group_by, summarise, select,mutate,etc. using
rlang’s style of non-standard
evaluation (NSE)
try it out!
Installation
You can install the development version of tidyrgee from GitHub with:
# install.packages("devtools")
devtools::install_github("r-tidy-remote-sensing/tidyrgee")
It is important to note that to use tidyrgee you must be signed up for a GEE developer account. Additionally you must install the rgee package following there installation and setup instructions here
Syntax Comparison
Below is a quick example demonstrating the simplified syntax. Note that
the rgee syntax is very similar to the syntax in the Javascript code
editor. In this example I want to simply calculate mean monthly NDVI
(per pixel) for every year from 2000-2015. This is clearly a fairly
simple analysis to verbalize/conceptualize. Yet, using using standard
GEE conventions, the process is not so simple. Aside, from many
peculiarities such as flattening a list and then calling and then
rebuilding the imageCollection at the end, I also have to write and
think about a double mapping statement using months and years (sort
of like a double for-loop). By comparison the tidyrgee syntax removes
the complexity and allows me to write the code in a more human
readable/interpretable format.
Example usage
Below are a couple examples showing some of the available functions.
To load images/imageCollections you follow the standard approach using
rgee:
- load libraries
- initialize the GEE session
- load
ee$ImageCollection/ee$Image
library(tidyrgee)
library(rgee)
ee_Initialize(quiet = T)
modis_ic <- ee$ImageCollection("MODIS/006/MOD13Q1")
Once the above steps are performed you can convert the
ee$ImageCollection to a tidyee object with the function as_tidyee.
The tidyee object stores the original ee$ImageCollection as ee_ob
(for earth engine object) and produces as virtual table/data.frame
stored as vrt. This vrt not only facilitates the use of
dplyr/tidyverse methods, but also allows the user to better view the
data stored in the accompanying imageCollection. The ee_ob and vrt
inside the tidyee object are linked, any function applied to the tidyee
object will apply to them both so that they remain in sync.
modis_tidy <- as_tidyee(modis_ic)
the vrt comes with a few built in columns which you can use off the
bat for filtering and grouping, but you can also mutate additional
info for filtering and grouping (i.e using lubridate to create new
temporal groupings)
knitr::kable(modis_tidy$vrt |> head())
| id | time_start | system_index | date | month | year | doy | band_names | |:-----------------------------|:-----------|:-------------|:-----------|------:|-----:|----:|:--------------------------------------------------------------------------------------------------------------------------------------------------------| | MODIS/006/MOD13Q1/2000_02_18 | 2000-02-18 | 2000_02_18 | 2000-02-18 | 2 | 2000 | 49 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA | | MODIS/006/MOD13Q1/2000_03_05 | 2000-03-05 | 2000_03_05 | 2000-03-05 | 3 | 2000 | 65 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA | | MODIS/006/MOD13Q1/2000_03_21 | 2000-03-21 | 2000_03_21 | 2000-03-21 | 3 | 2000 | 81 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA | | MODIS/006/MOD13Q1/2000_04_06 | 2000-04-06 | 2000_04_06 | 2000-04-06 | 4 | 2000 | 97 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA | | MODIS/006/MOD13Q1/2000_04_22 | 2000-04-22 | 2000_04_22 | 2000-04-22 | 4 | 2000 | 113 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA | | MODIS/006/MOD13Q1/2000_05_08 | 2000-05-08 | 2000_05_08 | 2000-05-08 | 5 | 2000 | 129 | NDVI , EVI , DetailedQA , sur_refl_b01 , sur_refl_b02 , sur_refl_b03 , sur_refl_b07 , ViewZenith , SolarZenith , RelativeAzimuth, DayOfYear , SummaryQA |
Next we demonstrate filtering by date, month, and year. The vrt and
ee_ob are always filtered together
- by date
modis_tidy |>
filter(date>="2021-06-01")
#> band names: [ NDVI, EVI, DetailedQA, sur_refl_b01, sur_refl_b02, sur_refl_b03, sur_refl_b07, ViewZenith, SolarZenith, RelativeAzimuth, DayOfYear, SummaryQA ]
#>
#> $ee_ob
#> EarthEngine Object: ImageCollection
#> $vrt
#> # A tibble: 28 x 9
#> id time_start syste~1 date month year doy band_~2
#> <chr> <dttm> <chr> <date> <dbl> <dbl> <dbl> <list>
#> 1 MODIS/006/M~ 2021-06-10 00:00:00 2021_0~ 2021-06-10 6 2021 161 <chr>
#> 2 MODIS/006/M~ 2021-06-26 00:00:00 2021_0~ 2021-06-26 6 2021 177 <chr>
#> 3 MODIS/006/M~ 2021-07-12 00:00:00 2021_0~ 2021-07-12 7 2021 193 <chr>
#> 4 MODIS/006/M~ 2021-07-28 00:00:00 2021_0~ 2021-07-28 7 2021 209 <chr>
#> 5 MODIS/006/M~ 2021-08-13 00:00:00 2021_0~ 2021-08-13 8 2021 225 <chr>
#> 6 MODIS/006/M~ 2021-08-29 00:00:00 2021_0~ 2021-08-29 8 2021 241 <chr>
#> 7 MODIS/006/M~ 2021-09-14 00:00:00 2021_0~ 2021-09-14 9 2021 257 <chr>
#> 8 MODIS/006/M~ 2021-09-30 00:00:00 2021_0~ 2021-09-30 9 2021 273 <chr>
#> 9 MODIS/006/M~ 2021-10-16 00:00:00 2021_1~ 2021-10-16 10 2021 289 <chr>
#> 10 MODIS/006/M~ 2021-11-01 00:00:00 2021_1~ 2021-11-01 11 2021 305 <chr>
#> # ... with 18 more rows, 1 more variable: tidyee_index <chr>, and abbreviated
#> # variable names 1: system_index, 2: band_names
#> # i Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
#>
#> attr(,"class")
#> [1] "tidyee"
- by year
modis_tidy |>
filter(year%in% 2010:2011)
#> band names: [ NDVI, EVI, DetailedQA, sur_refl_b01, sur_refl_b02, sur_refl_b03, sur_refl_b07, ViewZenith, SolarZenith, RelativeAzimuth, DayOfYear, SummaryQA ]
#>
#> $ee_ob
#> EarthEngine Object: ImageCollection
#> $vrt
#> # A tibble: 46 x 9
#> id time_start syste~1 date month year doy band_~2
#> <chr> <dttm> <chr> <date> <dbl> <dbl> <dbl> <list>
#> 1 MODIS/006/M~ 2010-01-01 00:00:00 2010_0~ 2010-01-01 1 2010 1 <chr>
#> 2 MODIS/006/M~ 2010-01-17 00:00:00 2010_0~ 2010-01-17 1 2010 17 <chr>
#> 3 MODIS/006/M~ 2010-02-02 00:00:00 2010_0~ 2010-02-02 2 2010 33 <chr>
#> 4 MODIS/006/M~ 2010-02-18 00:00:00 2010_0~ 2010-02-18 2 2010 49 <chr>
#> 5 MODIS/006/M~ 2010-03-06 00:00:00 2010_0~ 2010-03-06 3 2010 65 <chr>
#> 6 MODIS/006/M~ 2010-03-22 00:00:00 2010_0~ 2010-03-22 3 2010 81 <chr>
#> 7 MODIS/006/M~ 2010-04-07 00:00:00 2010_0~ 2010-04-07 4 2010 97 <chr>
#> 8 MODIS/006/M~ 2010-04-23 00:00:00 2010_0~ 2010-04-23 4 2010 113 <chr>
#> 9 MODIS/006/M~ 2010-05-09 00:00:00 2010_0~ 2010-05-09 5 2010 129 <chr>
#> 10 MODIS/006/M~ 2010-05-25 00:00:00 2010_0~ 2010-05-25 5 2010 145 <chr>
#> # ... with 36 more rows, 1 more variable: tidyee_index <chr>, and abbreviated
#> # variable names 1: system_index, 2: band_names
#> # i Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
#>
#> attr(,"class")
#> [1] "tidyee"
- by month
modis_tidy |>
filter(month%in% c(7,8))
#> band names: [ NDVI, EVI, DetailedQA, sur_refl_b01, sur_refl_b02, sur_refl_b03, sur_refl_b07, ViewZenith, SolarZenith, RelativeAzimuth, DayOfYear, SummaryQA ]
#>
#> $ee_ob
#> EarthEngine Object: ImageCollection
#> $vrt
#> # A tibble: 91 x 9
#> id time_start syste~1 date month year doy band_~2
#> <chr> <dttm> <chr> <date> <dbl> <dbl> <dbl> <list>
#> 1 MODIS/006/M~ 2000-07-11 00:00:00 2000_0~ 2000-07-11 7 2000 193 <chr>
#> 2 MODIS/006/M~ 2000-07-27 00:00:00 2000_0~ 2000-07-27 7 2000 209 <chr>
#> 3 MODIS/006/M~ 2000-08-12 00:00:00 2000_0~ 2000-08-12 8 2000 225 <chr>
#> 4 MODIS/006/M~ 2000-08-28 00:00:00 2000_0~ 2000-08-28 8 2000 241 <chr>
#> 5 MODIS/006/M~ 2001-07-12 00:00:00 2001_0~ 2001-07-12 7 2001 193 <chr>
#> 6 MODIS/006/M~ 2001-07-28 00:00:00 2001_0~ 2001-07-28 7 2001 209 <chr>
#> 7 MODIS/006/M~ 2001-08-13 00:00:00 2001_0~ 2001-08-13 8 2001 225 <chr>
#> 8 MODIS/006/M~ 2001-08-29 00:00:00 2001_0~ 2001-08-29 8 2001 241 <chr>
#> 9 MODIS/006/M~ 2002-07-12 00:00:00 2002_0~ 2002-07-12 7 2002 193 <chr>
#> 10 MODIS/006/M~ 2002-07-28 00:00:00 2002_0~ 2002-07-28 7 2002 209 <chr>
#> # ... with 81 more rows, 1 more variable: tidyee_index <chr>, and abbreviated
#> # variable names 1: system_index, 2: band_names
#> # i Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
#>
#> attr(,"class")
#> [1] "tidyee"
Putting a dplyr-like chain together:
In this next example we pipe together multiple functions (select,
filter, group_by, summarise) to
- select the
NDVIband from the ImageCollection - filter the imageCollection to a desired date range
- group the filtered ImageCollection by month
- summarizing each pixel by year and month.
The result will be an ImageCollection with the one Image per month
(12 images) where each pixel in each image represents the average NDVI
value for that month calculated using monthly data from 2000 2015.
modis_tidy |>
select("NDVI") |>
filter(year %in% 2000:2015) |>
group_by(month) |>
summarise(stat= "mean")
#> band names: [ NDVI_mean ]
#>
#> $ee_ob
#> EarthEngine Object: ImageCollection
#> $vrt
#> # A tibble: 12 x 7
#> month dates_summ~1 numbe~2 time_start time_end date
#> <dbl> <list> <int> <dttm> <dttm> <date>
#> 1 1 <dttm [30]> 30 2001-01-01 00:00:00 2001-01-01 00:00:00 2001-01-01
#> 2 2 <dttm [31]> 31 2000-02-18 00:00:00 2000-02-18 00:00:00 2000-02-18
#> 3 3 <dttm [32]> 32 2000-03-05 00:00:00 2000-03-05 00:00:00 2000-03-05
#> 4 4 <dttm [32]> 32 2000-04-06 00:00:00 2000-04-06 00:00:00 2000-04-06
#> 5 5 <dttm [32]> 32 2000-05-08 00:00:00 2000-05-08 00:00:00 2000-05-08
#> 6 6 <dttm [32]> 32 2000-06-09 00:00:00 2000-06-09 00:00:00 2000-06-09
#> 7 7 <dttm [32]> 32 2000-07-11 00:00:00 2000-07-11 00:00:00 2000-07-11
#> 8 8 <dttm [32]> 32 2000-08-12 00:00:00 2000-08-12 00:00:00 2000-08-12
#> 9 9 <dttm [32]> 32 2000-09-13 00:00:00 2000-09-13 00:00:00 2000-09-13
#> 10 10 <dttm [20]> 20 2000-10-15 00:00:00 2000-10-15 00:00:00 2000-10-15
#> 11 11 <dttm [28]> 28 2000-11-16 00:00:00 2000-11-16 00:00:00 2000-11-16
#> 12 12 <dttm [32]> 32 2000-12-02 00:00:00 2000-12-02 00:00:00 2000-12-02
#> # ... with 1 more variable: band_names <list>, and abbreviated variable names
#> # 1: dates_summarised, 2: number_images
#> # i Use `colnames()` to see all variable names
#>
#> attr(,"class")
#> [1] "tidyee"
You can easily group_by more than 1 property to calculate different
summary stats. Below we
- filter to only data from 2021-2022
- group by year, month and calculate the median NDVI pixel value
As we are using the MODIS 16-day composite we summarising approximately
2 images per month to create median composite image fo reach month in
the specified years. The vrt holds a list-col containing all the
dates summarised per new composite image.
modis_tidy |>
select("NDVI") |>
filter(year %in% 2021:2022) |>
group_by(year,month) |>
summarise(stat= "median")
#> band names: [ NDVI_median ]
#>
#> $ee_ob
#> EarthEngine Object: ImageCollection
#> $vrt
#> # A tibble: 20 x 8
#> year month dates_summarised number~1 time_start time_end
#> <dbl> <dbl> <list> <int> <dttm> <dttm>
#> 1 2021 1 <dttm [2]> 2 2021-01-01 00:00:00 2021-01-01 00:00:00
#> 2 2021 2 <dttm [2]> 2 2021-02-02 00:00:00 2021-02-02 00:00:00
#> 3 2021 3 <dttm [2]> 2 2021-03-06 00:00:00 2021-03-06 00:00:00
#> 4 2021 4 <dttm [2]> 2 2021-04-07 00:00:00 2021-04-07 00:00:00
#> 5 2021 5 <dttm [2]> 2 2021-05-09 00:00:00 2021-05-09 00:00:00
#> 6 2021 6 <dttm [2]> 2 2021-06-10 00:00:00 2021-06-10 00:00:00
#> 7 2021 7 <dttm [2]> 2 2021-07-12 00:00:00 2021-07-12 00:00:00
#> 8 2021 8 <dttm [2]> 2 2021-08-13 00:00:00 2021-08-13 00:00:00
#> 9 2021 9 <dttm [2]> 2 2021-09-14 00:00:00 2021-09-14 00:00:00
#> 10 2021 10 <dttm [1]> 1 2021-10-16 00:00:00 2021-10-16 00:00:00
#> 11 2021 11 <dttm [2]> 2 2021-11-01 00:00:00 2021-11-01 00:00:00
#> 12 2021 12 <dttm [2]> 2 2021-12-03 00:00:00 2021-12-03 00:00:00
#> 13 2022 1 <dttm [2]> 2 2022-01-01 00:00:00 2022-01-01 00:00:00
#> 14 2022 2 <dttm [2]> 2 2022-02-02 00:00:00 2022-02-02 00:00:00
#> 15 2022 3 <dttm [2]> 2 2022-03-06 00:00:00 2022-03-06 00:00:00
#> 16 2022 4 <dttm [2]> 2 2022-04-07 00:00:00 2022-04-07 00:00:00
#> 17 2022 5 <dttm [2]> 2 2022-05-09 00:00:00 2022-05-09 00:00:00
#> 18 2022 6 <dttm [2]> 2 2022-06-10 00:00:00 2022-06-10 00:00:00
#> 19 2022 7 <dttm [2]> 2 2022-07-12 00:00:00 2022-07-12 00:00:00
#> 20 2022 8 <dttm [1]> 1 2022-08-13 00:00:00 2022-08-13 00:00:00
#> # ... with 2 more variables: date <date>, band_names <list>, and abbreviated
#> # variable name 1: number_images
#> # i Use `colnames()` to see all variable names
#>
#> attr(,"class")
#> [1] "tidyee"
To improve interoperability with rgee we have included the as_ee
function to return the tidyee object back to rgee classes when
necessary
modis_ic <- modis_tidy |> as_ee()
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