-## Get the URLs of MODIS Terra LST daily
+## Get the URLs of MODIS Vegetation indexes
urls_mod11a1 <- mf_get_url(
collection = "MOD13A3.061",
variables = c("_1_km_monthly_NDVI","_1_km_monthly_EVI","_1_km_monthly_VI_Quality"), # get the available variables with : mf_list_variables("MOD13A3.061"), or set to NULL (default) to download all the variables
@@ -305,7 +308,7 @@ Import the data in Rncdf4
package.
-
⚠️
+⚠️ ⚠️ ⚠️ ⚠️
Important warning regarding the use of the data after download
through modisfast
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2. Get data on several regions or periods of interest at a glance
+
+ 3. Full use case
+
2. Get data on several regions or periods of
Paul
Taconet
- 2024-05-16
+ 2024-06-06
Source: vignettes/modisfast2.Rmd
modisfast2.Rmd
@@ -265,15 +268,15 @@ Get data ov
modis_ts
#> class : SpatRaster
-#> dimensions : 85, 50, 124 (nrow, ncol, nlyr)
+#> dimensions : 85, 50, 496 (nrow, ncol, nlyr)
#> resolution : 0.008417905, 0.008417905 (x, y)
#> extent : -5.818061, -5.397166, 8.830311, 9.545833 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source(s) : memory
#> names : LST_N~1km_1, LST_N~1km_2, LST_N~1km_3, LST_N~1km_4, LST_N~1km_5, LST_N~1km_6, ...
-#> min values : 285.8285, 287.4483, 288.2661, 285.307, 288.3886, 288.1431, ...
-#> max values : 295.6503, 294.3870, 295.9265, 293.285, 295.7804, 294.9189, ...
-#> time (days) : 2016-01-01 to 2016-01-31
+#> min values : 285.640, NaN, 288.2661, 285.8200, NaN, NaN, ...
+#> max values : 295.341, NaN, 294.6754, 290.4939, NaN, NaN, ...
+#> time (days) : 2016-01-01 to 2019-01-31
diff --git a/docs/articles/use_case.html b/docs/articles/use_case.html
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+3. Full use case • modisfast
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+ 3. Full use case
+ Paul
+Taconet
+
+ 2024-06-06
+
+ Source: vignettes/use_case.Rmd
+ use_case.Rmd
+
+
+
+
+
+
+Introduction
+
+In this use case, we showcase how, starting with a simple table
+containing sampling-like information (geographical coordinates and
+dates), it is possible to retrieve meteorological or environmental data
+around each of these sampling points at chosen lags before the sampling
+dates.
+Here, let’s say that we have a dataset containing abundances of
+mosquitoes vectors of malaria. These mosquitoes were periodically
+collected in several villages of a region of Northern Côte d’Ivoire
+during 1.5 years. In total, 8 entomological surveys took place in 32
+villages of this area. In order to analyse the effects of temperature
+and rainfall on the abundance of these mosquitoes, we want to get the
+average temperature and cumulated rainfall for each collection point, in
+a 2-km radius buffer zone, one month before each collection date.
+For information, the full ‘real’ dataset was published in the GBIF and
+exhaustively described in this data
+paper).
+
+
+Set-up the parameters
+
+First we load the useful packages :
+
+Then, let’s load the data and have a look at them :
+
+head(entomological_data)
+#> mission date village X Y n
+#> 1 1 2016-09-21 GUE -5.548970 9.271823 120
+#> 2 1 2016-09-21 LOK -5.605484 9.186781 156
+#> 3 1 2016-09-21 PEN -5.514849 9.253527 84
+#> 4 1 2016-09-22 KOL -5.524137 9.288387 131
+#> 5 1 2016-09-23 KAG -5.535555 9.282149 165
+#> 6 1 2016-09-23 NOK -5.614132 9.251798 188
+
+- ‘mission’ is the number of the entomological survey,
+- ‘date’ is the date of the survey
+- ‘village’ is a 3-digit code for the village of the survey
+- ‘X’ and ‘Y’ are longitude and latitude of the center of the
+village
+- ‘n’ is the number of mosquitoes collected
+
+The first step is to convert this dataset as an sf POINT
+object, and then generate its bounding box (ie. area of interest) to get
+a POLYGON object (which is the input type for modisfast)
+:
+
+entomological_data <- entomological_data %>%
+ st_as_sf(coords = c("X", "Y"), crs = 4326) %>%
+ mutate(date = as.Date(date))
+
+roi <- st_bbox(entomological_data)
+Since we will calculate the meteorological indicators in 2-km radius
+buffer zones, we need to enlarge a bit the ROI. Here, we enlarge it by
+3000 m in all directions (N, S, W, E) and we give the ROI the name
+“Korhogo” (the name of the main city in the area) :
+
+# function expand_bbox() (Thanks @Chrisjb for the function)
+source("https://raw.githubusercontent.com/Chrisjb/basemapR/master/R/expand_bbox.R")
+
+roi <- roi %>%
+ expand_bbox(.,3000,3000) %>%
+ sf::st_as_sfc() %>%
+ sf::st_sf()
+
+roi$id = "korhogo"
+Next, we define the time range of interest.
+Here we will first download the whole time series, and then (in step
+3) filter-out the specific dates for each
+collection point in the next step. So for download, we set the time
+range as the period going from 30 days before the first collection date
+to the last collection date.
+
+
+
+Download the meteorological data using modisfast
+
+We now download the data using modisfast :
+
+- “MOD11A2.061” = MODIS/Terra Land Surface Temperature/Emissivity
+8-Day L3 Global 1 km SIN Grid v061
+- “GPM_3IMERGDF.07” = GPM IMERG Final Precipitation L3 1 day 0.1
+degree x 0.1 degree V07
+
+
+log <- mf_login(credentials = c(Sys.getenv("earthdata_un"),Sys.getenv("earthdata_pw")))
+#> Checking credentials...
+#> Successfull login to earthdata
+
+urls_mod11a2 <- mf_get_url(
+ collection = "MOD11A2.061",
+ variables = c("LST_Day_1km","LST_Night_1km"),
+ roi = roi,
+ time_range = time_range )
+#> Building the URLs...
+#> OK
+
+
+urls_gpm <- mf_get_url(
+ collection = "GPM_3IMERGDF.07",
+ variables = c("precipitation"),
+ roi = roi,
+ time_range = time_range )
+#> Building the URLs...
+#> OK
+
+
+res_dl_modis <- mf_download_data(urls_mod11a2, path = "data_use_case")
+#> 1 datasets in total : 1 already downloaded and 0 datasets to download
+#>
+#> Data were all properly downloaded under the folder(s) data_use_case/korhogo/MOD11A2.061
+res_dl_gpm <- mf_download_data(urls_gpm, path = "data_use_case")
+#> 590 datasets in total : 590 already downloaded and 0 datasets to download
+#>
+#> Data were all properly downloaded under the folder(s) data_use_case/korhogo/GPM_3IMERGDF.07
+And then we import it in R :
+
+# import MODIS
+modis_ts <- mf_import_data(path = dirname(res_dl_modis$destfile[1]),
+ collection_source = "MODIS")
+# import GPM
+gpm_ts <- mf_import_data(path = dirname(res_dl_gpm$destfile[1]),
+ collection_source = "GPM")
+
+plot(modis_ts)
+
+
+plot(gpm_ts)
+
+Our time series are here, let’s finish the job now !
+
+
+Extract the data in 2-km radius buffer areas and 1 month before each
+collection date
+
+The last step is to extract the average temperature and cumulated
+rainfall for each collection point in a 2-km radius buffer zone, one
+month before each collection date.
+Below is some code for this - but feel free to adapt it, there are
+many ways of doing this, and the code suggested here may not be the best
+suited to your needs.
+
+# generate a 2-km radius buffer area around each collection point
+sp_buffer <- st_buffer(entomological_data, 2000)
+
+# write a function that summarizes the data, given as input :
+# - a buffer zone (within which the data will we summarized),
+# - a SpatRaster time series,
+# - a layer of interest for this SpatRaster,
+# - a time range of interest
+# - a function to summarize the data for the considered time frame
+
+fun_get_zonal_stat <- function(sp_buffer, raster_ts, variable, min_date, max_date, fun_summarize){
+
+ r_sub <- terra::subset(raster_ts, time(raster_ts) >= min_date & time(raster_ts) <= max_date)
+ r_agg <- terra::app(r_sub[variable], fun_summarize, na.rm = T)
+ val <- terra::extract(r_agg, sp_buffer, fun = mean, ID = F, touches=TRUE, na.rm = T)
+ val <- as.numeric(val)
+
+ return(val)
+}
+
+# split the dataset (needed for the execution of the function)
+sp_buffer_split <- split(sp_buffer, seq(nrow(sp_buffer)))
+
+# execute the function to get vectors
+LST_1_month_bef <- purrr::map_dbl(sp_buffer_split, ~fun_get_zonal_stat(., modis_ts, "LST_Day_1km", .$date - 30, .$date, "mean"))
+rain_1_month_bef <- purrr::map_dbl(sp_buffer_split, ~fun_get_zonal_stat(., gpm_ts, "precipitation", .$date - 30, .$date, "sum"))
+
+# add columns in the original table (entomological_data) containing the extracted temperature and rainfall data
+entomological_data$LST_1_month_bef <- LST_1_month_bef - 273.15 # the - 273.15 is to convert the temperature from kelvin to °C
+entomological_data$rain_1_month_bef <- rain_1_month_bef
+
+head(entomological_data)
+#> Simple feature collection with 6 features and 6 fields
+#> Geometry type: POINT
+#> Dimension: XY
+#> Bounding box: xmin: -5.614132 ymin: 9.186781 xmax: -5.514849 ymax: 9.288387
+#> Geodetic CRS: WGS 84
+#> mission date village n geometry LST_1_month_bef
+#> 1 1 2016-09-21 GUE 120 POINT (-5.54897 9.271823) 28.59190
+#> 2 1 2016-09-21 LOK 156 POINT (-5.605484 9.186781) 27.31314
+#> 3 1 2016-09-21 PEN 84 POINT (-5.514849 9.253527) 28.50162
+#> 4 1 2016-09-22 KOL 131 POINT (-5.524137 9.288387) 26.71242
+#> 5 1 2016-09-23 KAG 165 POINT (-5.535555 9.282149) 27.68345
+#> 6 1 2016-09-23 NOK 188 POINT (-5.614132 9.251798) 29.66437
+#> rain_1_month_bef
+#> 1 377.1600
+#> 2 366.2787
+#> 3 374.3600
+#> 4 362.1925
+#> 5 370.0350
+#> 6 368.2850
+
+
+Visualize the association between the meteorological variables and
+the abundance of mosquitoes
+
+We can now visualize the data :
+
+# association between diurnal temperature and mosquito abundance
+ggplot(entomological_data,aes(x=LST_1_month_bef, y=n)) + geom_point() + geom_smooth() + theme_bw() + labs(x="Average diurnal temperature\n1 month before collection", y = "Number of mosquitoes collected")
+
+
+# association between rainfall and mosquito abundance
+ggplot(entomological_data,aes(x=rain_1_month_bef, y=n)) + geom_point() + geom_smooth() + theme_bw() + labs(x="Cumulated rainfall\n1 month before collection", y = "Number of mosquitoes collected")
+
+We see that overall, there is :
+
+- a negative relationship between diurnal temperature and mosquito
+abundance (which makes sense : excessive temperatures might kill
+mosquitoes)
+- a positive relationship between rainfall and mosquito abundance
+(which makes sense too : rainfall creates puddles, which are breeding
+sites)
+
+There also seem to be non-linear relationships that could be further
+explored and explained.
+
+
+Next steps in the analyses of the data
+
+Next steps could imply modeling the abundances of mosquitoes using
+these meteorological data, for explanation or prediction purposes. To go
+deeper, we could also extract data from more time frames, to better
+apprehend several aspects of the ecology of the vectors in the study
+area (eg. impact of meteorological variables at the different life
+stages of the mosquitoes).
+You may see this
+paper and this
+one for examples of full data mining works using such data.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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--- a/docs/authors.html
+++ b/docs/authors.html
@@ -37,6 +37,9 @@
modisfast2.Rmd3. Full use case
+Paul +Taconet
+ +2024-06-06
+ + Source:vignettes/use_case.Rmd
+ use_case.RmdIntroduction +
+In this use case, we showcase how, starting with a simple table +containing sampling-like information (geographical coordinates and +dates), it is possible to retrieve meteorological or environmental data +around each of these sampling points at chosen lags before the sampling +dates.
+Here, let’s say that we have a dataset containing abundances of +mosquitoes vectors of malaria. These mosquitoes were periodically +collected in several villages of a region of Northern Côte d’Ivoire +during 1.5 years. In total, 8 entomological surveys took place in 32 +villages of this area. In order to analyse the effects of temperature +and rainfall on the abundance of these mosquitoes, we want to get the +average temperature and cumulated rainfall for each collection point, in +a 2-km radius buffer zone, one month before each collection date.
+For information, the full ‘real’ dataset was published in the GBIF and +exhaustively described in this data +paper).
+Set-up the parameters +
+First we load the useful packages :
+ +Then, let’s load the data and have a look at them :
+
+head(entomological_data)
+#> mission date village X Y n
+#> 1 1 2016-09-21 GUE -5.548970 9.271823 120
+#> 2 1 2016-09-21 LOK -5.605484 9.186781 156
+#> 3 1 2016-09-21 PEN -5.514849 9.253527 84
+#> 4 1 2016-09-22 KOL -5.524137 9.288387 131
+#> 5 1 2016-09-23 KAG -5.535555 9.282149 165
+#> 6 1 2016-09-23 NOK -5.614132 9.251798 188-
+
- ‘mission’ is the number of the entomological survey, +
- ‘date’ is the date of the survey +
- ‘village’ is a 3-digit code for the village of the survey +
- ‘X’ and ‘Y’ are longitude and latitude of the center of the +village +
- ‘n’ is the number of mosquitoes collected +
The first step is to convert this dataset as an sf POINT
+object, and then generate its bounding box (ie. area of interest) to get
+a POLYGON object (which is the input type for modisfast)
+:
+entomological_data <- entomological_data %>%
+ st_as_sf(coords = c("X", "Y"), crs = 4326) %>%
+ mutate(date = as.Date(date))
+
+roi <- st_bbox(entomological_data)Since we will calculate the meteorological indicators in 2-km radius +buffer zones, we need to enlarge a bit the ROI. Here, we enlarge it by +3000 m in all directions (N, S, W, E) and we give the ROI the name +“Korhogo” (the name of the main city in the area) :
+
+# function expand_bbox() (Thanks @Chrisjb for the function)
+source("https://raw.githubusercontent.com/Chrisjb/basemapR/master/R/expand_bbox.R")
+
+roi <- roi %>%
+ expand_bbox(.,3000,3000) %>%
+ sf::st_as_sfc() %>%
+ sf::st_sf()
+
+roi$id = "korhogo"Next, we define the time range of interest.
+Here we will first download the whole time series, and then (in step +3) filter-out the specific dates for each +collection point in the next step. So for download, we set the time +range as the period going from 30 days before the first collection date +to the last collection date.
+ +Download the meteorological data using modisfast
+
+We now download the data using modisfast :
-
+
- “MOD11A2.061” = MODIS/Terra Land Surface Temperature/Emissivity +8-Day L3 Global 1 km SIN Grid v061 +
- “GPM_3IMERGDF.07” = GPM IMERG Final Precipitation L3 1 day 0.1 +degree x 0.1 degree V07 +
+log <- mf_login(credentials = c(Sys.getenv("earthdata_un"),Sys.getenv("earthdata_pw")))
+#> Checking credentials...
+#> Successfull login to earthdata
+
+urls_mod11a2 <- mf_get_url(
+ collection = "MOD11A2.061",
+ variables = c("LST_Day_1km","LST_Night_1km"),
+ roi = roi,
+ time_range = time_range )
+#> Building the URLs...
+#> OK
+
+
+urls_gpm <- mf_get_url(
+ collection = "GPM_3IMERGDF.07",
+ variables = c("precipitation"),
+ roi = roi,
+ time_range = time_range )
+#> Building the URLs...
+#> OK
+
+
+res_dl_modis <- mf_download_data(urls_mod11a2, path = "data_use_case")
+#> 1 datasets in total : 1 already downloaded and 0 datasets to download
+#>
+#> Data were all properly downloaded under the folder(s) data_use_case/korhogo/MOD11A2.061
+res_dl_gpm <- mf_download_data(urls_gpm, path = "data_use_case")
+#> 590 datasets in total : 590 already downloaded and 0 datasets to download
+#>
+#> Data were all properly downloaded under the folder(s) data_use_case/korhogo/GPM_3IMERGDF.07And then we import it in R :
+
+# import MODIS
+modis_ts <- mf_import_data(path = dirname(res_dl_modis$destfile[1]),
+ collection_source = "MODIS")
+# import GPM
+gpm_ts <- mf_import_data(path = dirname(res_dl_gpm$destfile[1]),
+ collection_source = "GPM")
+
+plot(modis_ts)
+plot(gpm_ts)
Our time series are here, let’s finish the job now !
+Extract the data in 2-km radius buffer areas and 1 month before each +collection date +
+The last step is to extract the average temperature and cumulated +rainfall for each collection point in a 2-km radius buffer zone, one +month before each collection date.
+Below is some code for this - but feel free to adapt it, there are +many ways of doing this, and the code suggested here may not be the best +suited to your needs.
+
+# generate a 2-km radius buffer area around each collection point
+sp_buffer <- st_buffer(entomological_data, 2000)
+
+# write a function that summarizes the data, given as input :
+# - a buffer zone (within which the data will we summarized),
+# - a SpatRaster time series,
+# - a layer of interest for this SpatRaster,
+# - a time range of interest
+# - a function to summarize the data for the considered time frame
+
+fun_get_zonal_stat <- function(sp_buffer, raster_ts, variable, min_date, max_date, fun_summarize){
+
+ r_sub <- terra::subset(raster_ts, time(raster_ts) >= min_date & time(raster_ts) <= max_date)
+ r_agg <- terra::app(r_sub[variable], fun_summarize, na.rm = T)
+ val <- terra::extract(r_agg, sp_buffer, fun = mean, ID = F, touches=TRUE, na.rm = T)
+ val <- as.numeric(val)
+
+ return(val)
+}
+
+# split the dataset (needed for the execution of the function)
+sp_buffer_split <- split(sp_buffer, seq(nrow(sp_buffer)))
+
+# execute the function to get vectors
+LST_1_month_bef <- purrr::map_dbl(sp_buffer_split, ~fun_get_zonal_stat(., modis_ts, "LST_Day_1km", .$date - 30, .$date, "mean"))
+rain_1_month_bef <- purrr::map_dbl(sp_buffer_split, ~fun_get_zonal_stat(., gpm_ts, "precipitation", .$date - 30, .$date, "sum"))
+
+# add columns in the original table (entomological_data) containing the extracted temperature and rainfall data
+entomological_data$LST_1_month_bef <- LST_1_month_bef - 273.15 # the - 273.15 is to convert the temperature from kelvin to °C
+entomological_data$rain_1_month_bef <- rain_1_month_bef
+
+head(entomological_data)
+#> Simple feature collection with 6 features and 6 fields
+#> Geometry type: POINT
+#> Dimension: XY
+#> Bounding box: xmin: -5.614132 ymin: 9.186781 xmax: -5.514849 ymax: 9.288387
+#> Geodetic CRS: WGS 84
+#> mission date village n geometry LST_1_month_bef
+#> 1 1 2016-09-21 GUE 120 POINT (-5.54897 9.271823) 28.59190
+#> 2 1 2016-09-21 LOK 156 POINT (-5.605484 9.186781) 27.31314
+#> 3 1 2016-09-21 PEN 84 POINT (-5.514849 9.253527) 28.50162
+#> 4 1 2016-09-22 KOL 131 POINT (-5.524137 9.288387) 26.71242
+#> 5 1 2016-09-23 KAG 165 POINT (-5.535555 9.282149) 27.68345
+#> 6 1 2016-09-23 NOK 188 POINT (-5.614132 9.251798) 29.66437
+#> rain_1_month_bef
+#> 1 377.1600
+#> 2 366.2787
+#> 3 374.3600
+#> 4 362.1925
+#> 5 370.0350
+#> 6 368.2850Visualize the association between the meteorological variables and +the abundance of mosquitoes +
+We can now visualize the data :
+
+# association between diurnal temperature and mosquito abundance
+ggplot(entomological_data,aes(x=LST_1_month_bef, y=n)) + geom_point() + geom_smooth() + theme_bw() + labs(x="Average diurnal temperature\n1 month before collection", y = "Number of mosquitoes collected")
+# association between rainfall and mosquito abundance
+ggplot(entomological_data,aes(x=rain_1_month_bef, y=n)) + geom_point() + geom_smooth() + theme_bw() + labs(x="Cumulated rainfall\n1 month before collection", y = "Number of mosquitoes collected")
We see that overall, there is :
+-
+
- a negative relationship between diurnal temperature and mosquito +abundance (which makes sense : excessive temperatures might kill +mosquitoes) +
- a positive relationship between rainfall and mosquito abundance +(which makes sense too : rainfall creates puddles, which are breeding +sites) +
There also seem to be non-linear relationships that could be further +explored and explained.
+Next steps in the analyses of the data +
+Next steps could imply modeling the abundances of mosquitoes using +these meteorological data, for explanation or prediction purposes. To go +deeper, we could also extract data from more time frames, to better +apprehend several aspects of the ecology of the vectors in the study +area (eg. impact of meteorological variables at the different life +stages of the mosquitoes).
+You may see this +paper and this +one for examples of full data mining works using such data.
+-
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-modisfast(formerlyopendapr) is an R package that provides functions to speed-up the download of time-series data products derived from MODIS and VIIRS observations, as well as other widely-used satellite-derived environmental data (e.g. Global Precipitation Measurement Mission).
+modisfastuses the abilities offered by the OPeNDAP framework (Open-source Project for a Network Data Access Protocol) to download a subset of data cube, along spatial, temporal or any other data dimension (depth, …). This way, it reduces downloading time and disk usage to their minimum : no more 1° x 1° MODIS tiles when your region of interest is only 100 km x 100 km wide ! Moreover, modisfast supports parallelized downloads.
+modisfast(formerlyopendapr) is an R package that provides functions to speed-up the download of time-series raster data products derived from MODIS and VIIRS observations, as well as other widely-used satellite-derived environmental data (e.g. Global Precipitation Measurement Mission).modisfastuses the abilities offered by the OPeNDAP framework (Open-source Project for a Network Data Access Protocol) to download a subset of Earth science data cube, along spatial, temporal or any other data dimension (depth, …). This way, it reduces downloading time and disk usage to their minimum : no more 1° x 1° MODIS tiles when your region of interest is only 100 km x 100 km wide ! Moreover,modisfastsupports parallelized downloads.modisfastis hence particularly suited for retrieving MODIS or VIIRS data over long time series and over areas, rather than short time series and points.Below is a comparison of modisfast with other packages available for downloading chunks of remote sensing data :
