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aggregate temporal period #63

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Nov 23, 2023
Merged

aggregate temporal period #63

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08f1cbd
aggregate tempoal period
PondiB Nov 23, 2023
02c6788
aggregate temporal period
PondiB Nov 23, 2023
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1 change: 1 addition & 0 deletions R/api.R
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Original file line number Diff line number Diff line change
Expand Up @@ -293,6 +293,7 @@ addEndpoint = function() {
Session$assignProcess(load_collection)
Session$assignProcess(load_stac)
Session$assignProcess(save_result)
Session$assignProcess(aggregate_temporal_period)
Session$assignProcess(filter_bands)
Session$assignProcess(filter_bbox)
Session$assignProcess(filter_spatial)
Expand Down
107 changes: 77 additions & 30 deletions R/processes.R
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Expand Up @@ -100,6 +100,15 @@ load_collection <- Process$new(
)
)
),
Parameter$new(
name = "crs",
description = "Coordinate Reference System, default = 4326",
schema = list(
type = "number",
subtype = "epsg-code"
),
optional = TRUE
),
Parameter$new(
name = "temporal_extent",
description = "Limits the data to load from the collection to the specified left-closed temporal interval.",
Expand All @@ -115,38 +124,10 @@ load_collection <- Process$new(
type = "array"
),
optional = TRUE
),
### Additional variables for flexibility due to gdalcubes
Parameter$new(
name = "pixels_size",
description = "size of pixels in x-direction(longitude / easting) and y-direction (latitude / northing). Default is 300",
schema = list(
type = "number"
),
optional = TRUE
),
Parameter$new(
name = "time_aggregation",
description = "size of pixels in time-direction, expressed as ISO8601 period string (only 1 number and unit is allowed) such as \"P16D\".Default is monthly i.e. \"P1M\".",
schema = list(
type = "string",
subtype = "duration"
),
optional = TRUE
),
Parameter$new(
name = "crs",
description = "Coordinate Reference System, default = 4326",
schema = list(
type = "number",
subtype = "epsg-code"
),
optional = TRUE
)
),
returns = eo_datacube,
operation = function(id, spatial_extent, temporal_extent, bands = NULL, pixels_size = 300, time_aggregation = "P1M",
crs = 4326, job) {
operation = function(id, spatial_extent, crs = 4326, temporal_extent, bands = NULL, job) {
# Temporal extent preprocess
t0 <- temporal_extent[[1]]
t1 <- temporal_extent[[2]]
Expand Down Expand Up @@ -205,7 +186,7 @@ load_collection <- Process$new(
crs <- c("EPSG", crs)
crs <- paste(crs, collapse = ":")
v.overview <- gdalcubes::cube_view(
srs = crs, dx = pixels_size, dy = pixels_size, dt = time_aggregation,
srs = crs, dx = 30, dy = 30, dt = "P15D",
aggregation = "median", resampling = "average",
extent = list(
t0 = t0, t1 = t1,
Expand Down Expand Up @@ -362,6 +343,72 @@ load_stac <- Process$new(
}
)

#' aggregate temporal period
aggregate_temporal_period <- Process$new(
id = "aggregate_temporal_period",
description = "Computes a temporal aggregation based on calendar hierarchies such as years, months or seasons.",
categories = as.array("aggregate", "cubes", "climatology"),
summary = "Temporal aggregations based on calendar hierarchies",
parameters = list(
Parameter$new(
name = "data",
description = "The source data cube.",
schema = list(
type = "object",
subtype = "raster-cube"
)
),
Parameter$new(
name = "period",
description = "The time intervals to aggregate",
schema = list(
type = "string"
),
optional = FALSE
),
Parameter$new(
name = "reducer",
description = "A reducer to be applied for the values contained in each interval. A reducer is a single process such as mean or a set of processes, which computes a single value for a list of values",
schema = list(
type = "any"
),
optional = FALSE
),
Parameter$new(
name = "dimension",
description = "The name of the temporal dimension for aggregation",
schema = list(
type = "any"
),
optional = TRUE
),
Parameter$new(
name = "context",
description = "Additional data to be passed to the reducer",
schema = list(
type = "any"
),
optional = TRUE
)
),
returns = eo_datacube,
operation = function(data, period, reducer, dimension = NULL, context = NULL, job) {
dt_period <- switch(period,
week = "P7D",
month = "P1M",
year = "P1Y",
decade = "P10Y",
stop("The specified period is not supported")
)

message("Aggregate temporal period ...")
message("Aggregate temporal period:", dt_period, "using reducer:", reducer)

cube <- gdalcubes::aggregate_time(cube = data, dt = dt_period, method = reducer)
message(gdalcubes::as_json(cube))
return(cube)
}
)

#' filter bands
filter_bands <- Process$new(
Expand Down
26 changes: 13 additions & 13 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -126,18 +126,17 @@ datacube_init = p$load_collection(id = "sentinel-s2-l2a-cogs",
south=-1027138,
east=-7329987,
north=-1018790),
temporal_extent = c("2021-05-01", "2022-06-30"),
# extra args for data cubes regularization -> courtesy of gdalcubes
pixels_size = 30,
time_aggregation = "P1Y",
crs = 3857)
crs = 3857,
temporal_extent = c("2021-05-01", "2022-06-30"))

# filter the data cube for the desired bands
datacube_filtered = p$filter_bands(data = datacube_init, bands = c("B04", "B08"))

# aggregate data cube to a year
datacube_agg = p$aggregate_temporal_period(data = datacube_filtered, period = "year", reducer = "median")

# ndvi calculation
datacube_ndvi = p$ndvi(data = datacube_filtered, red = "B04", nir = "B08")
datacube_ndvi = p$ndvi(data = datacube_agg, red = "B04", nir = "B08")

# supported formats
formats = list_file_formats()
Expand Down Expand Up @@ -196,14 +195,15 @@ datacube_init = p$load_collection(id = "sentinel-s2-l2a-cogs",
south=5803577.5,
east=422094.8,
north=5807036.1),
temporal_extent = c("2016-01-01", "2020-12-31"),
# extra args for data cubes regularization
pixels_size = 10,
time_aggregation = "P1M",
crs = 32633)
crs = 32633,
temporal_extent = c("2016-01-01", "2020-12-31"))

# filter the data cube for the desired bands
datacube_filtered = p$filter_bands(data = datacube_init, bands = c("B04", "B08"))
datacube_filtered = p$filter_bands(data = datacube_init,
bands = c("B04", "B08"))
# aggregate data cube to monthly
datacube_agg = p$aggregate_temporal_period(data = datacube_filtered,
period = "month", reducer = "median")

# user defined R function - bfast change detection method
change_detection = "function(x) {
Expand All @@ -223,7 +223,7 @@ change_detection = "function(x) {
}"

# run udf
datacube_udf = p$run_udf(data = datacube_filtered, udf = change.detection, context = c("change_date", "change_magnitude"))
datacube_udf = p$run_udf(data = datacube_agg, udf = change.detection, context = c("change_date", "change_magnitude"))

# supported formats
formats = list_file_formats()
Expand Down
8 changes: 3 additions & 5 deletions examples/01-rgb-download/R-Client-truecolors-newyork.R
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Original file line number Diff line number Diff line change
Expand Up @@ -26,11 +26,9 @@ datacube_init = p$load_collection(id = "sentinel-s2-l2a-cogs",
south=4483092.4,
east=609472,
north=4530135),
temporal_extent = c("2021-06-01", "2021-06-30"),
# extra optional args for datacubes regularization -> courtesy of gdalcubes
pixels_size = 10,
time_aggregation = "P1M",
crs = 32618)
crs = 32618,
temporal_extent = c("2021-06-01", "2021-06-30")
)
# filter the data cube for the desired bands
datacube_filtered = p$filter_bands(data = datacube_init, bands = c("B02","B03","B04"))

Expand Down
21 changes: 11 additions & 10 deletions examples/02-ndvi/R-Client-ndvi-amazonia.R
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Original file line number Diff line number Diff line change
Expand Up @@ -26,22 +26,23 @@ p = processes()

# load the initial data collection and limit the amount of data loaded
datacube_init = p$load_collection(id = "sentinel-s2-l2a-cogs",
spatial_extent = list(west=-7338335,
south=-1027138,
east=-7329987,
north=-1018790),
temporal_extent = c("2021-05-01", "2022-06-30"),
# extra optional args for datacubes regularization -> courtesy of gdalcubes
pixels_size = 30,
time_aggregation = "P1Y",
crs = 3857)
spatial_extent = list(west=-7338335,
south=-1027138,
east=-7329987,
north=-1018790),
crs = 3857,
temporal_extent = c("2021-05-01", "2022-06-30"))



# filter the data cube for the desired bands
datacube_filtered = p$filter_bands(data = datacube_init, bands = c("B04", "B08"))

# aggregate data cube to a year
datacube_agg = p$aggregate_temporal_period(data = datacube_filtered, period = "year", reducer = "median")

# ndvi calculation
datacube_ndvi = p$ndvi(data = datacube_filtered, red = "B04", nir = "B08")
datacube_ndvi = p$ndvi(data = datacube_agg, red = "B04", nir = "B08")

# supported formats
formats = list_file_formats()
Expand Down
18 changes: 11 additions & 7 deletions examples/03-change-detection/R-Client-bfast-brandenburg.R
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Original file line number Diff line number Diff line change
Expand Up @@ -31,17 +31,21 @@ datacube_init = p$load_collection(id = "sentinel-s2-l2a-cogs",
east=422094.8,
north=5807036.1),
temporal_extent = c("2016-01-01", "2020-12-31"),
# extra optional args for datacubes regularization -> courtesy of gdalcubes
# extra args for data cubes regularization
pixels_size = 10,
time_aggregation = "P1M",
crs = 32633)

# filter the data cube for the desired bands
datacube_filtered = p$filter_bands(data = datacube_init, bands = c("B04", "B08"))
datacube_filtered = p$filter_bands(data = datacube_init,
bands = c("B04", "B08"))
# aggregate data cube to monthly
datacube_agg = p$aggregate_temporal_period(data = datacube_filtered,
period = "month", reducer = "median")

# bfast custom change detection method
change.detection = 'function(x) {
knr <- exp(-((x["B08",]/10000)-(x["B04",]/10000))^2/(2))
# user defined R function - bfast change detection method
change_detection = "function(x) {
knr <- exp(-((x[\"B08\",]/10000)-(x[\"B04\",]/10000))^2/(2))
kndvi <- (1-knr) / (1+knr)
if (all(is.na(kndvi))) {
return(c(NA,NA))
Expand All @@ -54,10 +58,10 @@ change.detection = 'function(x) {
}, error = function(x) {
return(c(NA,NA))
})
}'
}"

# run udf
datacube_udf = p$run_udf(data = datacube_filtered, udf = change.detection, context = c("change_date", "change_magnitude"))
datacube_udf = p$run_udf(data = datacube_agg, udf = change.detection, context = c("change_date", "change_magnitude"))

# supported formats
formats = list_file_formats()
Expand Down
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