Skip to contents

Prepare Dataframe with Pairs of NDVI and STR Values

Source: R/optram_ndvi_str.R
optram_ndvi_str.Rd

Collect all pixel values of both vegetation index, and Swir Transformed Reflectance, for a time series of images. Prepare data.frame of all pairs of values (as input for the optram_wetdry_coefficients function)

Usage

optram_ndvi_str(STR_list, VI_list, output_dir = tempdir(), aoi = NULL)

Arguments

STR_list,

list of paths to STR raster files

VI_list,

list of paths to NDVI raster files

output_dir,

string, path to save data.frames (in RDS format)

aoi,

sf POLYGON or MULTIPOLYGON, must have a numeric column named "ID" for coloring trapezoid points by features Default NULL, (no coloring)

Value

full_df, data.frame with 7 columns: X,Y,Date,Month,NDVI,STR,Density and optionally a 7th column with feature ID values from the AOI polygon. The columns Month, Density, Feature_ID can be used in plot_vi_str_cloud() function to color the points in the scatter plot in various ways.

Note

Use the option max_tbl_size (see optram_options) to limit size of the NDVI-STR data.frame. With a large area of interest, and long time frame, the number of data points can overrun the computation resources. This parameter sets a total size of data.frame from the max_tbl_size parameter, together with the number of image time slots in the time range.

In some cases (i.e. water surfaces) NDVI can have values below zero. These pixels can be removed from the trapezoid by setting rm.low.vi option to TRUE.

The vegetation index column is named "NDVI" even though it can represent other vegetation indices, such as SAVI, or MSAVI.

Examples

VI_list <- list.files(system.file("extdata", "NDVI", package = "rOPTRAM"),
        pattern = ".tif$", full.names = TRUE)
STR_list <- list.files(system.file("extdata", "STR", package = "rOPTRAM"),
        pattern = ".tif$", full.names = TRUE)
full_df <- optram_ndvi_str(STR_list, VI_list)
#> Saved: 96360 rows of VI-STR data to: /tmp/RtmpQfv12R/VI_STR_data.rds
# Show structure of output data.frame
str(full_df)
#> 'data.frame':	96360 obs. of  8 variables:
#>  $ x      : num  34.9 34.9 34.9 34.9 34.9 ...
#>  $ y      : num  31.6 31.6 31.6 31.6 31.6 ...
#>  $ VI     : num  0.432 0.418 0.428 0.528 0.538 ...
#>  $ STR    : num  1.72 1.95 1.96 2.11 2.13 ...
#>  $ Date   : Date, format: "2022-11-11" "2022-11-11" ...
#>  $ Tile   : chr  "T36RXV" "T36RXV" "T36RXV" "T36RXV" ...
#>  $ Month  : chr  "11" "11" "11" "11" ...
#>  $ Density: num  0.565 0.374 0.328 0.49 0.495 ...