gdalcubes-stac-cog.Rmd

---
title: "Mosaic images using STAC"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Mosaic images using STAC}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## *WORK IN PROGRESS*



High-resolution satellites generate many snapshot images each with a limited field of view or spatial extent.  In order to see a larger area in space, and/or observe changes across space and time, we need to assemble these many snapshots into a mosaic or "data cube" that we can analyze as a cohesive whole. 


[EARTHDATA STAC CATALOGS](https://radiantearth.github.io/stac-browser/#/external/cmr.earthdata.nasa.gov/stac/LPCLOUD?.language=en)


```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path="img/",
  message = FALSE,
  warning = FALSE
)
```

```{r setup, message=FALSE}
library(earthdatalogin)
library(rstac)
library(gdalcubes)
gdalcubes_options(parallel = TRUE)
```


### Earth Data Authentication

First let's get EDL authentication out of the way.
For cloud data from almost any other STAC catalog (NOAA, USGS, Planetary Computer, etc), authentication is either unnecessary or already provided by the STAC API, but NASA EDL is special. 


```{r}
library(earthdatalogin)
```

We could just use `edl_set_token()` here as usual to set the environmental variable. 
This works fine but can problems if we do not remember to `edl_unset_token()` before accessing other non-EDL resources over the http interface.
When using the `gdalcubes` package, we have support for a somewhat nicer, more localized authentication that uses the configuration options instead.
We tell `edl_set_token` not to set the environmental variable globally, but to return
in the header format which we can pass to `gdalcubes_set_gdal_config()`:

```{r}
header <- edl_set_token(set_env_var = FALSE, format = "header")
gdalcubes_set_gdal_config("GDAL_HTTP_HEADERS", header)
```

`earthdatalogin` also includes optional configuration settings for GDAL which can improve performance of cloud-based data access.  Set the GDAL environmental variables using `gdal_cloud_config()`

```{r}
gdal_cloud_config()
```

## Search via STAC

We will now use the `rstac` package to search one or more NASA collections for data that falls into our desired bounding 

Set a search box in space & time

```{r}
bbox <- c(xmin=-122.5, ymin=37.5, xmax=-122.0, ymax=38) 
start <- "2022-01-01"
end <- "2022-06-30"

# Find all assets from the desired catalog:
items <- stac("https://cmr.earthdata.nasa.gov/stac/LPCLOUD") |> 
  stac_search(collections = "HLSL30.v2.0",
              bbox = bbox,
              datetime = paste(start,end, sep = "/")) |>
  post_request() |>
  items_fetch() |>
  items_filter(filter_fn = \(x) {x[["eo:cloud_cover"]] < 20})
```


Note that `r length(items$features)` features have matched our search criteria! Each feature represents a 'snapshot' image taken by the satellite as it passes by (this is a harmonized product so actually there's quite a lot of post-processing.)  Each feature thus shares the same bounding box, projection, and timestamp, but may consist of many different 'assets', different COG files representing the different spectral bands on the satellite camera instrument.  Each feature can potentially include quite extensive metadata about the feature, including details of instrument itself or from post-processing, such as cloud cover.  Unfortunately, EarthData's STAC metadata tends to be quite sparse. 



## Building a Data Cube


```{r}
# Desired data cube shape & resolution
v = cube_view(srs = "EPSG:4326",
              extent = list(t0 = as.character(start), 
                            t1 = as.character(end),
                            left = bbox[1], right = bbox[3],
                            top = bbox[4], bottom = bbox[2]),
              nx = 512, ny = 512, dt = "P1M")
```


```{r}
# RGB bands + cloud cover mask
col <- stac_image_collection(items$features, 
                             asset_names = c("B02", "B03", "B04", "Fmask"))
```


```{r sf_bay}
# use a cloud mask -- not sure I have this correct
# https://lpdaac.usgs.gov/documents/1326/HLS_User_Guide_V2.pdf
cloud_mask <- image_mask("Fmask", values=1) # mask clouds and cloud shadows
rgb_bands <- c("B04","B03", "B02")

# Here we go! note eval is lazy
raster_cube(col, v, mask=cloud_mask) |>
  select_bands(rgb_bands) |>
  plot(rgb=1:3)
```


## Scaling up

Same code with larger search box:

```{r}
bbox <- c(xmin=-123, ymin=37, xmax=-121, ymax=39) 

start <- "2023-01-01"
end <-  "2023-01-31"
items <- stac("https://cmr.earthdata.nasa.gov/stac/LPCLOUD") |> 
  stac_search(collections = "HLSL30.v2.0",
              bbox = c(bbox),
              datetime =paste(start,end, sep = "/")) |>
  post_request() |>
  items_fetch() |>
  items_filter(filter_fn = \(x) {x[["eo:cloud_cover"]] < 20})
```


```{r}
view <- cube_view(srs = "EPSG:4326",
                        extent = list(t0 = as.character(start), 
                                      t1 = as.character(end),
                                      left = bbox[1], right = bbox[3],
                                      top = bbox[4], bottom = bbox[2]),
                        nx = 1024, ny = 1024, dt = "P1M")
assets <- stac_image_collection(items$features, 
                             asset_names = c("B02", "B03", "B04", "Fmask"))
```


```{r scalingup}
raster_cube(assets, view) |>
    select_bands(c("B04","B03", "B02")) |>
    plot(rgb=1:3)
```