add openEO example, improve descirption of gdalcubes example (see htt…

…ps://github.com/Robinlovelace/geocompr/pull/784)

10-gis.Rmd

@@ -916,7 +916,7 @@ The SpatioTemporal Asset Catalog (STAC)\index{STAC} is a general description for
 Besides simple static catalog descriptions, STAC-API presents a web service to query items (e.g. images) of catalogs by space, time, and other properties. 
 In R, the **rstac** package [@simoes_rstac_2021] allows to connect to STAC-API endpoints and search for items. 
 In the example below, we request all images from the [Sentinel-2 Cloud-Optimized GeoTIFF (COG) dataset on Amazon Web Services](https://registry.opendata.aws/sentinel-2-l2a-cogs)\index{COG} that intersect with a predefined area and time of interest. 
-The result contains all found images and their metadata and URLs pointing to actual files on AWS. 
+The result contains all found images and their metadata (e.g. cloud cover) and URLs pointing to actual files on AWS. 
 
 ```{r 09-stac-example, eval = FALSE}
 library(rstac)
@@ -940,7 +940,7 @@ As an R user, you don't have to install anything to work with COGs because [GDAL
 For larger areas of interest, requested images are still relatively difficult to work with: they may use different map projections, may spatially overlap, and the spatial resolution often depends on the spectral band. 
 The **gdalcubes** package [@appel_gdalcubes_2019] can be used to abstract from individual images and to create and process image collections as four-dimensional data cubes\index{data cube}.
 
-The code below shows a minimal example to create a lower resolution (250m) maximum NDVI composite from the Sentinel-2 images returned by the previous STAC-API search.
+The code below shows a minimal example to create a lower resolution (250m) maximum NDVI composite from the Sentinel-2 images returned by the previous STAC-API search. 
 
 ```{r 09-gdalcubes-example, eval = FALSE}
 library(gdalcubes)
@@ -961,6 +961,10 @@ cube = raster_cube(collection, v) |>
 # plot(cube, zlim = c(0,1))
 ```
 
+To filter images by cloud cover, we provide a property filter function that is applied on each STAC\index{STAC} result item while creating the image collection. 
+The function receives available metadata of an image as input list and returns a single logical value such that only images for which the function yields TRUE will be considered. 
+In this case, we ignore images with 10% or more cloud cover. 
+
 The combination of STAC\index{STAC}, COGs\index{COG}, and data cubes\index{data cube} forms a cloud-native workflow to analyze (larger) collections of satellite imagery in the cloud\index{cloud computing}. 
 For more details, please refer to this [tutorial presented at OpenGeoHub summer school 2021](https://appelmar.github.io/ogh2021/tutorial.html).
 
@@ -975,8 +979,47 @@ Implementations are available for eight different backends (see https://hub.open
 Since the functionality and data availability differs among the backends, the **openeo** R package [@lahn_openeo_2021] dynamically loads available processes and collections from the connected backend. 
 Afterwards, users can load image collections, apply and chain processes, submit jobs, and explore and plot results. 
 
-
-
+The following code will connect to the [openEO platform backend](https://openeo.cloud/), request available datasets, processes, and output formats, define a process graph to compute a maximum NDVI image from Sentinel-2 data, and finally executes the graph after logging in to the backend. 
+The openEO\index{openEO} platform backend includes a free tier and registration is possible from existing institutional or social platform accounts. 
+
+
+```{r 09-openeo-example, eval=FALSE}
+library(openeo)
+con = connect(host = "https://openeo.cloud")
+p = processes() # load available processes
+collections = list_collections() # load available collections
+formats = list_file_formats() # load available output formats
+
+# Load Sentinel-2 collection
+s2 = p$load_collection(id = "SENTINEL2_L2A",
+                       spatial_extent = list(west = 7.5, east = 8.5,
+                                             north = 51.1, south = 50.1),
+                       temporal_extent = list("2021-01-01", "2021-01-31"),
+                       bands=list("B04","B08"))
+
+# Compute NDVI vegetation index
+compute_ndvi = p$reduce_dimension(data = s2, dimension = "bands",
+                                  reducer = function(data,context) {
+                                    return((data[2]-data[1])/(data[2]+data[1]))
+                                  })
+
+# Compute maximum over time
+reduce_max = p$reduce_dimension(data=compute_ndvi, dimension = "t",
+                                reducer = function(x,y) {max(x)})
+
+# Export as GeoTIFF
+result = p$save_result(reduce_max, formats$output$GTiff)
+
+# Login, see https://docs.openeo.cloud/getting-started/r/#authentication
+login(login_type = "oidc",
+      provider = "egi",
+      config = list(
+        client_id= "...",
+        secret = "...")))
+
+# Execute processes
+compute_result(graph = result, output_file = tempfile(fileext = ".tif"))
+```
 
 ## Exercises