Author: Jose Samos (jsamos@ugr.es)
Date: 2024-12-07

# CORINE Land Cover Data and Styles

#### Jose Samos (jsamos@ugr.es)

#### 2024-12-07

# Introduction

This section introduces CORINE Land Cover. It also presents the `clc` package, highlighting its functionalities. Finally, the document’s structure is outlined.

## CORINE Land Cover

CORINE Land Cover (CLC) is a standardized geospatial dataset that provides detailed land cover and land use information across Europe. Established under the European Commission’s CORINE (Coordination of Information on the Environment) program, CLC offers consistent and comparable land cover data for environmental monitoring and spatial planning.

CLC aligns with the INSPIRE Directive (Infrastructure for Spatial Information in the European Community), which establishes a framework for spatial data interoperability across Europe. CLC datasets conform to INSPIRE’s standards for land cover data, ensuring compatibility with other datasets and supporting European environmental and planning initiatives.

CLC is available in both vector and raster formats:

- Vector Format: Represents land cover polygons, each categorized using predefined CLC codes. This format typically includes an associated style definition (e.g., QML or SLD) that ensures consistent visualization of land cover classes across platforms.

- Raster Format: Provides land cover data in a gridded format with cell values corresponding to CLC codes.

Each CLC code is associated with a specific color and description as part of its classification system. Style files (e.g., QGIS QML or SLD files) are often included with CLC datasets, enabling standardized visualization of land cover classes for accurate and intuitive map representation. In GeoPackage and PostGIS formats, styles are stored in a dedicated table, which contains metadata associated with each layer.

## The `clc` package

The goal of the `clc` package is to support operations with CLC data, addressing the challenges and specificities it presents, particularly those related to its associated styles. Its main characteristics are the following:

1. It automates the extraction, application, and preservation of styles across workflows.

2. Tasks such as reading CLC vector data, applying styles, clipping layers, and converting vector data to raster format involve multiple steps. The `clc` package provides simplified, high-level functions to handle these tasks efficiently.

3. It handles CLC data in both GeoPackage and PostGIS formats, supporting them as sources and destinations.

The `clc` package includes the `clc` class, designed as a comprehensive toolkit for managing and processing CLC data, enabling users to perform a wide range of geospatial operations efficiently.

## Document Structure

Apart from this introduction, the document begins by presenting the data available within the package. It then demonstrates the functionalities of the `clc` class through a complete usage example. Finally, it concludes with a summary of the functionalities and their benefits.

# Data Included in the Package

The package includes the following datasets to support workflows with CLC data:

- `clc.gpkg`, a GeoPackage file containing the following vector layers:
  - `clc`: A fragment of CLC data for a region in Granada, Spain, stored in vector format. This layer includes the associated style definitions, which are stored within the same GeoPackage. The data was sourced from the CNIG (Centro Nacional de Información Geográfica).
  - `lanjaron`: A polygonal vector layer representing the boundaries of the municipality of Lanjarón, located in Granada, Spain. This data was sourced from the DERA (Datos Espaciales de Referencia de Andalucía).
- `mdt.tif`, a raster file containing a Digital Terrain Model (DTM) for the same region as the `clc` and `lanjaron` layers. This data was also obtained from the CNIG. The DTM data is not actually used; only its format is needed as a template to create a base raster.

These datasets provide a foundational base for exploring the functionalities of the package, including vector and raster data processing and visualization.

# Class-Based Operations

Key features of the `clc` class include the ability to read CLC datasets directly from widely used geospatial databases, such as GeoPackage or PostGIS.

The class also facilitates advanced visualization of CLC vector data, complete with associated styles, to accurately represent land cover classifications. Users can further refine datasets by clipping CLC data to specific regions of interest while preserving style consistency, enabling localized analyses.

In addition to vector processing, the `clc` class supports the conversion of CLC vector data into raster formats, expanding its utility for raster-based analyses. Once rasterized, users can visualize CLC raster data with styles that mirror the original vector representation, maintaining interpretability.

To support data sharing and further processing, the `clc` class includes functionality for exporting both CLC data and their associated styles back to GeoPackage or PostGIS, ensuring data integrity and compatibility with external systems.

The following provides a detailed example illustrating the functionalities described earlier.

## Create an object of class `clc`

We start by showing how to create an object of class `clc` from a vector layer. The `source` parameter specifies the source of the vector data, which can be a path to a GeoPackage file or a `DBI` connection to a PostGIS database. The `layer_name` parameter is the name of the specific layer in the source to be used.

    library(clc)

    source_gpkg <- system.file("extdata", "clc.gpkg", package = "clc")

    clc_data <- clc(source = source_gpkg, layer_name = "clc")

To create a `clc` object, the vector layer must have associated styles in its source. These styles are essential as they define the visual appearance of the CLC data, including category colors and labels.

We can include a parameter called `field`, which is optional. It should only be provided if the function is unable to automatically determine the field containing the CLC codes in the layer. This can happen if there are multiple fields or none at all. If `field` is set to `NULL` (default), the function will attempt to locate the appropriate column that contains the CLC codes based on the layer’s structure.

The function returns an object of class `clc`, which can then be used for further analysis and visualization.

In this case, the following code would achieve the same result, assuming the database contains the same layer and styles stored.

    library(clc)

    conn <- RPostgres::dbConnect(
      RPostgres::Postgres(),
      dbname = 'exampledb',
      host = 'localhost',
      port = '5432',
      user = 'user',
      password = 'password'
    )

    clc_data <- clc(source = conn, layer_name = "clc")

    DBI::dbDisconnect(conn)

This example demonstrates how to use a PostGIS database connection instead of a GeoPackage file. However, since the example is designed to run within the package and without external dependencies like a live database, we use the local `clc.gpkg` file instead.

## Plot of Vector CLC Data

Once the object is created with its associated style, the next step is to visualize the data with the applied style. This is achieved by using the following function:

    clc_data |>
      plot_clc()

This command automatically considers the style that was previously associated with the `clc` object. It ensures that the vector data is displayed with the appropriate colors and descriptions, as defined in the source, without the need for further styling adjustments.

The vector plot for CLC data is created using the `ggplot2::ggplot` function. This function is defined using other functions from the `ggplot2` package to ensure the plot is displayed with appropriate styling and formatting.

However, instead of directly using `plot_clc`, the `prepare_plot` function is included in the package to define the basic elements of the plot. This function prepares the necessary components for visualization, but leaves the final presentation and customization up to the user.

To complete the plot configuration, the missing elements, such as color and labels, can be obtained using the `get_levels` function. These elements can then be used to define the presentation according to the user’s preferences. For example, the following code achieves the same result as `plot_clc`, but provides more flexibility for further customization:

    p <- clc_data |>
      prepare_plot()

    levels <- clc_data |>
      get_levels()

    p <- p +
      ggplot2::scale_fill_manual(
        values = stats::setNames(levels$color, levels$id),
        labels = stats::setNames(levels$description, levels$id),
        name = ""
      ) +
      ggplot2::theme(
        legend.position = "right",
        legend.key.height = ggplot2::unit(2, "cm"),
        legend.title = ggplot2::element_text(size = 12),
        legend.text = ggplot2::element_text(size = 10)
      ) +
      ggplot2::theme_minimal()

    p

This code customizes the plot by defining the color scale, adding labels, and modifying the appearance of the legend. It allows for full control over how the data is presented, beyond the default configuration provided by `plot_clc`.

## Clipping CLC Data to a Region of Interest

In this section, we focus on the process of clipping CLC data to a region of interest, using the provided `cut_to_extent` function. This function is an essential tool for isolating specific areas within the CLC data, allowing users to work with smaller, relevant subsets of the data.

First, we read the clipping layer (the region of interest) from the source GeoPackage, which defines the geographical boundaries for the clip. After loading the region, we use the `cut_to_extent` function to clip the CLC data to this region.

Once the data is clipped, it is visualized using the `plot_clc` function, which applies the appropriate styling and provides a clear representation of the clipped CLC data.

Here’s an example of how to clip the CLC data to a region and visualize the result:

    # Read the clipping layer (region of interest)
    region <- sf::st_read(source_gpkg, layer = "lanjaron", quiet = TRUE)

    # Clip the CLC data to the region of interest
    clc_clipped <- clc_data |>
      cut_to_extent(region)

    # Visualize the clipped CLC data with its associated style
    clc_clipped |>
      plot_clc()

This workflow demonstrates how the `cut_to_extent` function can be used to subset CLC data by a specific region and visualize the clipped data, all while retaining the correct styling.

## Saving CLC Data and Styles

In this section, we focus on how to save the CLC data, along with its associated styles, to a GeoPackage. This process ensures that the data is preserved in a standard format for future use, with the correct styling embedded in the file.

For example, after clipping the CLC data, you may want to store the resulting subset along with the style information in a GeoPackage file (new or existing one). The `save_to` function is used to write both the CLC data and its styles to the specified GeoPackage destination.

The following example demonstrates how to save the clipped CLC data and its styles to a new GeoPackage:

    # Define the output GeoPackage file
    output_gpkg <- tempfile(fileext = ".gpkg")

    # Capture output to suppress messages (optional)
    sink(tempfile())

    # Save the clipped data and its styles to the new GeoPackage
    clc_clipped |>
      save_to(output_gpkg)

    # Stop capturing output
    sink()

This workflow saves the clipped CLC data to a new GeoPackage file, ensuring that both the data and its associated styles are properly stored. You can later access the file, preserving the full context of the original dataset, including the applied styles.

In addition to saving the clipped data to a GeoPackage, you can also save it to a PostGIS database. The following example demonstrates it. However, this example is not executed here to avoid creating external dependencies on a database connection.

    conn <- RPostgres::dbConnect(
      RPostgres::Postgres(),
      dbname = 'exampledb2',
      host = 'localhost',
      port = '5432',
      user = 'user',
      password = 'password'
    )

    clc_clipped |>
      save_to(conn, 'exampledb2')

    DBI::dbDisconnect(conn)

The same `save_to` function can be used with a PostGIS connection, just like with GeoPackage, enabling flexibility in where the data is stored.

## Copying Styles

The `copy_to` function in the `clc` package enables you to copy only the style definitions from one source and apply them to a specified layer or all layers in the destination. If no specific layer is indicated, the style is applied to all layers in the destination.

Below is an example demonstrating how to use `copy_to`:

    # Create a new GeoPackage without style definition
    destination_gpkg <- tempfile(fileext = ".gpkg")
    clc_layer <- sf::st_read(source_gpkg, layer = "clc", quiet = TRUE)
    sf::st_write(
      clc_layer,
      destination_gpkg,
      layer = "clc2",
      delete_layer = TRUE,
      quiet = TRUE
    )

    # Copy the style to the new GeoPackage
    clc_clipped |>
      copy_to(to = destination_gpkg, layers = "clc2")

    # Create a clc object from the new GeoPackage and view it
    clc_data2 <- clc(source = destination_gpkg, layer_name = "clc2")

    clc_data2 |>
      plot_clc()

This workflow demonstrates the flexibility of the `clc` package in managing styles across multiple GeoPackages. The `copy_to` function can also be used with a PostGIS database as the destination.

## Converting CLC Data to Raster Format

The `as_raster` function in the `clc` package provides an efficient way to convert vector-based CLC data into a raster format. This is particularly useful when working with raster-based analysis workflows

The function allows specifying a base raster for consistent alignment and resolution with existing raster data. If no base raster is provided, a custom resolution can be specified to generate the raster grid.

The raster output retains the CLC categories, ensuring that the data remains consistent with its original classification.

Below are two examples demonstrating the use of `as_raster`:

- If a base raster is available, you can use it to define the extent, resolution, and coordinate reference system (CRS) for the output raster.

    raster_path <- system.file("extdata", "mdt.tif", package = "clc")
    base_raster <- terra::rast(raster_path)

    clc_raster1 <- clc_clipped |>
      as_raster(base_raster = base_raster)

- When no base raster is available, you can specify a resolution to define the raster grid.

    clc_raster2 <- clc_clipped |>
      as_raster(resolution = 50)

These examples demonstrate how `as_raster` streamlines the conversion process, making it easier to integrate vector-based CLC data into raster workflows.

## Applications of the Generated Raster

After converting CLC data to raster format using the `as_raster` function, the raster can be used for various applications. Below, we demonstrate two common use cases.

These examples illustrate the versatility of the raster output, making it suitable for visualization, sharing, or extended GIS workflows.

### Visualizing the Raster Data

For raster visualization, the `terra::plot` function is used with the color scheme configured (using the `col` parameter) to match the CLC styles. The `plot_clc` function allows customization of all other parameters supported by `terra::plot`.

    clc_raster1 |>
      plot_clc()

This renders the raster using the colors and descriptions associated with the original CLC styles, ensuring a clear and accurate representation of the data.

### Getting the Raster

The `get_raster` function extracts a `terra::SpatRaster` object for further customization, analysis, or storage.

Once the raster is obtained, it can be exported to a file format such as GeoTIFF. This is particularly useful for sharing the data or for further processing in GIS software.

    clc_r <- clc_raster1 |>
      get_raster()

    output_tif <- tempfile(fileext = ".tif")
    terra::writeRaster(clc_r,
                       output_tif,
                       filetype = "GTiff",
                       overwrite = TRUE)

This workflow creates a GeoTIFF file containing the rasterized CLC data, making it compatible with most GIS platforms and software.

# Conclusions

The `clc` package provides an efficient and user-friendly framework for managing and processing CLC data. By automating key operations such as style handling, clipping, rasterization, and data export, it simplifies workflows and enhances interoperability between geospatial formats like GeoPackage and PostGIS.

Through its flexible and well-documented functionality, the package addresses the complexities associated with CLC data, ensuring accurate visualization and seamless integration into GIS workflows. The detailed examples and workflows presented demonstrate its potential to streamline CLC data management, making it a valuable tool for environmental analysis, spatial planning, and research applications.