Notebook 2: Land health, soil health, and spatial workflows

This second notebook builds directly on the skills introduced in Notebook 1.

It continues the same core workflow in R within Google Colab, but now applies it to land health and soil datasets that are highly relevant to restoration decision-making.

Participants will build on their use of dplyr and ggplot2, and begin working with other spatial packages including leaflet for interactive maps and sf for introductory vector workflows.

Notebook 2 is designed to extend the same step-by-step approach used in Notebook 1. It helps participants strengthen their confidence with data handling and visualisation, while beginning to connect tabular datasets back to mapped locations and restoration areas.

What you will learn and do

This notebook is organised around three connected parts:

1. Work with land health datasets

The first section applies the same analytical workflow from Notebook 1 to land health indicators.

Loading and inspecting land health data
Working with indicators such as tree cover, erosion, and cropland
Checking values, variable types, and summary statistics
Using dplyr and ggplot2 to explore patterns

2. Work with soil health datasets

The second section introduces key soil variables and applies the same workflow to soil data used in restoration assessment and monitoring.

Loading and exploring soil datasets
Working with variables such as SOC, total nitrogen, sand, silt, and pH
Summarising and comparing soil indicators across locations
Interpreting how soil condition relates to restoration potential and land health

3. Working with maps

The final section introduces simple spatial workflows that help connect tabular datasets back to mapped features and locations.

Creating simple interactive maps with leaflet
Working with spatial data frames using sf
Viewing point and polygon data in a mapped environment
Exploring optional extensions into more advanced spatial analysis

Interactive mapping and spatial workflows in R

Land health and soil data in restoration

Land health and soil condition are central to restoration decision-making. Indicators such as tree cover, erosion prevalence, cropland extent, soil organic carbon, nitrogen, texture, and pH help describe how landscapes are functioning and where restoration challenges or opportunities may exist.

These variables can be used to:

Identify degraded or vulnerable areas
Compare conditions across sites or districts
Understand constraints on restoration success
Support targeting, planning, and monitoring

Figure 1. Examples of precipitation and temperature trend rasters from the GGW Dashboards.

In practice, land health and soil datasets are often interpreted together. Vegetation cover, land use, erosion, and soil properties interact to shape productivity, resilience, and the likely outcomes of different restoration interventions.