These tutorials instruct on a workflow for collecting, visualising, and analysing spatial data for mapping and monitoring diverse landscapes.

This workflow is comprised of open-source geospatial applications:

  • In the field: Use QField mobile GIS to map landscape resource use.
  • In the cloud: Server apps and databases to sync data collected on mobile devices.
  • On your desktop: Use map.landscape dashboard to analyse and visualise landscape data collected in-the-field.
  • At scale: Use satellite images and machine learning to monitor inter-annual landscape dynamics.

Please visit the Livelihoods and Landscapes project site for more information about how these geospatial applications are being used for monitoring agricultural landscapes.

Mobile GIS data collection

This workflow uses QField mobile GIS for mapping diverse landscapes and how landscape resources are used in-the-field. There are three introductory lessons that outline the steps required to setup a QField data collection project followed by tutorials demonstrating how QField has been used by landscape stakeholders in Fiji and Tonga.

Lesson 1

  1. QGIS and QField setup
  2. Project creation and form generation

Lesson 2

  1. Opening projects in QField
  2. Data collection using QField
  3. Syncing data from mobile devices

Lesson 3

  1. Generating complex data collection forms

Tonga Crop Survey

Slide deck demonstrating how QField can be used for farm and crop surveys.

Community mapping

Use QField to map landscape features used to support livelihoods

Ground truth data collection

Use QField to collect ground truth points for validating land cover maps generated from satellite images.

Ground Truth - ERPD Pilot

Second tutorial, using QField to collect ground truth points for validating land cover maps generated from satellite images.

Visualise and analyse

map.landscape overview

Introduction to map.landscape dashboard for syncing, visualising, and analysing landscape data collected in-the-field.

Map landscape dynamics

Collect Earth Online

Use machine learning and satellite images to map inter-annual landscape dynamics.