EUDR
As part of the EU Deforestation Regulation (EUDR), the European Union aims to reduce its carbon footprint by reducing the consumption of products that contribute to climate change. Better known as deforestation-free products, these are defined as not being produced on land deforested after December 31, 2020. To ensure that the products comply with this regulation, Apacheta developed a Tool using the web-based mapping platform GEE in collaboration with FAO. The EUDR Forest Dynamic Explorer analyses forest dynamics to verify their existence and confirm that the products are “deforestation-free”.
The maps illustrate forest cover and deforestation based on a comparison of six different high-resolution datasets. This allows users to assess the evidence convergence and identify areas with higher and lower levels of convergence across datasets. To increase sustainable trade, such tools support the EU and other organisations in verifying whether products and their source materials contribute to environmental risks.
GEO LDN Country Teams
As part of Working Group 4 of the Group on Earth Observations for Land Degradation (GEO LDN), Apacheta supported countries around the world with their specific needs regarding geospatial data for their use cases.
GEO LDN - Nigeria
As part of Working Group 4, we supported projects in countries worldwide that utilised geospatial data to combat Land Degradation. In the case of Nigeria, the country team focused on the Amba Community, which faces high levels of deforestation, degraded landscapes, low farm productivity, and food insecurity. We co-developed a questionnaire mapping tool adapted from the WOCAT-LADA (Land Degradation Assessment).
By adjusting the tool to the context of the Amba community’s degraded lands, the questionnaire focused on specific “mapping units” or spatially defined areas, based on characteristics such as land cover and slope. The questionnaires aimed to evaluate the type, extent and causes of degradation as well as the current land-use systems.
Concurrently, we developed a Decision Support System (DSS) utilising Google Earth Engine (GEE) to facilitate data-driven decision-making for land degradation monitoring and restoration planning. This involved training and capacity development on using GEE to map land based on the specific needs of a country. This included adding geospatial layers and assets to GEE, running scripts to visualise and analyse land degradation indicators and customising it for the team's specific context. As a result, they could use the tool to determine restoration planning and Sustainable Land Management implementation plans.
GEO LDN - Colombia
We strive for a balanced use of our land and ecosystems, especially in the case of the Tropical Dry Forests in the Alto Patia area of Colombia. Not only are they unique ecosystems, often undervalued by even environmental experts, but they also serve social and economic services. Our team met with the community members living in the area to assess how it was possible to conserve both the land and biodiversity of such ecosystems while allowing for continued agricultural practices and other developments.
By mapping the area using a GEE tool and developing a Decision Support System (DSS), we enhanced the local community's capacity to create a specific zonation that identifies the tropical dry forest as a protected area at the national level. In an effort to protect the biodiversity of this unique ecosystem, the team collaborated with the National Park Association to support the development of this project. By mapping Tropical Dry Forests and including them in national assessments, the team could push for them to be declared as a newly protected area. The GEE Tool and the incorporation of the Alto Patia Dry Forests into the DSS estimate the level of degraded lands at 75.5%, which is significantly higher than the national average of 29.7%, underscoring the need for immediate protection and restoration efforts. With its assessment of land cover changes, the tool also allowed them to prioritise what areas to restore.
Ghana
Land Degradation (LD) is worsened by and contributes to the climate crisis. As such in Ghana, the country team aimed to address LD and enhance climate resilience through community action planning. Through the integration of Spatial Data, we supported their goals of more effective decision-making by authorities and communities for land use planning. The LDN DSS tool enabled the characterisation of three different zones for Community Action Plans by converging diverse datasets, including those on key biodiversity areas, precipitation patterns, soil organic carbon, topography and land productivity dynamics.
With this multicriteria analysis approach, the Ghana team was able to prioritise communities and their action plans to increase climate resiliency for the most vulnerable members. This systematic approach supported the principle of convergence of evidence, ensuring that planning decisions were well-informed and data-driven. As part of this support, capacity development and training on the use of Google Earth Engine (GEE) became a cornerstone of the project's success, enabling them to carry out a national estimation of LDN indicators. The future goal is to continue on the ground assessments with communities in other districts to verify the datasets and continue improving upon current best practices, which, most importantly, are co-developed in a participatory approach with local stakeholders.
