A primary framework for building a forest landscape restoration programme from the ground-up is called the Restoration Opportunities Assessment Methodology (ROAM). This in-depth step-by-step methodology has been proven effective in assessing and laying the groundwork for FLR work with practical steps for diverse stakeholders to restore landscapes at any scale.
ROAM is a flexible and cost-effective analytic process for identifying restoration opportunities at national or sub-national levels, as well as describing how those opportunities relate to food, water and energy security. The application of ROAM generates good context-specific knowledge relevant to understanding and addressing forest and land-use planning and management. Specifically, it helps to:
- identify priority areas for restoration;
- prioritise relevant and feasible restoration intervention types across the assessment area;
- quantify costs and benefits of each intervention type;
- analyse the finance and investment options for restoration in the assessment area;
- estimate the values of additional carbon sequestered by these intervention types; and
- come up with a diagnostic of ‘restoration readiness’ and strategies for addressing major policy and institutional bottlenecks.
Through the participatory processes, the assessment provides a framework for a common setting of restoration goals at a landscape level. The assessment also provides a participatory framework that can be used to set common restoration goals that address immediate priorities, such as livelihoods.
The Guide to ROAM handbook has been developed to guide assessment teams through the ROAM framework – or any subsection of it. The guide is intended to engage a wide range of disciplines in the process of learning, thereby continuing to improve the methodology. It includes descriptions of the individual tools and components of ROAM as well as guidance on how they can be combined and sequenced to suit different needs. The handbook is available in English, French, Indonesian, Spanish, Portuguese and Russian.