Spatial optimisation

What is Spatial Optimisation?

Spatial optimisation is a technical discipline that uses computational methods and geographic data to find the best possible arrangement, allocation, or placement of resources, infrastructure, or land uses across a geographical area.

It essentially involves maximizing or minimizing an objective that is related to a geographic problem, while adhering to a set of spatial and non-spatial constraints.

Key components of a spatial optimisation problem include:

• The objective: The goal to be achieved (e.g., maximize biodiversity protection, minimize restoration costs, or find a balance between conflicting goals).
• Decision variables: The choices that can be made (e.g., where to locate a facility, which areas to restore, or how much of a resource to allocate).
• Constraints: The limitations that must be satisfied (e.g., limited budget, minimum habitat size, or existing land use boundaries).

This technique is rooted in geographic information systems (GIS) and operations research, and is used in fields like transportation, urban planning, and natural resource management.

Using spatial optimisation in forest restoration

Spatial optimisation is a powerful tool for developing efficient, transparent, and sustainable forest restoration plans by identifying and prioritizing the optimal locations for restoration efforts.

Forest restoration projects often involve multiple, sometimes conflicting, objectives (e.g., conservation, carbon sequestration, and local livelihoods) and are constrained by limited budgets and practical considerations. Spatial optimisation models help balance these trade-offs to achieve the greatest overall benefit.

Key applications in forest restoration:

• Prioritizing restoration sites: Optimisation algorithms can integrate various datasets—such as soil type, water availability, biodiversity indicators, current deforestation rates, and proximity to existing forests—to generate suitability maps. The models then use these maps to select the set of sites that offers the maximum ecological or socioeconomic benefit for a given budget.
• Balancing multiple goals: Complex models, like integer-linear programming (ILP), can be used to achieve multiple objectives simultaneously, such as:
• Maximizing biodiversity conservation (e.g., selecting areas that enhance habitat connectivity).
• Maximizing provision of ecosystem services (e.g., selecting areas that improve water conservation or soil stabilization).
• Minimizing negative impacts on local livelihoods (e.g., avoiding the conversion of highly productive agricultural land).
• Cost-effectiveness and efficiency: By factoring in the costs of different restoration actions and comparing them to the potential gains at specific locations, spatial optimisation helps determine the most cost-effective strategy. This ensures that limited resources are allocated where they will have the greatest impact.
• Designing connected landscapes: The models can prioritize the selection of sites that create compact patches and enhance ecological connectivity between existing forest fragments, which is crucial for the long-term resilience of ecosystems.

Utilizing Multi-Criteria Decision Making (MCDM)

Forest restoration must balance numerous, often qualitative, criteria such as soil suitability, water quality benefits, proximity to core habitat, and local stakeholder needs. Multi-Criteria Decision Making (MCDM) techniques provide a structured, transparent framework to weigh and rank these attributes before computational optimisation begins.

Techniques such as the Simple Multi-attribute Rating Technique (SMART) and the Analytic Hierarchy Process (AHP) are used to identify and rank attributes according to their perceived importance for prioritizing restoration areas. AHP, in particular, offers a structured framework for complex decision-making in reforestation planning, enabling the integration of both qualitative and quantitative criteria.

Some examples of spatial optimisation in forest restoration

Tools like the SEPAL-based forest restoration planning tool (se.plan) use spatial optimisation approaches to help decision-makers identify and prioritize restoration locations, supporting the development of strategic restoration plans. This video introduces a spatial suitability tool for forest restoration planning developed by FAO and partners.

Spatial optimisation was also used for designing a network of green infrastructure for the EU U to enhance connectivity among protected areas and warrant the provision of ecosystem services. In their study, the authors used the best available data on distribution of vertebrates, habitats and ecosystem services at a European scale and integrated them in a prioritisation exercise to identify new management areas outside the current Natura 2000 network that could help achieve the objectives of the EU green infrastructure network.

Beyond ecological and financial metrics, a study by Gopalakrishna et al. showed that spatial optimisation is essential for maximizing social welfare and ensuring political legitimacy. Integrated spatial optimisation plans deliver benefits evenly across potential restoration areas and are significantly more equitable in their impact compared to single-objective schemes. Distributional equity analysis revealed that integrated plans deliver benefits to a large proportion of individuals who are socioeconomically disadvantaged. In contrast, single-objective strategies, such as a carbon-centric plan, yielded below-average benefits for socio-economically disadvantaged people. This underlines that multi-objective optimisation is not only ecologically and economically important but also a crucial mechanism for achieving maximal social well-being.