Coupling spatial multiattribute analysis and optimization to identify reforestation priority areas : a case study in central Mexico

Abstract

Reforestation programs have been proposed as a remedial measure to tackle deforestation and forest ecosystems degradation. Because one of the main constraints to the implementation of restoration practices is lack of funding, these programs need to be carefully planned to efficiently use the economic and human resources invested. In this study we present a geospatial decision - making tool to identify suitable areas for restoration. The overall approach entails (1) the use of the simple multiattribute rating technique (SMART) to identify and rank the attributes according to their importance for prioritizing areas for restoration and (2) the implementation of 0–1 integer programming to select the areas that maximize the environmental benefit. The approach is exemplified through a case study in central Mexico’s mountainous state of de México, encompassing an area just above 2 million ha. Specialists in different aspects of reforestation selected the following attributes to identify priority areas for reforestation: erosion, land use/land cover, position in the watershed, soil type, terrain slope, and precipitation. In total, 644,642 ha were classified under very high priority for reforestation. Of these, 17,059 ha were selected to maximize the environmental benefit without exceeding the available budget. The elected sites were mainly in the forested zones of steeply sloped mountains. Although the multiattribute decision analysis, the optimization model, and the spatial analysis were only loosely coupled, their combination proved to be an innovative and practical approach to systematically identify priority areas for reforestation on a yearly basis.