Optimization of multi-objective multi-functional landuse zoning using a vector-based genetic algorithm

Abstract

Contemporary urban planners optimize economic and ecological tradeoffs from different landuses using raster models and homogeneous functional zones. However, ecosystem services depend on the diversity and connectivity of non-homogeneous ecological functions operating within functional zones. To account for these dependencies, we developed a multi-objective functional zoning optimization (MOFZO) model that considers the heterogeneity of internal ecological and economic functions within zones. Vector is used as optimization units, and blue-green-gray (BGG) composition mixture of water/vegetation/urban, within functional zones was used to describe the internal heterogeneity from mixed landuse in functional zones. Results from an extended NSGA-II model for Shenzhen in China, using three conflicting objectives (maximum GDP, maximum ESV, maximum Connectedness), showed patches around ports and coastal areas only changed BGG, which is consistent with the scarcity of coastal land resources and the need to internally optimize land use. Compared to the conventional NSGA-II case, ESV conservation performance was optimized in 41 % of the total changed patches through BGG changes only. These finding provide a practical functional zoning model for planners with deeper insights on implications of constraints and internal changes in functional zones, that materially improves overall ecosystem services within the limited land resources of coastal regions.