An integrated approach of remote sensing, GIS and swarm intelligence for zoning protected ecological areas

Abstract

Interest in protecting ecological areas is increasing because of land uses conflicts and environmental pressures. The optimal zoning of protected ecological areas belongs to a NP-hard problem because it is subject to both box and spatial constraints. A challenge in solving area optimization problems emerges with the increasing size of a study region. In this article, an integrated approach of remote sensing, GIS and modified ant colony optimization (ACO) is proposed for application in zoning protected ecological areas. Significant modifications have been made in the conventional ACO so that it can be further extended to solve zoning problems in large regions. An improved selection strategy is designed to accelerate the progress of sites selection for artificial ants. Another important modification in ACO is to incorporate the neighborhood diffusion strategy into pheromone updating. The optimal objective is to generate protected areas that maximize both ecological suitability and spatial compactness. The modified ACO model has been successfully applied to a case study involving an area of 25,483 cells in Dongguan, Guangdong, China. The experiments have demonstrated that the proposed model is an efficient and effective optimization technique for generating optimal protection. The modified ACO model only requires approximately 119 s for determining near-optimal solutions. Furthermore, the proposed method performs better than other methods, including simulated annealing, genetic algorithm, iterative relaxation, basic ACO, and density slicing.