Multi-Scenario Simulation of Green Space Landscape Pattern in Harbin City Based on FLUS Model.

In this study, the change in green space in different scenarios and the index characteristics of landscape patterns were analyzed and were conducive to providing the decision basis for future green space planning in Harbin, a city in Northeast China. The FLUS model was used to predict the layout of green space, and the prediction results were analyzed and evaluated using the landscape index method. Combined with the MOP model and LINGO12.0, the objective function of economic benefit and ecological benefit was established to maximize the comprehensive benefit. As revealed by the outcome, from 2010 to 2020, the fragmentation degree of cultivated land, forest, and grassland decreased, and the overall landscape level tended to be diversified and uniform. In the status quo scenario, the cultivated land and the forest land were increased, whereas the water area and the wetland changed little, and its overall benefit was the lowest. The forest was increased by 137.46 km² in the ecological protection scenario, the largest among the three scenarios, and the overall water quality improved. In the economic development scenario, the cultivated land tended to expand rapidly, the connectivity was increased, and the area of forest was decreased by 69.19 km², and its comprehensive benefit is lower than that under the scenario of ecological protection. The sustainable development scenario achieved the most significant economic and ecological benefits, with a total income of CNY 435,860.88 million. Therefore, the future green space pattern should limit the expansion of cultivated land, maintain the spatial pattern of woodland and wetland, and enhance the protection of water area. In this study, Harbin green space was studied from different scenario perspectives, combined with landscape pattern index and multi-objective planning, which is of great significance for Harbin green space planning decisions in the future and improving comprehensive benefits.