Planning to Practice: Impacts of Large-Scale and Rapid Urban Afforestation on Greenspace Patterns in the Beijing Plain Area

Abstract

(1) Research Highlights: Afforestation is one of the most effective urban greening practices for mitigating a variety of environmental issues. Globally, municipal governments have launched large-scale afforestation programs in metropolitan areas during the last decades. However, the spatiotemporal dynamics of urban greenspace patterns are seldom studied during such afforestation programs. (2) Background and Objectives: In this study, the Beijing Plain Afforestation Project (BPAP), which planted 70,711 ha of trees in only four years, was examined by integrating spatial and landscape analysis. To evaluate the real-world outcomes of this massive program, we investigated the spatial-temporal dynamics of landscape patterns during the implementation process to identify potential impacts and challenges for future management of new afforestation. (3) Materials and Methods: We analyzed the transition of various patch types and sizes, applied landscape indicators to measure the temporal changes in urban greenspace patterns, and used the landscape expansion index to quantify the rate and extent of greenspace spatial expansion. (4) Results: Our results illustrated that the implementation of afforestation in the Beijing plain area had generally achieved its initial goal of increasing the proportion of land devoted to forest (increased 8.43%) and parks (increased 0.23%). Afforestation also accelerated the conversion of small-size greenspaces to large-size patches. However, the significant discrepancies found between planned and actual afforestation sites, as well as the large conversion of cropland to forest, may present major challenges for project optimization and future management. (5) Conclusions: This study demonstrated that spatial analysis is a useful and potentially replicable method that can rapidly provide new data to support further afforestation ecosystem assessments and provide spatial insights into the optimization of large inner-city afforestation projects.