Spatial pattern of urban forest diversity and its potential drivers in a snow climate city, Northeast China

Abstract

Urban forest diversity is the cornerstone of providing ecosystem services and improving urban environment. However, the spatial pattern of urban forest (UF) diversity and its driving mechanisms are still not well understood. In our study, using 330 UF sample plots and the corresponding plot-based standard deviation values of remote sensing parameters with different spatial resolutions, we developed prediction models for the spatial pattern of UF diversity, and the driving factors of UF diversity were further explored in our study. Our findings indicated that the 3-m spatial resolution of remote sensing was optimal for predicting UF diversity. Both excessively high and excessively low resolutions can increase misclassification errors. In addition, among the three prediction models, the boosted regression trees (BRT) model exhibited greater robustness than the support vector machine (SVM) and random forest (RF) models for predicting UF diversity. UF species were abundant, with 112 tree species in Changchun City. Spatially, the UF diversity showed a significant gradient decreasing trend from suburban areas (outer rings) to downtown areas. We also observed higher diversity in the older communities with high housing prices, supporting the “legacy effect and luxury effect”. Additionally, our research revealed that socioeconomic factors, landscape cover, and patterns collectively explained 41.25% of the UF diversity variation, wherein socioeconomic factors contributed the most variation (17.33%). We further found that UF diversity does not increase more with increasing house prices when the house prices are higher than ¥ 12,000/m2. The higher patch density reduced UF diversity, while a higher UF landscape shape index (LSI) promoted diversity. The LSI threshold, which significantly impacted UF diversity, was about 12. Our research showed that high-resolution remote sensing offers a rapid, cost-effective approach to acquire UF diversity patterns, which could enhance urban forest diversity and contribute to sustainable urban development.