Urban scenic forests are a main component of forest parks, and a quantitative study of the landscape color of urban scenic forests can provide a scientific reference for the planning of forest parks and the design of scenic forest management measures. The purpose of the study is to find the quantitative correlation between autumn landscape color and ecological service functions and to obtain the important influencing factors.
This study focuses on Purple Mountain National Forest Park in Nanjing as a case study area and uses forest resource survey data from Purple Mountain National Forest Park in 2020, autumn landscape color photograph data from Purple Mountain National Forest Park in 2020, and digital elevation model data as the main information sources. The correlation between two ecological functions of above-ground biomass (AGB), tree species diversity (TSD), and influencing factors (including color factors, stand factors, and terrain factors) were both analyzed by Pearson correlation analysis. Then, multiple linear regression (MLR) and random forest (RF) methods were used to perform the quantitative relationship between the functions.
The results show that, in the established quantitative models of AGB, with TSD as the dependent variable, the correlation coefficients of the MLR model are both above 0.784, while the correlation coefficients of the RF model are all above 0.872. Moreover, the brightness value of the main color (BRI), the number of yellow-green blocks (NYG), and the number of yellow blocks (NY) have important effects on the two ecological service functions.
In conclusion, there are complex non-linear relationships between the ecological service functions of AGB, TSD, and influencing factors, and the landscape color can reflect the ecological function of the scenic forest to some extent. In addition, stand factors and color factors have important effects on the ecological function of AGB. Color factors and terrain factors have important effects on the ecological function of TSD. BRI, NYG, and NY have important effects on the two ecological functions. Finally, this quantitative method has universal applicability in the temperate zone, warm temperate zone, and subtropical zone of China.