A New Paradigm for Assessing Detailed Dynamics of Forest Landscape Fragmentation

Abstract

There is an urgent need for a thorough assessment of forest landscape fragmentation to inform forest protection and restoration, and reforestation policies. However, there is currently a lack of an effective comprehensive index for forest landscape fragmentation, and detailed knowledge of the forest landscape fragmentation dynamics remains insufficient. Here, taking Putian City of Fujian Province in Southeastern China as a case, we employed a forest fragmentation comprehensive index (FFCI) to capture key features of forest landscape fragmentation, such as patch size, number, and distribution. Then, bivariate spatial autocorrelation analysis was employed to identify the spatial associations between the static forest landscape fragmentation (FFCI) and the dynamic forest landscape fragmentation (ΔFFCI), and the spatial coupling modes among the three individual components of FFCI (mean patch area, MPA; aggregation index, AI; patch density, PD) were identified to explore the detail process of forest landscape fragmentation. Finally, the random forest model was applied to observe the impact factors of forest landscape fragmentation dynamics. The findings showed that forest landscapes with different degrees of fragmentation exhibited more noticeable changes at both ends (i.e., either high or lower-level fragmentation), with the intermediate level remaining consistent from 2000 to 2020. Around 18.3% of forest landscapes experienced a decrease in fragmentation, particularly in the northern part of the study area, while approximately 81.7% of forest landscapes exhibited an increasing trend in fragmentation. The bivariate spatial autocorrelation analysis indicated that the proportion of Low–High-type grids was the highest at 17.3%, followed by the High–High type at 7.0%. We also identified eight forest landscape fragmentation modes, which indicate the most significant forest landscape fragmentation pattern is a decrease in MPA and an increase in PD. Moreover, the anthropogenic factors (e.g., population density and night light intensity) were found to dominate the FFCI dynamics during 2000–2020. This study offers an efficient research paradigm for the dynamics of forest landscape fragmentation. The outcomes are conducive to an in-depth comprehension of the detailed dynamic information of forest landscape fragmentation, and supply a scientific foundation for enhancing the overall ecological service function of the forest.

Keywords

• forest landscape fragmentation
• bivariate spatial autocorrelation analysis
• random forest model
• deep learning algorithms