Spatial differentiation of the leaf area index in forests in ecological transition zones and its environmental response

The leaf area index (LAI) is a crucial vegetation parameter that characterizes leaf sparsity and canopy structure, and the study of the spatial distribution pattern of the forest LAI and its environmental response can help to reveal the adaptive capacity of forest vegetation to climate change in semiarid areas. In this paper, a remote sensing inversion model of the LAI, which pertains to the forest ecosystem of Xinglong Mountain in the transition zone between the Qinghai‒Tibet Plateau and Loess Plateau, was established by combining an optical instrumentation method, a remote sensing inversion method, and a generalized additive model (GAM). The results showed that (1) the Meris terrestrial chlorophyll index (MTCI) linear regression model provided the greatest explanatory power for the LAI in the Xinglong Mountain forest, with R² = 0.88 and RMSE = 0.32. (2) The LAI was influenced mainly by the altitude, slope, profile curvature, aspect, planform curvature, temperature, precipitation, and evapotranspiration. According to the single-factor GAM, altitude (R² = 0.43) explained most of the total variation in the LAI, followed by precipitation (R² = 0.36). According to the multifactor GAM, the above influencing factors could explain 84.2% of the total variation in the LAI, which was significant (P < 0.001). (3) Interaction analysis revealed that the LAI was significantly influenced by the interaction between topographic and meteorological factors (P < 0.001). It was revealed that the topography of Xinglong Mountain is fragmented, the vertical band spectrum of vegetation is notable, and the forest LAI exhibits high spatial heterogeneity under the interaction between topographic and meteorological factors, reflecting the environmental response mechanism of vegetation growth in forest ecosystems in ecological transition zones.