The leaf area index (LAI) is a crucial indicator for quantifying forest productivity and community ecological processes. Satellite remote sensing can achieve large-scale LAI monitoring, but it needs to be calibrated and validated according to the in situ measurements on the ground. In this study, we attempted to use different indirect methods to measure LAI in a tropical secondary forest. These methods included the LAI-2200 plant canopy analyzer (LAI-2200), Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS) (using single-station and multi-station measurements, respectively). Additionally, we tried to correct the measured LAI by obtaining indicators of woody components and clumping effects. The results showed that the LAI of this forest was large, with estimated values of 5.27 ± 1.16, 3.69 ± 0.74, 5.86 ± 1.09, 4.93 ± 1.33, and 3.87 ± 0.89 for LAI-2200, DHP, TRAC, TLS multi-station, and TLS single-station, respectively. There was a significant correlation between the different methods. LAI-2200 was significantly correlated with all other methods (p < 0.01), with the strongest correlation with DHP (r = 0.684). TRAC was significantly correlated with TLS single-station (p < 0.01, r = 0.283). TLS multi-station was significantly correlated with TLS single-station (p < 0.05, r = 0.266). With the multi-station measurement method, TLS could maximize the compensation for measurement bias due to the shadowing effects. In general, the clumping index of this forest was 0.94 ± 0.05, the woody-to-total area ratio was 3.23 ± 2.22%, and the total correction coefficient was 1.03 ± 0.07. After correction, the LAI estimates for all methods were slightly higher than before, but there was no significant difference among them. Based on the performance assessment of existing ground-based methods, we hope to enhance the inter-calibration between methods to improve their estimation accuracy under complex forest conditions and advance the validation of remote sensing inversion of the LAI. Moreover, this study also provided a practical reference to promote the application of LiDAR technology in tropical forests.

This study investigates how different vegetation types influence the molecular structure and abundance of soil organic carbon (SOC), as well as their influence on microbial metabolic pathways and community composition. Soil samples were collected from four different sites: a woodland dominated by Drypetes perreticulata (DP), a woodland dominated by Horsfieldia hainanensis (HM), a Zea mays L. field (ZL), and a citrus reticulata orchard (CB). The molecular structure of soil organic carbon (SOC) was characterised using Fourier Transform Infrared (FTIR) spectroscopy, identifying aromatic carbon (ArC), polysaccharide carbon (PSC), alkyl carbon (AlkC), amine carbon (AmC), ether carbon (EtC), and olefin carbon (OleC). Our results indicated significant variations across vegetation types: DG exhibited a significantly higher ArC content, while maize fields showed lower PSC levels. To analyse the relationships between different samples, we employed principal component analysis (PCA), which revealed distinct organic carbon structures across vegetation types, with the forests (DG and HM) significantly differing from agricultural sites (ZL and CB). Additionally, the 16S V3_V4 region of soil bacteria was sequenced using high-throughput sequencing. We employed PICRUSt2 to predict microbial metabolic pathways, revealing consistent core metabolic functions across samples but significant variations in secondary metabolism, with HM samples exhibiting the most distinctive metabolic profiles. Redundancy analysis (RDA) further demonstrated that microbial metabolic pathway variation explained 55.66% of organic carbon structure variance. Key microbial taxa exhibited significant associations with specific carbon source types and functional pathways. These findings highlight the pivotal mechanisms by which different vegetation types regulate soil organic carbon structure and composition by driving changes in microbial metabolic traits and community assembly. This study provides a mechanistic basis for understanding the coupling between vegetation, microorganisms, and carbon cycling, offering significant guidance for optimising vegetation restoration strategies, enhancing soil carbon sequestration capacity, and advancing carbon management practices based on microbial regulation.