Search

Soil microorganisms are vital components of the karst terrestrial ecosystem. However, their responses to the vegetation succession on karst mountain peaks remain unclear as to whether soil microbial diversity and community compositions change with vegetation succession. We investigated the diversity and community compositions of soil bacteria and fungi and associated environmental factors along a vegetation succession from moss crusts (MC) to moss crusts with sparse grasses (MCG) to sparse grasses (G) on karst mountain peaks. The results indicated that soil organic carbon and total nitrogen generally increased, and soil pH changed in the range of 8.19–8.44 and slightly declined with vegetation succession. Overall, there was an increase in microbial biomass along the vegetation succession, with the dominant phyla of bacteria, including Proteobacteria, Acidobacteria, Actinobacteria, Choroflexi, Gemmatiomnadates, Bacteroidetes, and Planctomycetes, and the dominated phyla of fungi, including Basidiomycota and Ascomycota. Notably, both the bacterial and fungal community compositions were different among the three successional stages. Spearman's correction analysis showed that soil organic carbon and total nitrogen had stronger and more significant influences on the soil microbial community compositions compared to soil water content, pH, and C:N ratio. Overall, our results provide evidence for the changes and influencing factors of the microbial community with the succession vegetation on karst mountain peaks. [ABSTRACT FROM AUTHOR]

In the low-lying, river-rich Pearl River Delta in South China, an extensive network of flood defense levees, spanning over 4400 km, plays a crucial role in urban flood management. These levees are designed to withstand floods and storm surges, yet their failure can lead to significant human and economic losses, highlighting the need for robust urban flood defense strategies. This necessitates the development of a sophisticated geographic information system for the levee network and rapid, accurate assessment methods for levee conditions to support water management and flood mitigation efforts. This study focuses on the levees along the Hengmen waterway in the Pearl River Delta, utilizing airborne Light Detection and Ranging (LiDAR) technology to gather 3D spatial data of the levees. Employing the Cloth Simulation Filter (CSF) algorithm, non-ground point cloud data were extracted. The study improved upon the region-growing algorithm, using a seed point set approach for the automatic extraction of levee point cloud data. The accuracy and completeness of levee extraction were evaluated using the quality index. This method achieved effective extraction of four levee types, showing significant improvements over traditional algorithms, with extraction quality ranging from 72% to 83%. Key research outcomes include the development of a novel method for detecting localized levee depressions based on the computation of the variance of angles between normal vectors in single-phase levee point cloud data. An adaptive optimal neighborhood approach was utilized to accurately determine the normal vectors, effectively representing the local morphology of the levee point clouds. Applied in three levee depression detection experiments, this method proved effective, demonstrating the capability of single-phase data in identifying regions of levee depression deformation. This advancement in levee monitoring technology marks a significant step forward in enhancing urban flood defense capabilities in regions such as the cities of the Pearl River Delta in China. [ABSTRACT FROM AUTHOR]