Mapping global lake aquatic vegetation dynamics using 10-m resolution satellite observations

Aquatic vegetation is crucial for improving water quality,supporting fisheries and preserving biodiversity in lakes.Monitoring the spatiotemporal dynamics of aquatic vegetation is indispensable for the assessment and protection of lake ecosystems.Nevertheless,a comprehensive global assessment of lacustrine aquatic vegetation is lacking.This study introduces an automatic identification algorithm(with a total accuracy of 94.4%)for Sentinel-2 MSI,enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to 2022.Results show that aquatic vegetation occurred in 81,116 lakes across six continents over the past four years,covering a cumulative maximum aquatic vegetation area(MVA)of 16,111.8 km^(2).The global median aquatic vegetation occurrence(VO,in%)is 3.0%,with notable higher values observed in South America(7.4%)and Africa(4.1%)compared with Asia(2.7%)and North America(2.4%).High VO is also observed in lakes near major rivers such as the Yangtze,Ob,and ParanáRivers.Integrating historical data with our calculated MVA,the aquatic vegetation changes in 170 lakes worldwide were analyzed.It shows that 72.4%(123/170)of lakes experienced a decline in aquatic vegetation from the early 1980s to 2022,encompassing both submerged and overall aquatic vegetation.The most substantial decrease is observed in Asia and Africa.Our findings suggest that,beyond lake algal blooms and temperature,the physical characteristics of the lakes and their surrounding environments could also influence aquatic vegetation distribution.Our research provides valuable information for the conservation and restoration of lacustrine aquatic vegetation.

Quantifying the characteristics of particulate matters captured by urban plants using an automatic approach

It is widely accepted that urban plant leaves can capture airborne particles. Previous studies on the particle capture capacity of plant leaves have mostly focused on particle mass and/or size distribution. Fewer studies, however, have examined the particle density, and the size and shape characteristics of particles, which may have important implications for evaluating the particle capture efficiency of plants, and identifying the particle sources. In addition, the role of different vegetation types is as yet unclear. Here, we chose three species of different vegetation types, and firstly applied an object-based classification approach to automatically identify the particles from scanning electron microscope（SEM）micrographs. We then quantified the particle capture efficiency, and the major sources of particles were identified. We found（1） Rosa xanthina Lindl（shrub species） had greater retention efficiency than Broussonetia papyrifera（broadleaf species） and Pinus bungeana Zucc.（coniferous species）, in terms of particle number and particle area cover.（2） 97.9% of the identified particles had diameter ≤10 μm, and 67.1% of them had diameter ≤2.5 μm. 89.8% of the particles had smooth boundaries, with 23.4% of them being nearly spherical.（3） 32.4%–74.1% of the particles were generated from bare soil and construction activities, and 15.5%–23.0% were mainly from vehicle exhaust and cooking fumes.