RESUMEN
Mangrove forests provide vital ecosystem services. The increasing threats to mangrove forest extent and fragmentation can be monitored from space. Accurate spatially explicit quantification of key vegetation characteristics of mangroves, such as leaf area index (LAI), would further advance our monitoring efforts to assess ecosystem health and functioning. Here, we investigated the potential of radiative transfer models (RTM), combined with active learning (AL), to estimate LAI from Sentinel-2 spectral reflectance in the mangrove-dominated region of Ngoc Hien, Vietnam. We validated the retrieval of LAI estimates against in-situ measurements based on hemispherical photography and compared against red-edge NDVI and the Sentinel Application Platform (SNAP) biophysical processor. Our results highlight the performance of physics-based machine learning using Gaussian processes regression (GPR) in combination with AL for the estimation of mangrove LAI. Our AL-driven hybrid GPR model substantially outperformed SNAP (R2 = 0.77 and 0.44 respectively) as well as the red-edge NDVI approach. Comparing two canopy RTMs, the highest accuracy was achieved by PROSAIL (RMSE = 0.13 m2.m-2, NRMSE = 9.57%, MAE = 0.1 m2.m-2). The successful retrieval of mangrove LAI from Sentinel-2 can overcome extensive reliance on scarce in-situ measurements for training seen in other approaches and present a more scalable applicability by relying on the universal principles of physics in combination with uncertainty estimates. AL-based GPR models using RTM simulations allow us to adapt the genericity of RTMs to the peculiarities of distinct ecosystems such as mangrove forests with limited ancillary data. These findings bode potential for retrieving a wider range of vegetation variables to quantify large-scale mangrove ecosystem dynamics in space and time.
RESUMEN
In the Mekong Delta in southern Vietnam, rats are commonly traded in wet markets and sold live for food consumption. We investigated seroprevalence to selected groups of rodent-borne viruses among human populations with high levels of animal exposure and among co-located rodent populations. The indirect fluorescence antibody test (IFAT) was used to determine seropositivity to representative reference strains of hantaviruses (Dobrava virus [DOBV], Seoul virus [SEOV]), cowpox virus, arenaviruses (lymphocytic choriomeningitis virus [LCMV]), flaviviruses (tick-borne encephalitis virus [TBEV]), and rodent parechoviruses (Ljungan virus), using sera from 245 humans living in Dong Thap Province and 275 rodents representing the five common rodent species sold in wet markets and present in peridomestic and farm settings. Combined seropositivity to DOBV and SEOV among the rodents and humans was 6.9% (19/275) and 3.7% (9/245), respectively; 1.1% (3/275) and 4.5% (11/245) to cowpox virus; 5.4% (15/275) and 47.3% (116/245) for TBEV; and exposure to Ljungan virus was 18.8% (46/245) in humans, but 0% in rodents. Very little seroreactivity was observed to LCMV in either rodents (1/275, 0.4%) or humans (2/245, 0.8%). Molecular screening of rodent liver tissues using consensus primers for flaviviruses did not yield any amplicons, whereas molecular screening of rodent lung tissues for hantavirus yielded one hantavirus sequence (SEOV). In summary, these results indicate low to moderate levels of endemic hantavirus circulation, possible circulation of a flavivirus in rodent reservoirs, and the first available data on human exposures to parechoviruses in Vietnam. Although the current evidence suggests only limited exposure of humans to known rodent-borne diseases, further research is warranted to assess public health implications of the rodent trade.