Spatial distribution of Culex mosquito abundance and associated risk factors in Hanoi, Vietnam.

Japanese encephalitis (JE) is the major cause of viral encephalitis (VE) in most Asian-Pacific countries. In Vietnam, there is no nationwide surveillance system for JE due to lack of medical facilities and diagnoses. Culex tritaeniorhynchus, Culex vishnui, and Culex quinquefasciatus have been identified as the major JE vectors in Vietnam. The main objective of this study was to forecast a risk map of Culex mosquitoes in Hanoi, which is one of the most densely populated cities in Vietnam. A total of 10,775 female adult Culex mosquitoes were collected from 513 trapping locations. We collected temperature and precipitation information during the study period and its preceding month. In addition, the other predictor variables (e.g., normalized difference vegetation index [NDVI], land use/land cover and human population density), were collected for our analysis. The final model selected for estimating the Culex mosquito abundance included centered rainfall, quadratic term rainfall, rice cover ratio, forest cover ratio, and human population density variables. The estimated spatial distribution of Culex mosquito abundance ranged from 0 to more than 150 mosquitoes per 900m2. Our model estimated that 87% of the Hanoi area had an abundance of mosquitoes from 0 to 50, whereas approximately 1.2% of the area showed more than 100 mosquitoes, which was mostly in the rural/peri-urban districts. Our findings provide better insight into understanding the spatial distribution of Culex mosquitoes and its associated environmental risk factors. Such information can assist local clinicians and public health policymakers to identify potential areas of risk for JE virus. Risk maps can be an efficient way of raising public awareness about the virus and further preventive measures need to be considered in order to prevent outbreaks and onwards transmission of JE virus.

Remote quantification of Cochlodinium polykrikoides blooms occurring in the East Sea using geostationary ocean color imager (GOCI).

Accurate and timely quantification of widespread harmful algal bloom (HAB) distribution is crucial to respond to the natural disaster, minimize the damage, and assess the environmental impact of the event. Although various remote sensing-based quantification approaches have been proposed for HAB since the advent of the ocean color satellite sensor, there have been no algorithms that were validated with in-situ quantitative measurements for the red tide occurring in the Korean seas. Furthermore, since the geostationary ocean color imager (GOCI) became available in June 2010, an algorithm that exploits its unprecedented observation frequency (every hour during the daytime) has been highly demanded to better track the changes in spatial distribution of red tide. This study developed a novel red tide quantification algorithm for GOCI that can estimate hourly chlorophyll-a (Chl a) concentration of Cochlodinium (Margalefidinium) polykrikoides, one of the major red tide species around Korean seas. The developed algorithm has been validated using in-situ Chl a measurements collected from a cruise campaign conducted in August 2013, when a massive C. polykrikoides bloom devastated Korean coasts. The proposed algorithm produced a high correlation (R2=0.92) with in-situ Chl a measurements with robust performance also for high Chl a concentration (300mg/m3) in East Sea areas that typically have a relatively low total suspended particle concentration (<0.5mg/m3).

Simple aerosol correction technique based on the spectral relationships of the aerosol multiple-scattering reflectances for atmospheric correction over the oceans.

An estimation of the aerosol multiple-scattering reflectance is an important part of the atmospheric correction procedure in satellite ocean color data processing. Most commonly, the utilization of two near-infrared (NIR) bands to estimate the aerosol optical properties has been adopted for the estimation of the effects of aerosols. Previously, the operational Geostationary Color Ocean Imager (GOCI) atmospheric correction scheme relies on a single-scattering reflectance ratio (SSE), which was developed for the processing of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data to determine the appropriate aerosol models and their aerosol optical thicknesses. The scheme computes reflectance contributions (weighting factor) of candidate aerosol models in a single scattering domain then spectrally extrapolates the single-scattering aerosol reflectance from NIR to visible (VIS) bands using the SSE. However, it directly applies the weight value to all wavelengths in a multiple-scattering domain although the multiple-scattering aerosol reflectance has a non-linear relationship with the single-scattering reflectance and inter-band relationship of multiple scattering aerosol reflectances is non-linear. To avoid these issues, we propose an alternative scheme for estimating the aerosol reflectance that uses the spectral relationships in the aerosol multiple-scattering reflectance between different wavelengths (called SRAMS). The process directly calculates the multiple-scattering reflectance contributions in NIR with no residual errors for selected aerosol models. Then it spectrally extrapolates the reflectance contribution from NIR to visible bands for each selected model using the SRAMS. To assess the performance of the algorithm regarding the errors in the water reflectance at the surface or remote-sensing reflectance retrieval, we compared the SRAMS atmospheric correction results with the SSE atmospheric correction using both simulations and in situ match-ups with the GOCI data. From simulations, the mean errors for bands from 412 to 555 nm were 5.2% for the SRAMS scheme and 11.5% for SSE scheme in case-I waters. From in situ match-ups, 16.5% for the SRAMS scheme and 17.6% scheme for the SSE scheme in both case-I and case-II waters. Although we applied the SRAMS algorithm to the GOCI, it can be applied to other ocean color sensors which have two NIR wavelengths.

Vicarious calibration of the Geostationary Ocean Color Imager.

Measurements of ocean color from Geostationary Ocean Color Imager (GOCI) with a moderate spatial resolution and a high temporal frequency demonstrate high value for a number of oceanographic applications. This study aims to propose and evaluate the calibration of GOCI as needed to achieve the level of radiometric accuracy desired for ocean color studies. Previous studies reported that the GOCI retrievals of normalized water-leaving radiances (nLw) are biased high for all visible bands due to the lack of vicarious calibration. The vicarious calibration approach described here relies on the assumed constant aerosol characteristics over the open-ocean sites to accurately estimate atmospheric radiances for the two near-infrared (NIR) bands. The vicarious calibration of visible bands is performed using in situ nLw measurements and the satellite-estimated atmospheric radiance using two NIR bands over the case-1 waters. Prior to this analysis, the in situ nLw spectra in the NIR are corrected by the spectrum optimization technique based on the NIR similarity spectrum assumption. The vicarious calibration gain factors derived for all GOCI bands (except 865nm) significantly improve agreement in retrieved remote-sensing reflectance (Rrs) relative to in situ measurements. These gain factors are independent of angular geometry and possible temporal variability. To further increase the confidence in the calibration gain factors, a large data set from shipboard measurements and AERONET-OC is used in the validation process. It is shown that the absolute percentage difference of the atmospheric correction results from the vicariously calibrated GOCI system is reduced by ~6.8%.