Turbidity is a commonly-used index of the factors that determine light penetration in the water column. Consistent estimation of turbidity is crucial to design environmental and restoration management plans, to predict fate of possible pollutants, and to estimate sedimentary fluxes into the ocean. Traditional methods monitoring fixed geographical locations at fixed intervals may not be representative of the mean water turbidity in estuaries between intervals, and can be expensive and time consuming. Although remote sensing offers a good solution to this limitation, it is still not widely used due in part to required complex processing of imagery. There are satellite-derived products, including the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance daily product (MOD09GQ) Band 1 (620–670 nm) which are now routinely available at 250 m spatial resolution and corrected for atmospheric effect. This study shows this product to be useful to estimate turbidity in Tampa Bay, Florida, after rainfall events (R² = 0.76, n = 34). Within Tampa Bay, Hillsborough Bay (HB) and Old Tampa Bay (OTB) presented higher turbidity compared to Middle Tampa Bay (MTB) and Lower Tampa Bay (LTB).

Using a geographic transect in Central Mexico, with an elevation/climate gradient, but uniformity in socio-economic conditions among study sites, this study evaluates the applicability of three widely-used remote sensing (RS) products to link weather conditions with the local abundance of the dengue virus mosquito vector, Aedes aegypti (Ae. aegypti). Field-derived entomological measures included estimates for the percentage of premises with the presence of Ae. aegypti pupae and the abundance of Ae. aegypti pupae per premises. Data on mosquito abundance from field surveys were matched with RS data and analyzed for correlation. Daily daytime and nighttime land surface temperature (LST) values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua cloud-free images within the four weeks preceding the field survey. Tropical Rainfall Measuring Mission (TRMM)-estimated rainfall accumulation was calculated for the four weeks preceding the field survey. Elevation was estimated through a digital elevation model (DEM). Strong correlations were found between mosquito abundance and RS-derived night LST, elevation and rainfall along the elevation/climate gradient. These findings show that RS data can be used to predict Ae. aegypti abundance, but further studies are needed to define the climatic and socio-economic conditions under which the correlations observed herein can be assumed to apply.

Dengue fever (DF), a vector-borne flavivirus, is endemic to the tropical countries of the world with nearly 400 million people becoming infected each year and roughly one-third of the world’s population living in areas of risk. The main vector for DF is the Aedes aegypti mosquito, which is also the same vector of yellow fever, chikungunya, and Zika viruses. To gain an understanding of the spatial aspects that can affect the epidemiological processes across the disease’s geographical range, and the spatial interactions involved, we created and compared Bernoulli and Poisson family Boosted Regression Tree (BRT) models to quantify the overall annual risk of DF incidence by municipality, using the Magdalena River watershed of Colombia as a study site during the time period between 2012 and 2014. A wide range of environmental conditions make this site ideal to develop models that, with minor adjustments, could be applied in many other geographical areas. Our results show that these BRT methods can be successfully used to identify areas at risk and presents great potential for implementation in surveillance programs.

The introduction and spread of West Nile virus and the recent introduction of chikungunya and Zika viruses into the Americas have raised concern about the potential for various tropical pathogens to become established in North America. A historical analysis of yellow fever and malaria incidences in the United States suggests that it is not merely a temperate climate that keeps these pathogens from becoming established. Instead, socioeconomic changes are the most likely explanation for why these pathogens essentially disappeared from the United States yet remain a problem in tropical areas. In contrast to these anthroponotic pathogens that require humans in their transmission cycle, zoonotic pathogens are only slightly affected by socioeconomic factors, which is why West Nile virus became established in North America. In light of increasing globalization, we need to be concerned about the introduction of pathogens such as Rift Valley fever, Japanese encephalitis, and Venezuelan equine encephalitis viruses.

Water clarity (generally quantified as the Secchi disk depth: SDD) is a key variable for assessing environmental changes in lakes. Using remote sensing we calculated and elucidated the SDD dynamics in lakes in the Inner Mongolia-Xinjiang Lake Zone (IMXL) from 1986 to 2018 in response to variations in temperature, rainfall, lake area, normalized difference vegetation index (NDVI) and Palmer's drought severity index (PDSI). The results showed that the lakes with high SDD values are primarily located in the Xinjiang region at longitudes of 75°-93° E. In contrast, the lakes in Inner Mongolia at longitudes of 93°-118° E generally have low SDD values. In total, 205 lakes show significant increasing SDD trends (P < 0.05), with a mean rate of 0.15 m per decade. In contrast, 75 lakes, most of which are located in Inner Mongolia, exhibited significant decreasing trends with a mean rate of 0.08 m per decade (P < 0.05). Pooled together, an overall increase is found with a mean rate of 0.14 m per decade. Multiple linear regression reveals that among the five variables selected to explain the variations in SDD, lake area accounts for the highest proportion of variance (25%), while temperature and rainfall account for 12% and 10%, respectively. In addition, rainfall accounts for 52% of the variation in humidity, 8% of the variation in lake area and 7% of the variation in NDVI. Temperature accounts for 27% of the variation in NDVI, 39% of the variation in lake area and 22% of the variation in PDSI. Warming and wetting conditions in IMXL thus promote the growth of vegetation and cause melting of glaciers and expansion of lake area, which eventually leads to improved water quality in the lakes in terms of higher SDD. In contrast, lakes facing more severe drought conditions, became more turbid.