We test the hypothesis that climate and environmental conditions are becoming favorable for dengue transmission in San Juan, Puerto Rico. Sea Level Pressure (SLP), Mean Sea Level (MSL), Wind, Sea Surface Temperature (SST), Air Surface Temperature (AST), Rainfall, and confirmed dengue cases were analyzed. We evaluated the dengue incidence and environmental data with Principal Component Analysis, Pearson correlation coefficient, Mann-Kendall trend test and logistic regressions. Results indicated that dry days are increasing and wet days are decreasing. MSL is increasing, posing higher risk of dengue as the perimeter of the San Juan Bay estuary expands and shorelines move inland. Warming is evident with both SST and AST. Maximum and minimum air surface temperature extremes have increased. Between 1992 and 2011, dengue transmission increased by a factor of 3.4 (95% CI: 1.9–6.1) for each 1 °C increase in SST. For the period 2007–2011 alone, dengue incidence reached a factor of 5.2 (95% CI: 1.9–13.9) for each 1 °C increase in SST. Teenagers are consistently the age group that suffers the most infections in San Juan. Results help understand possible impacts of different climate change scenarios in planning for social adaptation and public health interventions.

Habitat mapping can be accomplished using many techniques and types of data. There are pros and cons for each technique and dataset, therefore, the goal of this project was to investigate the capabilities of new satellite sensor technology and to assess map accuracy for a variety of image classification techniques based on hundreds of field-work sites. The study area was Masonboro Island, an undeveloped area in coastal North Carolina, USA. Using the best map results, a habitat change assessment was conducted between 2002 and 2010. WorldView-2, QuickBird, and IKONOS satellite sensors were tested using unsupervised and supervised methods using a variety of spectral band combinations. Light Detection and Ranging (LiDAR) elevation and texture data pan-sharpening, and spatial filtering were also tested. In total, 200 maps were generated and results indicated that WorldView-2 was consistently more accurate than QuickBird and IKONOS. Supervised maps were more accurate than unsupervised in 80% of the maps. Pan-sharpening the images did not consistently improve map accuracy but using a majority filter generally increased map accuracy. During the relatively short eight-year period, 20% of the coastal study area changed with intertidal marsh experiencing the most change. Smaller habitat classes changed substantially as well. For example, 84% of upland scrub-shrub experienced change. These results document the dynamic nature of coastal habitats, validate the use of the relatively new Worldview-2 sensor, and may be used to guide future coastal habitat mapping.

In September of 2017, Hurricane Irma made landfall within the Rookery Bay National Estuarine Research Reserve of southwest Florida (USA) as a category 3 storm with winds in excess of 200 km h⁻¹. We mapped the extent of the hurricane’s impact on coastal land cover with a seasonal time series of satellite imagery. Very high-resolution (i.e., <5 m pixel) satellite imagery has proven effective to map wetland ecosystems, but challenges in data acquisition and storage, algorithm training, and image processing have prevented large-scale and time-series mapping of these data. We describe our approach to address these issues to evaluate Rookery Bay ecosystem damage and recovery using 91 WorldView-2 satellite images collected between 2010 and 2018 mapped using automated techniques and validated with a field campaign. Land cover was classified seasonally at 2 m resolution (i.e., healthy mangrove, degraded mangrove, upland, soil, and water) with an overall accuracy of 82%. Digital change detection methods show that hurricane-related degradation was 17% of mangrove forest (~5 km²). Approximately 35% (1.7 km²) of this loss recovered one year after Hurricane Irma. The approach completed the mapping approximately 200 times faster than existing methods, illustrating the ease with which regional high-resolution mapping may be accomplished efficiently.