Douglas L. Rickman

Urban heat islands developing in coastal tropical cities

Beautiful and breezy cities on small tropical islands, it turns out, may not be exempt from the same local climate change effects and urban heat island effects seen in large continental cities such as Los Angeles or Mexico City. A surprising, recent discovery indicates that this is the case for San Juan, Puerto Rico, a relatively affluent coastal tropical city of about two million inhabitants that is spreading rapidly into the once-rural areas around it. A recent climatological analysis of the surface temperature of the city has revealed that the local temperature has been increasing over the neighboring vegetated areas at a rate of 0.06°C per year for the past 30 years. This is a trend that may be comparable to climate changes induced by global warming.

Using the surface reflectance MODIS Terra product to estimate turbidity in Tampa Bay, Florida.

Abstract: 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

Methods for Characterizing Fine Particulate Matter Using Ground Observations and Remotely Sensed Data: Potential Use for Environmental Public Health Surveillance

Abstract This study describes and demonstrates different techniques for surface fitting daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 μm (PM2.5) for the purpose of inte grating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC) pilot study of Health and Environment Linked for Information Exchange (HELIX)–Atlanta. It presents a methodology for estimating daily spatial surfaces of ground-level PM2.5 concentrations using the B-Spline and inverse distance weighting (IDW) surface-fitting techniques, leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM2.5 from the EPA database for the year 2003 as well as PM2.5 estimates derived from NASA’s satellite data. Hazard data have been processed to derive the surrogate PM2.5 exposure estimates. This paper shows that merging MODIS remote sensing data with surface observations of PM2.5 not only provides a more complete daily representation of PM2.5 than either dataset alone would allow, but it also reduces the errors in the PM2.5- estimated surfaces. The results of this study also show that although the IDW technique can introduce some numerical artifacts that could be due to its interpolating nature, which assumes that the maxima and minima can occur only at the observation points, the daily IDW PM2.5 surfaces had smaller errors in general, with respect to observations, than those of the B-Spline surfaces. Finally, the methods discussed in this paper establish a foundation for environmental public health linkage and association studies for which determining the concentrations of an environmental hazard such as PM2.5 with high accuracy is critical.

Climate Impacts of Land-Cover and Land-Use Changes in Tropical Islands under Conditions of Global Climate Change

AbstractLand-cover and land-use (LCLU) changes have significant climate impacts in tropical coastal regions with the added complexity of occurring within the context of a warming climate. The individual and combined effects of these two factors in tropical islands are investigated by use of an integrated mesoscale atmospheric modeling approach, taking the northeastern region of Puerto Rico as the test case. To achieve this goal, an ensemble of climate simulations is performed, combining two LCLU and global warming scenarios. Reconstructed agricultural maps and sea surface temperatures form the past (1955–59) scenario, while the present (2000–04) scenario is supported with high-resolution remote sensing LCLU data. Here, the authors show that LCLU changes produced the largest near-surface (2-m AGL) air temperature differences over heavily urbanized regions and that these changes do not penetrate the boundary layer. The influence of the global warming signal induces a positive inland gradient of maximum temp...

A Land–Atmospheric Interaction Study in the Coastal Tropical City of San Juan, Puerto Rico

Abstract This paper focuses on the surface–atmospheric interaction in a tropical coastal city including the validation of an atmospheric modeling and an impact study of land-cover and land-use (LCLU) changes. The Regional Atmospheric Modeling System (RAMS), driven with regional reanalysis data for a 10-day simulation, is used to perform the study in the San Juan metropolitan area (SJMA), one of the largest urban conglomerations in the Caribbean, which is located in the island of Puerto Rico and taken as the test case. The model’s surface characteristics were updated using airborne high-resolution remote sensing information to obtain a more accurate and detailed configuration of the SJMA. Surface and rawinsonde data from the San Juan Airborne Thermal and Land Applications Sensor (ATLAS) Mission are used to validate the modeling system, yielding satisfactory results in surface/canopy temperature, near-surface air temperatures, and vertical profiles. The impact analysis, performed with the updated SJMA confi...

Environmental Public Health Applications Using Remotely Sensed Data

We describe a remote sensing and geographic information system (GIS)-based study that has three objectives: (1) characterize fine particulate matter (PM2.5), insolation and land surface temperature (LST) using NASA satellite observations, Environmental Protection Agency (EPA) ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.

Using remote sensing to monitor the influence of river discharge on watershed outlets and adjacent coral Reefs: Magdalena River and Rosario Islands, Colombia

a b s t r a c t Worldwide, coral reef ecosystems are being increasingly threatened by sediments loads from river dis- charges, which in turn are influenced by changing rainfall patterns due to climate change and by growing human activity in their watersheds. In this case study, we explored the applicability of using remote sensing (RS) technology to detect and monitor the relationship between water quality at the coral reefs around the Rosario Islands, in the Caribbean Sea and the rainfall patterns in the Magdalena River water- shed. From the Moderate Resolution Imaging Spectroradiometer (MODIS), this study used the water surface reflectance product (MOD09GQ) to estimate water surface reflectance as a proxy for sediment concentration and the land cover product (MCD12Q1 V51) to characterize land cover of the watershed. Rainfall was estimated by using the 3B43 V7 product from the Tropical Rainforest Measuring Mission (TRMM). For the first trimester of each year, we investigated the inter-annual temporal variation in water surface reflectance at the Rosario Islands and at the three main mouths of the Magdalena River water- shed. No increasing or decreasing trends of water surface reflectance were detected for any of the sites for the study period 2001-2014 (p > 0.05) but significant correlations were detected among the trends of each site at the watershed mouths (r = 0.57-0.90, p < 0.05) and between them and the inter-annual variation in rainfall on the watershed (r = 0.63-0.67, p < 0.05). Those trimesters with above-normal water surface reflectance at the mouths and above-normal rainfall at the watershed coincided with La Ni ˜ na conditions while the opposite was the case during El Niconditions. Although, a preliminary analysis of inter-annual land cover trends found only cropland cover in the watershed to be significantly correlated with water surface reflectance at two of the watershed mouths (r = 0.58 and 0.63, p < 0.05), the validation analysis draw only a 40.7% of accuracy in this land cover classification. This requires further analysis to confirm the impact of the cropland on the water quality at the watershed outlets. Spatial analysis with MOD09GQ imagery detected the overpass of river plumes from Barbacoas Bay over the Rosario Islands waters.

Effects of Spatial and Spectral Resolutions on Fractal Dimensions in Forested Landscapes

Recent work has shown that more research is needed in applying fractal analysis to multi-resolution remote sensing data for landscape characterization. The purpose of this study was to closely examine the impacts that spatial and spectral resolutions have on fractal dimensions using real-world multi-resolution remotely sensed data as opposed to the more conventional single resolution and aggregation approach. The study focused on fractal analysis of forested landscapes in the southeastern United States and Central America. Initially, the effects of spatial resolution on the computed fractal dimensions were examined using data from three instruments with different spatial resolutions. Based on the criteria of mean value and variation within the accepted ranges of fractal dimensions, it was determined that 30-m Landsat TM data were best able to capture the complexity of a forested landscape in Central America compared to 4-m IKONOS data and 250-m MODIS data. Also, among the spectral bands of Landsat TM images of four national forests in the southeastern United States, tests showed that the spatial indices of fractal dimensions are much more distinguishable in the visible bands than they are in the near-mid infrared bands. Thus, based solely on the fractal analysis, the fractal dimensions could have relatively higher chances to distinguish forest characteristics (e.g., stand sizes and species) in the Landsat TM visible wavelength bands than in the near-mid infrared bands. This study has focused on a relative comparison between visible and near-mid infrared wavelength bands; however it will be important to study in the future the effect of a combination of those bands such as the Normalized Difference Vegetation Index (NDVI) on fractal dimensions of forested landscapes.

Relationship Between Watershed Land-Cover/Land-Use Change and Water Turbidity Status of Tampa Bay Major Tributaries, Florida, USA

The extent and change of land cover/land use (LCLU) across the Tampa Bay watershed, Florida, was characterized for the time period between 1996 and 2006. Likewise, the water turbidity trend was determined at a site near the Bay for each of four major tributaries to Tampa Bay (Hillsborough River, the Alafia River, the Little Manatee River, and the Manatee River). This study identifies consistent changes in LCLU across the Tampa Bay watershed and a decrease in water turbidity. LCLU change analysis as a percent of the total Tampa Bay watershed revealed an increase of 2.6% in developed area followed by a 0.9% in bare land and a 0.6% in water cover. A decrease of 1.8% of the total Tampa Bay watershed was found in agriculture, followed in order by 1.1% in wetland and 1.4% in scrub/shrub. Other land classes changed less than 0.2% of the total watershed. A linear mixed model (SAS procedure PROC MIXED) revealed an overall decreasing trend in water turbidity (p = 0.003, slope estimate = −0.02) across the four major Tampa Bay tributaries considered. This study suggests that development (urbanization) could be associated with decreasing water turbidity in Tampa Bay. Finally, although these results may help explain similar effects on other water bodies with similar conditions of adjacent urbanization and low slope, more analysis are needed considering a larger number of watersheds with similar scales and longer time period in order to confirm that the findings of this study are generally evident.

Characterization of Forested Landscapes from Remotely Sensed Data Using Fractals and Spatial Autocorrelation

The characterization of forested landscapes is frequently required in civil engineering practice. In this study, some spatial analysis techniques are presented that might be employed with Landsat TM data to analyze forest structure characteristics. A case study is presented wherein fractal dimensions (FDs), along with a simple spatial autocorrelation technique (Moran’s I), were related to stand density parameters of the Oakmulgee National Forest located in the southeastern United States (Alabama). The results indicate that when smaller trees do not dominate the landscape (<50%), forested areas can be differentiated according to breast sizes and thus important flood plain characteristics such as ratio of obstructed area to total area can be estimated from remotely sensed data using the studied indices. This would facilitate the estimation of hydraulic roughness coefficients for computation of flood profiles needed for bridge design. FD and Moran’s I remained fairly constant around the values of 2.7 and 0.9 (resp.) for samples with either greater than 50% saplings or less than 50% sawtimber and with ranges of 2.7–2.9 and 0.6–0.9 as the saplings decreased or the sawtimber increased. Those indices can also distinguish hardwood and softwood species facilitating forested landscapes mapping for preliminary environmental impact analysis.