Sharolyn Anderson

The Value of Coastal Wetlands for Hurricane Protection

Abstract Coastal wetlands reduce the damaging effects of hurricanes on coastal communities. A regression model using 34 major US hurricanes since 1980 with the natural log of damage per unit gross domestic product in the hurricane swath as the dependent variable and the natural logs of wind speed and wetland area in the swath as the independent variables was highly significant and explained 60% of the variation in relative damages. A loss of 1 ha of wetland in the model corresponded to an average USD 33 000 (median = USD 5000) increase in storm damage from specific storms. Using this relationship, and taking into account the annual probability of hits by hurricanes of varying intensities, we mapped the annual value of coastal wetlands by 1km × 1km pixel and by state. The annual value ranged from USD 250 to USD 51 000 ha−1 yr−1, with a mean of USD 8240 ha−1 yr−1 (median = USD 3230 ha−1 yr−1) significantly larger than previous estimates. Coastal wetlands in the US were estimated to currently provide USD 23.2 billion yr−1 in storm protection services. Coastal wetlands function as valuable, selfmaintaining “horizontal levees” for storm protection, and also provide a host of other ecosystem services that vertical levees do not. Their restoration and preservation is an extremely cost-effective strategy for society.

Estimation of Mexico’s Informal Economy and Remittances Using Nighttime Imagery

Abstract: Accurate estimates of the magnitude and spatial distribution of both formal and informal economic activity have many useful applications. Developing alternative methods for making estimates of these economic activities may prove to be useful when other measures are of suspect accuracy or unavailable. This research explores the potential for estimating the formal and informal economy for Mexico using known relationships between the spatial patterns of nighttime satellite imagery and economic activity in the United States (U.S.). Regression models have been developed between spatial patterns of nighttime imagery and Adjusted Official Gross State Product ( AGSP ) for the U.S. states. These regression parameters derived from the regression models of the U.S. were ‘blindly’ applied to Mexico to estimate the Estimated Gross State Income ( EGSI ) at the sub-national level and the Estimated Gross Domestic Income ( EGDI ) at the national level. Comparison of the EGDI estimate of Mexico against the official Gross National Income (

Paving the planet: impervious surface as proxy measure of the human ecological footprint

Fundamental questions regarding the human-environment-sustainability problematic remain contested. What are the relative roles of population, consumption, and technology with respect to sustainability? How can sustainability be measured? Numerous metrics have been developed to address these controversial questions including ideas of carrying capacity, environmental sustainability indices, and ecological footprints. This work explores the question: is pavement a proxy measure of human impact on the environment? We explore and evaluate the use of satellite derived density grids of constructed area (aka ‘pavement’ or ‘impervious surface’) in the calculation of national and subnational ‘ecological footprints’. We generated a global constructed area density grid for the 2000—2001 period using satellite observed nighttime lights and a population count grid from the US Department of Energy. Satellite data inputs to the population product include MODIS landcover, SRTM topography and high-resolution imagery. Calibration of the global constructed area density product was derived from high-resolution aerial photographs. We demonstrate that a satellite derived constructed area per person index can serve as a proxy measure of ecological footprints at both the national and subnational level. This relatively simple and globally uniform measure of human impact on the environment correlates strongly with other more difficult to obtain measures.

High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors

Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The study site was Veale Gardens in Adelaide, Australia. Image processing was applied on five images captured from February 2012 to February 2013 using ERDAS Imagine. From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences) was selected. Normalized Difference Vegetation Index (NDVI) values were derived for each category of landscape cover, namely trees, shrubs, turf grasses, impervious pavements, and water bodies. Urban landscape evapotranspiration rates for Veale Gardens were estimated through field monitoring using observational-based landscape coefficients. The relationships between remotely sensed NDVIs for the entire Veale Gardens and for individual NDVIs of different vegetation covers were compared with field measured urban landscape evapotranspiration rates. The water stress conditions experienced in January

Characterizing relationships between population density and nighttime imagery for Denver, Colorado: issues of scale and representation

This paper maps and characterizes the correlation between population density and nighttime imagery over Denver, Colorado. Photographs taken at night from the International Space Station (ISS) have finer spatial and spectral resolution than existing nocturnal observing satellites such as the Defense Meteorological Satellite Programs Operational Linescan System (DMSP OLS). We determined the correlation between the city lights of Denver, Colorado, and several representations of population and population density derived from census data. The DMSP OLS proved to have a stronger correlation than any of the finer resolution ISS photograph bands. This study suggests that exclusive use of nighttime images with finer spatial and spectral resolution will not necessarily improve our ability to use nighttime imagery for modelling traditional representations of population. However, analysis of the spatial patterns of error indicates that finer resolution imagery may be a good proxy of conceptualizations of population density that account for human spatial behaviour. Future research may demonstrate that imagery such as the ISS photographs may prove to be uniquely capable of informing more sophisticated representations of complex phenomena such as ambient population density, land-use intensity and impervious surface.

A Thermodynamic Geography: Night-Time Satellite Imagery as a Proxy Measure of Emergy

Night-time satellite imagery enables the measurement, visualization, and mapping of energy consumption in an area. In this paper, an index of the “sum of lights” as observed by night-time satellite imagery within national boundaries is compared with the emergy of the nations. Emergy is a measure of the solar energy equivalent used, directly or indirectly, to support the processes that characterize the economic activity in a country. Emergy has renewable and non-renewable components. Our results show that the non-renewable component of national emergy use is positively correlated with night-time satellite imagery. This relationship can be used to produce emergy density maps which enable the incorporation of spatially explicit representations of emergy in geographic information systems. The region of Abruzzo (Italy) is used to demonstrate this relationship as a spatially disaggregate case.

It Used To Be Dark Here

Nighttime satellite imagery from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) has a unique capability to observe nocturnal light emissions from sources including cities, wild fires, and gas flares. Data from the DMSP OLS is used in a wide range of studies including mapping urban areas, estimating informal economies, and estimations of population. Given the extensive and increasing list of applications a repeatable method for assessing geolocation accuracy would be beneficial. An array of portable lights was designed and taken to multiple field sites known to have no other light sources. The lights were operated during nighttime overpasses by the DMSP OLS and observed in the imagery. An assessment of the geolocation accuracy was performed by measuring the distance between the GPS measured location of the lights and the observed location in the imagery. A systematic shift was observed and the mean distance was measured at 2.9 km.

An ensemble approach to space-time interpolation

The availability of spatial data on an unprecedented scale as well as advancements in analytical and visualization techniques gives researchers the opportunity to study complex problems over large urban and regional areas. Nevertheless, few individual data sets exist that provide both the requisite spatial and/or temporal observational frequency to truly facilitate detailed investigations. Some data are collected frequently over time but only at a few geographic locations (e.g., weather stations). Similarly, other data are collected with a high level of spatial resolution but not at regular or frequent time intervals (e.g., satellite data). The purpose of this article is to present an interpolation approach that leverages the relative temporal richness of one data set with the relative spatial richness of another to fill in the gaps. Because different interpolation techniques are more appropriate than others for specific types of data, we propose a space–time interpolation approach whereby two interpolation methods – one for the temporal and one for the spatial dimension – are used in tandem to increase the accuracy results. We call our ensemble approach the space–time interpolation environment (STIE). The primary steps within this environment include a spatial interpolation processor, a temporal interpolation processor, and a calibration processor, which enforces phenomenon-related behavioral constraints. The specific interpolation techniques used within the STIE can be chosen on the basis of suitability for the data and application at hand. In this article, we first describe STIE conceptually including the data input requirements, output structure, details of the primary steps, and the mechanism for coordinating the data within those steps. We then describe a case study focusing on urban land cover in Phoenix, Arizona, using our working implementation. Our empirical results show that our approach increased the accuracy for estimating urban land cover better than a single interpolation technique.

Estimation of Mexico's informal economy using DMSP nighttime lights data

In many countries of the world governments are unable to accurately track the true magnitude of economic activity due to the large number of transactions upon which taxes are not paid. It is particularly easy to avoid paying taxes on cash transactions and on remittances transferred from outside of the country. In some cases the so called “informal economy” is believed to be a substantial fraction of a nations total gross domestic product (GDP). Using DMSP satellite observed nighttime lights we developed a calibration for estimating reported GDP for the 48 contiguous states of the USA, where the informal economy is rated as relatively low. We applied this calibration to estimate the GDP for the states of Mexico and compared these values to officially reported GDP and Gross National Income (GNI) values. We found that most states in Mexico have a surplus in lighting relative to their officially reported GDP. We attribute this surplus in lighting to the informal economy and have made estimates of the magnitude of this unreported component to the true GDP. The results are encouraging and suggest that this technique could be used in other countries where accurate GDP reporting is problematic.

How near is near? The distance perceptions of residents of a nuclear emergency planning zone

This paper reports the results of a study conducted within the emergency planning zone (EPZ) of the Nine Mile Point (NMP) nuclear complex located in Oswego County, New York. An EPZ is the area in the vicinity of a nuclear power plant for which detailed plans are implemented for the management of emergencies and for the communication of the risks of nuclear energy production. EPZs are subdivided into emergency response planning areas (ERPAs) according to distance from the plant. This study aimed at discovering how residents of the NMP EPZ perceived distance to the nuclear plant. Distance was conceptualized in five different and complementary ways: estimated straight line distance, estimated driving distance, actual straight distance, actual driving distance and perceived distance. The results indicate that people living at a very short distance from the nuclear plant perceive and estimate distance differently than people living farther away. These results have policy implications and suggest alternative and potentially more efficient ways to redesign ERPAs. Fundamental geographic variables and concepts such as distance, location, proximity and their associated human perceptions are important human dimensions of risk analysis. These results should be of interest to nuclear emergency planners and are probably applicable to many other hazard planning activities.