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.

We have developed a satellite data derived ''Night Light Development Index'' (NLDI) as a simple, objective, spatially explicit and globally available empirical measurement of human development derived solely from nighttime satellite imagery and population density. There is increasing recognition that the distribution of wealth and income amongst the population in a nation or region correlates strongly with both the overall happiness of that population and the environmental quality of that nation or region. Measuring the distribution of wealth and income at national and regional scales is an interesting and challenging problem. Gini coefficients derived from Lorenz curves are a well-established method of measuring income distribution. Nonetheless, there are many shortcomings of the Gini coefficient as a measure of income or wealth distribution. Gini coefficients are typically calculated using national level data on the distribution of income through the population. Such data are not available for many countries and the results are generally limited to single values representing entire countries. In this paper we develop an index for the co-distribution of nocturnal light and people that is derived without the use of monetary measures of wealth and is capable of providing a spatial depiction of differences in development within countries.

Improving human well-being is increasingly recognized as essential for movement toward a sustainable and desirable future. Estimates of different aspects of human well-being, such as Gross Domestic Product, or percentage of population with access to electric power, or measuring the distribution of income in society are often fraught with problems. There are few standardized methods of data collection; in addition, the required data is not obtained in a reliable manner and on a repetitive basis in many parts of the world. Consequently, inter-comparability of the data that does exist becomes problematic. Data derived from nighttime satellite imagery has helped develop various globally consistent proxy measures of human well-being at the gridded, sub-national, and national level. We review several ways in which nighttime satellite imagery has been used to measure the human well-being within nations.

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 (GNI) estimate suggests that the magnitude of Mexico’s informal economy and the inflow of remittances are 150 percent larger than their existing official estimates in the GNI.