Kimberly E. Baugh

Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption

The area lit by anthropogenic visible-near infrared emissions (i.e., lights) has been estimated for 21 countries using night-time data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). The area lit is highly correlated to gross domestic product and electric power consumption. Significant outliers exist in the relation between area lit and population. The results indicate that the local level of economic development must be factored into the apportionment of population across the land surface based on DMSP-OLS observed lights.

Night-time lights of the world: 1994–1995

Abstract The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) has a unique low-light imaging capability developed for the detection of clouds using moonlight. In addition to moonlit clouds, the OLS also detects lights from human settlements, fires, gas flares, heavily lit fishing boats, lightning and the aurora. By analysing the location, frequency, and appearance of lights observed in an image time series, it is possible to distinguish four primary types of lights present at the earths surface: human settlements, gas flares, fires, and fishing boats. We have produced a global map of the four types of light sources as observed during a 6-month time period in 1994–1995. We review a number of environmental applications that have been developed or proposed based on the night-time light data. We examine the relationship between area of lighting, population, economic activity, electric power consumption, and energy related carbon emissions for 200 nations, representing 99% of the worlds population.

Radiance calibration of DMSP-OLS low-light imaging data of human settlements

Abstract Nocturnal lighting is a primary method for enabling human activity. Outdoor lighting is used extensively worldwide in residential, commercial, industrial, public facilities, and roadways. A radiance calibrated nighttime lights image of the United States has been assembled from Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). The satellite observation of the location and intensity of nocturnal lighting provide a unique view of humanities presence and can be used as a spatial indicator for other variables that are more difficult to observe at a global scale. Examples include the modeling of population density and energy related greenhouse gas emissions.

Global Distribution and Density of Constructed Impervious Surfaces

We present the first global inventory of the spatial distribution and density of constructed impervious surface area (ISA). Examples of ISA include roads, parking lots, buildings, driveways, sidewalks and other manmade surfaces. While high spatial resolution is required to observe these features, the new product reports the estimated density of ISA on a one-km2 grid based on two coarse resolution indicators of ISA – the brightness of satellite observed nighttime lights and population count. The model was calibrated using 30-meter resolution ISA of the USA from the U.S. Geological Survey. Nominally the product is for the years 2000-01 since both the nighttime lights and reference data are from those two years. We found that 1.05% of the United States land area is impervious surface (83,337 km2) and 0.43 % of the worlds land surface (579,703 km2) is constructed impervious surface. China has more ISA than any other country (87,182 km2), but has only 67 m2 of ISA per person, compared to 297 m2 per person in the USA. The distribution of ISA in the worlds primary drainage basins indicates that watersheds damaged by ISA are primarily concentrated in the USA, Europe, Japan, China and India. The authors believe the next step for improving the product is to include reference ISA data from many more areas around the world.

Validation of urban boundaries derived from global night-time satellite imagery

Night-time imagery from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) has been proposed as a useful tool for monitoring urban expansion around the world, but determining appropriate light thresholds for delineating cities remains a challenge. In this paper we present a new approach. We used DMSP stable lights and radiance-calibrated images to delimit urban boundaries for San Francisco, Beijing and Lhasa, cities with different levels of urbanization and economic development, and compared the results against boundaries derived from high-resolution Landsat Thematic Mapper (TM) imagery. Unthresholded DMSP images exaggerate and shift the extent of these urban areas. We then calculated light thresholds that minimized the discrepancies between the DMSP- and TM-derived urban boundaries for each city. Our comparison highlights the difficulty of using DMSP data across areas with disparate urban characteristics, but suggests the possibility of calibrating this data source for monitoring growth of cities at comparable levels of development.

A global poverty map derived from satellite data

A global poverty map has been produced at 30arcsec resolution using a poverty index calculated by dividing population count (LandScan 2004) by the brightness of satellite observed lighting (DMSP nighttime lights). Inputs to the LandScan product include satellite-derived land cover and topography, plus human settlement outlines derived from high-resolution imagery. The poverty estimates have been calibrated using national level poverty data from the World Development Indicators (WDI) 2006 edition. The total estimate of the numbers of individuals living in poverty is 2.2 billion, slightly under the WDI estimate of 2.6 billion. We have demonstrated a new class of poverty map that should improve over time through the inclusion of new reference data for calibration of poverty estimates and as improvements are made in the satellite observation of human activities related to economic activity and technology access.

The new world atlas of artificial night sky brightness

The world atlas of zenith artificial night sky brightness is modelled with VIIRS DNB data and calibrated with more than 35,000 observations. Artificial lights raise night sky luminance, creating the most visible effect of light pollution—artificial skyglow. Despite the increasing interest among scientists in fields such as ecology, astronomy, health care, and land-use planning, light pollution lacks a current quantification of its magnitude on a global scale. To overcome this, we present the world atlas of artificial sky luminance, computed with our light pollution propagation software using new high-resolution satellite data and new precision sky brightness measurements. This atlas shows that more than 80% of the world and more than 99% of the U.S. and European populations live under light-polluted skies. The Milky Way is hidden from more than one-third of humanity, including 60% of Europeans and nearly 80% of North Americans. Moreover, 23% of the world’s land surfaces between 75°N and 60°S, 88% of Europe, and almost half of the United States experience light-polluted nights.

Spectral Identification of Lighting Type and Character

We investigated the optimal spectral bands for the identification of lighting types and the estimation of four major indices used to measure the efficiency or character of lighting. To accomplish these objectives we collected high-resolution emission spectra (350 to 2,500 nm) for forty-three different lamps, encompassing nine of the major types of lamps used worldwide. The narrow band emission spectra were used to simulate radiances in eight spectral bands including the human eye photoreceptor bands (photopic, scotopic, and “meltopic”) plus five spectral bands in the visible and near-infrared modeled on bands flown on the Landsat Thematic Mapper (TM). The high-resolution continuous spectra are superior to the broad band combinations for the identification of lighting type and are the standard for calculation of Luminous Efficacy of Radiation (LER), Correlated Color Temperature (CCT) and Color Rendering Index (CRI). Given the high cost that would be associated with building and flying a hyperspectral sensor with detection limits low enough to observe nighttime lights we conclude that it would be more feasible to fly an instrument with a limited number of broad spectral bands in the visible to near infrared. The best set of broad spectral bands among those tested is blue, green, red and NIR bands modeled on the band set flown on the Landsat Thematic Mapper. This set provides low errors on the identification of lighting types and reasonable estimates of LER and CCT when compared to the other broad band set tested. None of the broad band sets tested could make reasonable estimates of Luminous Efficacy (LE) or CRI. The photopic band proved useful for the estimation of LER. However, the three photoreceptor bands performed poorly in the identification of lighting types when compared to the bands modeled on the Landsat Thematic Mapper. Our conclusion is that it is feasible to identify lighting type and make reasonable estimates of LER and CCT using four or more spectral bands with minimal spectral overlap spanning the 0.4 to 1.0 um region.

The artificial night sky brightness mapped from DMSP satellite Operational Linescan System measurements

ABSTRA C T We present a method to map the artificial sky brightness across large territories in astronomical photometric bands with a resolution of approximately 1 km. This is of use in quantifying the situation regarding night sky pollution, recognizing potential astronomical sites and allowing future monitoring of trends. The artificial sky brightness present in the chosen direction at a given position on the surface of the Earth is obtained by the integration of the contributions produced by every surface area in the surroundings. Each contribution is computed via detailed models for the propagation in the atmosphere of the upward light flux emitted by the area. The light flux is measured with top-of-atmosphere radiometric observations made by the Defense Meteorological Satellite Program (DMSP) Operational Linescan System. We have applied the described method to Europe, obtaining maps of artificial sky brightness in the V and B bands.

VIIRS Nightfire: Satellite Pyrometry at Night

The Nightfire algorithm detects and characterizes sub-pixel hot sources using multispectral data collected globally, each night, by the Suomi National Polar Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The spectral bands utilized span visible, near-infrared (NIR), short-wave infrared (SWIR), and mid-wave infrared (MWIR). The primary detection band is in the SWIR, centered at 1.6 μm. Without solar input, the SWIR spectral band records sensor noise, punctuated by high radiant emissions associated with gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Planck curve fitting of the hot source radiances yields temperature (K) and emission scaling factor (ESF). Additional calculations are done to estimate source size (m2), radiant heat intensity (W/m2), and radiant heat (MW). Use of the sensor noise limited M7, M8, and M10 spectral bands at night reduce scene background effects, which are widely reported for fire algorithms based on MWIR and long-wave infrared. High atmospheric transmissivity in the M10 spectral band reduces atmospheric effects on temperature and radiant heat retrievals. Nightfire retrieved temperature estimates for sub-pixel hot sources ranging from 600 to 6,000 K. An intercomparison study of biomass burning in Sumatra from June 2013 found Nightfire radiant heat (MW) to be highly correlated to Moderate Resolution Imaging Spectrometer (MODIS) Fire Radiative Power (MW).