Cities are now home to more than 50% of the world’s population and emit large quantities of pollutants from sources such as fossil fuel combustion and the leakage of refrigerants. We demonstrate the utility of persistent synoptic longwave hyperspectral imaging to study the ongoing leakage of refrigerant gases in New York City, compounds that either deplete the stratosphere ozone or have significant global warming potential. In contrast to current monitoring programs that are based on country-level reporting or aggregate measures of emissions, we present the identification of gaseous plumes with high spatial and temporal granularity in real-time over the skyline of Manhattan. The reported data highlights the emission of chemicals scheduled for phase-out. Our goal is to contribute to better understanding of the composition, sources, concentration, prevalence and patterns of emissions for the purposes of both research and policy.

Despite decades of research seeking to derive the urban energy budget, the dynamics of thermal exchange in the densely constructed environment is not yet well understood. Using New York City as a study site, we present a novel hybrid experimental-computational approach for a better understanding of the radiative heat transfer in complex urban environments. The aim of this work is to contribute to the calculation of the urban energy budget, particularly the stored energy. We will focus our attention on surface thermal radiation. Improved understanding of urban thermodynamics incorporating the interaction of various bodies, particularly in high rise cities, will have implications on energy conservation at the building scale, and for human health and comfort at the urban scale. The platform presented is based on longwave hyperspectral imaging of nearly 100 blocks of Manhattan, in addition to a geospatial radiosity model that describes the collective radiative heat exchange between multiple buildings. Despite assumptions in surface emissivity and thermal conductivity of buildings walls, the close comparison of temperatures derived from measurements and computations is promising. Results imply that the presented geospatial thermodynamic model of urban structures can enable accurate and high resolution analysis of instantaneous urban surface temperatures.

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Data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) are being widely used for quantitatively estimating parameters in the study of atmospheric and land surface processes. Cloud detection is a preliminarily important step in remotely sensed image processing for many applications. The main purpose of this paper is classification of MODIS image into four classes including cloudy, semi cloudy, snow and clear areas. In this article, we use a new method for MODIS cloud detection based on spatial analysis and generation four RGB images. Several MODIS data were applied to evaluate the performance of the new algorithm and the results show that the new algorithm was acceptable result.