Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500 m spatial resolution

With the advent of “social sensing” in the Big Data era, location-based social media (LBSM) data are increasingly used to explore anthropogenic activities and their impacts on the environment. This study converts a typical kind of LBSM data, geo-tagged tweets, into raster images at the 500 m spatial resolution and compares them with the new generation nighttime lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly image composites. The results show that the monthly tweet images are significantly correlated with the VIIRS-DNB images at the pixel level. The tweet images have nearly the same ability on estimating electric power consumption and better performance on assessing personal incomes and population than the NTL images. Tweeted areas (i.e. the pixels with at least one posted tweet) are closer to satellite-derived built-up/urban areas than lit areas in NTL imagery, making tweet images an alternative to delimit extents of human activities. Moreover, the monthly tweet images do not show apparent seasonal changes, and the values of tweet images are more stable across different months than VIIRS-DNB monthly image composites. This study explores the potential of LBSM data at relatively fine spatiotemporal resolutions to estimate or map socioeconomic factors as an alternative to NTL images in the United States.     

Remote sensing of Carhuarazo volcanic complex using ASTER imagery in Southern Peru to detect alteration zones and volcanic structures – a combined approach of image processing in ENVI and ArcGIS/ArcScene

A combined approach to detect hydrothermal alteration zones and their mineral distribution is proposed for a relatively remote area around the Carhuarazo volcanic complex in southern Peru encompassing 2222 km². In this region, tertiary volcanic structures associated with hydrothermal alteration are well known to host epithermal ore deposits. We make an attempt to detect and to quantify alteration minerals based on spectral analysis using ASTER reflectance data product provided by LP-DAAC. Besides commonly used ratio images, mineral indices (MI) and relative band depth images (RBD), we also extracted endmember spectra using Pixel-Purity-Processing preceded by minimum noise fraction transformation. These spectra are thought to represent the spectrally purest pixel of the image and show the typical absorption features of the main constituents. Based on this assumption, we used different spectral analysis methods in order to extract the most important alteration minerals for such an environment. These minerals were then used for matched filter processing in areas showing high values in MIs and RBDs. Using this method, we detected and mapped argillic alteration and variations in the distribution of important minerals like alunite, kaolinite or nacrite. There were no indications for the presence of propilitization at ASTER spatial resolutions. Our method can be applied easily to any ASTER scene and provides information about the intensity of alteration and the character of alteration zones. The intensity is highest in the centre of the Carhuarazo volcanic complex and is mostly argillic with a high content of alunite, dickite and other clay minerals.