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This article proposes a simple statistical approach to combine nighttime light data with official national income growth figures. The suggested procedure arises from a signal-plus-noise model for official growth along with a constant elasticity relation between observed night lights and income. The methodology implemented in this paper differs from the approach based on panel data for several countries at once that uses World Bank ratings of income data quality for the countries under study to produce an estimate of true economic growth. The new approach: (a) leads to a relatively simple and robust statistical method based only on time series data pertaining to the country under study and (b) does not require the use of quality ratings of official income statistics. For illustrative purposes, some empirical applications are made for Mexico, China and Chile. The results show that during the period of study there was underestimation of economic growth for both Mexico and Chile, while official figures of China over-estimated true economic growth. [ABSTRACT FROM AUTHOR]

Particulate matter concentration and assessment of its movement pattern is crucial in air pollution studies. However, no study has been conducted to determine the PM concentration using atmospheric correction of thermal band by temperature of nearest dark pixels group (TNDPG) of this band. For that purpose, 16 Landsat Enhanced Thematic Mapper plus ETM+ images for Sanandaj and Tehran in Iran were utilized to determine the amount of PM concentration in the air. Thermal infrared (band 6) of all images was also used to determine the ground station temperature (GST b6) and temperature of nearest dark pixels group. Based on atmospheric correction of images using temperature retrieval from Landsat ETM+, three empirical models were established. Non-linear correlation coefficient with polynomial equation was used to analyze the correlations between particulate matter concentration and the ground station temperature for the three models. Similar analyses were also undertaken for three stations in Klang Valley, Malaysia, using 11 Landsat ETM+ images to show the effectiveness of the model in different region. The data analysis indicated a good correlation coefficient R = 0.89 and R = 0.91 between the trend of the result of temperature of nearest dark pixels group b6 − (GST b6 − GST) model and the trend of PM concentration in Iran and Malaysia, respectively. This study reveals the applicability of the thermal band of Landsat TM and ETM+ to determine the PM concentration over large areas. [ABSTRACT FROM AUTHOR]

This paper deals with the experiment of sediment microstructure analysis especially microfabric mapping by digital imaging. For that purpose the greyscale images (Red band from RGB combination) of the thin sections have been prepared from the selected 12 samples. The basis of this mapping is the reflectance capacity of different sediments which is influenced by the physical parameters like grain size and colour. The reflectances of different sediments are represented in digital format by different DN values from 0-255 within the radiometric ranges of '8 bit' data. Density slicing has been chosen as the method of microstructure mapping in this research. This study shows that lower DN values normally present dark coloured coarser sand and clay while higher DN values present light coloured finer sediment samples. In the selected samples for this study the maximum DN value has been found from micaceous materials. Another remarkable thing observed from the microfabric mapping is that the presence of coarser sediments forms complex microfabric pattern than the finer sediments in the study area. Though this method have some demerits, still its simple technique can be very useful for accurate microstructure analysis. [ABSTRACT FROM AUTHOR]

Background: Hemispherical photography (HP) is a popular method to estimate canopy structure and understorey light environment, which analyses photographs acquired with wide view-angle lens (i.e. fisheye lens). To increase HP accuracy, the approaches of most previous studies were to increase the preciseness of exposure and thresholding of photographs, while ours quantified effects of canopy properties (gap fraction and length of sky-canopy border (SCB)) and errors of exposure and thresholding on the accuracy of HP.Results: Through analysing photographs of real and model canopies, it was showed that HP inaccuracy resulted from the mismatch between exposure and thresholding rather than exposure or thresholding errors alone. HP inaccuracy was a function of the SCB length and the extent of exposure and thresholding errors, but independent of gap fraction.Discussion: In photographs, SCBs are recorded as grey pixels which greyness is in between that of sky and canopy pixels. When there are exposure and thresholding errors, grey pixels are those prone to be misclassified in image analysis. Longer (vegetation with taller canopies) and wider (lower image sharpness) SCBs in photographs can both result in a higher amount of grey pixels and ultimately higher HP inaccuracy for a given extent of exposure and threshold errors.Conclusions: Using lenses with view angle narrower rather than that of fisheye lens can shorten the SCB length in photographs and in turn reduce HP estimation inaccuracy for canopy structure and understorey light environment. Since short SCBs and low levels of exposure and thresholding errors can both result in low HP inaccuracy, to identify the true performance of new exposure and thresholding methods for HP, photographs recording canopies with long SCBs and acquired with fisheye lenses should be used. Because HP inaccuracy in a function of the amount of grey pixels resulting from SCBs, the amount of these pixels in photographs can be used as a universal parameter to quantify canopy properties influential to HP estimation and in turn make cross-study comparisons feasible. [ABSTRACT FROM AUTHOR]