Standoff Raman Spectroscopy of Explosive Nitrates Using 785 nm Laser
Sandra Sadate,
Carlton Farley III,
Aschalew Kassu,
Anup Sharma
Issue:
Volume 3, Issue 1, February 2015
Pages:
1-5
Received:
27 February 2015
Accepted:
17 March 2015
Published:
24 March 2015
Abstract: We have demonstrated that standoff Raman spectra of some nitrates could be obtained at distances ranging from 20 meters to 250 meters using an 11-inch reflecting telescope and a continuous wavelength 785 nm laser of 400 mW power coupled with a small portable spectrometer. The measurements were taken during both day and night, indoors and outdoors, for various integration times. Detection and identification of chemical and biological hazards within the forensic and homeland security contexts requires conducting the analysis in field while adapting a non-contact approach to the hazard. In this paper, we report the adequacy of a standoff Raman system with a 785nm laser for remote detection and identification of ammonium, sodium, and magnesium nitrates in bulk form. The results demonstrate that, as the standoff distances increases, there is a discernible attenuation in the intensity of the Raman signatures of the nitrates. To discriminate the background lights, improve the signal-to-noise ratio and trigger the weak characteristic bands, Raman spectra were also acquired using higher integration time. For the measurements taken at a 100 meter standoff distances, we were able to achieve a signal-to-noise ratio of about 10. For greater distances, due to the difficulty of locating the target using IR laser, a 50 mW green laser pointer at 485 nm wavelength is used before the IR laser is used for acquiring the Raman signals.
Abstract: We have demonstrated that standoff Raman spectra of some nitrates could be obtained at distances ranging from 20 meters to 250 meters using an 11-inch reflecting telescope and a continuous wavelength 785 nm laser of 400 mW power coupled with a small portable spectrometer. The measurements were taken during both day and night, indoors and outdoors, ...
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GIS and Logit Regression Model Applications in Land Use/Land Cover Change and Distribution in Usangu Catchment
Canute Hyandye,
Christina Geoffrey Mandara,
John Safari
Issue:
Volume 3, Issue 1, February 2015
Pages:
6-16
Received:
13 March 2015
Accepted:
31 March 2015
Published:
9 April 2015
Abstract: This study applied time series analysis to examine land use/land cover (LULC) change and distribution in Usangu watershed and multinomial logistic regression in the GIS environment to model the influence of the related driving factors. Historical land use/cover data of the watershed were extracted from the 2000, 2006 and 2013 Landsat images using GIS and remote sensing data processing and analysis techniques. Data was analyzed using ArcMap 10.1, ERDAS Imagine, SPSS and IDRISI Selva software. Eight factors likely to influence LULC change and LULC distribution were assessed. These include elevation, slope, distance from roads, distance from rivers networks, population density, Normalized Vegetation Index (NDVI), annual rainfall and soil types. Results show that LULC changes are mainly influenced by variations in annual rainfall, population density and distance from road networks. LULC distribution is determined mainly by terrain and edaphic factors namely elevation, slope and soil types. NDVI does not influence LULC change nor determine the LULC distribution, but can be used to show concentration of LULC types on a landscape. Combination of GIS, remote sensing and statistical analysis capabilities are powerful tools for assessing and model processes of land use change and their underlying causes in terms of time and space. It is concluded that ingeniously integration of remote sensing, GIS application combined with multi-source spatial data analysis give great possibility of quantifying and explaining the temporal and spatial LULC changes and distribution in a given watershed.
Abstract: This study applied time series analysis to examine land use/land cover (LULC) change and distribution in Usangu watershed and multinomial logistic regression in the GIS environment to model the influence of the related driving factors. Historical land use/cover data of the watershed were extracted from the 2000, 2006 and 2013 Landsat images using G...
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