Hydrothermal treatments of corn cob and hemicelluloses extraction

Corn cob samples were treated with water (autohydrolysis reaction) using a liquid to solid ratio of 10:1 w/w. The optimal condition for extraction of hemicelluloses was found at 185ºC for 30 min. This resulted in the release of 9.7% of hemicelluloses (% dry starting material), corresponding to the dissolution of 27.9% of the original hemicellulose. Chemical composition and physico-chemical properties of the samples were elucidated by a combination of sugar analyses and thermal analysis. The results showed that the treatment was effective on the extraction of hemicelluloses from corn cob and that the TGA analysis of xylan from birch wood was found to be initially degraded at about 220 ºC whereas hemicelluloses from corn cob would be degraded at about 225 ºC.Fundação para a Ciência e a Tecnologia (FCT).Erasmus Programme (Turkey)

Hemicelluloses fractions extraction of corn residue

Fundação para a Ciência e a Tecnologia (FCT)Erasmus Programm

Специфика организации транспортной службы предприятия

В данной статье были рассмотрены проблемы организации транспортной службы предприятия. Актуальность темы исследования обусловлена, тем, что любую готовую продукцию необходимо транспортировать, в связи с этим были рассмотрены общие характеристики транспортной службы предприятия, сделаны выводы, позволяющие повысить эффективность работы транспортного цеха предприятия за счет повышения качества надежности внешних и внутрипроизводственных перевозок, что обеспечит повышение конкурентоспособности предприятия в целом

Analysis of land use land cover classification results derived from sentinel-2 image

17th International Multidisciplinary Scientific GeoConference, SGEM 2017 -- 29 June 2017 through 5 July 2017 -- -- 130787In this study, object-based Land Use Land Cover (LULC) classification performance of Sentinel-2 image has been tested by comparing other medium resolution satellite dataset of Zonguldak test field. The test field covering a small area around Zonguldak is located in the Western Black Sea region of Turkey. It is noted for being one of the main coal mining areas in the world. For the purpose of the study, pan-sharpened Landsat 8 image was used because of its nearly similar ground sampling distance (GSD). The RGB and NIR bands of Sentinel-2 were used for classification and comparison. As a first step, Landsat-8 pan-sharpened image was created using High Pass Filtering (HPF) pan sharp algorithm in ERDAS software package. Following this, resulted images were handled by the eCognition v4.0.6 software with the main steps of segmentation and classification. After determining the optimal segmentation parameters correctly, classification of main Land use/Land cover classes were compared with by Landsat-8 derived LULC classes. Furthermore, the results were verified visually using high resolution satellite image Worldview-2. The accuracy assessment as Kappa statistics for Sentinel-2 and Landsat-8 are 0.74 and 0.66, respectively. The obtained results revealed that Sentinel-2 LULC images give better results than Landsat-8. © SGEM2017. All Rights Reserved

PIXEL-BASED CLASSIFICATION ANALYSIS OF LAND USE LAND COVER USING SENTINEL-2 AND LANDSAT-8 DATA

The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpening process was carried out for Landsat-8 data before classification because the spatial resolution of Landsat-8 (30m) is far from Sentinel-2 RGB and NIR bands (10m). LULC images were generated using pixel-based Maximum Likelihood (MLC) supervised classification method. As a result of the accuracy assessment, kappa statistics for Sentinel-2 and Landsat-8 data were 0.78 and 0.85 respectively. The obtained results showed that Sentinel-2 MSI presents more satisfying LULC images than Landsat-8 OLI data. However, in some areas of Sea class Landsat-8 presented better results than Sentinel-2

The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis

Rapid and irregular urbanization is an essential issue in terms of environmental assessment and management. The dynamics of landscape patterns should be observed and analyzed by local authorities for a sustainable environment. The aim of this study is to determine which spectral urban index, originated from old Landsat missions, represents impervious area better when new generation Earth observation satellite Landsat 8 data are used. Two datasets of Landsat 8, acquired on 2 September 2013 and 10 September 2016, were utilized to investigate the consistency of the results. In this study, commonly used urban indices namely normalized difference built-up index (NDBI), index-based built-up index (IBI), urban index (UI), and enhanced built-up and bareness index (EBBI) were utilized to extract impervious areas. The accuracy assessment of urban indices was conducted by comparing the results with pan-sharpened images, which were classified using maximum likelihood classification (MLC) method. The kappa values of MLC, IBI, NDBI, EBBI, and UI for 2013 dataset were 0.89, 0.79, 0.71, 0.59, and 0.49, respectively, and the kappa values of MLC, IBI, NDBI, EBBI, and UI for 2016 dataset were 0.90, 0.78, 0.70, 0.56, and 0.47, respectively. In addition, area information was extracted from indices and classified images, and the obtained outcomes showed that IBI presented better results than the other urban indices, and UI extracted impervious areas worse than the other indices in both selected cases. Consequently, Landsat 8 satellite data can be considered as an important source to extract and monitor impervious surfaces for the sustainable development of cities. © 2018, Springer International Publishing AG, part of Springer Nature

UPDATING OBJECT FOR GIS DATABASE INFORMATION USING HIGH RESOLUTION SATELLITE IMAGES: A CASE STUDY ZONGULDAK

Nowadays Geographic Information Systems (GIS) uses Remote Sensing (RS) data for a lot of applications. One of the application areas is the updating of the GIS database using high resolution imagery. In this context high resolution satellite imagery data is very important for many applications areas today's and future. And also, high resolution satellite imagery data will be used in many applications for different purposes. Information systems needs to high resolution imagery data for updating. Updating is very important component for the any of the GIS systems. One of this area will be updated and kept alive GIS database information. High resolution satellite imagery is used with different data base which serve map information via internet and different aims of information systems applications in future topographic and cartographic information systems will very important in our country in this sense use of the satellite images will be unavoidable. In this study explain to how is acquired to satellite images and how is use this images in information systems for object and roads. Firstly, pan-sharpened two of the IKONOS's images have been produced by fusion of high resolution PAN and MS images using PCI Geomatica v9.1 software package. Automatic object extraction has been made using eCognition v4.0.6. On the other hand, these objects have been manually digitized from high resolution images using ArcGIS v9.3. software package. Application section of in this study, satellite images data will be compared each other and GIS objects and road database. It is also determined which data is useful in Geographic Information Systems. Finally, this article explains that integration of remote sensing technology and GIS applications.</i