Application of spectral analysis to determine geothermal anomalies in the Tuzla region, NW Turkey

We used remote sensing data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite to identify the mineral properties and geothermal anomalies related to hot springs in the Tuzla area, including the fault system with NW-SE trend, which is located southwest of Çanakkale, NW Turkey. In the study area, the lithological units of the Tuzla geothermal field and the surrounding area consist of Miocene volcanic (trachyandesite, trachyte, and ignimbrites) and Pliocene sedimentary (conglomerate, sandstone, and mudstone) rocks with siliceous, argillaceous, and ferrous alteration linked to the geothermal fluid. ASTER visible/near-infrared (VNIR), short-wave infrared (SWIR), and TIR bands were analyzed by different approaches in order to highlight hot springs in the study area. From these approaches, band ratios were constructed from ASTER VNIR, SWIR, and TIR bands for obtaining geological properties of the region. The geothermal areas were defined by the minimum noise fraction (MNF) and principal component analysis (PCA) methods that was extracted from 5 thermal infrared (TIR) bands as well. Land surface temperatures (LST) support the results from MNF and PCA that were estimated for 5 TIR bands using the inversion of Planck function method. Four days of data including daytime and nighttime satellite images from ASTER were used for the analysis. The used procedure displayed a good match with the ground reality based on field observations in the Tuzla Region. © 2019, Saudi Society for Geosciences

Assessment of hotspots using sparse autoencoder in industrial zones

PubMedID: 31222399Remote sensing satellite systems can be used to detect industrial zones by means of thermal infrared bands. There are several satellite systems loaded with thermal infrared sensors such as Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). In this study, ASTER thermal infrared data were converted to land surface temperature (LST) in order to determine hotspots caused by industrial zones. High LST values surrounded by low LST values are called hotspots here. These hotspots can be determined by applying different methodologies. One of these methods of sparse autoencoder can be used to indicate hotspots using different sizes of hidden layers. The principle of sparse autoencoder depends on unlabeled data in unsupervised learning. It does not need any information about labeled data as in supervised learning. The autoencoder reproduces its output with the same dimensions as the input image by managing the size of the hidden layer. The reconstruction of the image depends on the minimization of a cost function. The size of the hidden layer sets the fitting degree of the function for the reproduced image. A low-order reproduced image is the main target for hotspot detection. In this study, the difference between the original image and the reproduced image was analyzed for hotspot detection. Sparse autoencoder was successfully applied to ASTER thermal band 10 for hotspot detection in 7 pre-defined sites of a region known for steel industry for the two different days. © 2019, Springer Nature Switzerland AG.U.S. Geological Survey National Aeronautics and Space Administration U.S. Geological SurveyFor LST retrieval, ASTER data used in this study was downloaded from the Distributed Active Archive Center (DAAC) operated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and National Aeronautics and Space Administration (NASA). The ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST L1_T) data was obtained in WGS-84 datum and Universal Transverse Mercator (UTM) projection with zone 37N. The borders of the cropped image were defined fo

ZmYS1 functions as a proton-coupled symporter for phytosiderophore- and nicotianamine-chelated metals.

Among higher plants graminaceous species have the unique ability to efficiently acquire iron from alkaline soils with low iron solubility by secreting phytosiderophores, which are hexadentate metal chelators with high affinity for Fe(III). Iron(III)-phytosiderophores are subsequently taken up by roots via YS1 transporters, that belong to the OPT oligopeptide transporter family. Despite its physiological importance at alkaline pH, uptake of Fe-phytosiderophores into roots of wild-type maize plants was greater at acidic pH and sensitive to the proton uncoupler CCCP. To access the mechanism of Fe-phytosiderophore acquisition, ZmYS1 was expressed in an iron uptake-defective yeast mutant and in Xenopus oocytes, where ZmYS1-dependent Fe-phytosiderophore transport was stimulated at acidic pH and sensitive to CCCP. Electrophysiological analysis in oocytes demonstrated that Fephytosiderophore transport depends on proton cotransport and on the membrane potential, which allows ZmYS1-mediated transport even at alkaline pH. We further investigated substrate specificity and observed that ZmYS1 complemented the growth defect of the zinc uptake-defective yeast mutant zap1 and transported various phytosiderophore-bound metals into oocytes, including zinc, copper, nickel, and, at a lower rate, also manganese and cadmium. Unexpectedly, ZmYS1 also transported Ni(II), Fe(II), and Fe(III) complexes with nicotianamine, a structural analog of phytosiderophores, which has been shown to act as an intracellular metal chelator in all higher plants. Our results show that ZmYS1 encodes a proton-coupled broad-range metal-phytosiderophore transporter that additionally transports Fe- and Ni-nicotianamine. These biochemical properties indicate a novel role of YS1 transporters for heavy metal homeostasis in plants