Lithological, structural and hydrothermal alteration mapping utilizing remote sensing datasets: a case study around Um Salim area, Egypt

Remote sensing datasets have introduced remarkable advancements in mapping rock units, structural elements, and hydrothermal alteration zones. This study applied Landsat Operational Land Imager (OLI) multispectral dataset in discriminating the intricate basement of Um Salim area, Central Eastern Desert (CED), Egypt. Moreover, a panchromatic 15m pixel size band is implemented to extract the study area’s linear structural features. Several image processing methods including False Color Combination (FCC), Band Ratio (BR), Optimum Index Factor (OIF), and Density slicing were utilized in lithological and alteration mapping. The widely used, LINE module of the PCI Geomatica is applied for lineament extraction. Results reasonably discriminate the complicated rock units using selected composites depending on OIF results. A photo-geological map is constructed and shows greater coincidence with recently published maps. Lineaments map and its density revealed the preponderance of NE-SW and WNW-ESE structural trends. The spatial relationship between the resultant hydrothermally-altered zones and the detected structural features strongly recommends further detailed examination for ore deposits within the study area besides manifesting the efficiency of the utilized data and methods.

Aplikasi Citra Landsat 8 OLI/TIRS Dalam Mengidentifikasi Alterasi Hidrotermal Skala Regional; Studi Kasus Kecamatan Suoh dan Bandar Negeri Suoh

Telah dilakukan penelitian mengenai eksplorasi geothermal menggunakan Remote Sensing atau penginderaan jauh. Secara umum, penginderaan jauh merupakan metode yang dilakukan untuk mengumpulkan data atau informasi tanpa perlu menyentuh objek yang akan diteliti. Remote sensing dilakukan dengan menggunakan foto udara ataupun dengan menggunakan citra satelit seperti Landsat-8, ALOS PALSAR, Radar, dan Sentinel. Penginderaan jauh sering digunakan dalam eksplorasi geothermal karena memiliki keunggulan yang dapat mengakses hingga ke daerah terpencil dan tidak memerlukan biaya yang besar. Penelitian ini dilakukan dengan menggunakan Citra Landsat 8 OLI/TIRS yang bertujuan untuk memetakan daerah alterasi hidrotermal secara regional dengan studi kasus Suoh, Lampung Barat. Penelitian dilakukan dengan cara memproses beberapa band dengan memiliki rentang pixel yang dapat merepresentasikan mineral alterasi mineral hidrotermal pada daerah penelitian. Di daerah penelitian, mineral alterasi tersebut terdeteksi dengan warna kuning – orange pada citra komposit 4/2, 6/7, 5 dan 10, dengan menggunakan metode density slicing. Dari penelitian ini didapatkan luas daerah dengan batuan teralterasi hidrotermal mencapai 223H.

Preliminary Study of Mining Material Prospects Based on Hydrothermal Alteration Distribution Using Composite and Density Slicing of Landsat 8 Image in Ulubongka Regency, Central Sulawesi

Difficult access to the field in Central Sulawesi causes the process of identifying prospects for mining minerals requires a relatively large time and cost. Therefore, the remote sensing approach can be carried out as a preliminary study to determine the distribution of hydrothermal alterations that characterize the presence of mine minerals. This study utilizes Landsat 8 imagery to obtain the distribution of alteration minerals, especially the group of ferrous mineral oxide and hydroxide minerals, clay and carbonate minerals and Ferromagnesian minerals. The method used is composite (RGB) 4/2, 6/7, 5 and 4/2, 6/7, 10 to detect the distribution of alteration minerals. Whereas the Density Slicing method uses 4/2, 5/6 and 6/7 ratio images to detect Ferrugitation, Ferromagnesian, clay and carbonate minerals. The results of the analysis showed the distribution of iron oxide and hydroxide minerals (Ferrugination) dominated the area in alluvium and opiolite rocks. This alteration distribution is interpreted as a prospect of further studies of the presence of mining minerals such as nickel and iron. Whereas ferromagnesian minerals, clays and carbonates dominate in conglomerate and limestone rocks.

Mapping Urbanization and Evaluating Its Possible Impacts on Stream Water Quality in Chattanooga, Tennessee, Using GIS and Remote Sensing

Impervious surfaces (IS) produced by urbanization can facilitate pollutants’ movement to nearby water bodies through stormwater. This study mapped and estimated the IS changes in Chattanooga, Tennessee, using satellite imagery acquired in 1986 and 2016. A model was developed utilizing the Normalized Difference Vegetation Index coupled with density slicing to detect and map urbanization through IS growth. Urban growth was quantified at USGS HUC12 watershed level including stream riparian areas. The obtained results show a net growth of 45.12 km2 of IS with a heterogeneous distribution. About 9.96 km2 of this growth is within 90 m of streams, about 6% of the study site’s land cover. The Lower South Chickamauga Creek watershed experienced the largest urban growth with a change from 24.2 to 48.5 km2. Using the riparian zone percent imperviousness, a stream risk assessment model was developed to evaluate potential stream impairment due to this growth. Approximately 87, 131, and 203 km lengths of streams identified as potentially at high, very high, and extreme risks, respectively, to be impaired due to urban growth from the last 30 years. These findings would benefit to proactively implement sustainable management plans for the streams near rapidly urbanizing areas in the study site.

Extraction of Hidden Information under Sootiness on Murals Based on Hyperspectral Image Enhancement

Changes in the environment and human activities can cause serious deterioration of murals. Hyperspectral imaging technology can observe murals in the range of visible to near infrared light, providing a scientific and non-destructive way for mural digital preservation. An effective method to extract hidden information from the sootiness of murals in order to enhance the visual value of patterns in ancient murals using hyperspectral imaging is proposed in this paper. Firstly, Minimum Noise Fraction transform was applied to reduce sootiness features in the background of the mural. Secondly, analysis of spectral characteristics and image subtraction were used to achieve feature enhancement of the murals. Finally, density slicing was performed to extract the patterns under the sootiness. The results showed that the extraction of hidden information was achieved with an overall accuracy of 88.97%.

Aplication Multi Vegetation Index to Mapping Magrove Distribution Coast Environtment Northeast Province of Aceh, Indonesia

The mangrove such forest very important at coastal ecosystem and environment. The purpose of the research to mapping mangrove distribution at the coast environment using multi vegetation index, comparison accuracy assessment to mapping mangrove area. The method of the research use by multi imagery transformation as NDVI, Infrared II, SAVI, EVI and Maximum Likelihood. Data on the research have using by Landsat OLI8, tools use by ENVI 5.0 and ArcGIS 10.1. Optimizing the used of data from Landsat satellite imagery for mapping mangrove found where sharper appearance mangrove area in the gray scale image of the results of the analysis of vegetation transformation NDVI, Infrared II, SAVI and EVI showing difference specification, but also found has founded difference objects of interpreted it was showing like shadows of cloud be the another object. To classification on mangrove object is seen from the results of density slicing of transformation value to classing vegetation. The percentage accuracy of image prove some dominant image transformation is able to indicate a more optimal mangrove and mangrove separating the object is not present, but the accuracy of the data analysis result has variations, refers to the number of samples used

Separation of Different Vegetation Types in ASTER and Landsat Satellite Images Using Satellite‐derived Vegetation Indices

Separation of different vegetation types in satellite images is a critical issue in remote sensing. This is because of the close reflectance between different vegetation types that it makes difficult segregation of them in satellite images. In this study, to facilitate this problem, different satellite derived vegetation indices including: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index 2 (EVI2) were derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat-5 TM data. The obtained NDVI, EVI, and EVI2 images were then analyzed and interpreted in order to evaluate their effectiveness to discriminate rice and citrus fields from ASTER and Landsat data. In doing so, the Density Slicing (DS) classification technique followed by the trial and error method was implemented. The results indicated that the accuracies of ASTER NDVI and ASTER EVI2 for citrus mapping are about 75% and 65%, while the accuracies of Landsat NDVI and Landsat EVI for rice mapping are about 60% and 65%, respectively. The achieved results demonstrated higher performance of ASTER NDVI for citrus mapping and Landsat EVI for rice mapping. The study concluded that it is difficult to detect and map rice fields from satellite images using satellite-derived indices with high accuracy. However, the citrus fields can be mapped with the higher accuracy using satellite-derived indices.