ASTER-Based Remote Sensing Image Analysis for Prospection Criteria of Podiform Chromite at the Khoy Ophiolite (NW Iran)

A single chromite deposit occurrence is found in the serpentinized harzburgite unit of the Khoy ophiolite complex in northwest Iran, which is surrounded by dunite envelopes. This area has mountainous features and extremely rugged topography with difficult access, so prospecting for chromite deposits by conventional geological mapping is challenging. Therefore, using remote sensing techniques is very useful and effective, in terms of saving costs and time, to determine the chromite-bearing zones. This study evaluated the discrimination of chromite-bearing mineralized zones within the Khoy ophiolite complex by analyzing the capabilities of ASTER satellite data. Spectral transformation methods such as optimum index factor (OIF), band ratio (BR), spectral angle mapper (SAM), and principal component analysis (PCA) were applied on the ASTER bands for lithological mapping. Many chromitite lenses are scattered in this ophiolite, but only a few have been explored. ASTER bands contain improved spectral characteristics and higher spatial resolution for detecting serpentinized dunite in ophiolitic complexes. In this study, after the correction of ASTER data, many conventional techniques were used. A specialized optimum index factor RGB (8, 6, 3) was developed using ASTER bands to differentiate lithological units. The color composition of band ratios such as RGB ((4 + 2)/3, (7 + 5)/6, (9 + 7)/8), (4/1, 4/7, 4/5), and (4/3 × 2/3, 3/4, 4/7) produced the best results. The integration of information extracted from the image processing algorithms used in this study mapped most of the lithological units of the Khoy ophiolitic complex and new prospecting targets for chromite exploration were determined. Furthermore, the results were verified by comprehensive fieldwork and previous studies in the study area. The results of this study indicate that the integration of information extracted from the image processing algorithms could be a broadly applicable tool for chromite prospecting and lithological mapping in mountainous and inaccessible regions such as Iranian ophiolitic zones.

DETEKSI AWAL HABITAT PERAIRAN LAUT DANGKAL MENGGUNAKAN TEKNIK OPTIMUM INDEX FACTOR PADA CITRA SPOT 7 DAN LANDSAT 8

Informasi keberadaan habitat perairan laut dangkal semakin dibutuhkan terutama dalam kegiatan pelestarian lingkungan dan monitoring di wilayah pesisir. Komponen penyusun ekosistem habitat dasar perairan laut dangkal di antaranya terumbu karang dan lamun dimana lokasi keberadaan obyek habitat ini cenderung berdekatan. Dalam interpretasi ekosistem habitat dasar perairan laut dangkal terkendala oleh lokasi keberadaan ekosistem yang berasosiasi dengan obyek lainnya. Tujuan penelitian ini adalah menentukan kombinasi komposit kanal terbaik dalam mengidentifikasi obyek habitat dasar perairan laut dangkal di Pantai Pemuteran, Bali. Data citra satelit yang digunakan dalam penelitian ini adalah citra SPOT 7 akuisisi tanggal 11 April 2018 dan citra Landsat 8 akuisisi tanggal 14 April 2018, sedangkan data terkait informasi sebaran habitat dasar perairan laut dangkal diperoleh berdasarkan hasil survei lapangan yang telah dilakukan pada tanggal 7-13 April 2018 di Pantai Pemuteran, Bali. Data citra satelit diperoleh dari Pusat Teknologi dan Data LAPAN. Untuk menentukan kombinasi dari 3 (tiga) kanal terbaik dalam interpretasi habitat dasar perairan laut dangkal digunakan metode Optimum Index Factor (OIF) dimana metode ini menggunakan nilai standar deviasi dan koefisien korelasi dari kombinasi 3 (tiga) kanal citra yang digunakan. Hasil penelitian menunjukkan kombinasi komposit 2 (hijau), 3 (merah) dan 4 (NIR) mempunyai nilai OIF tertinggi untuk citra SPOT 7, sedangkan kombinasi komposit 2 (biru), 4 (merah) dan 6 (SWIR 1) Mempunyai nilai OIF tertinggi untuk citra Landsat 8. Interpretasi sebaran habitat dasar perairan laut dangkal dapat dilakukan secara efektif dengan menggunakan citra komposit RGB 423 untuk citra SPOT 7 dan RGB 642 untuk citra Landsat 8.DETECTION OF SHALLOW WATER HABITATS USING OPTIMUM INDEX FACTORS TECHNIQUE ON SPOT 7 AND LANDSAT 8 IMAGERY. Information of the existence of the shallow water habitat is required especially in environmental conservation and monitoring of activities in coastal areas. The component of the shallow water habitat including coral reefs and seagrass where the location of the existence of these relatively close together. Interpretation of the shallow water habitat is constrained by the location of ecosystem associated with other objects. The aim of study is to determine the best combination of band composites in identifying the shallow water habitat in Pemuteran Beach, Bali. The study used SPOT 7 imagery (acquisition on April 11, 2018) and Landsat 8 imagery (acquisition on April 14, 2018). The data of the shallow water habitat based on the result of field survey was conducted on 7-13 April 2018 at Pemuteran Beach, Bali. Image data obtained from Remote Sensing Technology and Data Center of LAPAN. Determination of combination of 3 (three) bands the shallow water habitat using Optimum Index Factor (OIF) method where this method used standard deviation value and correlation coefficient from combination of 3 (three) bands. The results show the composite combinations of band 2 (green), band 3 (red) and band 4 (NIR) have the highest OIF values for SPOT 7 image, while the composite combinations of band 2 (blue), band 4 (red) and band 6 (SWIR 1) have the highest OIF values for Landsat 8 image. Interpretation of distribution of shallow water habitat can be done effectively using RGB 423 composite image (SPOT 7) and RGB 642 composite image (Landsat 8).

Application of Remote Sensing Data in Lithological Discrimination of Kerdous Inlier in the Anti Atlas Belt of Morocco

Remote sensing data reveals a great importance for lithological mapping due to their spatial, spectral and radiometric characteristics. Lithological mapping using spatial data is a preliminary and important step to mineral mapping. In this work, several spectral and radiometric transformations methods were applied on Landsat 8 OLI data to enhance lithological units in the study area situated in the Anti Atlas belt. The methods of Optimum Index Factor (OIF), Decorrelation Stretching (DS), Principal Components Analysis (PCA) and Band Ratioing (BR) showed good results for lithological mapping in comparison with the existing geological and field investigation. An RGB color composite of OLI bands 651 was developed for mapping lithological units of the study area by fusing optimum index factor (OIF) and decorrelation stretching methods. furthermore, Band ratios derived from image spectra were applied in two RGB color composites (7+4/2, PC1, PC2) and (PC1, 7/6, 3/7) providing good discrimination of the lithological units. The Landsat-8 OLI data significantly provided satisfied results for lithological mapping.

APPLICATION OF REMOTE SENSING IN GEOLOGICAL MAPPING, CASE STUDY Al MAGHRABAH AREA – HAJJAH REGION, YEMEN

Remote sensing technology plays an important role today in the geological survey, mapping, analysis and interpretation, which provides a unique opportunity to investigate the geological characteristics of the remote areas of the earth's surface without the need to gain access to an area on the ground. The aim of this study is achievement a geological map of the study area. The data utilizes is Sentinel-2 imagery, the processes used in this study, the OIF Optimum Index Factor is a statistic value that can be used to select the optimum combination of three bands in a satellite image. It’s based on the total variance within bands and correlation coefficient between bands, ICA Independent component analysis (3, 4, 6) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals, MNF Minimum Noise Fraction (1, 2, 3) is used to determine the inherent dimensionality of image data to segregate noise in the data and to reduce the computational requirements for subsequent processing, Optimum Index Factor is a good method for choosing the best band for lithological mapping. ICA, MNF, also a practical way to extract the structural geology maps. The results in this paper indicate that, the studied area can be divided into four main geological units: Basement rocks (Meta volcanic, Meta sediments), Sedimentary rocks, Intrusive rocks, volcanic rocks. The method used in this study offers great potential for lithological mapping, by using Sentinel-2 imagery, the results were compared with existing geologic maps and were superior and could be used to update the existing maps.