scholarly journals Mineral Prοpecting and Lithοlοgical Mapping Using Remοte Sensing Apprοaches in Between Yazihan-Heki̇mhan (Malatya) Turkey

2021 ◽  
Author(s):  
ULAŞ İNAN SEVİMLİ ◽  
MAMADOU TRAORE ◽  
YUSUF TOPAK ◽  
SENEM TEKİN
Keyword(s):  
Remote Sensing ◽  
Landsat 8 ◽  
Potential Zone ◽  
Landsat 8 Oli ◽  
Ophiolite Complex ◽  
Band Ratio ◽  
Lithological Mapping ◽  
Chromite Ore ◽  
Aster Data ◽  
Basic Rocks

Abstract The Remote Sensing processing analysis have become a directing and hopeful instrument for mineral investigation and lithological mapping. Mineral exploration in general and bearing chromites associated with ultrabasic and basic rocks of the ophiolite complex in particular has been successfully carried out in recent years using Remote Sensing techniques. Yazıhan-Hekimhan (Malatya) region of East Taurus mountain belt, ranks second in terms of iron mineralization in Turkey are accepted. The area is characterized by high grade iron ore deposits in use, development and exploration. Lithological mapping and chromite ore exploration of this area is challenging owing to difficult access (High Mountain 2243 m) using the traditional method of exploration. The main objective of this research is to evaluate the capacity of Landsat-8 OLI and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery to discriminate and detect the potential zone of chromites bearing mineralized in Malatya (Yazıhan). Several images processing techniques, Vegetation Mask, Band Ratio (BR), Band Ratio Color Composite (BRCC), Principal Component Analysis (PCA), Decorrelation Stretch, Minimum Noise Fraction and Supervised classification using Spectral Angle Mapper (SAM) exist in previous studies have been performed for lithological mapping. The obtained results show that, BR, PCA and Decorrelation Stretch methods applied on NVIR-SWIR bands of Landsat-8 and ASTER were clearly discriminate the ophiolite rocks at a regional scale. In Addition, SAM classification was applied on a spectral signature of differents ultrabasic and basic rocks extracted from ASTER data. The results are promising in identifying the potentials zones of chromite ore mineralization zones within the ophiolite region. Thus, the techniques used in this research are suitable to detect or identify the high-potential chromite bearing areas in the ophiolite complex rocks using Landsat-8 OLI and ASTER data.

scholarly journals Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

2019 ◽  
Vol 8 (6) ◽  
pp. 248 ◽  
Author(s):  
Imane Bachri ◽  
Mustapha Hakdaoui ◽  
Mohammed Raji ◽  
Ana Cláudia Teodoro ◽  
Abdelmajid Benbouziane
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Lithological Mapping ◽  
Sensing Data

Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.

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

2019 ◽  
Vol 3 (2-2) ◽  
Author(s):  
Amine Jellouli ◽  
Abderrazak El Harti ◽  
Zakaria Adiri ◽  
El Mostafa Bachaoui ◽  
Abderrahmane El Ghmari
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Remote Sensing Data ◽  
Field Investigation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Lithological Mapping ◽  
Sensing Data ◽  
Color Composite ◽  
Optimum Index Factor

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.

scholarly journals Lithological mapping using remote sensing techniques: A case study of Alagbayan area, Dornogobi province, Mongolia

2021 ◽  
Vol 26 (53) ◽  
pp. 37-54
Author(s):  
Badrakh Munkhsuren ◽  
Batkhuyag Enkhdalai ◽  
Tserendash Narantsetseg ◽  
Khurelchuluun Udaanjargal ◽  
Demberel Orolmaa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Granite Gneiss ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat Images ◽  
Band Ratio ◽  
Lithological Mapping ◽  
Remote Sensing Techniques ◽  
Thin Section Analysis

This study investigated the multispectral remote sensing techniques including ASTER, Landsat 8 OLI, and Sentinel 2A data in order to distinguish different lithological units in the Alagbayan area of Dornogobi province, Mongolia. Therefore, Principal component analysis (PCA), Band ratio (BR), and Support Vector Machine (SVM), which are widely used image enhancement methods, have been applied to the satellite images for lithological mapping. The result of supervised classification shows that Landsat data gives a better classification with an overall accuracy of 93.43% and a kappa coefficient of 0.92 when the former geologic map and thin section analysis were chosen as a reference for training samples. Moreover, band ratios of ((band 7 + band 9)/band 8) obtained from ASTER corresponds well with carbonate rocks. According to PCs, PC4, PC3 and PC2 in the RGB of Landsat, PC3, PC2, PC6 for ASTER data are chosen as a good indicator for different lithological units where Silurian, Carboniferous, Jurassic, and Cretaceous formations are easily distinguished. In terms of Landsat images, the most efficient BR was a ratio where BRs of 5/4 for alluvium, 4/7 for schist and 7/6 to discriminate granite. In addition, as a result of BR as well as PCA, Precambrian Khutag-Uul metamorphic complex and Norovzeeg formation can be identified but granite-gneiss and schist have not given satisfactory results.

scholarly journals Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

scholarly journals FOREST DISTURBANCE ANALYSIS WITH LANDSAT-8 OLI DATA RELATED TO A PARAMETRIC WIND FIELD: A CASE STUDY FOR TYPHOON RAMMASUN (201409)

2018 ◽  
Vol XLII-3 ◽  
pp. 1629-1633
Author(s):  
C. Tan ◽  
W. Fang
Keyword(s):  
Remote Sensing ◽  
Wind Field ◽  
Forest Disturbance ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Dependence Relation ◽  
Wind Field Model ◽  
Wind Speeds ◽  
Sensing Technology ◽  
And Function

Forest disturbance induced by tropical cyclone often has significant and profound effects on the structure and function of forest ecosystem. Detection and analysis of post-disaster forest disturbance based on remote sensing technology has been widely applied. At present, it is necessary to conduct further quantitative analysis of the magnitude of forest disturbance with the intensity of typhoon. In this study, taking the case of super typhoon Rammasun (201409), we analysed the sensitivity of four common used remote sensing indices and explored the relationship between remote sensing index and corresponding wind speeds based on pre-and post- Landsat-8 OLI (Operational Land Imager) images and a parameterized wind field model. The results proved that NBR is the most sensitive index for the detection of forest disturbance induced by Typhoon Rammasun and the variation of NBR has a significant linear dependence relation with the simulated 3-second gust wind speed.

scholarly journals MANEJO, QUALIDADE E DINÂMICA DA DEGRADAÇÃO DE PASTAGENS NA MATA ATLÂNTICA DE MINAS GERAIS - BRASIL

Nativa ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 370 ◽  
Author(s):  
Luís Flávio Pereira ◽  
Cecilia Fátima Carlos Ferreira ◽  
Ricardo Morato Fiúza Guimarães
Keyword(s):  
Remote Sensing ◽  
Atlantic Forest ◽  
Quality Index ◽  
Minas Gerais ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Mata Atlântica ◽  
Mata Atlantica ◽  
Forest Biome

Pastagens sob práticas de manejo ineficientes tornam-se degradadas, provocando sérios problemas socioambientais e econômicos. Assim, entender a dinâmica dos sistemas pastoris e suas interações com o meio físico torna-se essencial na busca de alternativas sustentáveis para a agropecuária. Estudou-se manejo, dinâmica anual e interações socioambientais em pastagens de uma bacia hidrográfica no bioma Mata Atlântica em Minas Gerais, Brasil, durante o ano hidrológico 2016/2017. Utilizou-se dados de campo, relatos de agricultores e sensoriamento remoto via imagens LANDSAT 8 OLI e Google Earth Pro®. Foi proposto um índice de qualidade para pastagens da região. As pastagens apresentaram, em média, qualidade moderada. Níveis de degradação foram altos, oscilando de forma quadrática (níveis 2, 4, 5 e IDP) e potencial (nível 1) com a precipitação (p < 0,01), o que sugere que a irrigação possa ser prática eficiente no controle da degradação. Durante o ano, pelo menos 51,27% das pastagens apresentaram algum sinal de degradação, atingindo-se a marca de 91,32%, no período seco. Os resultados sugerem pior qualidade e maiores níveis de degradação de pastagens em terras elevadas e declivosas. Devido às condições socioambientais locais, indica-se o uso de sistemas silvipastoris agroecológicos no manejo das pastagens.Palavras-chave: uso da terra, sensoriamento remoto, relação solo paisagem, Zona da Mata, índice de qualidade. MANAGEMENT, QUALITY AND DEGRADATION DYNAMICS OF PASTURES IN ATLANTIC FOREST BIOME, MINAS GERAIS – BRASIL ABSTRACT:Pastures under inefficient management practices get degraded, leading to serious socioeconomic and environmental issues. That being said, understanding the dynamics of such systems and their interaction with the environment is essential when it comes to looking towards sustainable alternatives for livestock activities. The management, annual dynamics and socio-environmental interactions in pastures in an hydrographic basin located in Atlantic Forest biome, Minas Gerais, Brasil, were studied during the hydrological year of 2016/2017. Field data and farmers reports were utilized, such as remote sensing via images from LANDSAT 8 OLI and Google Earth Pro®. A quality index was proposed for the pastures, which usually presented medium quality. Degradation levels were high, oscillating in a quadratic basis (levels 2, 4, 5 and IDP) and potential (level 1) with precipitation (p < 0,01), which suggests that irrigation might be an efficient practice when it comes to degradation control. During the year, at least 51,27% of pastures have presented signs of degradation, achieving 91,32% in dry periods. The results suggest less quality and bigger degradation levels in pastures located in high and steep areas. Considering the local environmental conditions, agroecological silvopasture systems are recommended regarding the pastures management.Keywords: land use, remote sensing, soil/landscape relationships, Zona da Mata, quality index.

Penentuan Luas Area Sawah Kabupaten Sleman Pada Citra Landsat 8 Operational Land Imager (OLI)

Respati ◽  
2018 ◽  
Vol 13 (3) ◽  
Author(s):  
Sulidar Fitri ◽  
Novi Nurjanah
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Support Vector Machine ◽  
Rice Field ◽  
Image Data ◽  
Rice Fields ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Learning Machine

INTISARITeknologi penginderaan jauh sangat baik dijadikan data pembuatan peta penggunaan lahan, karena kebutuhan pemetaan semakin tinggi terutama untuk mendeteksi perubahan penggunaan lahan terutama untuk penentuan luas area khususnya sawah di kabupaten Sleman. Untuk mendapatkan informasi luasan area sawah dari interpretasi citra landsat-8 OLI (Operational Land Imager) diperlukan metode khusus, terutama untuk pengolahan data citra penginderaan jauh secara digital. Salah satu metode pengolahan citra penginderaan jauh adalah metode Support Vector Machine (SVM). Metode SVM merupakan metode learning machine (Pembelajaran mesin) yang dapat mengklasifikasikan pola serta mengenali pola dari inputan atau contoh data yang diberikan dan juga termasuk ke dalam supervised learning. Hasil area sawah yang didapati dari citra Landsat 8 OLI dengan pengolahan metode SVM didapati berada di 18 kecamatan dala Kabupaten Sleman. Luasan tertinggi ada di kecamatan Ngaglik dengan 19,78 KM2 dan terendah di kecamatan Turi seluas 2,14 KM2. Nilai keseluruhan akurasi yang didapat untuk kelas lahan sawah dan area non sawah adalah adalah 53%.Kata kunci— Landsat-8 OLI, SVM, Data Citra, Geospasial, Luas Area Sawah ABSTRACTRemote sensing technology is very well used as a data for making land use maps, because mapping needs are increasingly high especially for detecting land use changes, especially for determining the area, especially rice fields in Sleman district. To get information about the area of the rice fields from the interpretation of Landsat-8 OLI (Operational Land Imager), special methods are needed, especially for processing remote sensing image data digitally. One method of processing remote sensing images is the Support Vector Machine (SVM) method. The SVM method is a learning machine method that can classify patterns and recognize patterns from input or sample data provided and also includes supervised learning. The results of the rice field that were found from the Landsat 8 OLI image by processing the SVM method were found in 18 sub-districts in Sleman Regency. The highest area is in Ngaglik sub-district with 19.78 KM2 and the lowest in Turi sub-district is 2.14 KM2. The overall value of the accuracy obtained for the class of rice field and non-rice field is 53%.Kata kunci—  Landsat-8 OLI, SVM, Image Data, Geospatial, Area of Rice Fields

Application of optical and radar data for lineaments mapping in Kerdous inlier of the Anti Atlas belt, Morocco

2021 ◽  
Author(s):  
Amine Jellouli ◽  
Abderrazak El Harti ◽  
Zakaria Adiri ◽  
Mohcine Chakouri ◽  
Jaouad El Hachimi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Radar Data ◽  
Ore Deposits ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Alos Palsar ◽  
Geological Map ◽  
Color Composite ◽  
Manual Extraction

&lt;p&gt;Lineament mapping is an important step for lithological and hydrothermal alterations mapping. It is considered as an efficient research task which can be a part of structural investigation and mineral ore deposits identification. The availability of optical as well as radar remote sensing data, such as Landsat 8 OLI, Terra ASTER and ALOS PALSAR data, allows lineaments mapping at regional and national scale. The accuracy of the obtained results depends strongly on the spatial and spectral resolution of the data. The aim of this study was to compare Landsat 8 OLI, Terra ASTER, and radar ALOS PALSAR satellite data for automatic and manual lineaments extraction. The module Line of PCI Geomatica software was applied on PC1 OLI, PC3 ASTER and HH and HV polarization images to automatically extract geological lineaments. However, the manual extraction was achieved using the RGB color composite of the directional filtered images N - S (0&amp;#176;), NE - SW (45&amp;#176;) and E - W (90&amp;#176;) of the OLI panchromatic band 8. The obtained lineaments from automatic and manual extraction were compared against the faults and photo-geological lineaments digitized from the existing geological map of the study area. The extracted lineaments from PC1 OLI and ALOS PALSAR polarizations images showed the best correlation with faults and photo-geological lineaments. The results indicate that the lineaments extracted from HH and HV polarizations of ALOS PALSAR radar data used in this study, with 1499 and 1507 extracted lineaments, were more efficient for structural lineament mapping, as well as the PC1 OLI image with 1057 lineaments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt; Remote Sensing . OLI. ALOS PALSAR . ASTER . Kerdous Inlier . Anti Atlas&lt;/p&gt;

scholarly journals Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors

2020 ◽  
Vol 12 (8) ◽  
pp. 1263 ◽  
Author(s):  
Yingfei Xiong ◽  
Shanxin Guo ◽  
Jinsong Chen ◽  
Xinping Deng ◽  
Luyi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Land Cover Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remote Sensing Images ◽  
The Cross ◽  
Model Training

Detailed and accurate information on the spatial variation of land cover and land use is a critical component of local ecology and environmental research. For these tasks, high spatial resolution images are required. Considering the trade-off between high spatial and high temporal resolution in remote sensing images, many learning-based models (e.g., Convolutional neural network, sparse coding, Bayesian network) have been established to improve the spatial resolution of coarse images in both the computer vision and remote sensing fields. However, data for training and testing in these learning-based methods are usually limited to a certain location and specific sensor, resulting in the limited ability to generalize the model across locations and sensors. Recently, generative adversarial nets (GANs), a new learning model from the deep learning field, show many advantages for capturing high-dimensional nonlinear features over large samples. In this study, we test whether the GAN method can improve the generalization ability across locations and sensors with some modification to accomplish the idea “training once, apply to everywhere and different sensors” for remote sensing images. This work is based on super-resolution generative adversarial nets (SRGANs), where we modify the loss function and the structure of the network of SRGANs and propose the improved SRGAN (ISRGAN), which makes model training more stable and enhances the generalization ability across locations and sensors. In the experiment, the training and testing data were collected from two sensors (Landsat 8 OLI and Chinese GF 1) from different locations (Guangdong and Xinjiang in China). For the cross-location test, the model was trained in Guangdong with the Chinese GF 1 (8 m) data to be tested with the GF 1 data in Xinjiang. For the cross-sensor test, the same model training in Guangdong with GF 1 was tested in Landsat 8 OLI images in Xinjiang. The proposed method was compared with the neighbor-embedding (NE) method, the sparse representation method (SCSR), and the SRGAN. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were chosen for the quantitive assessment. The results showed that the ISRGAN is superior to the NE (PSNR: 30.999, SSIM: 0.944) and SCSR (PSNR: 29.423, SSIM: 0.876) methods, and the SRGAN (PSNR: 31.378, SSIM: 0.952), with the PSNR = 35.816 and SSIM = 0.988 in the cross-location test. A similar result was seen in the cross-sensor test. The ISRGAN had the best result (PSNR: 38.092, SSIM: 0.988) compared to the NE (PSNR: 35.000, SSIM: 0.982) and SCSR (PSNR: 33.639, SSIM: 0.965) methods, and the SRGAN (PSNR: 32.820, SSIM: 0.949). Meanwhile, we also tested the accuracy improvement for land cover classification before and after super-resolution by the ISRGAN. The results show that the accuracy of land cover classification after super-resolution was significantly improved, in particular, the impervious surface class (the road and buildings with high-resolution texture) improved by 15%.

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