Landsat-8 OLI Research Articles

Comparative Analysis of Sentinel-2 MSI and Landsat-8 OLI for Enhanced Land Monitoring

The usage of satellite imagery has been extremely beneficial to many industries, such as environmental monitoring, medical mapping, urban planning, and agriculture. Among the several satellite pictures that are emerging as significant sources of multispectral data that provide crucial insights into the dynamics of the Earth’s surface are Sentinel-2 MSI and Landsat-8 OLI. Understanding the differences between these two satellite systems is essential to maximizing their use in various applications. This study examines the advantages and disadvantages of Sentinel-2 and Landsat- 8 OLI satellite data. The results enabled the use of the pertinent satellite images to create various themed memories, such as the connection between human health and the environment while creating medical-related geographic maps.

Геоэкологическое картирование застройки на паводкоопасных территориях городов Шилки и Нерчинска Забайкальского края с использованием спектрального индекса NDBI и нейронной сети

At all times, the problem of floods has been relevant due to their destructiveness, frequency and poor predictability. The situation is complicated by the fact that floodplains are used for economic, recreational and other activities, thereby exposing the risk of destruction and degradation of any objects and human waste products during floods. Therefore, mapping and spatiotemporal monitoring of flood-prone areas in order to analyze and predict the likely material and economic damage to facilities of economic activity from the threat of floods is an urgent scientific problem that can be solved by geographic information systems using satellite data processed by spectral indices and convolutional neural networks. The object of the study is the flood-prone territories of Shilka and Nerchinsk (Transbaikal Region). The purpose is to assess the accuracy of economically developed territories decryption in flood-prone areas of Shilka and Nerchinsk in order to determine the effective threshold for separation of the studied facilities. Research objectives are as follows: collection and formation of an archive of Earth remote sensing data of the Landsat-8 OLI program; inference of satellite images on a neural network and their use as a reference image; calculation of NDBI spectral indices based on Landsat-8 data; determination of the areas of buil­­t-up and economically developed territories in the images; assessment of the accuracy and comparative analysis of the results of the development facilities allocation and economic activity; identification of advantages, disadvantages, applicability limits of the methods used to decrypt buildings and objects of economic activity in flood-prone areas. The accuracy and reliability of the results of the decrypting buildings and objects of economic activity by calculating the NDBI spectral index, the inference of a convolutional neural network according to Landsat 8 OLI data in flood-prone areas of Shilka and Nerchinsk have been evaluated. The most effective methods, parameters and thresholds for decrypting buildings and objects of economic activity have been determined in order to monitor the development of flood-prone areas and control the flood-prone situation.

Drought Analysis in Right Bank Main Canal Command Area of Dharoi Reservoir using Meteorological and Remote Sensing Indices

Drought is characterized by a prolonged and abnormal lack of moisture, often stemming from insufficient or uneven distribution of precipitation, resulting in water shortages in streams or reservoirs, with detrimental impacts on both natural and socioeconomic systems. The paper focuses on the climatic drought patterns within the Right Bank Main Canal Command Area of Gujarat’s Dharoi Reservoir. To evaluate drought conditions, the study employs several indices including the Standardized Precipitation Index (SPI)and Normalized Difference Vegetation Index (NDVI). Daily rainfall data, obtained from IMD gridded data and extracted using ArcGIS software, is utilized to calculate SPI values at a three-month time scale. NDVI is determined using Landsat-8 OLI data spanning from 2013 to 2022 to analyze changes in agricultural vegetation. Comparisons among NDVI and SPI reveal that the year 2018 experienced significant rainfall deficits across the region, leading to severe water scarcity. This shortage impacted agricultural practices, evident in reduced crop cultivation areas as indicated by NDVI results and validated using the crop yield data for the time series. Specifically, the NDVI analysis highlights that 33.5% of the RBMC command area exhibited signs of dryness, with an additional 64.5% showing moderate dryness. The study endeavors to pinpoint drought-affected regions utilizing precipitation data, reservoir storage information, and remote sensing methodologies.

Land use land cover change detection using multi-temporal Landsat imagery in the North of Congo Republic: a case study in Sangha region

In recent years, satellite data have become available for free to the remote sensing community. Land use and land cover (LULC) changes are identified in remote sensing applications using Landsat satellite data. However, there is a lack of studies that utilize these data to assess the performance of satellite data on LULC classification and monitoring changes in complex landscapes. This study aims at evaluating LULC changes for the years 2013, 2018, and 2023 in the Sangha area using Landsat-8 OLI images. The Support Vector Machine (SVM) algorithm was implemented for detecting changes in the Sangha area. The results revealed that wetland forest and water bodies drastically declined, with a net change of −33.78 and-19.22%, respectively, while open forest, urban area, and bare soils with +77.91, +52.81, and +40.52% correspondingly substantially increased between 2013 and 2023. The overall accuracy and Kappa statistics achieved were above 91% and 0.85, respectively.

The Mapping of Vegetation Density Changes Based on the Normalized Difference Vegetation Index Using Landsat OLI in The Coastal Region of Aceh Barat

Damage to coastal vegetation caused by the tsunami occurred rapidly, despite the critical role of vegetation in supporting life. Vegetation density is quantified through the use of an index known as the NDVI (Normalized Difference Vegetation Index). This index can be applied to assess changes in vegetation density over several years in coastal regions. The focus of this study was to analyze the changes in vegetation density along the coast of West Aceh in the years 2013, 2015, and 2020. The findings indicated that vegetation density in 2013 was notably low, with NDVI values falling within the range of -0.1495 to 0.1591, indicating a lack of vegetation. Furthermore, the percentage of areas with moderate vegetation density appears to have increased steadily year by year, with values of 26.73% in 2013, 27.50% in 2015, and a notable acceleration to 37.61% in 2020.

Temporal variation in glacier surface area and glacial lakes in glaciated river basins of Arunachal Pradesh

Abstract Climate change-induced glacier recession has sparked a dynamic transformation of glaciers in high-mountain areas worldwide, resulting in genesis, expansion, and dissipation of glacial lakes that pose a potential threat to downstream communities, underscoring the need for regular monitoring. This study incorporates the automated glacier extraction index to improve debris-free glacier mapping accuracy by reducing water and shadow area categorization errors using multi-temporal Landsat-5 Thermal Mapper, Landsat-8 Operational Land Imager, and digital elevation model (Shuttle Radar Topography Mission) to obtain precise glacier extent. Waterbodies were delineated using thresholding techniques applied to water indices: normalized difference water index, modified normalized difference water index, normalized difference turbidity index, and slope information. The study attempted to map and analyze the temporal variations of glaciers and glacier lakes in the glaciated sub-basins of Arunachal Pradesh: Dibang Basin, Mago Basin, and Subansiri Basin. Glaciers receded at a shrinkage rate of 1.08% per year (1988–2017) in Mago Basin, 1.01% per year (2013–2022) in Subansiri Basin, and 1.42% per year (1995–2021) in Dibang Basin. Glacier lakes showed an increasing trend in number and area due to glacier melt, expanding at rates of 3.31% in the Mago Basin, 0.48% in the Subansiri Basin, and 0.68% in the Dibang Basin per year. Findings suggest that global climate change is likely the primary factor driving glacier changes in these basins.

Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling

This research uses a Classification and Regression Tree (CART) model with Google Earth Engine (GEE) to assess the winter season’s land cover and change detection mapping impact on the evapotranspiration (crop water requirement) parameters. Winter seasons, crucial for agricultural planning, and irrigation water requirement challenges in accurately mapping land cover and detecting changes due to the dynamic nature of farming practices during this period. In this study, Landsat-8 OLI images have been combined to map Land use and Land cover (LULC) and other change detection mapping in Akola Block, Maharashtra, India, during the 2018–2022 winter season. As an discoverer researcher that found detailed information of LULC classes during last 2018 to 2022 winter seasons, the use of the CART model in combination with a cloud-computing GEE demonstrates to be a practical approach for accurate land cover classification and change detection maps to create a pixel-based winter seasons information of study area. The novelty of this study lies in its innovative use of GEE, a powerful platform for remote sensing and geospatial analysis, to create LULC maps with remarkable accuracy. Achieving a 100% training accuracy across the four years under consideration is an exceptional feat, highlighting the reliability and stability of the methodology. Furthermore, the validation accuracy values, ranging from 89 to 94% for the winter seasons of 2018 to 2022, underscore the robustness of this approach. Such consistently high accuracy in mapping LULC over time is a groundbreaking achievement and offers a new dimension to the field of hydrology. For the hydrological community, the implications of this study are profound. Accurate LULC mapping and change detection provide critical data for modeling and analyzing the effects of land use changes on water resources, watershed management, and water quality. The User, Kappa, and Producer accuracy metrics used in this research highlight the model’s performance and its suitability for hydrological applications. These accurate LULC maps can aid in the development of hydrological models, forecasting, and decision-making processes, ultimately contributing to more effective water resource management and environmental conservation. In summary, this study’s innovative use of GEE, its remarkable accuracy in LULC mapping, and its relevance to the hydrological community demonstrate the potential for advanced remote sensing and geospatial tools to significantly improve our understanding of land use changes and their implications for water resources and environmental management.

Indexing and segment-level mapping of soil quality in a spatially complex watershed in northern Iran.

Soil quality (SQ) modeling and mapping is a leading research field aiming to provide reproducible and cost-effective yet accurate SQ predictions at the landscape level. This endeavor was conducted in a complex watershed in northern Iran. We classified the region into spectrally and topographically homogenous land units (average area of 48 ± 23ha) using object-based segmentation analysis. Following the physicochemical analysis of soil samples from 98 stations, the Nemoro soil quality index (SQIn) was produced using the minimum dataset procedure and a non-linear sigmoid scoring function. SQIn values averaged 0.21 ± 0.06 and differed statistically between major land uses. To predict and map SQIn for each land unit, the best-performing regression model (F(3, 84) = 45.57, p = 0.00, R2 = 0.62) was built based on the positive contribution of the mean Landsat 8-OLI band 5, and negative influence of land surface temperature retrieved from Landsat 8-OLI band 10 and surface slope (t-test p-values < 0.01). Results showed that dense-canopy woodlands located in low-slope land units exhibit higher SQIn while regions characterized by either low-vegetation or steep-sloped land units had SQ deficits. This study provides insights into SQ prediction and mapping across spatially complex large-scale landscapes.

DEVELOPING A MULTI-VARIABLE FOREST FIRE RISK MODEL AND FIRE RISK ZONE MAPPING

Abstract. Forest steppe and taiga regions with a dry climate, small fires occur frequently between large fires, and therefore groups of trees of various ages and structures are formed in these forests, which has the effect of increasing fire exposure and "fuel reserves". The main goal of this research is to develop a multi-variable forest fire risk model and to mapping forest fire risk. The study area is the forest community in Bulgan province. Forest cover maps are essential for current research of community. We used in this research Landsat OLI and Digital Elevation Model (DEM) data. Soil moisture index and land surface temperature data from Landsat satellite data and elevation data (DEM) were used to determine slope and aspect. A multi-variable forest fire risk model was developed in this research. The final output delineated fire risk zones in the study area in four categories that include high-risk, moderate-risk, low-risk, and non-fire risk zones. Accuracy assessments on the two Landsat scenes indicates that forest/non-forest maps derived using the forest index (FI) have high accuracy.

Multi-property digital soil mapping at 30-m spatial resolution down to 1 m using extreme gradient boosting tree model and environmental covariates

Information on the variability of soil properties including clay, calcium carbonate equivalent (CCE) and acidity (pH) in space and depth play major role in sustainable agricultural development but scarce in many regions in the world, specifically in developing countries. Digital soil mapping technique has shown strong success in the prediction of soil properties both in space and depth from their relationship with environmental covariates. The aim of the study was to digitally map soil properties including clay, CCE and pH at multiple depths and identify the dominant factors controlling their variability using Minadoab region of Iran as a case study area due to its significance for agricultural production and economic development in the country. A total of 104 soil profiles were randomly selected and described in field during 15–30 July 2017, and 386 samples were collected from each genetic horizon with variable depths. A continuous spline function was fitted to the horizon data and properties were estimated at five standardized depths following GlobalSoilMap project. The soil layers were then grouped into three classes: i) H1: top (0–5 cm); ii) H2–H4: middle (5–15, 15–30 and 30–60 cm); and iii) H5: bottom (60–100 cm) for easy interpretation. A total of 17 static environmental covariates were derived from terrain-related attributes. Additionally, matching with the soil sampling date, a total of eight band ratios in addition to six individual bands were derived from Landsat-8 OLI (Product ID: LC08_L1TP_168034_20170721_20170728_01_T1) for the study area. Finally, all covariates were categorized in nine groups representing various factors controlling soil variations. The digital maps associated with uncertainties were prepared using extreme gradient boosting (XGBoost) Tree model and bootstrapping method, respectively. Overall, the highest accuracy using XGBoost Tree model was found for CCE (R2 = 0.73, on average), followed by pH (R2 = 0.72, on average) and clay (R2 = 0.70, on average). The highest amount of clay was observed for the bottom layer, followed by middle and top layers across the study area. H1 to H3 layers were subjected to tillage operations over the time and may have contributed to the redistribution of clays. A similar trend was also observed for CCE and pH as the location of the study area is underlain by massive limestone. The uncertainties helped quantify over- and under-estimation of the predictions and the factors with the highest variable importance was identified as the dominant factor. The information generated for the study area provides soil variability information that is critical for developing management strategies for sustainable production in the area.

Delineation of high‐resolution soil carbon management zones using digital soil mapping: A step towards mitigating climate change in the Northeastern Himalayas, India

AbstractDelineation of carbon management zones (CMZs) by capturing geospatial distribution of soil organic carbon (SOC) stock down the profile is an effective strategy for precision agriculture and climate change mitigation. Satellite (Landsat OLI 8), terrain (SRTM 30 m DEM) and bioclimatic (WorldClim dataset) factors were used as covariables in this digital soil mapping approach. Depth harmonization using the quadratic spline method (equal‐area) was carried out prior to quantile regression forest (QRF) algorithm‐based modelling to estimate SOC stock at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). Soil depth and SOC stock for the whole soil profile were used for the delineation of CMZs using fuzzy k‐means clustering. The predicted SOC stock, varied from 14.68 to 42.35 Mg ha−1 in the top layer (0–5 cm depth), while 17.91 to 36.88, 14.15 to 34.70, 12.55 to 35.59, 10.30 to 28.52 and 7.26 to 20.16 Mg ha−1 in the depths of 5–15, 15–30, 30–60, 60–100 and 100–200 cm, respectively. The QRF algorithm performed well in predicting SOC stock with high R2, which ranged from .67 to .83 for all the soil depths. To delineate three CMZs, modified partitioning entropy and the fuzzy performance index were used. In CMZ2, there was a significant increase in SOC stock, followed by CMZ1 and CMZ3. This zone (CMZ2) was located in the central region of the study area and was mostly covered by dense forest and perennial plantations (rubber). The CMZs provided the necessary foundation for the development of site‐specific carbon management techniques that can enhance ecosystem service and meet climate change mitigation goals.

Assessing Spatio-temporal Changes of Floating Aquatic Vegetation in Lake Sevan Using Landsat Imagery and Vegetation Indices

This research was carried out as part of the "Copernicus assisted environmental monitoring across the Black Sea Basin – PONTOS" project, which aimed to support and enhance environmental monitoring in the Black Sea Basin region by utilizing Earth Observation products obtained from satellite, airborne, and ground sources. The project team evaluated the environmental monitoring system in pilot sites across Armenia, Greece, Georgia, and Ukraine. The current study focused on assessing changes in wetland and floating vegetation cover from 2009-2015 in Lake Sevan, the largest freshwater source in Armenia and one of the project's pilot sites. Monitoring spatio-temporal changes in aquatic vegetation is crucial for understanding the ecological and socioeconomic functions of lake ecosystems, and requires standardized methods. In order to identify floating aquatic vegetation in Lake Sevan, this study utilized Landsat TM and OLI imagery that were acquired during the main growing season from middle May to middle September of the years 2009-2015. To enhance the classification process, vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Aquatic Vegetation Index (NDAVI), and Normalized Difference Water Index (NDWI) were applied. The findings of this study indicate that medium-resolution Landsat and similar satellite images, which are freely available, can be effectively used to monitor spatiotemporal changes in lakes in a reproducible and continuous manner. However, it was also discovered that algal blooms can significantly hinder the accurate detection of floating vegetation from satellite imagery.

Mapping potential hydrocarbon sites in Upper Assam shelf using geospatial and geo-structural techniques

Remote Sensing and Spatial analysis tools are used to identify the hydrocarbon prospective zones as subsurface hydrocarbon traps are not perfectly sealed and moves in the upward direction from the reservoir to the surface in the form of traces and alter the vegetation which can be recognized through several techniques. Landsat-8 OLI and Sentinel 2A satellite images were used and spectral signatures such as Mineral Indices, Vegetation Index, Hydrothermal Alteration were analyzed to detect possible hydrocarbon sites in the study area. Mineral Indices namely clay carbonate, ferrous, and ferric indices were used to confirm and locate the hydrocarbon zones which is directly related to the detection of hydrocarbon micro seepage on the surface. Vegetation indices such as NDVI, GNDVI, SAVI, and Chlorophyll Index Green were investigated to narrow down the prospective areas, indicating chemical alteration in the soil and vegetation, giving an idea about the hydrocarbon prospective zones. Magnetic and gravity data were also collected to correlate these findings which shown anomalies in the study area and gave evidence for the prospect of hydrocarbon. Lineament extraction was performed to delineate the micro seepage zones in the study area. After combining all the data, the prospective zones of hydrocarbon and micro seepage in Upper Assam Shelf were located. Furthermore, a comparison study was conducted to identify prospective areas from analyzing both Sentinel 2A and Landsat 8 images. The results of the study demonstrated the effectiveness of remote sensing and spatial analysis tools in identifying hydrocarbon prospective areas in the region.

KAJIAN KUALITAS AIR DI PERAIRAN DESA SUMBERKIMA DAN DESA PEMUTERAN, KECAMATAN GEROKGAK, KABUPATEN BULELENG, PROVINSI BALI

Water quality has a significant impact on aquaculture productivity. Water quality characteristics influence fishing production. This study employs numerous prior research techniques to assess air quality factors such as Total Suspend Solid (TSS), salinity, Sea Surface Temperature (SST), and dissolved oxygen. However, physical elements such as rainfall, which are separated into wet and dry months in this study, have an impact on water quality. The approach was developed using Landsat-8 OLI satellite images. The algorithm's output is validated for data accuracy using Pearson correlation, root mean square error (RMSE), and R-square. The findings suggest that the distribution of water quality in dry and rainy months is low in coastal areas and high in locations adjacent to open waters. Furthermore, it was discovered that the average value of the distribution of TSS in dry months was lower than in wet months, the mean value of the distribution of salinity in dry months was higher than in wet months, the average value of the SST distribution in dry months was higher than in wet months, and the mean value of the dissolved oxygen distribution in dry months was lower than in wet months.&#x0D; Keywords: Water suitability; Landsat-8; Water Quality; Remote Sensing

Automated mapping of regolith units with support vector machine and artificial neural network using data from Landsat-8 OLI, ALOS PALSAR DEM, and Sentinel-1A radar images: the case of the Sissingué Gold Project, Northern Côte d’Ivoire

ABSTRACT The Sissingué Gold Project is in Northern Côte d’Ivoire and part of the southern extension, a large peneplain of the landscape in South Mali. The area has experienced extensive weathering and erosion over a long time, resulting in a widespread and complex regolith cover. This diverse and vast regolith cover poses considerable difficulties for exploration. To overcome this problem, we adopted mapping of regolith units using machine learning (ML) with support vector machine (SVM) and artificial neural network (ANN) algorithms. The main objectives were to (1) make the predictive regolith map of the study region using these algorithms with data obtained from Landsat-8 operational land imager, Advanced Land Observing Satellite Phased Array Type L-Band Synthetic Aperture Radar digital elevation model and Sentinel-1A; (2) show the method of pre-processing and processing the required data and (3) develop the regolith landform unit (RLU) map from the predictive regolith map and collected data based on the Relict – Erosional – Depositional model. Tests using the SVM and ANN algorithms showed that both ML tools could accurately map regolith landforms. An innovative method of optimizing data using parameters is presented. The results showed that ANN outperformed SVM with an overall accuracy of 87.01% and a kappa coefficient of 0.84, whereas the corresponding values for SVM were 86.69% and 0.84, respectively. However, the validation data obtained from SVM-based prediction exhibited a better score than that of validation data obtained from ANN-based prediction. The Sentinel-1A radar band combined with Landsat-8 data reduced the vegetation-masking effect and improved the classification results. The RLUs of this area are composed primarily of relicts at 24.91%, including lateritic residuum and soil, depositional material at 36.39% with exotic sediments and ferrierite and the remaining 38.7% of erosional material made of saprolite, colluvial fragments, and mottled zones on flanks.

SOC content of global Mollisols at a 30 m spatial resolution from 1984 to 2021 generated by the novel ML-CNN prediction model

Carbon cycle is influenced by agricultural soils, and accurately mapping the soil organic carbon (SOC) content of global Mollisols at a 30 m spatial resolution can contribute to clarifying the carbon sequestration capacity of each region, facilitate the quantification of agroecosystems and contribute to global food security. However, the high heterogeneity of environmental variables in global regions, coupled with the challenges posed by small-sample tasks, creates significant obstacles to producing reliable SOC content datasets. In this study, we collected 191,465 scenes of Landsat TM and OLI images and elevation model data to calculate spectral indices that can represent soil formation information based on a soil-pedogenic model. Second, a local strategy (LS) was proposed to reduce the influence of the high heterogeneity of SOC content and environmental variables on the prediction results. More importantly, the first meta-learning convolutional neural network (ML-CNN) model was proposed. It provides high prediction accuracy for small-sample tasks and was used to generate the first high-resolution global Mollisol region SOC content product (GMR-MCNN). Finally, we compared GMR-MCNN with the existing SoilGrids250m and Soil SubCenter products. The results showed that long-term, high-accuracy and high-resolution prediction of the SOC content in global Mollisol regions was achieved by the ML-CNN model (RMSE = 4.84 g kg−1, R2 = 0.75, RPIQ = 2.43). Compared with a CNN, ML-CNN can continuously optimize the meta-task, thus improving the performance of the model in small-sample tasks. Compared to the prediction model that combined the recursive feature elimination technique with the random forest model (RFE-RF), ML-CNN can efficiently extract high-level features from time-series data, thus improving the model performance. Compared with that of the global strategy, the RMSE of the LS decreased by 0.20 g kg−1, and R2 and RPIQ increased by 13.00% and 0.22, respectively. In addition, the GMR-MCNN results illustrated that the SOC content in the global Mollisol regions shows a decreasing trend, and the trend can be divided into significant decrease (1984–2000) and moderate decrease (2001−2021) phases. Different products were tested based on laboratory-measured SOC contents, and GMR-MCNN (RMSE = 6.13 g kg−1, R2 = 0.63) displayed better performance than SoilGrids250m (RMSE = 23.37 g kg−1, R2 = 0.28) and the Soil SubCenter map (RMSE = 8.59 g kg−1, R2 = 0.43). The developed methodology can provide a reference for the long-term observation of soil and crop properties at moderate and high resolutions globally.

Analysis of coastline changes under the impact of human activities during 1985-2020 in Tianjin, China.

The accurate evaluation of shoreline movement is a crucial aspect for managing highly dynamic coasts. This study employed Landsat TM and OLI data through the Digital Shoreline Analysis System model to quantify changes in the spatiotemporal distribution of Tianjin's coastline from 1985 to 2020. The results showed that the coastline length (CL) increased by 178 km and 151% over the past 36 years, with an average increase of 5.1 km/a. Accretion and erosion processes along the entire coast were observed at rates of 83.9% and 16.1%, respectively. Notably, the Tianjin Port Area and Nangang Industrial Zone showed remarkable changes in the shoreline in 2009. Night lights (NL) were used to display the intensity of human activity in this area, and the spatial heterogeneity of night light intensity was significant. Compared to the total night light (TNL) in 1985, it increased by 116% in 2020. The relationship between TNL and CL was then established and displayed a significant positive correlation (r = 0.91). With the increasing total night light, the growth of the CL presented changes with an initial slow increase, then rapid increase, and finally slow increase. In the second phase of TNL, the CL experienced a considerable increase due to anthropogenic activities such as land reclamation and port construction, fueled primarily by government policies during the period of 2005-2013. Subsequently, there was little change in the coastline. These findings provide valuable insights into spatiotemporal coastline monitoring programs and sustainable coastal management.

Contribution of Landsat 8 OLI imagery to mapping of lithological series and lineaments: implications for Pb-Zn mineralization exploration in the Boudahar Massif, Eastern High Atlas, Morocco

The Boudahar massif is located in the southern part of the eastern belt of the Moroccan High Atlas and it corresponds to a reef mass of about ten kilometers in length. Jbel Boudahar is a district of lead-zinc-barite mineralization, known by a strong artisanal exploitation of its ores. This district is characterized by its topography with a contrast of altitude and an arid environment, which makes the information acquired more valuable. The processing of Landsat 8 OLI data allowed us to extract a lithological and structural map, aiming to make correlations to understand the distribution of lead-zinc mineralization. To this end, several tests were carried out, including the composition of color, principal component analysis and band ratio transformation. The location of the main deposits at Jbel Boudhar coincides well with the lineament derived from band 1 of principal component analysis and with the dolomitic pliensbachian formations mapped by MNF and BR from Landsat Oli8. A remote sensing geological prospecting model has been established for the Jbel Boudhar lead-zinc deposits, providing a basis for future prospecting of new deposits in a similar context.

Evaluating the consistency of surface brightness, greenness, and wetness observations between Landsat-8 OLI and Landsat-9 OLI2 through underfly images

Evaluating the consistency of surface brightness, greenness, and wetness observations between Landsat-8 OLI and Landsat-9 OLI2 through underfly images

Contribution of Remote Sensing to the Mapping of Lineaments and Ore-Mineral Occurrences in the Taghbalt Region, Moroccan Eastern Anti-Atlas

The Taghbalet region is a part of the Paleozoic cover of the Moroccan Anti-Atlas, known by a rich mineral potential of vein type. This paper presents the results of a field investigation and processing of Landsat 8oli and Aster satellite images. The pre-processing allows for improving the image quality by applying a radiometric calibration, atmosphere correction and a pan-sharpening correction. Landsat-8 oli data is used to map structural lineaments including fracture and/or vein. C compositions ( RGB: 2, 5 and 7) and band ratios (5/7, 3/1, 4/3 and 5/7, 5/4, 3/1) were applied, both of which showed the lithological contrast between formations. Then directional filters (N-S, NE-SW, E-W and NW-SE) were applied on the spectral bands and principal component analysis to map the different lineaments. Validation and correction of lineaments obtained by automatic extraction are based on visual interpretation and field verification. The mining prospecting is performed using ASTER multispectral images. Therefore the results obtained show that we have two main directions; one E-W and the other NE-SW. The majority of these lineaments are concentrated in the south of the area. The results of mapping the hydrothermal alteration zones show us that these zones have a clear relationship with the fault.