Landsat-8 Operational Land Imager Research Articles

On the Retrieval of the Water Quality Parameters from Sentinel-3/2 and Landsat-8 OLI in the Nile Delta’s Coastal and Inland Waters

Reduced water quality due to the eutrophication process causes large economic losses worldwide. Multi-source remotely-sensed water quality monitoring can help provide effective water resource management. The research evaluates the retrieval of the water quality parameters: chlorophyll-a (Chl-a), total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM), over optically different water types. Cross-sensor performance analysis of three satellite data sources: Sentinel-3 Ocean Land Color Imager (OLCI), Sentinel-2A Multi-Spectral Instrument (MSI), and Landsat-8 Operational Land Imager (OLI), acquired during a 45 min overpass on the Nile Delta coast on 22 March 2020 was performed. Atmospheric correction using the case 2 Regional Coast Color (C2RCC) was applied using local water temperature and salinity averages. Owing to the lack of ground-truth measurements in the coastal water, results were inter-compared with standard simultaneous color products of the Copernicus Marine Environment Monitoring Service (CMEMS), OLCI water full resolution (WFR), and the MODIS Aqua, in order to highlight the sensor data relative performance in the Nile Delta’s coastal and inland waters. Validation of estimates was carried out for the only cloud-free MSI data available in the 18–20 September 2020 period for the Burullus Lake nearly contemporaneous with in situ measurements in the 22–25 September 2020. Inter-comparison of the retrieved parameters showed good congruence and correlation among all data in the coastal water, while this comparison returned low positive or negative correlation in the inland lake waters. In the coastal water, all investigated sensors and reference data showed Chl-a content average of 3.14 mg m−3 with a range level of 0.39–4.81 mg m−3. TSM averaged 7.66 g m−3 in the range of 6.32–10.18 g m−3. CDOM clarified mean of 0.18 m−1 in the range level of 0.13–0.30 m−1. Analysis of the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) clarified that the MSI sensor was ranked first achieving the smallest MAE and RMSE for the Chl-a contents, while the EFR proved superior for TSM and CDOM estimates. Validation of results in Burullus Lake indicated a clear underestimation on average of 35.35% for the Chl-a induced by the land adjacency effect, shallow bottom depths, and the optical dominance of the TSM and the CDOM absorption intermixed in turbid water loaded with abundant green algae species and counts. The underestimation error increased at larger estimates of the algal composition/abundance (total counts, Chlorophyacea, Euglenophycaea, and Bacillariophycaea) and the biological contents (carbohydrates, lipids, and proteins), arranged in decreasing order. The largest normalized RMSE estimates marked the downstream areas where the inflow of polluted water persistently brings nutrient loads of nitrogen and phosphorous compounds as well as substantial amounts of detrital particles and sediments discharged from the agricultural and industrial drains and the land use changes related to agricultural practices, resulting in the increase of water turbidity giving rise to inaccurate Chl-a estimates.

Comparative analysis of AVIRIS-NG and Landsat-8 OLI data for lithological mapping in parts of Sittampundi layered complex, Tamil Nadu, India

The present study addresses a comparative analysis of the potentials of Airborne Visible and infrared imaging spectrometer -next generation (AVIRIS-NG) data, with respect to Landsat-8 Operational Land Imager (OLI) data for mapping of sporadic exposures of mafic cumulates within the anorthosite intrusion and delineation of major lithological units of Sittampundi layered complex. AVIRIS-NG and Landsat-8 OLI data were initially compared using scene-based relative signal-to-noise ratio (SNR) analysis. Subsequently, spectral anomaly maps were generated to map different lithological rock units using the constrained energy minimization (CEM) approach. Spectral maps derived using CEM were further filtered by low-pass filtering to control the false-positive result. Spectral angle mapper (SAM) classification technique was applied to analyze the suitability of AVIRIS-NG and Landsat-8 OLI data for lithological mapping. Confusion matrix analysis was carried out to assess the SAM classified maps. The first vertical derivative (FVD) map of magnetic anomaly and field evidence/geological exposures were used to validate the results. SNR analysis indicates the superior signal strength of AVIRIS-NG data than Landsat-8 OLI data. Moreover, combined analysis based on CEM and SAM of remote sensing data and FVD of magnetic anomaly reveals that the false-positive area indicating metagabbro/mafic cumulate is lower in AVIRIS-NG data than Landsat-8 OLI data. This is because of the relatively poor SNR, spectral and spatial resolutions of Landsat-8 OLI data; however, it can be good enough for mapping of metagabbro/mafic cumulate when AVIRIS-NG data is unavailable.

ASSESSMENT OF AFFORESTATION PROGRAMME AND COMMUNITY PERCEPTION IN GUMEL LOCAL GOVERNMENT AREA, JIGAWA STATE

This study assessed afforestation programme and community perception in Gumel Local Government area of Jigawa state. Field data collection was done using structured questionnaire and Landsat satellite imageries. A total of two hundred (200) questionnaire were distributed to the 200 sample size drawn from the sample area using a multi-stage sampling design technique. In a similar way, Landsat data were collected for a period of thirty (30) years using Landsat TM for 1988 and 1998 with Landsat ETM for 2008 and landsat-8 operational land imager (OLI) for 2018 using Earthexplorer. A 30m by 30m resolution Landsat image of the study area was obtained from NASA website. The images were downloaded from the Global Land Cover Facility of the University of Maryland (GLCF, 2018) and the United States Geological Survey (USGS) and spatially referenced in the Universal Transverse Mercator (UTM) projection with datum World Geodetic System (WGS). The images were extracted to Tiff formats for processing and classification using maximum likelihood classifier in Idrisi Selva. The collected data were subjected to analysis such as mean, simple percentage, graphs and tables. Results shows that between the year 1988 to 2018, afforestation in Gumel Local Government Area of Jigawa State increases significantly due to the success of the afforestation programme. It was however recommended that Government agencies in charge of forestry activity are thereby encouraged to put up measures in ensuring the avoidance of forest encroachment activities so as to ensure abundance trees and land covers.

Development of a three-source remote sensing model for estimation of urban evapotranspiration

Estimation of urban evapotranspiration (ET) is meaningful for evaluating the urban heat island effect and promoting urban water conservation. While multi-source satellite-based ET models for farmland and natural ecosystems have been developed, these models lack the object-oriented solutions for the urban areas. In view of this, the present study proposed a Three-source Remote sensing model for Urban areas (TRU) to simulate the urban ET, which can discriminate between urban soil evaporation, vegetation transpiration, and impervious surface evaporation. The new parameterization scheme proposed herein is characterized by the theory of complementary relationship to estimate the urban soil evaporation. Evaporation losses in the impervious areas were independently estimated in accordance with the distribution of urban land use and land cover (LULC). The developed TRU model was evaluated for 63 cloud-free test days in Tianjin, China by using 30 m Landsat Operational Land Imager (OLI)/Enhanced Thematic Mapper Plus (ETM+) images. The outcomes of this study revealed a root-mean-square-error (RMSE) of 40.2 W/m2 and a bias of 10.8 W/m2 compared with the two eddy correlation (EC) observations for the instantaneous latent heat flux (LE) simulation. Moreover, the RMSE and bias were 0.092 and −0.023, respectively in simulating the vegetation latent heat flux fraction (LEv/LE) when verified with respect to the stable water isotope measurements. Due to limited water availability, the impervious urban surface evaporation magnitude was found quite low with minimal variation on different test days. The results emphasize the importance of coupling LULC information in the urban ET modeling conceptualization. Certainly, the TRU model improved the ET simulation accuracy in urban areas and could facilitate the mapping of urban ET components at high spatial resolution.

Change detection of Jamuna River and its impact on the local settlements

ABSTRACT Spatio-temporal change detection analysis of Jamuna River using low and medium-resolution satellite data between the years of 1972 and 2013 are dynamic to assess river spatio-temporal changes and their impacts on the local settlements over the specific time. This study employs remote sensing and geographic information system (GIS) proficiencies to identify the river bank shifting, erosion, and depositional features along with the population displacement. The satellite image of LANDSAT multispectral (MSS), thematic mapper (TM), and operational land imager (OLI) and thermal infrared sensor (TIRS) were used in this study. Supervised and unsupervised classification techniques have been used to identify land cover types and detect the changes using ArcGIS Pro 2.3.2. Supervised classifications performed well by validating ≥90% overall accuracy. The result shows total erosion was 3356 ha from 1972 to 2013, while the deposition was 5342 ha. Erosion and deposition cause the bank line shifting, river widening, and settlement displacement. Hundreds of displaced people suffered from loss of land, livelihood, increased poverty, food insecurity, lack of sanitation, and drinking water facilities. Thus, this study will be earnest in assisting the environmental management and associated planning including necessary measures.

Examining the Integration of Landsat Operational Land Imager with Sentinel-1 and Vegetation Indices in Mapping Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines)

The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this study was to examine the integration of Landsat Operational Land Imager (OLI ) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data—normalized difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, transformed soil-adjusted vegetation index, and infrared percentage vegetation index; and 4) Landsat OLI with Sentinel-1 and vegetation indices. The results showed that the integration of Landsat OLI reflectance bands with Sentinel-1 backscattering coefficients and vegetation indices yielded the best overall classification accuracy, about 77%, and standalone Landsat OLI the weakest accuracy, approximately 67%. The findings in this study demonstrate that the addition of backscattering coefficients from Sentinel-1 and vegetation indices positively contributed to the mapping of southern yellow pines.

Fusing Sentinel-2 and Landsat-8 Surface Reflectance Data via Pixel-Wise Local Normalization

Medium spatial resolution surface reflectance image series from the combination of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Imager (MSI) observations have great importance to the land surface monitoring tasks, for which great efforts have been paid for blending the two data. However, most of the efforts focus on the image series with spatial resolution of 30 m, which can not meet the data demand of some applications. Therefore, it is necessary to fuse Landsat-8 and Sentinel-2 images to provide 10 m image series. Currently, there are three means to achieve that, including the area-to-point regression kriging fusion approach (ATPRK), spatiotemporal fusion methods and deep learning based fusion models. However, the ATPRK and spatiotemporal fusion methods suffer from the limited fusion performance, while the deep learning based fusion models are hardware dependent, i.e., requiring the graphics processing units (GPUs), which may not be satisfied sometimes. To address these issues, in this paper, we develop a new pixel-wise local normalization-based fusion method (LN-FM) for fusing Sentinel-2 and Landsat-8 images. The newly proposed LN-FM is compared to ATPRK and three representative spatiotemporal fusion methods in experiments, which use imagery collected from both a rural area and an urban area. The experimental results demonstrate that the newly developed LN-FM exhibits excellent qualitative and quantitative performance, as well as remarkable spatial, spectral and pixel distribution fidelity. Furthermore, this approach is fast, which may improve its applicability.

Study of morphometry and land coverage to help urban planning in Barbosa and Barbosinha Brooks watershed, Lins - SP

Urbanization in hydrographic basins promotes changes in the hydrological cycle through the impermealized areas. Not only surface runoff is increased, reducing infiltration, but also the susceptibility to extreme hydrological events. The objective of this study is to analyze the natural susceptibility of Córregos Barbosa e Barbosinha (Barbosa and Barbosinha Brooks) watershed to flooding. Morphometric parameters of land use and occupation were analyzed, in order to subsidize management and planning in an area of urban expansion. The analysis of land use and occupation was based on the supervised classification method of Landsat-5 Thematic Mapper (TM) sensor and Landsat-8 Operational Land Imager sensor (OLI) satellite images dated 1990, 2006 and 2020. Morphometric indices were calculated using the SPRING 5.4.3 software and a SRTM image with a spatial resolution of 30 meters. The results indicate that the natural susceptibility to flooding of the study area is medium to high, and can be intensified by the dynamics of land use and occupation and increasing impervious areas in the basin. Over the period of study, the growth of impervious areas was 133% relative to 1990.

Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible.

Assessment of spatio-temporal direction of impervious surface area surface temperature in Pretoria, South Africa

Over the years, rapid urban growth has led to the conversion of natural lands into large man-made landscapes due to enhanced political and economic growth. This study assessed the spatio-temporal change characteristics of impervious surface area (ISA) expansion using its surface temperature (LST) at selected administrative subplace units (i.e., local region scale). ISA was estimated for 1995, 2005 and 2015 from Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images using a Random Forest (RF) algorithm. The spatio-temporal trends of ISA were assessed using an optimal analytical scale to aggregate ISA LST coupled with weighted standard deviational ellipse (SDE) method. The ISA was quantified with high predictive accuracy (i.e., AUROC = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015) using RF classifier. More than 70% of the selected administrative subplaces in Pretoria experienced an increase in growth rate (415.59%) between 1995 and 2015. LST computations from the Landsat TIRS bands yielded good results (RMSE = ∼1.44OC, 1.40OC, ∼0.86OC) for 1995, 2005 and 2015 respectively. Based on the hexagon polygon grid (90x90), the aggregated ISA surface temperature weighted SDE analysis results indicated ISA expansion in different directions at the selected administrative subplace units. Our findings can represent useful information for policymakers in evaluating urban development trends in Pretoria, City of Tshwane (COT).

Multi-Source Hydrological Data Products to Monitor High Asian River Basins and Regional Water Security

This project explored the integrated use of satellite, ground observations and hydrological distributed models to support water resources assessment and monitoring in High Mountain Asia (HMA). Hydrological data products were generated taking advantage of the synergies of European and Chinese data assets and space-borne observation systems. Energy-budget-based glacier mass balance and hydrological models driven by satellite observations were developed. These models can be applied to describe glacier-melt contribution to river flow. Satellite hydrological data products were used for forcing, calibration, validation and data assimilation in distributed river basin models. A pilot study was carried out on the Red River basin. Multiple hydrological data products were generated using the data collected by Chinese satellites. A new Evapo-Transpiration (ET) dataset from 2000 to 2018 was generated, including plant transpiration, soil evaporation, rainfall interception loss, snow/ice sublimation and open water evaporation. Higher resolution data were used to characterize glaciers and their response to environmental forcing. These studies focused on the Parlung Zangbo Basin, where glacier facies were mapped with GaoFeng (GF), Sentinal-2/Multi-Spectral Imager (S2/MSI) and Landsat8/Operational Land Imager (L8/OLI) data. The geodetic mass balance was estimated between 2000 and 2017 with Zi-Yuan (ZY)-3 Stereo Images and the SRTM DEM. Surface velocity was studied with Landsat5/Thematic Mapper (L5/TM), L8/OLI and S2/MSI data over the period 2013–2019. An updated method was developed to improve the retrieval of glacier albedo by correcting glacier reflectance for anisotropy, and a new dataset on glacier albedo was generated for the period 2001–2020. A detailed glacier energy and mass balance model was developed with the support of field experiments at the Parlung No. 4 Glacier and the 24 K Glacier, both in the Tibetan Plateau. Besides meteorological measurements, the field experiments included glaciological and hydrological measurements. The energy balance model was formulated in terms of enthalpy for easier treatment of water phase transitions. The model was applied to assess the spatial variability in glacier melt. In the Parlung No. 4 Glacier, the accumulated glacier melt was between 1.5 and 2.5 m w.e. in the accumulation zone and between 4.5 and 6.0 m w.e. in the ablation zone, reaching 6.5 m w.e. at the terminus. The seasonality in the glacier mass balance was observed by combining intensive field campaigns with continuous automatic observations. The linkage of the glacier and snowpack mass balance with water resources in a river basin was analyzed in the Chiese (Italy) and Heihe (China) basins by developing and applying integrated hydrological models using satellite retrievals in multiple ways. The model FEST-WEB was calibrated using retrievals of Land Surface Temperature (LST) to map soil hydrological properties. A watershed model was developed by coupling ecohydrological and socioeconomic systems. Integrated modeling is supported by an updated and parallelized data assimilation system. The latter exploits retrievals of brightness temperature (Advanced Microwave Scanning Radiometer, AMSR), LST (Moderate Resolution Imaging Spectroradiometer, MODIS), precipitation (Tropical Rainfall Measuring Mission (TRMM) and FengYun (FY)-2D) and in-situ measurements. In the case study on the Red River Basin, a new algorithm has been applied to disaggregate the SMOS (Soil Moisture and Ocean Salinity) soil moisture retrievals by making use of the correlation between evaporative fraction and soil moisture.

Mapping and characterizing the Kīlauea (Hawaiʻi) lava lake through Sentinel-2 MSI and Landsat-8 OLI observations of December 2020–February 2021

On December 20, 2020, a new eruption started at Kīlauea volcano (Hawaiʻi, USA). During the eruption, the lava cascaded into the summit water lake, generating a vigorous steam plume and forming a new lava lake at the base of the crater. In this study, we investigate the lava lake through the Normalized Hot Spot Indices (NHI) algorithm. The latter runs operationally within a Google Earth Engine (GEE) App (NHI tool), using Operational Land Imager (OLI) and Multispectral Instrument (MSI) data, to map volcanic thermal anomalies at global scale. Results show that the used algorithm provided information about the lava lake and relative space-time variations in agreement with field observations. By correcting daytime data for the influence of the solar irradiation, we estimated values of the radiant flux in the range 1–5 GW from hottest pixels. This study opens some challenging scenarios about the quantitative characterization of volcanic thermal features through the NHI tool.

A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 × 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region’s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.

Land Suitability Mapping for Optimum Soybean Production in Konshisha Local Government Area, Benue State, Nigeria.

Land suitability mapping is a pre-requisite for optimum crop production and sustainable use of the land to provide food security. Soya bean is one of the most valuable food and cash crops produced in Konshisha LGA, Benue State, Nigeria. However, the production is constrained by low yield. Thus, the aim of this study is to map land suitable for optimum and sustainable soya bean production in the study area. As an effort toward achieving the Sustainable Development Goal (SDG) target of eradicating hunger by 2030. Multi spectral satellite imageries from Landsat-8 Operational Land Imager, Climate Research Unit (CRU), National Aeronautical Space Agency’s (NASA) Shuttle Radar Topographic Mission (SRTM) and ORLN DAAC soil data were collected using remote sensing and GIS techniques for Multi-criteria evaluation (MCE). GIS analysis and Analytical Hierarchical Process (AHP) were carried out. Result of the multi criteria evaluation shows that climate, slope, land use land cover, and available phosphorus are favorable factors, while soil organic carbon, pH and drainage are limiting factors. The result of the overall suitability mapping shows that 30.4% of the land is highly suitable (S1), 69.5% moderately suitable (S2), and 0.1% marginally suitable (S3) for soya bean production in the study area. Based on the findings, the study concluded that low soil organic carbon, pH and poor soil drainage are the main land limiting factors for optimum soya bean production. Therefore, the study recommends application of mixed organic and inorganic fertilizers, agricultural lime, composite manure, agro-forestry, crop rotation, creation of open deep drains and channelization as supplement of the limiting factors for optimum soya bean production in the study area.

International geological significance of the potential Al-Medina volcanic UNESCO Global Geopark Project in Saudi Arabia revealed from multi-satellite remote sensing data

UNESCO Global Geoparks should be of international geological significance in terms of their scientific quality, rarity, aesthetic appeal and tourism value. At least nine Cenozoic volcanic fields are developed in the western Saudi Arabia, with a total area of 180,000 km2. In this paper, the geological and geomorphological features of these volcanic fields induced by the expansion of the Red Sea are interpreted and displayed using multi-source satellite images, such as Landsat-8 OLI (Operational Land Imager) and Gaofen-2 data. Our results show that the Al-Medina volcanic field (AMVF) has great prospects as the aspiring volcanic Geopark in Saudi Arabia. This study indicates that: (1) AMVF has international geoscientific significance and rare natural attribute because the volcanic fields are induced by the rising mantle plume and rare examples of white volcanoes comprised mainly from felsic rock; (2) AMVF has abundant volcanic landscapes, including completely preserved cones and craters and multi-phase lava flows from different eruption stages, which have great aesthetic appreciation and tourism values to attract the general public; (3) AMVF is close to the second holy city of Islam-Medina, which has convenient transportation and is suitable to develop tourism to promote the development of the local economy; (4) AMVF had erupted repeatedly (the latest eruption was in 1256 AD), establishment of the UNESCO Global Geopark Project can not only prevent potential geological hazard risks to the people living in Medina city from future volcanic eruption events, but also provide better protection and conservation to geoheritage sites being damaged by human activities, for example setting protected boundaries of AMVF to avoid the destruction of volcanic landscape integrity caused by urban expansion.

Synergy of Remote Sensing Data for Exploring Hydrothermal Mineral Resources Using GIS-Based Fuzzy Logic Approach

The Arabian Nubian Shield (ANS) contains a variety of gold deposits in the form of veins and veinlets formed by hydrothermal fluids. Characterizing potential areas of hydrothermal alteration zones therefore provides a significant tool for prospecting for hydrothermal gold deposits. In this study, we develop a model of exploration for hydrothermal mineral resources in an area located in the ANS, Egypt, using multiple criteria derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat-Operational Land Imager (OLI), and Sentinel-2 data and field work through GIS-based fuzzy logic approach. The hydrothermal alteration zones (HAZs) map extracted from combining mineral indices, spectral bands, and ratios is consistent with observed argillic alteration zones around the mineralized veins. Combining HAZs and lineament density led to identification of six zones based on their mineralization potential, and provides a tool for successful reconnaissance prospecting for future hydrothermal mineral deposits. The detected zones are labeled as excellent, very high, high, moderate, low, and very low, based on their potential for Au production, and the predictive excellent and very high zones cover about 1.6% of the study area. This model also shows that target prospective zones are quartz veins controlled by NNW-SSE trending fracture/fault zones all crosscutting Precambrian rocks of the ANS. Field observations and petrographic and X-ray diffraction analyses were performed to validate the mineral prospective map and revealed that quartz veins consist of gold–sulfide mineralization (e.g., gold, pyrite, chalcopyrite, and sphalerite). Consistency between the high potential hydrothermal alterations zones (HAZs) and the location of gold mineralization is achieved.

APPLICATION OF LANDSAT-8 OPERATIONAL LAND IMAGER AND SHUTTLE RADAR TOPOGRAPHY MISSION-DIGITAL ELEVATION MODEL IN THE STUDY OF IKARA AND ITS ENVIRONS, NORTHWESTERN NIGERIA

Images of the earth’s surface captured by radiometric detectors on air-borne (satellite) sensors were employed in geologic reconnaissance survey. They were used to study rock units, pattern and trends of geologic structures on the surface in 2D. Multispectral resolution Landsat-8 Operational Land Imager (Landsat-8 OLI), have nine spectral band with spatial resolution of 30 m (bands 1 to 7, and 9) and 15 m for band 8. The aim of this research is to discriminate lithological units/complexes and delineate fractures (lineaments), hence, only bands 2 to 7 were used for this study. This technique was used to discriminate existing lithological units and identify major and minor extensive geologic structures within the study area. These selected bands were selected, grouped and combined (Band Combination (BC)) and processed for interpretation using applicable Software Packages. Hence, for each procedure and maximum clarity in the output of results, three bands were combined for each analytical process. Bands 4,3,2; 6,5,4; 7,5,3 were combined for Natural Colour Composite (NCC), False Colour Composite (FCC) and Principle Component Analysis (PCA) respectively. The results revealed two distinct lithological Complexes due to their similarities in appearance and differences in mineralogical compositions. SRTM-DEM was used to highlight the topography showing the lowest relief at the northeast and the highest relief at the south east. More so, elongated ridges of intermediate-high reliefs were exposed at the southern part of the study area trending NW-SE. A lineament map and Rose plot shows dominant trend of fractures to be NNW-SSE, NW-SE, N-S,

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.

The Estimation of Iron Oxide Content in Soil based on Landsat 8 OLI TIRS Imagery in Wetland Areas

Wetland areas are volatile and have high iron content. In this study, through a remote sensing approach, especially using Landsat Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery, we discussed the method to estimate the presence of iron oxide in the wetlands of South Kalimantan in 2018, 2019, and 2020. Interpretation of the Landsat OLI TIRS was employed in April 2018, August 2018, February 2019, August 2019, March 2020, and August 2020. The band ratio method was used to determine the distribution of samples in this study. The results of the iron oxide index from the image were performed regression and correlation analysis with field measurement and laboratory test results to validate the oxide index values. The results showed that the iron oxide index value in the dry season was higher than in the rainy season. Iron oxide index value in open land was higher than in vegetation cover. The wetland was in dry condition during the dry season, making it easier to detect iron oxide values. Vegetation cover could reduce the iron oxide index value on the soil surface so that the iron oxide value was more easily identified in open land. The results of linear regression testing for the wet season sample obtained a coefficient of determination R² = 0.413, while the results of linear regression testing for the dry season sample obtained a coefficient of determination R² = 0.667. Thus, the Landsat image has strong enough to estimate the iron oxide index in the wetland area of Kalimantan.

A boundary finding-based spatiotemporal fusion model for vegetation index

ABSTRACT The spatiotemporal image fusion technique is regarded as an effective and efficient remedy to blend two or more geometrically registered remote sensing images of the same scene acquired from different sensors into a single image with both high temporal and high spatial resolutions. A number of existing fusion methods developed for generating Landsat-like images from the Moderate Resolution Imaging Spectroradiometer (MODIS) have successfully solved limitations on vegetation dynamics monitoring. However, these methods either treat the weighting parameters as constants or adopt insufficient linear regression coefficients (considering only spatial, temporal or spectral weights), which may contribute to blocky artefacts and dissimilar pixels in the predicted image. In this study, a boundary finding-based vegetation index spatiotemporal fusion model (BESFM) is newly proposed to generate high spatial and high temporal resolutions Enhanced Vegetation Index (EVI) images in homogeneous and heterogeneous regions with rapid vegetation changes. The cloud-free Landsat-8 Operational Land Imager (OLI) and the MODIS (MOD09GA) data acquired from two study sites, located in Queensland and New South Wales and Australia, were selected to evaluate the performance of the proposed method. Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and flexible spatiotemporal data fusion (FSDAF), the BESFM outperforms in both visible image quality (reducing the spatial distortion and blocky artefacts in prediction caused by abrupt land cover changes) and quantitative indices (with RSME minimum to 0.011 and R2 maximum to 0.956). Since the BESFM method not only uses adjustable linear regression assumptions for heterogeneous and homogeneous regions differently but also implements boundary detection-based spatial weight with temporal and spectral weights together to enhance the predicted spatial details, it has the potential to increase the availability of fine spatial resolution data.