False Colour Composite Research Articles

Evaluating Changes in Land Use Land Cover using Remote Sensing Satellite Data and GIS (A Case Study in Patna Municipal Corporation Area) Patna, Bihar

Evaluation of land use land cover (LULC) change is an essential aspect of development in rural and urban sectors. This paper investigates the changes in LULC aspects of an environmentally vulnerable Patna Municipal Corporation (PMC) area in the middle-Ganga Plain, India. We offer Remote Sensing (RS), and Geographic Information System (GIS) techniques delineated LULC types include water bodies, agriculture land, fallow land, wasteland, built-up land and vegetation of the study area. LULC mapping of the study area was done through False Color Composite (FCC) Satellite image Resourcesat-1 (IRS P6 LISS-IV) and Resourcesat 2A (IRS-R2A LISS-IV) with 5.8-meter spatial resolution data of the year 2007 and 2018 respectively. The supervised classification and maximum likelihood equation were used to classified two multi-temporal images. Then temporal changes were detected by comparison between two LULC classified maps of 2007 and 2018, which was produced independently. Patna Municipal Corporation (PMC) area, Patna is one of the environmentally vulnerable areas under the threat of environmental and ecological degradation as a result of human activities due to improper land cover management. The main objective of using change detection is an important technique to detect changes in LULC over time in PMC, Patna between 2007-2018, and it is significant for updating land cover or natural resource management. The interpretation of this study has substantial changes in LULC occurred in the Patna Municipal Corporation (PMC) area, Patna within the period 2007-2018, related to urbanisation and economic development. The analysis outcome indicates the most remarkable changes occurred an increase in Built-up, (+) 21.86 % between 2007-2018, whereas the area of cropland and vegetation decreased (-) 8.95 % and (-) 5.8% respectively between 2007-2018. In the spatial distribution pattern, other changes have also occurred. This study will give the benefit in future action plans in land use and urban development and avoid LULC changes without proper planning. It will be most significant for the natural environment.

Using remote sensing techniques to interpret geomorphological features along the east coast of the Red Sea, at Yanbu, Saudi Arabia

Yanbu is the second largest port and population settlement on the Saudi Arabian Red Sea coast. With the increasing development of its nearby coast, the geomorphological characteristics of the region must be studied in detail in order to inform future developments of the region. This study examines the region’s suitability for future development. A high-resolution map of the coastal area under study was drawn by analysing Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM) data and Advanced Spaceborne Thermal Emission and Reflection Radiometer-Digital Elevation Model (ASTER-DEM) data, using remote sensing images from Landsat satellites. Landsat ETM+ images taken in 2000 and 2015, alongside remote sensing analyses, field observations, and geological mapping, were used to describe the geomorphological and topographical characteristics of the Yanbu coast. Both principal component analysis (PCA) and false colour composite (FCC) were the most effective remote sensing techniques for mapping the geomorphological characteristics of the coastal plain. The true colour of the combination bands 3, 2, and 1 was the best at showing the areas and features of coral reefs. Thus, the main geomorphological units in the study area were identified, including wadi systems, dunes, alluvial fans and fan deltas, fluvial terraces, sabkhas, coral reefs, platform reefs, islands, sharms, and sea heads.

Lithological and alteration mineral mapping for alluvial gold exploration in the south east of Birao area, Central African Republic using Landsat-8 Operational Land Imager (OLI) data

Geological field mapping and mineral exploration still remain very laborious in many parts of the inter tropical regions of African plate due to remoteness, low security and high cost. Currently, a number of remote sensing satellite imagery are freely available and can be broadly used for lithological mapping and mineral exploration in inaccessible regions of African plate. The Birao region of the Central African Republic is well known for its alluvial deposits of gold and copper. The Birao region is located in subtropical belt of Africa plate. In this study, Landsat-8 Operational Land Imager (OLI) satellite imagery was used for lithological and alteration mapping in the south-eastern part (inaccessible zone) of Birao region for alluvial gold exploration. Image processing techniques such as False Color Composite (FCC), Band Ratio (BR), Principal component analysis (PCA), minimum noise fraction (MNF) and Spectral Angle Mapper (SAM) were executed to extract spectral information related to lithological units and alteration minerals. Clay and carbonate minerals, iron oxides, ferrous silicates and lithological units, including the Neo-Chadian Formation, the Lower Proterozoic Formations and the Recent Alluvial Formation were identified and discriminated in the study area. The band ratio of 6/7, 4/2 and 6/5 were typically used to map clay, iron oxides and ferrous silicates, respectively. The results of band ratio and PCA techniques were integrated with fuzzy logic modeling that considerably highlighted hydrothermal alteration minerals and high perspective zones for alluvial gold exploration. Additionally, an updated lithological-mineralogical map was generated for south-eastern part of the Birao region by fusing the FCC, MNF and SAM techniques. Comparison with previous lithological map of the study area, virtual verification, fieldwork and laboratory data confirmed good rate of accuracy for the result obtained from the Landsat-8 OLI satellite data. The approach presented in this study is a low-cost technique that highly applicable for lithological mapping and mineral exploration in remote subtropical regions of African plate.

Tracing and assessment of simultaneous dust storms in the cities of Ahvaz and Kermanshah in western Iran based on the new approach

One of the most important and relatively new natural disaster compared with others is the dust-storm phenomenon in today’s world, which has been aggravated by rising drought and global warming. Studies on the phenomenon of dust have different aspects and characteristics. To study this phenomenon, the use of meteorological models such as HYSPLIT, as well as satellite images with high temporal resolution and storm detection in terms of intensity, etc., is necessary. In this research, modeling in HYSPLIT system with back trajectory analysis was performed at three levels of altitude to determine the direction of motion of suspended particles in the atmosphere and its source in the interval 48 h prior to the occurrence of two simultaneous dust storm phenomena in destinations of Ahwaz and Kermanshah with a new approach. The dust detection was done by analyzing MODIS images using the iTIIDI (Improved Thermal Infrared Integrated Dust Index) method along with bands different method to remove cloudy pixels by determination of an appropriate threshold in digital numbers. The comparison of the transmission trajectories of dust yielded by HYSPLIT model for two destinations and false color composite of detected dust in MODIS images demonstrated the same sources for the dust phenomenon at the time of the study. Subsequently, the hydrological parameters of the identified region as the source were also evaluated; all of these, reflected the region's ability to produce suspended particles. Generally, the primary sources of dust on the two cities of Ahvaz and Kermanshah were desert parts of the northern of Iraq, a region between latitudes 33.80° N to 35.85° N and longitudes 42.00° E to 44.76° E in Tigris and Euphrates alluvial plain.

Application of remote-sensing techniques in geological and structural mapping of Atalla Shear Zone and Environs, Central Eastern Desert, Egypt

The Atalla Shear Zone (ASZ) is one of the crucial megashears which are geometrically and kinematically akin to the Najd-Shear Corridor in the central Eastern Desert of Egypt. The Landsat-8 and ASTER-based mapping techniques integrated with extensive petrographic and structural-field investigations enhanced the geological and structural maps of the study area. The Neoproterozoic basement complex in the study area discriminated into ditinctive rock suites: Meatiq Group, ophiolitic melange, arc assemblage, syn- to late-tectonic granitoids, Dokhan Volcanics, Hammamat Sediments, post-Hammamat Felsites and post-tectonic granitoids. The true color composites (TCC) (4 3 2 for Landsat-8; 3 2 1 for ASTER), false color composites (FCC) (1 6 4, 1 6 7 and 7 5 1 for Landsat-8; 5 4 3 and 9 6 4 for ASTER), principal component analysis (PCA) (1 2 3, 3 2 1 and 6 4 2 for Landsat-8; 1 2 3 and 5 4 2 for ASTER), minimum noise fraction (MNF) (1 2 3 and 3 5 1 for Landsat-8; 1 2 4 and 3 2 1 for ASTER) and band ratios (BR) (6/2, 6/7, 6/5*4/3 and 7/6, 7/5, 5/3 for Landsat-8; 2/6, 7/5, 7/6 and 4/7, 3/4, 2/1 for ASTER besides the new created ratio 2/6, 7/5, 7/6) are the best combinations that demonstrate efficiency in discrimination of lithologic contacts and structural elements, using the spectral signature of different rock units. Moreover, the BR b7/b5, b5/b7 and b6/b4 of grey scale for Landsat-8 are used as a tool for mineral detection along with the spectral indices created to detect muscovite (b7/b6), ferrous silicates b5/b4 (biotite, chlorite and amphiboles) and ferrous iron b5/b3 (mafic minerals). The automatic lineament extraction utilizing the SRTM data advocated the role of NE- to NNE-trending lineaments in the tectonic framework of the study area. The structural fabric of the study area is evaluated in three main structural domains: Meatiq (MD), Atalla Shear Zone (ASZD) and Wadi Hammamat (WHD). The pronounced structural implication is the transposition of NE-oriented fabric in MD into NW-oriented penetrative shear-related fabric, fully in the ASZD and partly in WHD. The combination of transcurrent shearing with ENE-directed shortening along the ASZ that was resulted in noteworthy transpressive structures resemble in many respects those observed in the Ajjaj Shear Belt in Western Arabia. Among these structures are shear zone-related folds, imbricated thrust sheets, antiformal stacks and thrust duplexes. The deformation history of the investigated area proposed four phases of deformation (D1–D4). It depicts a long lasted deformational event covering the main accretion-collision phases concurrent with the E-W assembly of Gondwana, and the subsequent post-collision Najd-related transpressional phase which resulted in several major left-lateral transcurrent shear zones along with the exhumation of gneissic core complexes in the Arabian-Nubian Shield.

Seasonal Cropping Pattern Extraction Using NDVI from IRS LISS-III Image of Kangsabati Commanded Area

This paper presents the analysis of IRS LISS III satellite image based on NDVI (Normalized Difference Vegetation Index) for extracting the crop pattern of rabi season of Kangsabati commanded area located at Bankura district of West Bengal, India. NDVI was calculated through creation of FCC (False Colour Composite) followed by supervised classification by applying ground truth data obtained after physical survey on three districts, covered under Kangsabati reservoir commanded area. Final crop map is generated to extract different crop classes from which crop sown in rabi season is estimated. It has been found that potato crop is sown on much more area than rice. Finally, accuracy is checked, and 87% accuracy was achieved after successful identification of crop. This study suggest that this approach will be effective with an acceptable accuracy for crop map generation to classify the different crops and thereby calculate the actual crop water demand of commanded area of Kangsabati reservoir and thereby evaluate the performance of the canal system in near future.

Utilizing landsat-8 sensor operational land image data for hydrothermal alteration mapping within Anka Schist Belt, northwestern Nigeria

Exploring for mineral deposits within the Anka Schist Belt involves the use of traditional geological techniques such as geochemical and geophysical studies that are very expensive and time consuming. There is therefore need for a better alternative that will provide accurate and reliable information with cost effective and time efficient solution. This effort seeks to explore the potential of remotely sensed digital data in highlighting mineralized zones through hydrothermal alteration studies. Landsat 8 OLI data covering the investigated area was used to detect and map locations of hydrothermal alterations. Image processing methods used were spectral enhancement, false colour composites, band rationing and Principal Component Analysis. Results of false colour composites of band 5: 7: 3 highlighted generally locations of hydrothermal alterations. Band ratios of 4/2, 6/7 and 6/5 revealed the presence of ferric iron minerals, clay rich minerals and ferrous minerals respectively. Principal Components (PCs) of two sets of images (2, 4, 5, 7 H-image and 2,5,6,7 F-image) depicting iron-oxide and hydroxyl mineral deposits as bright pixels were generated. Colour composite of H, F and H+F images enhanced the location of the mineral deposits, by showing areas of mineralization in dark blue (Fe rich), bright yellow (clay rich) and white (Fe and clay rich areas) pixels. Field coordinates of mining locations were superimposed on the remote sensing generated mineral map. The results were found to be in tune. This study recommends the use of remote sensing and geospatial technology in mineral studies through hydrothermal alteration within the basement complex rocks of Nigeria.

EMMCNN: An ETPS-Based Multi-Scale and Multi-Feature Method Using CNN for High Spatial Resolution Image Land-Cover Classification

Land-cover information is significant for land-use planning, urban management, and environment monitoring. This paper presented a novel extended topology-preserving segmentation (ETPS)-based multi-scale and multi-feature method using the convolutional neural network (EMMCNN) for high spatial resolution (HSR) image land-cover classification. The EMMCNN first segmented the images into superpixels using the ETPS algorithm with false-color composition and enhancement and built parallel convolutional neural networks (CNNs) with dense connections for superpixel multi-scale deep feature learning. Then, the multi-resolution segmentation (MRS) object hand-delineated features were extracted and mapped to superpixels for complementary multi-segmentation and multi-type representation. Finally, a hybrid network was designed to consist of 1-dimension CNN and multi-layer perception (MLP) with channel-wise stacking and attention-based weighting for adaptive feature fusion and comprehensive classification. Experimental results on four real HSR GaoFen-2 datasets demonstrated the superiority of the proposed EMMCNN over several well-known classification methods in terms of accuracy and consistency, with overall accuracy averagely improved by 1.74% to 19.35% for testing images and 1.06% to 8.78% for validating images. It was found that the solution combining an appropriate number of larger scales and multi-type features is recommended for better performance. Efficient superpixel segmentation, networks with strong learning ability, optimized multi-scale and multi-feature solution, and adaptive attention-based feature fusion were key points for improving HSR image land-cover classification in this study.

Análise Espaço-Temporal de uma Bacia Hidrográfica de Abastecimento Urbano

Sustainable management of water and land resources is currently a top priority on the agenda of many countries around the world, which demands information on the land cover changes in watersheds. In this study, maps of 1984, 1996, 2005 and 2016 were developed using false-color composites and the Normalized Difference Vegetation Index (NDVI) to identify the main changes occurred in the Corrego Guariroba’s stream catchment. This watershed is an Environmental Protection Area since 1995, when it became the urban water supply of the state’s capital of Mato Grosso do Sul, Brazil. In order to do that, the open source software QGIS and Landsat imagery were used to determine the land use and land cover changes. This information allowed the discussion of the possible pressures and environmental consequences of the changes in these periods. The watershed has been under intense agriculture and livestock farming activities, with most of the land surface occupied by pasturelands for cattle ranching and eucalyptus forestry. Results indicate that the direct influence of human activities drastically reduced the natural cover in the area, with implication for the compliance of the Forest Code legislation. Anthropogenic land uses represent more than 57% of the total land cover in all years, reaching 79.70% in 2005. In 2016, there was an increase in native vegetation recovery, but despite this enhancement the human land use continues high (74.09%). Part of the small gain in native vegetation is due to reforestation plans carried out from 2010. The sedimentation of the water reservoir is visible in the 2016 image, forming sandbanks that extends for about 700 m in the distal portion of the Guariroba stream. The results of this study can be applied to a possible revision of the management plan of the watershed, considering the management of its water resources integrated to environmental planning and economic development. This type of analysis may aid in the decision-making process of governmental agencies, so that watersheds used for public water supply have restrictions of land use due to the risk of reducing the quantity and quality of water.

Assessment of sedimentation in kharkhara reservoir using digital image processing techniques

Dams or reservoirs have proven to be very beneficial for the sustained development of human beings since its evolution. The usefulness of dam depends upon its capacity to store water. Sedimentation is a process which involves deposition of silt carried by flowing water from erosion of soil of upstream catchment area. Sedimentation has proven to be very detrimental for the capacity of dams or reservoirs. Sedimentation results in huge loss of storage capacity of dams or reservoirs thus reducing its life. Many methods have developed to measure the reservoir sedimentation like hydrographic survey, inflow-outflow approaches, remote sensing method etc. Out of these, remote sensing method is widely used as it is very simple and involves very less human survey thus reducing the chances of error. In remote sensing method, revised water spread area at different levels of reservoir is calculated and used for computation of loss of capacities between these levels. The present study has been carried out on Kharkhara reservoirs situated in Chhattisgarh state. Multi–date satellite data of IRS-P6, LISS-III is used for Kharkhara dam to estimate revised capacity. The normalized difference water index (NDWI), band ratioing technique (BRT) and false color composite (FCC) along with field truth verification were used to differentiate water pixels from rest of image. As the revised water spread at dead storage and full reservoir levels were not available, best –fit curve has been used to get revised spreads on these levels. From the analysis, it has been observed that Kharkhara reservoir has lost 8.41 MCM of gross storage against its total capacity of 169.54 MCM during 50 years(1967-2017). The average rate of sedimentation in Kharkhara reservoir is 16.82 Ha-m per year.

Determination of Hydrothermal Alteration Zones Using Remote Sensing Methods in Tirka Area, Toroud, NE Iran

The aim of current study is the identification of hydrothermal alteration by remote sensing methods in Tirka area, NE Iran. The field of study with area about 13 km2 (scale: 1:20,000) is part of the Toroud–Chahshirin magmatic belt, which is located in central Iran structural zone. Hydrothermal alteration zones are interpreted based on Advanced Spaceborne Thermal Emission and Reflection Radiometer remote sensing data. In the first stage, atmospheric, topographic and geometric corrections were performed on satellite imagery. Then, different image interpretation techniques consisting of band ratio, principal component analysis, minimum noise fraction, least squares fitting (LS-Fit), spectral angle mapper and spectral feature fitting (SFF) methods were used. Therefore, VNIR bands for iron oxide alteration, SWIR bands for argillic, phyllic and propylitic alterations, and TIR bands for silicification were applied. The results showed iron oxide and propylitic alterations occurred at the SW and the argillic and phyllic alteration zones were existed in the northern and SE parts of the area. Silicification happened sporadically in the region. Lineaments were processed by false color composite, high-pass filters and hill-shade digital elevation model methods, and two NW–SE and NE–SW major trends were revealed. Field observation and laboratory analysis were used for accuracy of satellite studies. Finally, alteration and lineament maps were integrated for the recognition of high-potential mineralization. Petrographic studies showed that SFF and LS-Fit methods are more reliable than other techniques for the determination of hydrothermal alterations.

Use of Logistic Regression in Land-Cover Classification with Moderate-Resolution Multispectral Data

The current study highlights the use of binary logistic regression for land-use land-cover (LULC) classification. The moderate-resolution Sentinel-2 multispectral data was used for LULC map generation for the post-monsoon season. The main focus of this study is to present a simple and precise approach for image classification using binary logistic regression (BLR) technique. The study was carried out in cropland, fallow land, forest and water body dominated subtropical region of India located in the eastern coastal region. The cropland and fallow lands are mostly dependent on the monsoon and reciprocal land covers. A large number of training and testing data points were collected viewing the image in a standard false-color composite. ArcGIS, Microsoft Office Excel and R software were used for classification. In addition to BLR, the training and testing data points were also used to perform the classification with ‘random forest’ classifier in R. We observed higher classification accuracy for spectrally pure classes and pixels and lower for closely associated mix-pixels. Lower user’s and producer’s accuracies (< 90%) were observed for fallow land, water body and grassland class during training and model building and for fallow land and forest during accuracy assessment, whereas the accuracies were more than 90% for the rest of classes during both training and testing. Misclassifications were mostly observed between forest, fallow land, grassland and water body during training, which were forest and fallow land in testing, due to their lower spectral difference with reference to classified classes. However, the overall accuracy and kappa value during training and testing were more than 94% and 0.98, respectively. Similar accuracies and misclassification were also obtained with the results of random forest model, validating the adopted methodology. Regardless of the seasonal variations in cropland and fallow land, the field observations (52 locations) also corroborated the estimated classification accuracy. The easy implementation and comparatively higher classification accuracy with the binary logistic technique are believed to increase its intense use in land-cover classification.

Landsat 8: Utilizing sensitive response bands concept for image processing and mapping of basalts

The spectral signature curve behavior of basalt and its relationship with the surface reflectance (SR) values of Landsat 8 OLI data, (Operational Land Imager), is considered as the key factor for determination of the sensitive response bands for basalt interaction to be used in both Object-Oriented Principal Components (PCA) & Independent Component (ICA) analyses. Also, it is extremely beneficial in develop mathematical formulas which express the best band ratios for the precise illustration and mapping of basalts. The integration of those sensitive bands and the three mentioned methods are the main objectives of the present investigation. This should permit to create the basalt spatial distribution map for a given area. These methods were applied in west central Sinai, Egypt.The measured spectral signature reflectance curve of the basalt was resampled to meet the spectral characteristics of Landsat 8 bands, both curves were carefully examined to determine the most significant response bands for basalts; which were found to be bands 4, 5, 6 and 7. The examined spectral behavior led to three significant band ratios. Moreover, two false color composite (PC1, PC2, PC4) and (IC4, IC3, IC1) in RGB, were determined by execution and examination of the PCA and ICA images, respectively of the study area.ERDAS Imagine (2010) and ArcGIS 10.3 packages were used for digital/mathematical processing steps and to apply the resulted models on the study area.

Discriminating between large-scale oil palm plantations and smallholdings on tropical peatlands using vegetation indices and supervised classification of LANDSAT-8

ABSTRACTTropical peat swamp forest is rapidly being converted into industrial-scale oil palm agriculture across South-east Asia. This has global and local environmental implications including habitat loss and carbon release. Monitoring oil palm expansion or land-use/land-cover on peatlands is feasible using remote sensing technology, however, there are few examples of assessments of different oil palm management systems. Three peatland land cover types were assessed and mapped using vegetation indices and supervised classifications of Landsat-8 level-2 imagery: peat swamp forest, large-scale oil palm plantation and smallholding. Firstly, we calculated and compared differences in vegetation indices (NDVI, EVI and SAVI) values between each of the land covers. Next, we mapped land cover using three classification methods – Support Vector Machine, Random Trees and Maximum Likelihood – with a single date and multi-temporal datasets. These were applied to the vegetation indices and also a three band Landsat image using band 2 (blue band), band 4 (red band) and band 5 (near-infrared band) i.e. false colour composite. The vegetation index assessment found significant differences in the NDVI, EVI and SAVI between peat swamp forest, large-scale plantation and smallholding. The NDVI in peat swamp forest was highest. In contrast, the EVI and SAVI in the peat swamp forest were the lowest. While the NDVI, EVI and SAVI in the large-scale plantations were greater than the smallholdings. Irrespective of landscape type, there were strong positive correlations between NDVI and EVI, NDVI and SAVI, and EVI and SAVI. For the land cover mapping, the best classifier was Random Trees with the multi-temporal Landsat bands 543 datasets with a mapping accuracy of 95.54% and a Kappa coefficient of 0.933. This study demonstrated that both vegetation indices and land cover mapping can provide planners and decision-makers with tools to monitor natural and production landscapes, managed under different systems in the tropics.

Detection of mineral alteration induced by hydrocarbon microseepages by using remotely sensed data in the Fateh Jang area of the Northern Potwar region of Pakistan

Mineral alteration can be induced by the hydrocarbon seepages at the earth surface that can be detected by the use of earth observation data. The long-term seepage of hydrocarbons in the subsurface can induce different types of chemical and mineralogical alterations in the rosk and soil. Mapping of these mineral alterion is important to explore hydrocarbon. In this study, satellite images were used to identify the surface mineral alterations due to hydrocarbon microseepages in the Fateh Jang area of the Northern Potwar region, Islamabad, Pakistan. Landsat TM band ratios of 7/5, 3/1, and 5/4 are used to detect ferric minerals, clay minerals, and ferrous iron minerals bearing sedimentary rocks, respectively, which are applied to distinguish the altered and unaltered rocks. Such rocks have specific spectral responses in various bands of the electromagnetic spectrum and thus give rise to a number of anomalies indicating the presence of hydrocarbons. This study uses enhancement techniques of principal component analysis (PCA), band ratio, false color composite (FCC), and thermal anomaly composition for surface expressions caused by microseeps. It is revealed that spectral reflectance signify that the ferrous iron and red bed bleaching have reflection band and absorption band in TM bands 1, 3, and 4, respectively. Clay minerals and kaolinite have enormous reflection in band 5 and absorption capacity in TM band 7. These results can be used as a model to identify the alteration induced by hydrocarbon seepages. The study area is categorized into northern, eastern, southern, and western zones on the basis mineral alteration. The northern zone is characterized by strong anomalies and is relatively rich in clay alteration and ferrous as compared to other zones. Two types of hydrocarbon microseepages were observed in the Fateh Jang area which includes active seepages (gas microseepages and oil seepage) and passive seepage (bleaching effects).

Geological mapping of the Central Cairo-Suez District of Egypt, using space-borne optical and radar dataset

The Central Cairo-Suez District (CCSD) has a special urban extension, particularly the fast growth of the new Cairo and New Administration Capital of Egypt (NACE). Accurate discrimination of the lithology and surface and near-surface structures will be a valuable contribution to the geotechnical characterization of the foundation rocks and geohazards. The present work utilizes remote sensing datasets; including Phased Array L-band Synthetic Aperture Radar (PALSAR-2) scenes, Landsat-8 OLI/TIRS data and high-resolution images of Google Earth accompanied with field work to update the geological map and highlighting the information on the spatial lithological distribution and the faulting history of the CCSD. Radar data is utilized to make use of its penetration capabilities to image the possible near-surface structures and rock contacts. The PALSAR-2 data improved the surface and shallow structural imaging around Gebel Oweibed and in other selected subsets. The NW-striking shear faults in Quaternary sediments were recognized for the first time. Their recent activation has been proved by the recent seismic events. The False Color Composites (FCC), Band ratios (BR), Decorrelation Stretch (DS) image processing techniques have been successfully applied to differentiate between sandstones, shales, marls, carbonates, basalts, and gravels which represent eight lithological units of Middle Eocene to Quaternary time. The results were enhanced and mapped by supervised classification with accuracy assessment exceeding 88%. Integration of remote sensing data, previous mapping, and field check resulted in a more accurate mapping of the study area with new important modifications.

MINERAL MAPPING USING CHANDRAYAAN-1 HYPERSPECTRAL (HYSI) DATA FROM MARE VAPORUM

Abstract. The imaging spectroscopy offers an opportunity to map and discriminate different minerals on the lunar surface which further helps to understand the origin, evolution process, and the crustal composition on the surface of the moon. Compositional mapping of the lunar surface is considered as a standard approach for mineral mapping. This paper reports surface mineralogy of the lunar surface from Mare Vaporum using Chandrayaan-1 Hyperspectral remotely sensed data from HySi sensor. False color composite is created using different band shaping algorithms like band strength; band curve and band tilt parameters at crucial wavelength for spatial analysis. The Spectral analysis has been done by deriving reflectance spectra at varying locations from the area under study. The Study shows the mineral map with different categories of minerals which are high-Ca pyroxene and/or olivine and low Ca-pyroxene. However because of the limited spectral coverage of HySi, data at the longer wavelengths required to discriminate among different group of minerals.

El empleo de fotogrametría mediante vehículos aéreos no tripulados (VANT/dron) como herramienta de evaluación del patrimonio en riesgo: chinampas arqueológicas de Xochimilco

The development of the technology of unmanned aerial vehicles (UAV, drone) in the last decade has been vertiginous. Its use in the study of cultural heritage is growing and in this study we expose a workflow using an automated system of remote sensing by aerial photography through the use of a device that integrates a hexacopter to which two cameras have been adapted, one of them to obtain visible light (VIS) images, the other modified to capture the near infrared spectrum of light. We have taken as a case study a chinampa site in Xochimilco (El Japon) to evaluate its usefulness in the record and recognition of traits buried at shallow depth. By means of the photogrammetric processing of the stereopairs, the digital elevation model was generated to record a high resolution topography similar to that obtained through a LIDAR device, as well as other images in false color composition for the creation of standardized differential vegetation index (NDVI) maps that allow us to enhance archaeological features based on differences in growth and health of the vegetation

AN ALGORITHM FOR GENERATING NATURAL COLOR IMAGES FROM FALSE COLOR USING SPECTRAL TRANSFORMATION TECHNIQUE WITH HIGHER POLYNOMIAL ORDER

Abstract. Satellite imageries in True color composite or Natural Color composite (NCC) serves the best combination for visual interpretation. Red, Green and Infrared channels form false color composite which might not be as useful as NCC to a non-remote sensing professional. As blue band is affected by large atmospheric scattering, satellites like IRS-LISS IV, SPOT do not have blue band. To generate NCC from such satellite data blue band must be simulated. Existing algorithms of spectral transformation do not provide robust coefficients leading to wrong NCC colors especially in water bodies. To achieve more robust coefficients, we have proposed new algorithm to generate NCC for IRS-LISS IV data using second order polynomial regression technique. Second order polynomial transformation functions consider even minor variability present in the image as compared to 1st order so that the derived coefficients are adjustable to accommodate spatial and temporal variability while generating NCC. In this study, Sentinel-2 image was used for deriving coefficients with blue band as dependent and green, red and infrared as independent variables. Simulated Sentinel band showed high accuracy with correlation of 0.93 and 0.97 for two test sites. Using the same coefficients, blue band was simulated for LISS-IV which also showed good correlation of 0.90 with sentinel original blue band. On comparing LISS-IV simulated NCC with simulated NCC from other algorithms, it was observed that higher order polynomial transformation was able to achieve higher accuracy especially for water bodies where expected color is green. Thus, proposed algorithms can be used for transforming false color image to natural color images.

Superpixel segmentation for hyperspectral images based on false colour composition with colour histogram driving

ABSTRACTHyperspectral images (HSIs) segmentation has gradually become an important basis for HSIs processing, such as the classification and unmixing. In this paper, we proposed a superpixel segmentation algorithm based on principal component (PC) weighted false colour composition (FCC) with colour histogram driving (FCC-CHD). First, the dimensionality of HSIs is reduced by using FCC algorithm based on weighted PC, so that the main spectral information of HSIs is transformed to colour information of false colour images. To scale difference of ground objects in HSIs, we propose colour histogram driving (CHD) function to guarantee the accuracy of metric functions and computational efficiency. In segmentation process, the hill climbing optimization (HCO) algorithm is used to transform a global optimization problem into a local optimization problem, ensuring the high efficiency of the algorithm. Finally, parameters selection experiments provide reasonable parameter selection references, and the comparison experiments on two datasets demonstrate that the proposed algorithm is more effective and accurate than other superpixel segmentation methods.