PROCESSING AND QUALITATIVE VISUALIZATION IN PSEUDO-TRUE COLOURS OF LONG-TERM SERIES OF SATELLITE DATA

2020 ◽  
Author(s):  
Lyudmila Shagarova ◽  
Mira Muratova ◽  
Aray Yermenbay
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Remote Sensing Data ◽  
Aral Sea ◽  
Free Access ◽  
Landsat 8 ◽  
Visible Radiation ◽  
Sensing Data ◽  
Earth’S System

Free access to moderate resolution remote sensing data enable the worldwide users for their studies of many key geophysical parameters of the Earth’s system, solving various tasks on regular monitoring of natural phenomena, including tasks on ecological space monitoring. This requires multilevel processing of satellite data. The processing results are given for the Aral Sea. This endorheic salt lake is located in Central Asia on the border of Kazakhstan and Uzbekistan. Aral was chosen as an example not by chance as because before shallowing, it was the fourth-largest lake in the world. During the process of drying, the lake was divided into three parts. Currently, the eastern part of the lake has completely disappeared. To the Aral Sea is happening a real ecological disaster. A long-term series of satellite data are needed to monitor the dynamics of changes. The active operation of remote sensing satellites usually exceeds their estimated lifetime. For example, spacecrafts “Terra” and “Aqua”, launched in 1999 and 2002, respectively, have an estimated lifetime of sensor MODIS as 6 years, but they are still used in the NASA EOS program aimed at Earth exploration. With the aging sensors has been a degradation of its optics equipment which affects the quality of the data in some channels. It limits the simple creation of a color image in TRUE colors by put the bands spectral range of visible radiation to corresponding layers RGB-composite. The article describes the technology of making quality images by digital operations with MODIS channels. It eliminate such a problem as “banding” of the image and create new synthesized bands. The results of processing are demonstrated using annual Terra/MODIS data for the autumn period from 2000 to 2019. Besides, taking into account that a water body has been chosen as the object of monitoring, the article presents the options of water surface detection based on spectral indices - indices calculated in mathematical operations with different spectral ranges (channels) of remote sensing data related to certain parameters. Thematic processing in Geomatica software is shown on Landsat-8 images: the sample profile of index image is demonstrated. Taking into account that the survey area exceeds the size of the standard Landsat scene, a mosaic image was made for complete coverage of the region.

scholarly journals Archiving and Managing Remote Sensing Data using State of the Art Storage Technologies

2014 ◽  
Vol XL-8 ◽  
pp. 1153-1158
Author(s):  
B. Lakshmi ◽  
C. Chandrasekhara Reddy ◽  
S. V. S. R. K. Kishore
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Satellite Data ◽  
High Speed ◽  
Remote Sensing Data ◽  
Multiple Systems ◽  
Sensing Data ◽  
Ground Segment ◽  
Data Products

Integrated Multi-mission Ground Segment for Earth Observation Satellites (IMGEOS) was established with an objective to eliminate human interaction to the maximum extent. All emergency data products will be delivered within an hour of acquisition through FTP delivery. All other standard data products will be delivered through FTP within a day. The IMGEOS activity was envisaged to reengineer the entire chain of operations at the ground segment facilities of NRSC at Shadnagar and Balanagar campuses to adopt an integrated multi-mission approach. To achieve this, the Information Technology Infrastructure was consolidated by implementing virtualized tiered storage and network computing infrastructure in a newly built Data Centre at Shadnagar Campus. One important activity that influences all other activities in the integrated multi-mission approach is the design of appropriate storage and network architecture for realizing all the envisaged operations in a highly streamlined, reliable and secure environment. Storage was consolidated based on the major factors like accessibility, long term data protection, availability, manageability and scalability. The broad operational activities are reception of satellite data, quick look, generation of browse, production of standard and valueadded data products, production chain management, data quality evaluation, quality control and product dissemination. For each of these activities, there are numerous other detailed sub-activities and pre-requisite tasks that need to be implemented to support the above operations. <br><br> The IMGEOS architecture has taken care of choosing the right technology for the given data sizes, their movement and long-term lossless retention policies. Operational costs of the solution are kept to the minimum possible. Scalability of the solution is also ensured. The main function of the storage is to receive and store the acquired satellite data, facilitate high speed availability of the data for further processing at Data Processing servers and help to generate data products at a rate of about 1000 products per day. It also archives all the acquired data on tape storage for long-term retention and utilization. Data sizes per satellite pass range from hundreds of megabytes to tens of gigabytes <br><br> The images acquired from remote sensing satellites are valuable assets of NRSC and are used as input for further generation of different types of user data products through multiple Data Processing systems. Hence, it is required to collect and store the data within a shared, high speed repository concurrently accessible by multiple systems. After the raw imagery is stored on a high-speed repository, the images must be processed in order for them to be useful for value-added processing or for imagery analysts. The raw image file has to be copied on to data processing servers for further processing. Given the large file sizes, it is impractical to transfer these files to processing servers via a local area network. Even at gigabit Ethernet rates (up to 60 MB/s), a 5 GB file will take at least 83 seconds. For this reason, it is useful to employ a shared file system which allows every processing system to directly access the same pool where raw images were stored. Concurrent access by multiple systems is ensured for processing and generation of data products. With the above reasons, it was chosen to have high speed disk arrays for acquisition and processing purposes and tape based storage systems for long-term huge data (Peta Bytes) archival in a virtualized multitier storage architecture. <br><br> This paper explains the architecture involved in a virtualized tiered storage environment being used for acquisition, processing and archiving the remote sensing data. It also explains the data management aspects involved in ensuring data availability and archiving Peta bytes sized, remote sensing data acquired over the past 40 years.

scholarly journals The Sector Development at Tanjung Sakti, Mount Dempo, Bengkulu by Using Integration of Remote Sensing Data and TOPEX Gravity Satellite

2021 ◽  
Vol 873 (1) ◽  
pp. 012015
Author(s):  
Zahrah Athirah ◽  
Muhammad Dhery Mahendra
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Land Surface ◽  
Vegetation Index ◽  
Hot Springs ◽  
Gravity Data ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Volcanic Complex ◽  
Sensing Data

Abstract Mount Dempo is the highest volcano in South Sumatra, which lies between the Bukit Barisan mountains and Gumai. The mountain located in Dempo Makmur Village, Sub-district of Pagar Alam, Lahat Regency, South Sumatra is located at an altitude of 3173 meters above sea level with coordinates of 4.03 ° S 103.13 °E. Mount Dempo’s morphology is formed by pyroclastic deposits consisting of Tuff and Sand rocks. Mount Dempo’s vegetation is dominated by Cassia sp. and Camellia sinensis for upper vegetation, while Strobilanthes hamiltoniana and Strophanthus membranifolium dominate the undergrowth. The purpose of this study is to identify geological structures to predict geothermal prospect areas by integrating remote sensing data and TOPEX Gravity Satellite Data. The remote sensing data used in this study is Landsat 8. This data is used to analyze Land Surface Temperature (LST) from a single thermal infrared band, surface emissivity based on Normalization Difference Vegetation Index (NDVI) from the study area and determine structure delineation. Gravity Satellite Data is used to map gravity anomalies in the volcanic complex of Mount Dempo. Gravity data processing produces a high anomaly zone in the northern part of the study area and is predicted as a prospect area because it is assumed to be related to the plutonic body. High density contrast indicates that there is an error in that area. In line with the error, there are several hot springs because the error serves as a pathway for geothermal fluid to rise to the surface. The study believes that with all the facts stated above, the spots which are located in Tanjung Sakti, Mount Dempo district are very prospective to be developed as a geotourism complex, in which could also increase the welfare of the local citizens.

scholarly journals Actual evapotranspiration evaluation based on multi-sensed data

2021 ◽  
pp. 95-102
Author(s):  
Mohammed Ahmed El-Shirbeny ◽  
Samir Mahmoud Saleh
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Remote Sensing Data ◽  
Actual Evapotranspiration ◽  
Landsat 8 ◽  
Crop Water Stress Index ◽  
Study Region ◽  
Sensing Data ◽  
Passive Remote Sensing ◽  
Combination Methods

The importance of active and passive remote sensing data integration appears strongly on cloudy days. The lack of passive remote sensing data on cloudy days prevents the benefit of large-scale satellite data in cloudy areas, while the advantage of active remote sensing, it could penetrate the cloud and collect data underneath the cloud. The main objective of this paper is to determine the benefits of combining active and passive remote sensing data to detect actual evapotranspiration (ETa). Sentinel-1 radar data represents active data, while Landsat-8 represents passive data. Multi-date data for Landsat-8 and Sentinel-1 were used during the 2016 summer season. The characteristic soil texture in the study region is clay. The meteorological data were used to estimate ETo based on the FAO-Penman-Monteith (FPM) process, while the Lysimeter data were used to test the estimated ETa. Landsat-8 data are used to measure the Normalized Difference Vegetation Index (NDVI) and the Crop Water Stress Index (CWSI). Crop Coefficient (Kc) is calculated on the basis of NDVI. The CWSI, Kc, and ETo were then used to determine ETa. Backscattering (dB) C-band Synthetic Aperture Radar (SAR) data extracted from the Sentinel-1 satellite was correlated with Kc and used to estimate ETa. The Root Mean Square Error (RMSE) reported relevant results for active and passive satellite data separately and the combination process. For Sentinel-1, Landsat-8 and combination methods, the RMSE reported 0.89, 0.24, and 0.31 (mm/day) respectively.

Evaluation of Capacity of Salandi Reservoir Using Remote Sensing Data Along With Qgis Software

2018 ◽  
Vol 6 (8) ◽  
pp. 339
Author(s):  
Rupali Dhal ◽  
D. P. Satapathy
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Remote Sensing Data ◽  
Project Work ◽  
Space Technology ◽  
Reservoir Water ◽  
Sediment Distribution ◽  
Sensing Data ◽  
Spread Area ◽  
Water Spread

The dynamic aspects of the reservoir which are water spread, suspended sediment distribution and concentration requires regular and periodical mapping and monitoring. Sedimentation in a reservoir affects the capacity of the reservoir by affecting both life and dead storages. The life of a reservoir depends on the rate of siltation. The various aspects and behavior of the reservoir sedimentation, like the process of sedimentation in the reservoir, sources of sediments, measures to check the sediment and limitations of space technology have been discussed in this report. Multi satellite remote sensing data provide information on elevation contours in the form of water spread area. Any reduction in reservoir water spread area at a specified elevation corresponding to the date of satellite data is an indication of sediment deposition. Thus the quality of sediment load that is settled down over a period of time can be determined by evaluating the change in the aerial spread of the reservoir at various elevations. Salandi reservoir project work was completed in 1982 and the same is taken as the year of first impounding. The original gross and live storages capacities were 565 MCM& 556.50 MCM respectively. In SRS CWC (2009), they found that live storage capacity of the Salandi reservoir is 518.61 MCM witnessing a loss of 37.89 MCM (i.e. 6.81%) in a period of 27 years.The data obtained through satellite enables us to study the aspects on various scales and at different stages. This report comprises of the use of satellite to obtain data for the years 2009-2013 through remote sensing in the sedimentation study of Salandi reservoir. After analysis of the satellite data in the present study(2017), it is found that live capacity of the reservoir of the Salandi reservoir in 2017 is 524.19MCM witnessing a loss of 32.31 MCM (i.e. 5.80%)in a period of 35 years. This accounts for live capacity loss of 0.16 % per annum since 1982. The trap efficiencies of this reservoir evaluated by using Brown’s, Brune’s and Gill’s methods are 94.03%, 98.01and 99.94% respectively. Thus, the average trap efficiency of the Salandi Reservoir is obtained as 97.32%.

Application of geoinformation technologies for arranging a satellite monitoring system

2019 ◽  
Vol 943 (1) ◽  
pp. 110-118
Author(s):  
A.A. Kadochnikov
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Remote Sensing Data ◽  
Web Gis ◽  
Nature Management ◽  
Satellite Monitoring ◽  
Technological Support ◽  
Sensing Data ◽  
Territorial Planning ◽  
Operational Information

Today, remote sensing data are an important source of operational information about the environment for thematic GIS, this data can be used for the development of water, forestry and agriculture management, in the ecology and nature management, with territorial planning, etc. To solve the problem of ensuring the effective use of the space activities’results in the Krasnoyarsk Territory a United Regional Remote Sensing Center was created. On the basis of the Center, a new satellite receiving complex of FRC KSC SB RAS was put into operation. It is currently receiving satellite data from TERRA, AQUA, Suomi NPP and FENG-YUN satellites. Within the framework in cooperation with the Siberian Regional Center for Remote Sensing the Earth, an archive of satellite data from domestic Resource-P and Meteor-M2 satellites was created. The work considers some features of softwaredevelopment and technological support tools for loading, processing and publishing remote sensing data. The product is created in the service-oriented paradigm based on geoportal technologies and interactive web-cartography. The focus in this article is paid to the peculiarities of implementing the software components of the web GIS, the efficient processing and presentation of geospatial data.

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

scholarly journals Risk management support through India Remote Sensing Satellites

2014 ◽  
Vol XL-8 ◽  
pp. 1-6
Author(s):  
N. Aparna ◽  
A. V. Ramani ◽  
R. Nagaraja
Keyword(s):  
Remote Sensing ◽  
Risk Management ◽  
Satellite Data ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Temporal Variations ◽  
Geographical Information ◽  
Indian Remote Sensing ◽  
Management Support ◽  
Sensing Data

Remote Sensing along with Geographical Information System (GIS) has been proven as a very important tools for the monitoring of the Earth resources and the detection of its temporal variations. A variety of operational National applications in the fields of Crop yield estimation , flood monitoring, forest fire detection, landslide and land cover variations were shown in the last 25 years using the Remote Sensing data. The technology has proven very useful for risk management like by mapping of flood inundated areas identifying of escape routes and for identifying the locations of temporary housing or a-posteriori evaluation of damaged areas etc. The demand and need for Remote Sensing satellite data for such applications has increased tremendously. This can be attributed to the technology adaptation and also the happening of disasters due to the global climate changes or the urbanization. However, the real-time utilization of remote sensing data for emergency situations is still a difficult task because of the lack of a dedicated system (constellation) of satellites providing a day-to-day revisit of any area on the globe. The need of the day is to provide satellite data with the shortest delay. Tasking the satellite to product dissemination to the user is to be done in few hours. Indian Remote Sensing satellites with a range of resolutions from 1 km to 1 m has been supporting disasters both National &amp; International. In this paper, an attempt has been made to describe the expected performance and limitations of the Indian Remote Sensing Satellites available for risk management applications, as well as an analysis of future systems Cartosat-2D, 2E ,Resourcesat-2R &amp;RISAT-1A. This paper also attempts to describe the criteria of satellite selection for programming for the purpose of risk management with a special emphasis on planning RISAT-1(SAR sensor).

scholarly journals A Minimum Cloud Cover Mosaic Image Model of the Operational Land Imager Landsat-8 Multitemporal Data using Tile based

2018 ◽  
Vol 8 (1) ◽  
pp. 360
Author(s):  
Ratih Dewanti Dimyati ◽  
Projo Danoedoro ◽  
Hartono Hartono ◽  
Kustiyo Kustiyo
Keyword(s):  
Remote Sensing ◽  
Cloud Cover ◽  
National Development ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Sensing Data ◽  
Tile Size ◽  
Mosaic Image ◽  
Clear Area ◽  
Mosaic Images

<p>The need for remote sensing minimum cloud cover or cloud free mosaic images is now increasing in line with the increased of national development activities based on one map policy. However, the continuity and availability of cloud and haze free remote sensing data for the purpose of monitoring the natural resources are still low. This paper presents a model of medium resolution remote sensing data processing of Landsat-8 uses a new approach called mosaic tile based model (MTB), which is developed from the mosaic pixel based model (MPB) algorithm, to obtain an annual multitemporal mosaic image with minimum cloud cover mosaic imageries. The MTB model is an approach constructed from a set of pixels (called tiles) considering the image quality that is extracted from cloud and haze free areas, vegetation coverage, and open land coverage of multitemporal imageries. The data used in the model are from Landsat-8 Operational Land Imager (OLI) covering 10 scenes area, with 2.5 years recording period from June 2015 to June 2017; covered Riau, West Sumatra and North Sumatra Provinces. The MTB model is examined with tile size of 0.1 degrees (11x11 km2), 0.05 degrees (5.5x5.5 km2), and 0.02 degrees (2.2x2.2 km2). The result of the analysis shows that the smallest tile size 0.02 gives the best result in terms of minimum cloud cover and haze (or named clear area). The comparison of clear area values to cloud cover and haze for three years (2015, 2016 and 2017) for the three mosaic images of MTB are 68.2%, 78.8%, and 86.4%, respectively.</p>

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