scholarly journals Estimation of Sediments in Rengali Reservoir, Odisha (India) Using Remote Sensing

2021 ◽  
pp. 205-225
Author(s):  
Siba Prasad Mishra ◽  
Chandan Kumar ◽  
Abhisek Mishra ◽  
Saswat Mishra ◽  
Ashish Patel
Keyword(s):  
Remote Sensing ◽  
Dynamic Assessment ◽  
Geographical Information ◽  
Web Based ◽  
Spread Area ◽  
Using Data ◽  
Decadal Rate ◽  
State Of The Environment ◽  
Water Spread ◽  
Operation And Management

Reservoir sedimentation is a regular process and sequential path of sedimentation in reservoirs comprising of erosion, entrainment, transference, deposition and compaction of dregs carried into artificial lakes formed behind the dams. India houses 5334 large dams in function (2329 numbers before 1980) and 411 dams are in pipeline. The Rengali dam, functioned from 1984, that traps 50% of the total sediment load of the Brahmani River continues to thwart the growth and buffering of the Brahmani delta. Remote sensing (RS) and Geographical Information System (GIS) have emerged as powerful tools to create spatial inventory on Hydro-Bio-geo resources and the state of the environment. The RS/GIS and process-based modelling employed in spatial and dynamic assessment of loss in live storage of the reservoir by developing contour, aspect and slope map by using data received from LANDSAT sources. The sedimentation of the Rengali reservoir (functional from 1984) studied for three decades 1990-2000; 2000-2010 and 2010- 2020 by constructing contour, aspect and water spread area maps by using web based data (satellite downloads). The web based water spread area data analysed by GIS tool for integration, spatial analysis, and visual presentations. The results revealed that the decadal rate of sedimentation of Rengali reservoir is reducing with age. An appropriate reservoir operation and management system as per defined protocols considering sediment related problems is essential for controlling the ageing processes that may diminish the safety and shorten the reservoir life.

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%.

Optimized Airborne Oil Spill Remote Sensing: POSEIDON, the Quantitative Approach

2017 ◽  
Vol 2017 (1) ◽  
pp. 1594-1611
Author(s):  
Guilherme Pinho ◽  
Alessandro Vagata ◽  
Theo Hengstermann
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Oil Spill ◽  
Oil Spills ◽  
Response Strategy ◽  
Geographical Information ◽  
Web Based ◽  
Incident Command ◽  
Automated Processing ◽  
The Right

ABSTRACT Aerial surveillance is becoming a foundation on the overall oil spill response strategy due to the ability to plan and tactically position response resources in the optimal areas of oil migration. It takes a complete multitasking approach to effectively respond to oil spills. While much of the regulatory focus to date has been on the resources on the sea - vessels, skimmers, dispersants - the reality is that they are only one of the components and not necessarily the most important in combating oil spills. It is imperative to determine the location of oil that is most recoverable, and give quantitative information - thickness, volume, area, classification - whether day or night. Having the right information at the right time optimizes dramatically the use of all the response resources. And assess the effectiveness of the response and make an accurate natural resources damage assessment is critical and requires as well quantitative and timely information. In the past the main effort has been directed towards developing airborne sensors with enhanced spill monitoring capability. Recently, more and more attention has been paid to the automated processing of oil spill data acquired by integrated airborne sensor platforms. Automated processing and real time relay of immediately usable information to the Incident Command Center is critical during all phases of response. This paper focuses on advanced data processing and presents ways of improving the usability of airborne multi-sensor oil spill monitoring systems. In this context, is given an overview of currently existing oil spill remote sensing technology like infrared/ultraviolet line scanners, microwave radiometers, laser fluorosensors and radar system. The paper presents POSEIDON, a system for network-based real-time data acquisition, analysis and fusion of multi-sensor data. Also, a method for the distribution of oil spill data and related data products using web-based geographical information systems is described; automated generation of thematic maps of the oil spill scene along with their real-time web-based distribution is becoming more important in marine incident management.

scholarly journals Assessment of sedimentation in kharkhara reservoir using digital image processing techniques

2019 ◽  
Vol 34 (02) ◽  
Author(s):  
Kumar Jaiswal ◽  
Anoop Kumar Rai ◽  
Ravi Galkate ◽  
T. R. Nayak
Keyword(s):  
Remote Sensing ◽  
Average Rate ◽  
Human Beings ◽  
Water Index ◽  
Band Ratioing ◽  
Normalized Difference Water Index ◽  
Spread Area ◽  
Total Capacity ◽  
Color Composite ◽  
Water Spread

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.

scholarly journals ROLE OF SAR DATA IN WATER BODY MAPPING AND RESERVOIR SEDIMENTATION ASSESSMENT

2018 ◽  
Vol IV-5 ◽  
pp. 151-158 ◽  
Author(s):  
N. R. Prasad ◽  
V. Garg ◽  
P. K. Thakur
Keyword(s):  
Remote Sensing ◽  
Storage Capacity ◽  
Optical Data ◽  
Reservoir Sedimentation ◽  
Reservoir Level ◽  
Body Mapping ◽  
Spread Area ◽  
Sar Data ◽  
Water Spread ◽  
Microwave Data

<p><strong>Abstract.</strong> Reservoir sedimentation is the major problem, due to it every year the reservoir capacity is lost to considerable amount. Surveying for assessment of the reservoir by conventional approach is time and money consuming. Geospatial technology provides ample opportunity in this field through the availability of high resolution satellite data from sensors such as Sentinel, Indian Remote Sensing Satellite, Landsat, and SPOT have been used to calculate the water spread area of the reservoir. However, due to presence of cloud in most of the optical data during onset of monsoon, the water spread at the lowest reservoir level could not be mapped. In turn the revised capacity or sedimentation is generally assessed between either below full reservoir level (FRL) or above maximum draw down level (MDDL). Nowadays, the microwave synthetic aperture radar (SAR) data at reasonable spatial resolution is available freely in public domain. Moreover, microwave data has capability to penetrate cloud and the information below cloud can easily be retrieved. To overcome the issues related to optical data, in the present study, the reservoir sedimentation for Ghataprabha reservoir was estimated using SAR data. Sentinel-1A data was used to delineate the water spread area for the water year of 2016–17. The original live storage capacity (1974) was estimated to be 1434.14<span class="thinspace"></span>Mm<sup>3</sup> at FRL 662.940<span class="thinspace"></span>m by the authorities using the hydrographic survey during the commissioning of the reservoir in the year 1974. The live storage capacity was found out to be 1366.14<span class="thinspace"></span>Mm<sup>3</sup> at FRL, however, as per original elevation-area-capacity curve the live capacity is around 1262.404<span class="thinspace"></span>Mm<sup>3</sup> at 660.50<span class="thinspace"></span>m. Estimated live storage capacity from Remote sensing approach (2016–17) was assesses as 1182.5<span class="thinspace"></span>Mm<sup>3</sup> at 660.51<span class="thinspace"></span>m. The storage capacity has reduced from 1262.40<span class="thinspace"></span>Mm<sup>3</sup> (1974) to 1182.51<span class="thinspace"></span>Mm<sup>3</sup> i.e. around 171.732<span class="thinspace"></span>Mm<sup>3</sup>. As per present analysis the rate of sedimentation is around 4<span class="thinspace"></span>Mm<sup>3</sup>/yr. It was realized that using the SAR microwave data, the revised capacity of the reservoir from its near MDDL to FRL could be assessed through remote sensing approach.</p>

System Information Model of Obtaining and Using Data of Remote Sensing the Earth

2008 ◽  
Vol 40 (11) ◽  
pp. 46-56
Author(s):  
Ludmila I. Samoilenko ◽  
Sergey A. Baulin ◽  
Tatyana V. Ilyenko ◽  
Margarita A. Kirnosova ◽  
Ludmila N. Kolos ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Model ◽  
The Earth ◽  
System Information ◽  
Using Data

ANALISIS KESESUAIAN LAHAN DAN EVALUASI JENIS TANAH DALAM BUDIDAYA TANAMAN TEBU UNTUK PENGEMBANGAN DAERAH KABUPATEN TEGAL

2013 ◽  
Vol 13 (2) ◽  
Author(s):  
Daru Mulyono
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Spatial Analysis ◽  
Geographical Information System ◽  
Plant Productivity ◽  
Saccharum Officinarum ◽  
Geographical Information ◽  
Sugarcane Plant ◽  
Plant Cultivation ◽  
Suitability Map

The objectives of the research were to make land suitability map for sugarcane plant (Saccharum officinarum), to give recommendation of location including area for sugarcane plant cultivation and to increase sugarcane plant productivity. The research used maps overlay and Geographical Information System (GIS) which used Arch-View Spatial Analysis version 2,0 A in Remote Sensing Laboratory, Agency for the Assessment and Application of Technology (BPPT), Jakarta. The research was carried out in Tegal Regency starting from June to October 2004.The results of the research showed that the suitable, conditionally suitable, and not suitable land for sugarcane cultivation in Tegal Regency reached to a high of 20,227 ha, 144 ha, and 81,599 ha respectively. There were six most dominant kind of soil: alluvial (32,735 ha), grumosol 5,760 ha), mediteran (17,067 ha), latosol   (18,595 ha), glei humus (596 ha), and regosol (22,721 ha).

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