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>

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%.

scholarly journals Discriminating Wet Snow and Firn for Alpine Glaciers Using Sentinel-1 Data: A Case Study at Rofental, Austria

Geosciences ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 69 ◽  
Author(s):  
Achim Heilig ◽  
Anna Wendleder ◽  
Andreas Schmitt ◽  
Christoph Mayer
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Optical Data ◽  
Optical Remote Sensing ◽  
Mountain Ranges ◽  
Snow Covered Area ◽  
Covered Area ◽  
Wet Snow ◽  
Sar Data ◽  
Sar Analysis

Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the recession of wet snow area extent per season for three different glacier areas of the Rofental, Austria. For four glaciological years (GYs, 2014/2015–2017/2018), Sentinel-1 (S1) SAR data were acquired and processed. For all four GYs, the seasonal snow retreated above the elevation range of perennial firn. The described processing routine is capable of discriminating wet snow from firn areas for all GYs with sufficient accuracy. For a short in situ transect of the snow—firn boundary, SAR derived wet snow extent agreed within an accuracy of three to four pixels or 30–40 m. For entire glaciers, we used optical remote sensing imagery and field data to assess reliability of derived wet snow covered area extent. Differences in determination of snow covered area between optical data and SAR analysis did not exceed 10% on average. Offsets of SAR data to results of annual field assessments are below 10% as well. The introduced workflow for S1 data will contribute to monitoring accumulation area extent for remote and hazardous glacier areas and thus improve the data basis for such locations.

crops planting area identification and analysis based on multi-source high resolution remote sensing data

2020 ◽  
Author(s):  
Lei Wang ◽  
Haoran Sun ◽  
Wenjun Li ◽  
Liang Zhou
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Classification Accuracy ◽  
Kappa Coefficient ◽  
Optical Data ◽  
Accurate Estimation ◽  
Support Vector ◽  
Random Forest Method ◽  
Multi Temporal ◽  
Sar Data

&lt;p&gt;Crop planting structure is of great significance to the quantitative management of agricultural water and the accurate estimation of crop yield. With the increasing spatial and temporal resolution of remote sensing optical and SAR(Synthetic Aperture Radar) images, &amp;#160;efficient crop mapping in large area becomes possible and the accuracy is improved. In this study, Qingyijiang Irrigation District in southwest of China is selected for crop identification methods comparison, which has heterogeneous terrain and complex crop structure . Multi-temporal optical (Sentinel-2) and SAR (Sentinel-1) data were used to calculate NDVI and backscattering coefficient as the main classification indexes. The multi-spectral and SAR data showed significant change in different stages of the whole crop growth period and varied with different crop types. Spatial distribution and texture analysis was also made. Classification using different combinations of indexes were performed using neural network, support vector machine and random forest method. The results showed that, the use of multi-temporal optical data and SAR data in the key growing periods of main crops can both provide satisfactory classification accuracy. The overall classification accuracy was greater than 82% and Kappa coefficient was greater than 0.8. SAR data has high accuracy and much potential in rice identification. However optical data had more accuracy in upland crops classification. In addition, the classification accuracy can be effectively improved by combination of classification indexes from optical and SAR data, the overall accuracy was up to 91.47%. The random forest method was superior to the other two methods in terms of the overall accuracy and the kappa coefficient.&lt;/p&gt;

scholarly journals COMBINED ANALYSIS OF RADARSAT-2 SAR AND SENTINEL-2 OPTICAL DATA FOR IMPROVED MONITORING OF TUBER INITIATION STAGE OF POTATO

2018 ◽  
Vol XLII-5 ◽  
pp. 275-279 ◽  
Author(s):  
D. Mandal ◽  
V. Kumar ◽  
Y. S. Rao ◽  
A. Bhattacharya ◽  
S. Bera ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Temporal Dynamics ◽  
Potato Production ◽  
Tuber Initiation ◽  
Optical Data ◽  
Growth Monitoring ◽  
Tuber Bulking ◽  
Potato Tuberization ◽  
Sar Data ◽  
Sentinel 2

<p><strong>Abstract.</strong> Tuber initiation and tuber bulking stages are critical part of various phenological phases for potato production. Tuber initiation covers the period from the formation of spherical rhizome ends, the flowering and the start of tuber bulking. In general, the tuberization spans from 3 to 5 weeks after emergence and ends with the row closer i.e. canopies in adjacent rows touch each other across the furrow. Hence, this rapid growth seeks critical agronomic management practices such as irrigation and fertilization. It majorly influences the growth of stems, foliar area, dry weight and number of tubers particularly at the phase of tuber initiation. During these phenological stages, potato crops are susceptible to the infestation of late blight diseases caused by <i>Phytophthora infestans</i> and largely affects the potato production. Thus identifying the crop risk using remote sensing approaches can provide an efficient potato growth monitoring framework. In the context of monitoring crop dynamics, quad-pol Synthetic Aperture Radar (SAR) data has proven to be effective due to its sensitivity towards dielectric and geometric properties. In addition to SAR data, optical remote sensing data derived vegetation information can provide an improved insight into crop growth when combined with SAR data. In this research, quad-pol RADARSAT-2 and Sentinel-2 optical data are analyzed to monitor potato tuberization phase over Bardhaman district in the state ofWest Bengal, which is one of the major potato growing regions in India. The proposed approach uses polarimetric parameters such as backscatter intensities, ratio (HH/VV, VH/VV, linear depolarization ratio), and co-pol correlation (<i>&amp;rho;<sub>HH–VV</sub></i>) along with the vegetation indices derived from the Sentinel-2 data for understanding the spatio-temporal dynamics. The initial results show a promising accuracy in monitoring the dynamics of potato tuberization. Integration of such earth observation (EO) data, in conjunction with in-situ field measurements, might significantly enhance the current capabilities for crop monitoring.</p>

scholarly journals FLOOD MONITORING USING SENTINEL-1 SAR DATA: A CASE STUDY BASED ON AN EVENT OF 2018 AND 2019 SOUTHERN PART OF KERALA

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 37-41
Author(s):  
S. P. Dhanabalan ◽  
S. Abdul Rahaman ◽  
R. Jegankumar
Keyword(s):  
Remote Sensing ◽  
Natural Hazards ◽  
Satellite Imagery ◽  
Heavy Rainfall ◽  
Microwave Remote Sensing ◽  
Optical Data ◽  
Flood Hazards ◽  
Night Time ◽  
South West Monsoon ◽  
Sar Data

Abstract. Flood is a natural hazard influenced by rainfall and dam collapse, which propels release of enormous amount of water. In the last two decades flood is the second largest natural hazards occurred worldwide, which caused serious damage to life properties, settlements and economic activities. Flood mapping is a process that is useful for assessment and reduces the risk factor during the flood. An effective monitoring of flood prone area is necessary to handle GIS techniques and without remote sensing data it is difficult to identify the flooded area in this study Microwave remote sensing plays a lead role in natural hazards, here Synthetic Aperture Radar (SAR) data is the best way for monitoring flood hazards. In this study Southern part of the Kerala is chosen as the study area, In August 2018, during the south west monsoon due to heavy rainfall a severe flood affected the southern part of Kerala which saw a 37% increase in the rate of normal rainfall. The objective of the study is to find the flood zone area using SAR data and estimate the flood occurrence over a period of time. However a satellite imagery of optical data is used to analyse the pre and post event of flood, but during a heavy rainfall, cloud may interrupt the data acquisition. SAR satellite imagery fromSentinel-1A is a cloud penetrating data available in all kind of weather conditions during day and night time, which provides a good source of high resolution data sets. To identify the flood affected area an adapted technology of threshold methodology developed by using SAR data and change detection for the year 2018 and 2019, will illustrate flood extended part in southern part of Kerala. The result shows the estimation of flood extended part of the study area and the damages occurred during a flooded time period of post and pre event, vulnerability assessing of crop and agriculture is to obtain an intensity of the damaged areas which is closely associated with the river channel, the Polarization displays a similar sequence for amount of flooding. The study helps to find the reason of flood extent and to equip with better planning for risk reduction and management during a flooding period.

Remote sensing of terrestrial non-photosynthetic vegetation using hyperspectral, multispectral, SAR, and LiDAR data

2015 ◽  
Vol 40 (2) ◽  
pp. 276-304 ◽  
Author(s):  
Zhaoqin Li ◽  
Xulin Guo
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Wildlife Habitat ◽  
Optical Data ◽  
Lidar Data ◽  
Natural Fire ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Sar Data ◽  
Ecological Importance

Quantifying non-photosynthetic vegetation (NPV) is important for ecosystem management and studies on climate change, ecology, and hydrology because it controls uptake of carbon, water, and nutrients together with frequency and intensity of natural fire, and serves as wildlife habitat. The ecological importance of NPV has driven considerable research on quantitatively estimating NPV in diverse ecosystems including croplands, forests, grasslands, savannah, and shrublands using remote sensing data. However, a comprehensive review is not available. This review highlights the theoretical bases and the critical elements of remote sensing for NPV estimation, and summarizes research on estimating fractional cover of NPV (NPV cover) and biomass using passive optical hyperspectral and multispectral remote sensing data, active synthetic aperture radar (SAR) and light detection and ranging (LiDAR), and integrated multi-sensorial data. We also discuss advantages and disadvantages of optical, LiDAR, and SAR data and pinpoint future direction on NPV estimation using remote sensing data. Currently, most NPV research has been mainly focused on NPV cover, not NPV biomass, using passive optical data, while a few studies have used LiDAR data to quantify NPV biomass in forests and SAR data on NPV estimation in croplands and grasslands. In the future, more efforts should be made to estimate NPV biomass and to investigate the best use of hyperspectral, LiDAR, SAR data, and their integration. The upcoming new optical sensor on Sentinel-2 satellites, Radarsat-2 constellation and NovaSAR, technological innovation in hyperspectral, LiDAR, and SAR, and improvements on methodology for information extraction and combining multi-sensorial data will provide more opportunities for NPV estimation.

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.

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.

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