scholarly journals A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

2015 ◽  
Vol 9 (2) ◽  
pp. 113-133 ◽  
Author(s):  
Min Feng ◽  
Joseph O. Sexton ◽  
Saurabh Channan ◽  
John R. Townshend
Keyword(s):  
High Resolution ◽  
Water Body ◽  
Classification Algorithm ◽  
Spectral Classification ◽  
Inland Water ◽  
First Results ◽  
Inland Water Body

Bathymetry Estimation using Multispectral Imagery over an Inland Water Body – Vembanad Lake, Kerala, India

2020 ◽  
Vol 89 (sp1) ◽  
pp. 126
Author(s):  
Kumari Rupam ◽  
Vincent Arul Ferrer ◽  
Kizhur Kandathil Ramachandran
Keyword(s):  
Water Body ◽  
Multispectral Imagery ◽  
Inland Water ◽  
Inland Water Body

Results of the Global WaterPack: a novel product to assess inland water body dynamics on a daily basis

2015 ◽  
Vol 6 (1) ◽  
pp. 78-87 ◽  
Author(s):  
Igor Klein ◽  
Andreas Dietz ◽  
Ursula Gessner ◽  
Stefan Dech ◽  
Claudia Kuenzer
Keyword(s):  
Water Body ◽  
Daily Basis ◽  
Inland Water ◽  
Body Dynamics ◽  
Inland Water Body

SARAL/AltiKa Altimetry Data for Monitoring of Inland Water Body: a Case Study of Mayurakshi Reservoir, India

2016 ◽  
Vol 44 (5) ◽  
pp. 797-802 ◽  
Author(s):  
A. Sai krishnaveni ◽  
V. M. Chowdary ◽  
D. Dutta ◽  
J. R. Sharma ◽  
V. K. Dadhwal
Keyword(s):  
Water Body ◽  
Altimetry Data ◽  
Inland Water ◽  
Inland Water Body

Feasibility of Using Sentinel-3 in Estimating Lake Nasser Water Depths

2021 ◽  
Author(s):  
Marwa Khairy ◽  
Hickmat Hossen ◽  
Mohamed Elsahabi ◽  
Shenouda Ghaly ◽  
Andrea Scozzari ◽  
...  
Keyword(s):  
Water Level ◽  
Water Depth ◽  
Water Body ◽  
Optical Data ◽  
Inland Water ◽  
Depth Data ◽  
Nasser Lake ◽  
Inland Water Body ◽  
The Relationship

<p><strong>Abstract</strong>  After the construction of the Grand Ethiopian Renaissance Dam (GERD), Nasser Lake (NL)became one of the most challenging hot spots at both local and global level. It is one of the biggest manmade reservoirs in the world and the most important in Egypt. It is created  in the southern part of the Nile River in Upper Egypt after the construction of Aswan High Dam (AHD). The water level in NL might fluctuate between 160 to 182 m above the mean sea level. Monitoring NL  water depth is an expensive and time-consuming activity. This work investigates the possibility to use information from the Sentinel missions to estimate the depth of NL as an inland water body, in the frame of estimating storage variations from satellite measurements. In this preliminary study, we investigated the relationship between the radiance /reflectance of optical imagery from two instruments SLSTR and OLCI instruments hosted by the Sentinel-3A platform and in situ water depth data using the Lyzenga equation. The results  indictaed  that there was a reasonable correlation between Sentinel-3 optical data and in situ water depth data. Also, Heron's formula was used to estimate water storage variations of NL with limited in situ data. In addition, equations governing the relationship between water level and both surface area and water volume were worked out. This study is in the framework of a bilateral project between ASRT of Egypt and CNR of Italy which is still running.</p><p> </p><p><strong>Keywords</strong>: Sentinel, SLSTR, OLCI, Inland water body, Nasser Lake, Egypt, Water Depth, GERD, AHD, Egypt</p>

scholarly journals ASSESSMENT ON SUITABILITY OF NORMALIZING AND PROBABILITY TYPE BENTHIC INDICES FOR PREDICTION OF BEHAVIOR OF INLAND WATER BODY

2015 ◽  
Vol 3 (9SE) ◽  
pp. 1-5
Author(s):  
Gayatri Gautam
Keyword(s):  
Water Body ◽  
Sampling Period ◽  
Wiener Index ◽  
Pollution Level ◽  
Inland Water ◽  
Inappropriate Use ◽  
Inland Water Body ◽  
Probabilistic Type ◽  
Tank Water

Natural variability in diversity of benthic macro invertebrate fauna or benthos has become a valuable tool in assessing, modeling and monitoring of water quality of inland water body with respect to its portability and of pollution level.  However, inappropriate use of these indices may leads to false interpretation or prediction of behavior of inland water body. In this study we have employed two normalizing (i.e Shannon-Wiener Index (H’), Pielou´s Evenness (J’)) and one probability type (HurlbertPIE Index(PIE)) indices to evaluate behavior of Baikona tank water body between Jan-2008 to Dec 2009.Sampling was carried out for period of 24 months at three different location and diversity of benthos for each location was tabulated. It is observed that monthly variation these indices show same trend during observation period. Coefficient of correlation among normalizing indices is observed lesser degree when compared to correlation between normalizing and probability type index. Degree of correlation is equitable when samples in all three sampling stations are mapped as single for each sampling period. It can be summarized that to model the tank behavior (i)both normalizing type and probabilistic type indices to be considered.(ii)all samples taken at a time at different locations are to be mapped as single sample.

scholarly journals Effects of inland water body on ambient air specific humidity

1993 ◽  
Vol 5 (4) ◽  
pp. 289-298
Author(s):  
Wang Hao ◽  
◽  
Fu Baopu
Keyword(s):  
Water Body ◽  
Ambient Air ◽  
Specific Humidity ◽  
Inland Water ◽  
Inland Water Body

Performances of conventional fusion methods evaluated for inland water body observation using GF-1 image

2019 ◽  
Vol 38 (1) ◽  
pp. 172-179
Author(s):  
Yong Du ◽  
Xiaoyu Zhang ◽  
Zhihua Mao ◽  
Jianyu Chen
Keyword(s):  
Water Body ◽  
Inland Water ◽  
Fusion Methods ◽  
Inland Water Body

scholarly journals Potential of Large-Scale Inland Water Body Mapping from Sentinel-1/2 Data on the Example of Bavaria’s Lakes and Rivers

2020 ◽  
Vol 88 (3-4) ◽  
pp. 271-289 ◽  
Author(s):  
Michael Schmitt
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Large Scale ◽  
Water Bodies ◽  
Training Data ◽  
Body Mapping ◽  
Inland Water ◽  
Fully Automatic ◽  
Inland Water Body ◽  
Sentinel 2

Abstract The mapping of water bodies is an important application area of satellite-based remote sensing. In this contribution, a simple framework based on supervised learning and automatic training data annotation is shown, which allows to map inland water bodies from Sentinel satellite data on large scale, i.e. on state level. Using the German state of Bavaria as an example and different combinations of Sentinel-1 SAR and Sentinel-2 multi-spectral imagery as inputs, potentials and limits for the automatic detection of water surfaces for rivers, lakes, and reservoirs are investigated. Both quantitative and qualitative results confirm that fully automatic large-scale inland water body mapping is generally possible from Sentinel data; whereas, the best result is achieved when all available surface-related bands of both Sentinel-1 and Sentinel-2 are fused on a pixel level. The main limitation arises from missed smaller water bodies, which are not observed in bands with a resolution of about 20 m. Given the simplicity of the proposed approach and the open availability of the Sentinel data, the study confirms the potential for a fully automatic large-scale mapping of inland water with cloud-based remote sensing techniques.

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