scholarly journals Peer Review #2 of "Waterbird counts on large water bodies: comparing ground and aerial methods during different ice conditions (v0.1)"

2018 ◽  
Author(s):  
V Kowal
Keyword(s):  
Peer Review ◽  
Water Bodies ◽  
Ice Conditions ◽  
Large Water

Risk of infestation of population by opisthorchiasis causative agent in the middle and lower current of the Iset river. influence of loimopotential of the focus of infestation on the risk values

2017 ◽  
pp. 52-56
Author(s):  
A.V. Ushakov ◽  
R.G. Fattakhov ◽  
T.F. Stepanova
Keyword(s):  
Causative Agent ◽  
Water Bodies ◽  
Risk Of Infection ◽  
Natural Focus ◽  
Opisthorchis Felineus ◽  
Large Water

The risk of infestation of the population by the opisthorchiasis causative agent in the middle and lower reaches of the Iset River was estimated. Areas with the highest risk of peoples infection by Opisthorchis felineus’ metacercaria are identified. These territories are confined to the zones of removal of the opisthorchiasis causative agent, which are river beds and large water bodies that constantly connect with rivers. Steady risk of infection of the population is determined by the loimopotential of the opisthorchiasis natural focus. The general infestation of juveniles fishes in the middle and lower current of the Iset River made 9,9 %, annuals – 21,5 %, two-year-olds – 19,5 %.

Using Cyber Physical Systems to Map Water Quality Over Large Water Bodies

2018 ◽  
Author(s):  
P. Hirani ◽  
S. Balivada ◽  
R. Chauhan ◽  
G. Shaikh ◽  
L. Murthy ◽  
...  
Keyword(s):  
Water Quality ◽  
Cyber Physical Systems ◽  
Water Bodies ◽  
Physical Systems ◽  
Large Water

scholarly journals Reduced precipitation over large water bodies in the Brazilian Amazon shown from TRMM data

2011 ◽  
Vol 38 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Rodrigo Cauduro Dias Paiva ◽  
Diogo Costa Buarque ◽  
Robin T. Clarke ◽  
Walter Collischonn ◽  
Daniel Gustavo Allasia
Keyword(s):  
Brazilian Amazon ◽  
Water Bodies ◽  
Large Water

scholarly journals Dynamic Threshold Selection for the Classification of Large Water Bodies within Landsat-8 OLI Water Index Images

2016 ◽  
Author(s):  
Fangfang Zhang ◽  
Junsheng Li ◽  
Qian Shen ◽  
Bing Zhang ◽  
Huping Ye ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Landsat 8 ◽  
Threshold Selection ◽  
Water Index ◽  
Sensing Data ◽  
Twin Peaks ◽  
Large Water ◽  
Threshold Range

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

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