scholarly journals Surface Water Body Detection in Polarimetric SAR Data Using Contextual Complex Wishart Classification

2019 ◽  
Vol 55 (8) ◽  
pp. 7047-7059 ◽  
Author(s):  
E. Goumehei ◽  
V. Tolpekin ◽  
A. Stein ◽  
W. Yan
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Polarimetric Sar ◽  
Surface Water Body ◽  
Sar Data

scholarly journals Variations Size Investigation in Vegetation and Surface Water Body for Central Part of Iraq using Satellite Imagery Bands

2020 ◽  
Vol 928 ◽  
pp. 022064
Author(s):  
Malik R. Abbas ◽  
Mahir Mahmod Hason ◽  
Baharin Bin Ahmad ◽  
Abd Wahid Bin Rasib ◽  
Talib R. Abbas
Keyword(s):  
Surface Water ◽  
Satellite Imagery ◽  
Water Body ◽  
Surface Water Body

scholarly journals Automatic Surface Water Mapping Using Polarimetric SAR Data for Long-Term Change Detection

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 872
Author(s):  
Wen Zhang ◽  
Baoxin Hu ◽  
Glen S. Brown
Keyword(s):  
Surface Water ◽  
Open Water ◽  
Polarimetric Sar ◽  
Dynamic Events ◽  
Term Change ◽  
Microwave Signals ◽  
Timely Fashion ◽  
Sar Data ◽  
Accuracy Of Results ◽  
Water Saturated

Mapping the distribution and persistence of surface water in a timely fashion has broad value for tracking dynamic events like flooding, and for monitoring the effects of climate and human activities on natural resource values and biodiversity. Traditionally, surface water is mapped from optical imagery using semi-automatic approaches. However, this process is time-consuming and the accuracy of results can vary among image interpreters. In recent years, Synthetic Aperture Radar (SAR) images have been increasingly used. Microwave signals sensitive to water content make SAR systems useful for mapping surface water, saturated soils, and flooded vegetation. In this study, a fully automatic method based on robust stepwise thresholding was developed to map and track the change in the extent of surface water using Polarimetric SAR data. The application of this method in both Radarsat-2 and Sentinel-1 data in central Ontario, Canada demonstrates that the developed robust stepwise thresholding approach could facilitate rapid mapping of open water areas with a promising accuracy of over 95%. In addition, the time-series extent of surface water extracted from May 2008 to August 2016 reveals the dynamic nature of surface inundation, and the trend was consistent with the local precipitation data.

Extraction of Coal Under a Surface Water Body – a Strata Control Investigation

2005 ◽  
Vol 38 (5) ◽  
pp. 399-410 ◽  
Author(s):  
A. Gandhe ◽  
V. Venkateswarlu ◽  
R. N. Gupta
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Strata Control ◽  
Surface Water Body

Applications of mathematical morphology in surface water body studies

1995 ◽  
Vol 16 (8) ◽  
pp. 1495-1502 ◽  
Author(s):  
B. S. DAYA SAGAR ◽  
G. GANDHI ◽  
B. S. PRAKASA RAG
Keyword(s):  
Surface Water ◽  
Mathematical Morphology ◽  
Water Body ◽  
Body Studies ◽  
Surface Water Body

scholarly journals Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine

2021 ◽  
Vol 13 (1) ◽  
pp. 1290-1302
Author(s):  
Ruimeng Wang ◽  
Li Pan ◽  
Wenhui Niu ◽  
Rumeng Li ◽  
Xiaoyang Zhao ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Vegetation Index ◽  
Yellow River ◽  
Google Earth ◽  
Body Type ◽  
Body Area ◽  
Surface Water Body ◽  
Google Earth Engine ◽  
Xiaolangdi Reservoir

Abstract Xiaolangdi Reservoir is a key control project to control the water and sediment in the lower Yellow River, and a timely and accurate grasp of the reservoir’s water storage status is essential for the function of the reservoir. This study used all available Landsat images (789 scenes) and adopted the modified normalized difference water index, enhanced vegetation index, and normalized difference vegetation index to map the surface water from 1999 to 2019 in Google Earth Engine (GEE) cloud platform. The spatiotemporal characteristics of the surface water body area changes in the Xiaolangdi Reservoir in the past 21 years are analyzed from the water body type division, area change, type conversion, and the driving force of the Xiaolangdi water body area changes was analyzed. The results showed that (1) the overall accuracy of the water body extraction method was 98.86%, and the kappa coefficient was 0.96; (2) the maximum water body area of the Xiaolangdi Reservoir varies greatly between inter-annual and intra-annual, and seasonal water body and permanent water body have uneven spatiotemporal distribution; (3) in the conversion of water body types, the increased seasonal water body area of the Xiaolangdi Reservoir from 1999 to 2019 was mainly formed by the conversion of permanent water body, and the reduced permanent water body area was mainly caused by non-water conversion; and (4) the change of the water body area of the Xiaolangdi Reservoir has a weak negative correlation with natural factors such as precipitation and temperature, and population. It is positively correlated with seven indicators such as runoff and regional gross domestic product (GDP). The findings of the research will provide necessary data support for the management and planning of soil and water resources in the Xiaolangdi Reservoir.

scholarly journals Spatially distributed sensitivity of simulated global groundwater heads and flows to hydraulic conductivity, groundwater recharge and surface water body parameterization

2019 ◽  
Author(s):  
Robert Reinecke ◽  
Laura Foglia ◽  
Steffen Mehl ◽  
Jonathan D. Herman ◽  
Alexander Wachholz ◽  
...  
Keyword(s):  
Surface Water ◽  
Hydraulic Conductivity ◽  
Groundwater Recharge ◽  
Water Body ◽  
Distributed Parameter ◽  
Groundwater Model ◽  
Surface Water Body ◽  
Spatially Distributed ◽  
Gradient Based ◽  
The Impact

Abstract. In global hydrological models, groundwater storages and flows are generally simulated by linear reservoir models. Recently, the first global gradient-based groundwater models were developed in order to improve the representation of groundwater-surface water interactions, capillary rise, lateral flows and human water use impacts. However, the reliability of model outputs is limited by a lack of data as well as model assumptions required due to the necessarily coarse spatial resolution. The impact of data quality is presented by showing the sensitivity of a groundwater model to changes in the only available global hydraulic conductivity data-set. To better understand the sensitivity of model output to uncertain spatially distributed parameter inputs, we present the first application of a global sensitivity method for a global-scale groundwater model using nearly 2000 steady-state model runs of the global gradient-based groundwater model G3M. By applying the Morris method in a novel domain decomposition approach that identifies global hydrological response units, spatially distributed parameter sensitivities are determined for a computationally expensive model. Results indicate that globally simulated hydraulic heads are equally sensitive to hydraulic conductivity, groundwater recharge and surface water body elevation, though parameter sensitivities vary regionally. For large areas of the globe, rivers are simulated to be either losing or gaining, depending on the parameter combination, indicating a high uncertainty of simulating the direction of flow between the two compartments. Mountainous and dry regions show a high variance in simulated head due to numerical difficulties of the model, limiting the reliability of computed sensitivities in these regions. This instability is likely caused by the uncertainty in surface water body elevation. We conclude that maps of spatially distributed sensitivities can help to understand complex behaviour of models that incorporate data with varying spatial uncertainties. The findings support the selection of possible calibration parameters and help to anticipate challenges for a transient coupling of the model.

scholarly journals Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery

2018 ◽  
Vol 219 ◽  
pp. 259-270 ◽  
Author(s):  
Xiucheng Yang ◽  
Qiming Qin ◽  
Pierre Grussenmeyer ◽  
Mathieu Koehl
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Urban Surface ◽  
Surface Water Body ◽  
Water Indices ◽  
Sentinel 2
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