scholarly journals Combining high-resolution satellite images and altimetry to estimate the volume of small lakes

2014 ◽  
Vol 18 (5) ◽  
pp. 2007-2020 ◽  
Author(s):  
F. Baup ◽  
F. Frappart ◽  
J. Maubant
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Temporal Variations ◽  
Water Levels ◽  
Water Volume ◽  
Small Scale ◽  
Altimetry Data ◽  
Small Lakes ◽  
High Resolution Satellite Images

Abstract. This study presents an approach to determining the volume of water in small lakes (<100 ha) by combining satellite altimetry data and high-resolution (HR) images. In spite of the strong interest in monitoring surface water resources on a small scale using radar altimetry and satellite imagery, no information is available about the limits of the remote-sensing technologies for small lakes mainly used for irrigation purposes. The lake being studied is located in the south-west of France and is only used for agricultural irrigation purposes. The altimetry satellite data are provided by an RA-2 sensor onboard Envisat, and the high-resolution images (<10 m) are obtained from optical (Formosat-2) and synthetic aperture radar (SAR) antenna (Terrasar-X and Radarsat-2) satellites. The altimetry data (data are obtained every 35 days) and the HR images (77) have been available since 2003 and 2010, respectively. In situ data (for the water levels and volumes) going back to 2003 have been provided by the manager of the lake. Three independent approaches are developed to estimate the lake volume and its temporal variability. The first two approaches (HRBV and ABV) are empirical and use synchronous ground measurements of the water volume and the satellite data. The results demonstrate that altimetry and imagery can be effectively and accurately used to monitor the temporal variations of the lake (R2ABV = 0.98, RMSEABV = 5%, R2HRBV = 0.90, and RMSEABV = 7.4%), assuming a time-varying triangular shape for the shore slope of the lake (this form is well adapted since it implies a difference inferior to 2% between the theoretical volume of the lake and the one estimated from bathymetry). The third method (AHRBVC) combines altimetry (to measure the lake level) and satellite images (of the lake surface) to estimate the volume changes of the lake and produces the best results (R2AHRBVC = 0.98) of the three methods, demonstrating the potential of future Sentinel and SWOT missions to monitor small lakes and reservoirs for agricultural and irrigation applications.

scholarly journals Combining high-resolution satellite images and altimetry to estimate the volume of small lakes

2013 ◽  
Vol 10 (12) ◽  
pp. 15731-15770 ◽  
Author(s):  
F. Baup ◽  
F. Frappart ◽  
J. Maubant
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Temporal Variations ◽  
Water Levels ◽  
Water Volume ◽  
Altimetry Data ◽  
Small Lakes ◽  
In Situ Data ◽  
High Resolution Satellite Images

Abstract. This study presents an approach to determine the volume of water in small lakes (<100 ha) by combining satellite altimetry data and high-resolution (HR) images. The lake being studied is located in the south-west of France and is only used for agricultural irrigation purposes. The altimetry satellite data are provided by RA-2 sensor on board Envisat, and the high-resolution images (<10 m) are obtained from optical (Formosat-2) and synthetic aperture radar (SAR) sensors (Terrasar-X and Radarsat-2) satellites. The altimetry data (data are obtained every 35 days) and the HR images (45) have been available since 2003 and 2010, respectively. In situ data (for the water levels and volumes) going back to 2003 have been provided by the manager of the lake. Three independent approaches are developed to estimate the lake volume and its temporal variability. The first two approaches are empirical and use synchronous ground measurements of the water volume and the satellite data. The results demonstrate that altimetry and imagery can be effectively and accurately used to monitor the temporal variations of the lake (R2altimetry = 0.97, RMSEaltimetry = 5.2%, R2imagery = 0.90, and RMSEimagery = 7.4%). The third method combines altimetry (to measure the lake level) and satellite images (of the lake surface) to estimate the volume changes of the lake and produces the best results (R2 = 0.99) of the three methods, demonstrating the potential of future Sentinel and SWOT missions to monitor small lakes and reservoirs for agricultural and irrigation applications.

scholarly journals EXTRACTION OF SCATTERED SMALL-SCALE LANDSLIDES DISTRIBUTION BY OBJECT-BASED CLASSIFICATION USING OPTICAL HIGH-RESOLUTION SATELLITE IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1213-1218
Author(s):  
H. Hashiba ◽  
M. Sonobe
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Image Interpretation ◽  
Heavy Rain ◽  
Wide Area ◽  
Small Scale ◽  
Superpixel Segmentation ◽  
Long Duration ◽  
High Resolution Satellite Images ◽  
Landslide Event

Abstract. Continuous heavy rain for a long duration over mountainous terrain, where the elevation is relatively low and the topography is complex leads to multiple small-scale landslides over a wide area. Detailed investigations of small-scale landslides have been effectively carried out using optical high-resolution satellite images with spatial resolution of about 2 m or less. In this study, the sediment-related disaster caused by heavy rain in northern Kyushu, Japan that occurred in July 2017 was selected as a typical example of small-scale landslide. For this landslide event, the applicability of the conventional superpixel segmentation for landslide separation was examined. The applicability of the representative SLIC and SLICO methods in the superpixel segmentation method by image interpretation in the case of a large number of small-scale landslides in a wide area was assessed. These results suggest that in the case of such a disaster, segmentation by the SLICO method will be better. In addition, the set value of the area size for the area division was systematically examined from the distribution tendency of the average NDVI value in the divided area. It was shown that the landslide region can be extracted with relatively high accuracy by the land cover classification process by the NN method by using the appropriate region size examined by the SLICO method.

scholarly journals Construction of nearshore elevation model using open satellite data

2021 ◽  
Author(s):  
A.E. Hmelnov ◽  
A.S. Gachenko
Keyword(s):  
High Resolution ◽  
Water Level ◽  
Satellite Data ◽  
Satellite Images ◽  
Water Levels ◽  
Water Level Changes ◽  
Subpixel Accuracy ◽  
Contour Lines ◽  
Elevation Model

For the tasks considering changes of water level it is required to have a combined (above water and underwater) elevation model. And the highest accuracy requirements are imposed on the parts of the model, which produce the contour lines in the range of the actual water level changes, while the information about the underwater elevation is usually very scarce and rough. In the article we consider the possibility to obtain this part of the elevation model using open high resolution (10 m/pixel) satellite images corresponding to different water levels. Here we describe the technique, which allows us to obtain the subpixel accuracy of the resulting contour lines. And we consider the problems in the quality of the satellite images that the contour lines reveal, and some ways to deal with the problems.

An Approach for Building Rooftop Border Extraction from Very High-Resolution Satellite Images

2021 ◽  
pp. 1-11
Author(s):  
Yasser Mostafa ◽  
Mahmoud Nokrashy O. Ali ◽  
Faten Mostafa ◽  
Mohamed Yousef
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
High Resolution Satellite Images ◽  
Very High
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