Estimation of sediment capacity of Aswan High Dam Lake utilizing remotely sensed bathymetric data: Case Study Active Sedimentation portion of Lake Nubia

2021 ◽  
Author(s):  
Abdelazim Negm ◽  
Hickmat Hossen ◽  
Mohamed Elsahabi ◽  
Omar Makboul ◽  
Andrea Scozzari
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Quantitative Estimation ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Sensing Data ◽  
Bathymetric Data ◽  
Aswan High Dam ◽  
Mlr Model

<p>This study deals with the quantitative estimation of the accumulated sediment capacity within the period from the initiation of the storage process of Lake Nubia in 1964 until 2012, by using field measurements and remote sensing data.. The bed levels of the study area related to year 1964 were extracted from a tri-dimensional model of the lake derived from a topographic map, based on observations anterior to lake filling. This map was compared with the bed levels estimated for the year 2012, which were extracted from remote sensing data, with the aim to estimate the sediment capacity. The utilized technique for estimating the bathymetric data (depths) from satellite images relies on establishing a Multiple Linear Regression (MLR) model between in situ measurements and reflectance data from multi-spectral optical satellite observations. The Multiple Linear Regression (MLR) model showed good results in the correlation between field measurements and remote sensing data. The current approach provides flexibility as well as effective time and cost management in calculating depths from remote sensing data when compared to the traditional method applied by Aswan High Dam Authority (AHDA). 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>

Atmospheric boundary layer parameters retrieval from Stepped Frequency Microwave Radiometer measurements in tropical cyclones

2020 ◽  
Author(s):  
Nikita Rusakov ◽  
Evgeny Poplavsky ◽  
Olga Ermakova ◽  
Yuliya Troitskaya ◽  
Daniil Sergeev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Microwave Radiometer ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Radar Signal ◽  
Time And Space ◽  
Sensing Data ◽  
Radar Images

<p>Active microwave sensing using satellite instruments has great advantages, since in this range the absorption by clouds and atmospheric gases is noticeably reduced, it allows for round-the-clock and all-weather monitoring of the ocean. One of the main problems is concerned with obtaining the dependency between the RCS of radar signal scattered by the wavy water surface and the parameters of the atmospheric boundary layer in hurricane conditions. To obtain this dependence, we used field measurements of wind speed in a hurricane from falling NOAA GPS-sondes and SAR images from the Sentinel-1 satellite. However, there is the problem of correct collocation of remote sensing data with field measurements of the atmospheric boundary layer parameters, since they are separated in time and space. In this regard, the amount of data suitable for analysis is very limited, which forces us to look for new data sources for processing. A six-channel SFMR radiometer is also installed on board of NOAA research aircraft that measures the emissivity of the ocean surface beneath the aircraft. Thus, it becomes possible to relate the radiometric measurements of SFMR with the parameters of the atmospheric boundary layer in a tropical cyclone obtained from wind velocity profiles, since they are carried out as close as possible in time and space. Using this relation, the SFMR data and the hurricane radar images were analyzed together and an alternative method was found for constructing the dependence of the RCS on the parameters of the boundary layer.</p><p>This work was supported by the RFBR projects No. 19-05-00249, 19-05-00366, 18-35-20068 (remote sensing data analysis) and RSF No. 19-17-00209 (GPS-sondes data assimilation and processing).</p><p> </p>

scholarly journals Use of remote sensing data obtained from UAVs to assess the biomass productivity of Silphium perfoliatum

2021 ◽  
Vol 59 (2) ◽  
pp. 186-197
Author(s):  
T. N. Myslyva ◽  
B. V. Sheliuta ◽  
P. P. Nadtochy ◽  
A. A. Kutsayeva
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Vegetation Indices ◽  
Approximation Error ◽  
Implementation Process ◽  
Remote Sensing Data ◽  
Biomass Productivity ◽  
Surface Model ◽  
Forage Crops ◽  
Sensing Data

Agromonitoring is one of the most important sources of obtaining up-to-date and timely information about the state of agricultural crops. It is possible to speed up and reduce the cost of its implementation process using remote sensing data (RSD) obtained with the help of unmanned aerial vehicles (UAVs). Possibility of using ultra-high-resolution remote sensing to determine productivity of Silphium perfoliatum biomass has been evaluated using Phantom-4ProV 2.0 UAV. The shooting was carried out in RGB mode, the shooting height was 50 m, the spatial resolution was 2.5 cm. Based on the results of the survey, a height map and orthomosaic were created, which were later used to assess productivity of plants. To obtain the plant height values, the difference between the vegetation cover heights obtained from the surface model raster and the minimum height determined within the raster has been calculated. The actual height of plants measured in the field was compared with the data obtained using the UAV, and after the biomass productivity calculated from the actual and predicted heights was determined. The determination coefficient for equation of paired linear regression between the actual and predicted values of productivity made 0.97, and the value of the average approximation error was 3.3 %. To verify the results obtained, 60 samples of biomass were taken in the field within the study area, with the length of the plants determined using a tape measure, and the sampling sites coordinated using GPS positioning. 13 vegetation indices have been determined using pixel-based calibrated orthomosaic and normalized RGB channels, four of which (ExG, VARI, WI, and EXGR) showed to be suitable for creating a predictive model of multiple linear regression, which allows estimating and predicting the productivity of Silphium perfoliatum biomass during stemming phase with an error not exceeding 2 %. The results of the study can be useful both in development of prediction methods and in the direct prediction of Silphium perfoliatum biomass and other forage crops productivity, in particular Helianthus annuus and Helianthus tuberosus.

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