Comparative assessment of remote sensing–based water dynamic in a dam lake using a combination of Sentinel-2 data and digital elevation model

2022 ◽  
Vol 194 (2) ◽  
Author(s):  
Muhittin Karaman ◽  
Emre Özelkan
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Comparative Assessment ◽  
Digital Elevation ◽  
Elevation Model ◽  
Sentinel 2

scholarly journals Remote sensing of terrestrial impact craters: The TanDEM-X digital elevation model

2016 ◽  
Vol 52 (7) ◽  
pp. 1412-1427 ◽  
Author(s):  
Manfred Gottwald ◽  
Thomas Fritz ◽  
Helko Breit ◽  
Birgit Schättler ◽  
Alan Harris
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Impact Craters ◽  
Digital Elevation ◽  
Elevation Model

scholarly journals DEMNAS: MODEL DIGITAL KETINGGIAN NASIONAL UNTUK APLIKASI KEPESISIRAN

OSEANA ◽  
2018 ◽  
Vol 43 (4) ◽  
Author(s):  
Marindah Yulia Iswari ◽  
Kasih Anggraini
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Spatial Resolution ◽  
Digital Data ◽  
Coastal Hazards ◽  
Remote Sensing Images ◽  
Geospatial Information ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model

DEMNAS : NATIONAL DIGITAL ELEVATION MODEL FOR COASTAL APPLICATION. DEM is a digital data which contain information about elevation. In Indonesia, DEM can be generated from elevation points or contours in RBI (Rupabumi Indonesia). DEM can be performed to research of coastal application i.e. inundation or tsunami. DEM can help to analyze vulnerability or evacuation zone for coastal hazards. DEMNAS is one product of BIG (Geospatial Information Agency) which consist of elevation data from remote sensing images. DEMNAS data has not been widely used and is still being developed but DEMNAS has an advantage of spatial resolution. DEMNAS has spatial resolution 0.27 arc-second, which is bigger than the spatial resolution of global DEM.

scholarly journals EVALUATION DIGITAL ELEVATION MODEL GENERATED BY SYNTHETIC APERTURE RADAR DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 57-62
Author(s):  
H. B. Makineci ◽  
H. Karabörk
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Interpolation Method ◽  
Digital Elevation Models ◽  
European Space Agency ◽  
Mountain Area ◽  
Synthetic Aperture Radar Data ◽  
Radar Images ◽  
Digital Elevation ◽  
Elevation Model

Digital elevation model, showing the physical and topographical situation of the earth, is defined a tree-dimensional digital model obtained from the elevation of the surface by using of selected an appropriate interpolation method. DEMs are used in many areas such as management of natural resources, engineering and infrastructure projects, disaster and risk analysis, archaeology, security, aviation, forestry, energy, topographic mapping, landslide and flood analysis, Geographic Information Systems (GIS). Digital elevation models, which are the fundamental components of cartography, is calculated by many methods. Digital elevation models can be obtained terrestrial methods or data obtained by digitization of maps by processing the digital platform in general. Today, Digital elevation model data is generated by the processing of stereo optical satellite images, radar images (radargrammetry, interferometry) and lidar data using remote sensing and photogrammetric techniques with the help of improving technology. <br><br> One of the fundamental components of remote sensing radar technology is very advanced nowadays. In response to this progress it began to be used more frequently in various fields. Determining the shape of topography and creating digital elevation model comes the beginning topics of these areas. <br><br> It is aimed in this work , the differences of evaluation of quality between Sentinel-1A SAR image ,which is sent by European Space Agency ESA and Interferometry Wide Swath imaging mode and C band type , and DTED-2 (Digital Terrain Elevation Data) and application between them. The application includes RMS static method for detecting precision of data. Results show us to variance of points make a high decrease from mountain area to plane area.

scholarly journals CARTOGRAPHY OF MOROCCAN ARGAN TREE USING COMBINED OPTICAL AND SAR IMAGERY INTEGRATED WITH DIGITAL ELEVATION MODEL

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 211-217
Author(s):  
E. Elmoussaoui ◽  
A. Moumni ◽  
A. Lahrouni
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Digital Elevation Model ◽  
Optical Data ◽  
Support Vector ◽  
Ground Truth Data ◽  
Argan Tree ◽  
Digital Elevation ◽  
Elevation Model ◽  
Sentinel 2

Abstract. Forest tree species mapping became easier due to the global availability of high spatio-temporal resolution images acquired from multiple sensors. Such data can lead to better forest resources management. Machine-learning pixel based analysis was performed to multi-spectral Sentinel-2 and Synthetic Aperture Radar Sentinel-1 time series integrated with Digital Elevation Model acquired over Argan forest of Essaouira province, Morocco. The argan tree constitutes a fundamental resource for the populations of this arid area of Morocco. This research aims to use the potential of the combination of multi-sensor data to detect, map and identify argan tree from other forest species using three Machine Learning algorithms: Support Vector Machine (SVM), Maximum Likelihood (ML) and Artificial Neural Networks (ANN). The exploited datasets included Sentinel-1 (S1), Sentinel-2 (S2) time series, Shuttle Radar Topographic Missing Digital Elevation Model (DEM) layer and Ground truth data. We tested several sets of scenarios, including single S1 derived features, single S2 time series and combined S1 and S2 derived layers with DEM scene acquisition. The best results (overall accuracy OA and Kappa coefficient K) obtained from time series of optical data (NDVI): OA = 86.87%, K = 0.84, from time series of SAR data (VV+VH/VV): OA = 45.90%, K = 0.36, from the combination of optical and SAR time series (NDVI+VH+DEM): OA = 93.01%, K = 0.914, and from the fusion of optical time series and DEM layer (NDVI+DEM): OA = 93.25%, K = 0.91. These results indicate that single-sensor (S2) integrated with the DEM layer led us to obtain the highest classification results.

Terrace extraction based on remote sensing images and digital elevation model in the loess plateau, China

2020 ◽  
Vol 13 (2) ◽  
pp. 433-446 ◽  
Author(s):  
Lanhua Luo ◽  
Fayuan Li ◽  
Ziyang Dai ◽  
Xue Yang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Loess Plateau ◽  
The Loess Plateau ◽  
Remote Sensing Images ◽  
Digital Elevation ◽  
Elevation Model

Improving a digital elevation model of Antarctica using radar remote sensing data and GIS techniques1

1999 ◽  
Vol 23 (3) ◽  
pp. 185-200 ◽  
Author(s):  
Kenneth C. Jezek ◽  
Hongxing Liu ◽  
Zhiyuan Zhao ◽  
Biyan Li
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Remote Sensing Data ◽  
Radar Remote Sensing ◽  
Sensing Data ◽  
Digital Elevation ◽  
Elevation Model
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