Derivation of spatially detailed lentic habitat map and inventory at a basin scale by integrating multispectral Sentinel-2 satellite imagery and USGS Digital Elevation Models

2021 ◽  
Vol 603 ◽  
pp. 126876
Author(s):  
Yang Liu ◽  
Hongxing Liu ◽  
Lei Wang ◽  
Min Xu ◽  
Sagy Cohen ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Digital Elevation Models ◽  
Digital Elevation ◽  
Basin Scale ◽  
Sentinel 2

scholarly journals Quantifying the performance of automated GIS-based geomorphological approaches for riparian zone delineation using digital elevation models

2012 ◽  
Vol 16 (10) ◽  
pp. 3851-3862 ◽  
Author(s):  
D. Fernández ◽  
J. Barquín ◽  
M. Álvarez-Cabria ◽  
F. J. Peñas
Keyword(s):  
River Basin ◽  
Riparian Zone ◽  
Digital Elevation Models ◽  
Riparian Zones ◽  
River Networks ◽  
Riparian Areas ◽  
River Basin Scale ◽  
Digital Elevation ◽  
Basin Scale ◽  
Objective Quantification

Abstract. Riparian zone delineation is a central issue for managing rivers and adjacent areas; however, criteria used to delineate them are still under debate. The area inundated by a 50-yr flood has been indicated as an optimal hydrological descriptor for riparian areas. This detailed hydrological information is usually only available for populated areas at risk of flooding. In this work we created several floodplain surfaces by means of two different GIS-based geomorphological approaches using digital elevation models (DEMs), in an attempt to find hydrologically meaningful potential riparian zones for river networks at the river basin scale. Objective quantification of the performance of the two geomorphologic models is provided by analysing coinciding and exceeding areas with respect to the 50-yr flood surface in different river geomorphological types.

scholarly journals ON THE DEVELOPMENT OF A NOVEL APPROACH FOR IDENTIFYING PERENNIAL DRAINAGE IN SOUTHERN BRAZIL: A STUDY CASE INTEGRATING SENTINEL-2 AND HIGH-RESOLUTION DIGITAL ELEVATION MODELS WITH MACHINE LEARNING TECHNIQUES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 161-168
Author(s):  
A. Montibeller ◽  
M. Vilela ◽  
F. Hino ◽  
P. Mallmann ◽  
M. Nadas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
High Resolution ◽  
Digital Elevation Models ◽  
Machine Learning Techniques ◽  
Water Runoff ◽  
Learning Techniques ◽  
Digital Elevation ◽  
Sentinel 2

Abstract. Riparian vegetation plays a key role in maintaining water quality and preserving the ecosystems along riverine systems, as they prevent soil erosion, retain water by increased infiltration, and act as a buffer zone between rivers and their surroundings. Within urban spaces, these areas have also an important role in preventing illegal occupation in areas of hydrologic risk, such as in floodplains. The goal of this research is to propose a framework for identifying areas of permanent protection associated with perennial drainage, utilizing satellite imagery and digital elevation models (DEM) in association with machine learning techniques. The specific objectives include the development of a decision tree to retrieve perennial drainage over high resolution, 1-meter DEM’s, and the development of digital image processing workflow to retrieve surface water bodies from Sentinel-2 imagery. In-situ information on perennial and ephemeral conditions of streams and rivers were obtained to validate our results, that happened in the first trimester of 2020. We propose a minimum of 7 days without precipitation prior to in-situ validation, for more accurate assessment of streamflow conditions, in order to minimize impacts of surface water runoff in flow regime. The proposed method will benefit decision makers by providing them with reliable information on drainage network and their buffer zones, as well as yield detailed mapping of the areas of permanent protection that are key to urban planning and management.

scholarly journals EVALUATION OF DIGITAL ELEVATION MODELS FOR GEOMORPHOMETRIC ANALYSES ON DIFFERENT SCALES FOR NORTHERN CHILE

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1229-1235 ◽  
Author(s):  
T. Kramm ◽  
D. Hoffmeister
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Thermal Emission ◽  
Digital Elevation Models ◽  
Regional Scale ◽  
Laser Altimeter ◽  
Absolute Deviation ◽  
Digital Elevation ◽  
Very High

<p><strong>Abstract.</strong> The resolution and accuracy of digital elevation models (DEMs) have direct influence on further geoscientific computations like landform classifications and hydrologic modelling results. Thus, it is crucial to analyse the accuracy of DEMs to select the most suitable elevation model regarding aim, accuracy and scale of the study. Nowadays several worldwide DEMs are available, as well as DEMs covering regional or local extents. In this study a variety of globally available elevation models were evaluated for an area of about 190,000&amp;thinsp;km<sup>2</sup>. Data from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m, Shuttle Radar Topography Mission (SRTM) 30&amp;thinsp;m and 90&amp;thinsp;m, Advanced Land Observing Satellite (ALOS) World 3D 30&amp;thinsp;m and TanDEM-X WorldDEM&amp;trade; &amp;ndash; 12&amp;thinsp;m and 90&amp;thinsp;m resolution were obtained. Additionally, several very high resolution DEM data were derived from stereo satellite imagery from SPOT 6/7 and Pléiades for smaller areas of about 100&amp;ndash;400&amp;thinsp;km<sup>2</sup> for each dataset. All datasets were evaluated with height points of the Geoscience Laser Altimeter System (GLAS) instrument aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite on a regional scale and with nine very high resolution elevation models from UAV-based photogrammetry on a very large scale. For all datasets the root mean square error (RMSE) and normalized median absolute deviation (NMAD) was calculated. Furthermore, the association of errors to specific terrain was conducted by assigning these errors to landforms from the topographic position index (TPI), topographic roughness index (TRI) and slope. For all datasets with a global availability the results show the highest overall accuracies for the TanDEM-X 12&amp;thinsp;m (RMSE: 2.3&amp;thinsp;m, NMAD: 0.8&amp;thinsp;m). The lowest accuracies were detected for the 30&amp;thinsp;m ASTER GDEM v3 (RMSE: 8.9&amp;thinsp;m, NMAD: 7.1&amp;thinsp;m). Depending on the landscape the accuracies are higher for all DEMs in flat landscapes and the errors rise significantly in rougher terrain. Local scale DEMs derived from stereo satellite imagery show a varying overall accuracy, mainly depending on the topography covered by the scene.</p>

scholarly journals The generation of digital elevation models using open source images

2021 ◽  
Author(s):  
Nithin Santosh Kumar
Keyword(s):  
Open Source ◽  
Spatial Resolution ◽  
Digital Elevation Models ◽  
Landsat 8 ◽  
Web Mapping ◽  
Digital Elevation ◽  
Satellite Sensors ◽  
Global Coverage ◽  
Comparable Accuracy ◽  
Sentinel 2

Digital Elevation Models are a representation of Earth’s surface and are used in many areas of research. There are a number of freely available DEMs with near-global coverage, which have elevation accuracies ranging between 10 to 25 m. This project attempts to generate DEMs of comparable accuracy using open source images from satellite sensors and web mapping services. Images from Landsat 8, ASTER, and Sentinel-2 satellites, and from Microsoft’s Bing Maps were used to generate DEMs for a 6.633 km2 area in Oshawa, Canada. It was found that it is key that when combining images from different spaceborne sensors, the spatial resolution should be within 10 m of one another. Additionally, the radiometry of the images, in terms of intensity and contrast, must be similar. The highest accuracies of DEMs had RMSE values of 20.047 m and 20.579 m, when combining images from Sentinel-2 with ASTER and Landsat 8, respectively.

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