scholarly journals High resolution lidar-derived elevation data for Barry Arm landslide, Southcentral Alaska, June 26, 2020

2021 ◽  
Author(s):  
R.P. Daanen ◽  
G.J. Wolken ◽  
Katreen Wikstrom Jones ◽  
A.M. Herbst
Keyword(s):  
High Resolution ◽  
Southcentral Alaska ◽  
Elevation Data

scholarly journals Assessing Optimal Digital Elevation Model Selection for Active River Area Delineation Across Broad Regions

2021 ◽  
Author(s):  
Shizhou Ma ◽  
Karen Beazley ◽  
Patrick Nussey ◽  
Chris Greene
Keyword(s):  
High Resolution ◽  
Digital Elevation Model ◽  
Smoothing Algorithm ◽  
Processing Efficiency ◽  
Spatial Approach ◽  
Digital Elevation ◽  
Elevation Data ◽  
Computer Based ◽  
Lidar Dem ◽  
Elevation Model

Abstract The Active River Area (ARA) is a spatial approach for identifying the extent of functional riparian area. Given known limitations in terms of input elevation data quality and methodology, ARA studies to date have not achieved effective computer-based ARA-component delineation, limiting the efficacy of the ARA framework in terms of informing riparian conservation and management. To achieve framework refinement and determine the optimal input elevation data for future ARA studies, this study tested a novel Digital Elevation Model (DEM) smoothing algorithm and assessed ARA outputs derived from a range of DEMs for accuracy and efficiency. It was found that the tested DEM smoothing algorithm allows the ARA framework to take advantage of high-resolution LiDAR DEM and considerably improves the accuracy of high-resolution LiDAR DEM derived ARA results; smoothed LiDAR DEM in 5-meter spatial resolution best balanced ARA accuracy and data processing efficiency and is ultimately recommended for future ARA delineations across large regions.

scholarly journals HIGH-RESOLUTION TSUNAMI-BEDLOAD COUPLED COMPUTATION IN AMR ENVIRONMENT

2018 ◽  
pp. 21
Author(s):  
Yasunori Watanabe ◽  
Yuta Mitobe ◽  
Hitoshi Tanaka ◽  
Kazuya Watanabe
Keyword(s):  
Boundary Layer ◽  
Land Use ◽  
High Resolution ◽  
Boundary Layer Theory ◽  
Wall Roughness ◽  
Pressure Reduction ◽  
High Resolution Data ◽  
Public Facilities ◽  
Elevation Data ◽  
Resolution Data

Conventional tsunami computations on coarser grids have employed Manning’s friction coefficients of subgrid equivalent roughness for buildings, vegetation and public facilities (roads, dikes and so on), depending on land-use at the grid location. This equivalent roughness macroscopically models to integrate all effects of resistances against the flow within the computational cells; that is, drag force and pressure reduction behind structures in addition to wall roughness defined in turbulent boundary layer theory. Recently high-resolution land elevation data (2-m resolution), measured by an aerial laser profiler, has been used for computing local inundation of tsunami flood. Since the high-resolution data resolves major buildings and facilities, the mechanical contributions of the structures, such as drag and pressure reduction, are included in the computed result. In this case, conventional equivalent friction may be unacceptable to use.

scholarly journals Improvement of High-Resolution Satellite Rainfall Product for Typhoon Morakot (2009) over Taiwan

2013 ◽  
Vol 14 (6) ◽  
pp. 1859-1871 ◽  
Author(s):  
Aina Taniguchi ◽  
Shoichi Shige ◽  
Munehisa K. Yamamoto ◽  
Tomoaki Mega ◽  
Satoshi Kida ◽  
...  
Keyword(s):  
High Resolution ◽  
Classification Scheme ◽  
Rain Gauge ◽  
Upward Motion ◽  
Infrared Images ◽  
Horizontal Scale ◽  
Typhoon Morakot ◽  
Horizontal Length ◽  
Elevation Data ◽  
Microwave Radiometers

Abstract The authors improve the high-resolution Global Satellite Mapping of Precipitation (GSMaP) product for Typhoon Morakot (2009) over Taiwan by using an orographic/nonorographic rainfall classification scheme. For the estimation of the orographically forced upward motion used in the orographic/nonorographic rainfall classification scheme, the optimal horizontal length scale for averaging the elevation data is examined and found to be about 50 km. It is inferred that as the air ascends en masse on the horizontal scale, it becomes unstable and convection develops. The orographic/nonorographic rainfall classification scheme is extended to the GSMaP algorithm for all passive microwave radiometers in orbit, including not just microwave imagers but also microwave sounders. The retrieved rainfall rates, together with infrared images, are used for the high-resolution rainfall products, which leads to much better agreement with rain gauge observations.

scholarly journals Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data

2009 ◽  
Vol 13 (5) ◽  
pp. 567-576 ◽  
Author(s):  
H. Zwenzner ◽  
S. Voigt
Keyword(s):  
High Resolution ◽  
Flood Mapping ◽  
Scaling Effects ◽  
Data Set ◽  
Digital Elevation ◽  
Geometric Precision ◽  
Elevation Data ◽  
Lidar Dem ◽  
Sar Data ◽  
Very High

Abstract. Severe flood events turned out to be the most devastating catastrophes for Europe's population, economy and environment during the past decades. The total loss caused by the August 2002 flood is estimated to be 10 billion Euros for Germany alone. Due to their capability to present a synoptic view of the spatial extent of floods, remote sensing technology, and especially synthetic aperture radar (SAR) systems, have been successfully applied for flood mapping and monitoring applications. However, the quality and accuracy of the flood masks and derived flood parameters always depends on the scale and the geometric precision of the original data as well as on the classification accuracy of the derived data products. The incorporation of auxiliary information such as elevation data can help to improve the plausibility and reliability of the derived flood masks as well as higher level products. This paper presents methods to improve the matching of flood masks with very high resolution digital elevation models as derived from LiDAR measurements for example. In the following, a cross section approach is presented that allows the dynamic fitting of the position of flood mask profiles according to the underlying terrain information from the DEM. This approach is tested in two study areas, using different input data sets. The first test area is part of the Elbe River (Germany) where flood masks derived from Radarsat-1 and IKONOS during the 2002 flood are used in combination with a LiDAR DEM of 1 m spatial resolution. The other test data set is located on the River Severn (UK) and flood masks derived from the TerraSAR-X satellite and aerial photos acquired during the 2007 flood are used in combination with a LiDAR DEM of 2 m pixel spacing. By means of these two examples the performance of the matching technique and the scaling effects are analysed and discussed. Furthermore, the systematic flood mapping capability of the different imaging systems are examined. It could be shown that the combination of high resolution SAR data and LiDAR DEM allows the derivation of higher level flood parameters such as flood depth estimates, as presented for the Severn area. Finally, the potential and the constraints of the approach are evaluated and discussed.

High Resolution Elevation Data Derived from Stereoscopic CORONA Imagery with Minimal Ground Control

2008 ◽  
Vol 74 (9) ◽  
pp. 1093-1106 ◽  
Author(s):  
Nikolaos Galiatsatos ◽  
Daniel N.M. Donoghue ◽  
Graham Philip
Keyword(s):  
High Resolution ◽  
Ground Control ◽  
Elevation Data

scholarly journals IMPROVE THE ZY-3 HEIGHT ACCURACY USING ICESAT/GLAS LASER ALTIMETER DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 37-42
Author(s):  
Guoyuan Li ◽  
Xinming Tang ◽  
Xiaoming Gao ◽  
Chongyang Zhang ◽  
Tao Li
Keyword(s):  
High Resolution ◽  
Bundle Adjustment ◽  
Experimental Result ◽  
Altimeter Data ◽  
Surface Model ◽  
Laser Altimetry ◽  
Laser Altimeter ◽  
Ice Cloud ◽  
Elevation Data ◽  
Polar Ice Sheets

ZY-3 is the first civilian high resolution stereo mapping satellite, which has been launched on 9th, Jan, 2012. The aim of ZY-3 satellite is to obtain high resolution stereo images and support the 1:50000 scale national surveying and mapping. Although ZY-3 has very high accuracy for direct geo-locations without GCPs (Ground Control Points), use of some GCPs is still indispensible for high precise stereo mapping. The GLAS (Geo-science Laser Altimetry System) loaded on the ICESat (Ice Cloud and land Elevation Satellite), which is the first laser altimetry satellite for earth observation. GLAS has played an important role in the monitoring of polar ice sheets, the measuring of land topography and vegetation canopy heights after launched in 2003. Although GLAS has ended in 2009, the derived elevation dataset still can be used after selection by some criteria. <br><br> In this paper, the ICESat/GLAS laser altimeter data is used as height reference data to improve the ZY-3 height accuracy. A selection method is proposed to obtain high precision GLAS elevation data. Two strategies to improve the ZY-3 height accuracy are introduced. One is the conventional bundle adjustment based on RFM and bias-compensated model, in which the GLAS footprint data is viewed as height control. The second is to correct the DSM (Digital Surface Model) straightly by simple block adjustment, and the DSM is derived from the ZY-3 stereo imaging after freedom adjustment and dense image matching. The experimental result demonstrates that the height accuracy of ZY-3 without other GCPs can be improved to 3.0 meter after adding GLAS elevation data. What’s more, the comparison of the accuracy and efficiency between the two strategies is implemented for application.

scholarly journals Urban Density Indices Using Mean Shift-Based Upsampled Elevetion Data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1353-1357
Author(s):  
E. Charou ◽  
S. Gyftakis ◽  
E. Bratsolis ◽  
T. Tsenoglou ◽  
Th. D. Papadopoulou ◽  
...  
Keyword(s):  
High Resolution ◽  
Mean Shift ◽  
Urban Land ◽  
Optical Data ◽  
Surface Model ◽  
Land Classification ◽  
Urban Density ◽  
Coverage Ratio ◽  
Area Of Interest ◽  
Elevation Data

Urban density is an important factor for several fields, e.g. urban design, planning and land management. Modern remote sensors deliver ample information for the estimation of specific urban land classification classes (2D indicators), and the height of urban land classification objects (3D indicators) within an Area of Interest (AOI). In this research, two of these indicators, Building Coverage Ratio (BCR) and Floor Area Ratio (FAR) are numerically and automatically derived from high-resolution airborne RGB orthophotos and LiDAR data. In the pre-processing step the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an improved normalized digital surface model (nDSM) is an upsampled elevation data with considerable improvement regarding region filling and “straightness” of elevation discontinuities. In a following step, a Multilayer Feedforward Neural Network (MFNN) is used to classify all pixels of the AOI to building or non-building categories. For the total surface of the block and the buildings we consider the number of their pixels and the surface of the unit pixel. Comparisons of the automatically derived BCR and FAR indicators with manually derived ones shows the applicability and effectiveness of the methodology proposed.

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