Estimation of stem volume in hemi-boreal forests using airborne low-frequency Synthetic Aperture Radar and lidar data

2013 ◽  
Author(s):  
Johan E. S. Fransson ◽  
Jorgen Wallerman ◽  
Anders Gustavsson ◽  
Lars M. H. Ulander
Keyword(s):  
Synthetic Aperture Radar ◽  
Boreal Forests ◽  
Low Frequency ◽  
Synthetic Aperture ◽  
Stem Volume ◽  
Lidar Data ◽  
Aperture Radar

Low‐frequency synthetic aperture radar data‐dome collection with the Bright Sapphire II instrument

2017 ◽  
Vol 53 (15) ◽  
pp. 981-983
Author(s):  
S.G. Doody ◽  
N. Hughes ◽  
L. Ramio‐Tomas ◽  
E. Mak ◽  
D.G. Muff ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Low Frequency ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Synthetic Aperture Radar Data ◽  
Aperture Radar

Progress in operational flood mapping using satellite synthetic aperture radar (SAR) and airborne light detection and ranging (LiDAR) data

2016 ◽  
Vol 40 (2) ◽  
pp. 196-214 ◽  
Author(s):  
Kyle M. Brown ◽  
Crispin H. Hambidge ◽  
Jonathan M. Brownett
Keyword(s):  
Synthetic Aperture Radar ◽  
Light Detection And Ranging ◽  
Surface Elevation ◽  
Synthetic Aperture ◽  
Lidar Data ◽  
Light Detection ◽  
Elevation Data ◽  
Sar Data ◽  
Automated Methods ◽  
Aperture Radar

During flooding, operational tools for mapping flood extent and depth of water in flood-prone areas are required by those planning emergency response, including UK statutory agencies such as the Environment Agency. Satellite data have become a source of information to map and monitor floods, but many of the methods developed to process the data are unsuitable for accurate, near real-time production of flood information products. This paper describes a new semi-automated methodology developed to provide operational mapping of flood extent and flood depth using satellite synthetic aperture radar (SAR) data combined with light detection and ranging (LiDAR) elevation data. In this method, an analyst uses the flood boundary derived from 8 m spatial resolution satellite SAR data to estimate the flood surface elevation at points around a flooded area using a digital terrain model derived from LiDAR data. This method is compared to a simple satellite ‘SAR-only’ method for generating flood extent and alternative, automated methods of generating flood extent and depth that also used SAR and LiDAR. TerraSAR-X and SPOT 5 data were used from an area including the UK Somerset Levels which suffered a major flood event in February 2014. The new semi-automated method produced similar overall accuracy to the SAR-only method ( Po = 95.8% and Po = 95.3%, respectively), but was more accurate at mapping flood extent where large vegetation or other objects appeared in the satellite SAR data. The automated methods were relatively inaccurate (overall accuracy ranged from Po = 83.4% to Po = 88.8%), but were used to identify where further work on improving the semi-automated-elevation method could be carried out. In addition to the flood extent information provided by the semi-automated-elevation method, flood surface elevation data were produced that could be used to estimated flood depths and flood volumes. The accuracy of the flood elevation surface was tested using LiDAR data acquired of the water surface during the flooding (root mean square error = 0.152 m). The paper discusses progress towards operational flood monitoring using SAR and LiDAR remote sensing products.

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