Analysis of X-SAR SRTM elevation data to estimate surface cover heights over land areas

Weydahl, Dan Johan; Sagstuen, J.; Dick, Øystein B.; Rönning, Helén; Hansen, L.

doi:10.1109/igarss.2003.1293695

Cited by 4 publications

(2 citation statements)

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“…One is located in a mountainous region with hydroelectric dams and sparsely vegetated surface (Bykle), while the other (Vestfold) holds agricultural fields, forested areas and cities, see figure 1. The Vestfold test site was first analysed using the SRTM X-band data together with a reference DEM (N50) originating from 1 : 50 000 topographic data with 20 m elevation contours (Sagstuen 2003, Weydahl et al 2003. Later, the SRTM C-band data were also made available and compared with the X-band DEM (Weydahl 2004).…”

Section: Introductionmentioning

confidence: 99%

SRTM DEM accuracy assessment over vegetated areas in Norway

Weydahl

Sagstuen

Dick

et al. 2007

International Journal of Remote Sensing

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Results from the Shuttle Radar Topography Mission (SRTM) are presented. The SRTM C-band and X-band digital elevation models (DEMs) are evaluated with regard to elevation accuracies over agricultural fields, forest areas and man-made features in Norway. High-resolution digital maps and satellite images are used as background data. In general, many terrain details can be observed in the SRTM elevation datasets. The elevation accuracy (90% confidence level) of the two SRTM systems is estimated to less than 6.5 m for open agricultural fields and less than 11 m considering all land surface covers. This is better than specifications. Analysis of dense Norwegian forest stands shows that the SRTM system will produce elevation data that are as much as 15 m higher than the ground surface. The SRTM DEM products will therefore partly indicate canopy elevations in forested areas. We also show that SRTM data can be used to update older DEMs obtained from other sources, as well as estimating the volume of rock removed from large man-made gravel pits.

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Section: Introductionmentioning

confidence: 99%

SRTM DEM accuracy assessment over vegetated areas in Norway

Weydahl

Sagstuen

Dick

et al. 2007

International Journal of Remote Sensing

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“…For example, Hagberg et al (1995) studied a dense boreal forest with repeat-pass European Remote Sensing satellite (ERS-1) SAR interferometry and found that the interferometric height discontinuity at the forest to non-forest boundary showed good agreement with in situ tree height measurements, suggesting the possibility of estimating bole volume from the interferometric tree height and a bare ground elevation model. Weydahl et al (2003) showed that the Shuttle Radar Topography Mission (SRTM) elevation gave surface cover elevations rather than ground elevations in areas of dense forest and the SRTM elevation offsets compared with the 1:50 000 topographic elevation in forested areas may be used to categorize the forest further, such as for density and tree height. The National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) operates a multi-frequency (P: 0.44 GHz, 68 cm wavelength; L: 1.225 GHz, 24 cm; and C: 5.3 GHz, 5.6 cm) topographical SAR (TopSAR) system on board a DC-8 aircraft, producing simultaneous L-and P-band fully polarimetric plus C-band VV polarization backscatter images in addition to the C-band InSAR Digital Elevation Model (DEM) product , Madsen et al 1993.…”

Section: Introductionmentioning

confidence: 99%

Error analysis and correction for extracting the forest height from airborne C-band interferometric SAR and national elevation datasets

Huang

Crabtree

Swanson

et al. 2010

International Journal of Remote Sensing

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The Interferometric Synthetic Aperture Radar (InSAR) signal is returned from the canopy of the obscuring trees instead of bare ground when land is covered by forests. Therefore, the difference between an InSAR elevation and a bare earth model might contain information on forest height. The objective of this paper was to investigate if the difference between an airborne C-band InSAR from the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) and a bare earth model of 1/3 arcsecond National Elevation Datasets can be used for regional forest height estimation. The error sources of vertical offset, uncompensated roll angle, residual vertical bias, and scattering phase centre height conversion were analysed and corrected in this estimation. The results were validated by the leastsquare linear regression analysis between Light Detection and Ranging (LiDAR) and the estimated height at different forest stand sizes within different slope categories. In areas with slopes less than 5 , the correlation coefficients increased when forest stand sizes increased. In the area of slope ranging from 5 to 10 , a similar trend of increasing correlation coefficients with increasing stand size could also be observed, but with smaller corresponding correlation coefficients than those of slope 0-5 . In areas with slopes larger than 10 , the correlation coefficients were very poor. These results indicate the difference between airborne C-band InSAR and the accurate bare earth model has the potential for regional forest height estimation in flat areas with a minimum unit of 3750-5000 m 2 . However, to accurately estimate forest height in a mountainous terrain a solution must be found to correct the significant amount of noise caused by the terrain in these areas.

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Tree height estimation at plateau mountains, northwestern Sichuan, China using dual Pol-InSAR data

Wang

Luo

2016

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Analysis of X-SAR SRTM elevation data to estimate surface cover heights over land areas

Cited by 4 publications

References 1 publication

SRTM DEM accuracy assessment over vegetated areas in Norway

SRTM DEM accuracy assessment over vegetated areas in Norway

Error analysis and correction for extracting the forest height from airborne C-band interferometric SAR and national elevation datasets

Tree height estimation at plateau mountains, northwestern Sichuan, China using dual Pol-InSAR data

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