Name
Abstract | ||
|---|---|---|
This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method's idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest ground-to-volume amplitude ratio (GVR) by employing an additional baseline to constrain the inversion procedure. In this paper, we present for the first time an assessment of such a method on real PolInSAR data over boreal forest. Additionally, we propose an improvement on the original DBPI method by incorporating the sloped random volume over ground (S-RVoG) model in order to reduce the range terrain slope effect. Therefore, a digital elevation model (DEM) is needed to provide the slope information in the proposed method. Three scenes of P-band airborne PolInSAR data acquired by E-SAR and light detection and ranging (LIDAR) data available in the BioSAR2008 campaign are employed for testing purposes. The performance of the SBPI, DBPI, and modified DBPI methods is compared. The results show that the DBPI method extracts forest heights with an average root mean square error (RMSE) of 4.72 m against LIDAR heights for trees of 18 m height on average. It presents a significant improvement of forest height accuracy over the SBPI method (with a stand-level mean improvement of 42.86%). Concerning the modified DBPI method, it consistently improves the accuracy of forest height inversion over sloped areas. This improvement reaches a stand-level mean of 21.72% improvement (with a mean RMSE of 4.63 m) for slopes greater than 10 degrees. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/rs9080819 | REMOTE SENSING |
Keywords | Field | DocType |
forest height,polarimetric SAR interferometry (PolInSAR),dual-baseline,synthetic aperture radar (SAR),sloped random volume over ground (S-RVoG) model,P-band | Inversion (meteorology),Terrain,Remote sensing,Mean squared error,Amplitude ratio,Digital elevation model,Ranging,Lidar,Geology,Inverse transform sampling | Journal |
Volume | Issue | ISSN |
9 | 8 | 2072-4292 |
Citations | PageRank | References |
2 | 0.40 | 18 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinghua Xie | 1 | 9 | 3.55 |
| ZHU Jian-jun | 2 | 78 | 29.76 |
| Changcheng Wang | 3 | 19 | 12.94 |
| Haiqiang Fu | 4 | 7 | 14.71 |
| Juan Manuel Lopez-Sanchez | 5 | 13 | 3.67 |
| J. David Ballester-Berman | 6 | 180 | 19.10 |