Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2277-2281, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2277-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2277-2281, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2277-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

EVALUATION OF DATA APPLICABILITY FOR D-INSAR IN AREAS COVERED BY ABUNDANT VEGETATION

P. Zhang1,2 and Z. Zhao1,2 P. Zhang and Z. Zhao
  • 1Shandong University of Science and Technology, Qingdao, China
  • 2Key Laboratory of Earth Observation and Geospatial Information Science of NASG, Chinese Academy of Surveying and Mapping, Beijing, China

Keywords: Evaluation of Data Applicability, D-InSAR, Geologic Hazard, Areas Covered by Abundant Vegetation, Spatial Decorrelation, Interferometric Constraints

Abstract. In the past few years, the frequent geological disasters have caused enormous casualties and economic losses. Therefore, D-InSAR (differential interferometry synthetic aperture radar) has been widely used in early-warning and post disaster assessment. However, large area of decorrelation often occurs in the areas covered with abundant vegetation, which seriously affects the accuracy of surface deformation monitoring. In this paper, we analysed the effect of sensor parameters and external environment parameters on special decorrelation. Then Synthetic Aperture Radar (SAR) datasets acquired by X-band TerraSAR-X, Phased Array type L-band Synthetic Aperture Satellite-2 (ALOS-2), and C-band Sentinel-1 in Guizhou province were collected and analysed to generate the maps of coherence, which were used to evaluating the applicability of datasets of different wavelengths for D-InSAR in forest area. Finally, we found that datasets acquired by ALOS-2 had the best monitoring effect.