Modeling Interferometric SAR Features of Forest Canopies Over Mountainous Area at Landscape Scales

作者:Ni, WJ (Ni, Wenjian)[ 1 ] ; Zhang, ZY (Zhang, Zhiyu)[ 1 ] ; Sun, GQ (Sun, Guoqing)[ 2 ] ; Liu, QH (Liu, Qinhuo)[ 1 ]

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

卷: 56  期: 5  页: 2958-2967

DOI: 10.1109/TGRS.2017.2787704

出版年: MAY 2018

文献类型:Article

摘要

High-quality interferometric synthetic aperture radar (InSAR) data have become more available in recent years with InSAR missions such as the TanDEM-X. Theoretical coherent backscattering models of forest canopies at landscape scales are necessary for understanding the relationship between positions of scattering phase center extracted from InSAR data and spatial structures of forest stands growing on complex mountainous terrain. Unlike mast existing scattering models of forest canopies that focus on the prediction of the scattering behavior within a pixel, a new model, referred to as LandSAR, was developed to fully account for compound effects of forest spatial structure, terrain, and geometrical distortion caused by slant range imaging on the scattering phase center. The LandSAR model was validated over a mountainous forest scene (about 8.8 km by 9.5 km) imaged by an airborne laser scanner. The interferogram, flattened interferogram, coherence, unwrapped phase, and digital surface model were successfully extracted from simulated InSAR data that have been processed as real InSAR data. The effects of wavelength, baseline length, land cover, and terrain features on decorrelation of simulated InSAR data were consistent with theoretical expectations. Roth the height of the scattering phase center and the penetration depth were strongly correlated with forest heights. These results demonstrated that the LandSAR successfully modeled the InSAR features of forest canopies over a mountainous area at landscape scales.

通讯作者地址: Sun, GQ (通讯作者)

Univ Maryland, Dept Geog Sci, College Pk, MD 20740 USA.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20740 USA