Analysis of seasonal effects on forest parameter estimation of Indian deciduous forest using TerraSAR-X PolInSAR acquisitions

Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR Data

“…Especially the motion of scatterers, like wind motion of trees, results in lower coherences and inaccuracies in the canopy height estimation [52]. In addition, during this time period results of the inversion approach are not only affected by the motion of canopies but also by changes of dielectric properties [53]. Since all datasets of this study are multi-pass interferometric, temporal decorrelation is always present.…”

“…The R 2 lied between 0.08-0.64 and the RMSE varied from 4.8 to 13.5 m [55]. Khati et al [53] analyzed Indian tropical canopy heights based on TS-X/TanDEM-X data and attained RMSEs from 2.71-3.17 m for different seasons using the RVoG inversion approach. Other studies in tropical forests showed an average error between 3-5 m in estimating tree heights using Pol-InSAR likewise [56].…”

“…Apart from the temporal baseline, the critical perpendicular baseline can cause decorrelation within an interferogram. The critical baseline is defined as the perpendicular baseline at which the interferometric correlation becomes zero and thus is completely decorrelated [53]. Longer wavelengths are less sensitive to decorrelation due to the perpendicular baseline as found in the comparison between X-and C-band.…”

“…Nevertheless, for two TS-X datasets, the canopy height could not be estimated since the perpendicular baseline was too long and thus the HoA to low. The inversion process requires a HoA not smaller than the highest measured plot canopy height [53]. Since the HoA of two datasets was approximately 30 m and the tallest trees in the LiDAR reference data are about 35 m, an adequate inversion model performance was not possible.…”

Detection of Forest Disturbance Using Tandem-X Pol-Insar Coherence

“…Inputs on accurate estimate on forest disturbance are emphasized in strategizing climate change mitigation measures, since forests plays key role in regulation mechanism on terrestrial ecosystem. With varying penetration capability depending on the wavelength of the SAR sensors, along with the advancement in techniques such as PolSAR and PolInSAR, SAR remote sensing has emerged as a suitable alternative to study forest structural changes such as phenology (Khati et al, 2017), clear cuts (Olesk et al, 2015) and growth dynamics (Kobayashi et al, 2015).…”