Enhancing Hyrcanian Forest Height and Aboveground Biomass Predictions: A Synergistic Use of TanDEM-X InSAR Coherence, Sentinel-1, and Sentinel-2 Data

Abstract

Forest height (FH) is an important driver for aboveground biomass (AGB) that can be obtained using interferometric synthetic aperture radar (InSAR). However, the limited access to the quad-polarimetric data or high-accuracy terrain model makes FH retrieval a challenging task. This study aimed to retrieve FH and further predict AGB by combining TanDEM-X InSAR coherence, Sentinel-1 (S-1), and Sentinel-2 (S-2) data. A total of 125 sample plots with a size of 900 m2 were established in a broadleaved forest of Kheyroud, Iran. The linear and sinc models obtained by simplification of the random volume over ground model were used for deriving FHLin and FHSinc. Further investigation was conducted when S-1 and S-2 features including backscatters and multispectral information were added to FH predictions. Using the above-mentioned datasets and FH as an additional predictor, AGB was also predicted. K-nearest neighbor (k-NN), random forest (RF), and support vector regression (SVR) were employed for prediction. Lorey's mean height and AGB at sample plots were used in the accuracy assessment. Using the SVR method and synergy of FHSinc, S-1, and S-2 features, the FH prediction was improved (FHimp) with RMSE of 3.18 m and R2 = 0.59. The AGB prediction with RF and the combination of S-1 and S-2 features resulted in RMSE = 62.88 Mg·ha-1 (19.77%) that was improved to RMSE = 51.27 Mg·ha-1 (16.12%) when FHimp included. This study highlighted the capability of TanDEM-X InSAR coherence with certain geometry for FH prediction. Also, the importance of FH in AGB predictions can stimulate further attempts aiming at higher spatiotemporal accuracies.

Keywords

• Machine learning
• multispectral
• random volume over ground (RVoG)
• sinc model
• single-pass