Multi-Sensor Aboveground Biomass Estimation in the Broadleaved Hyrcanian Forest of Iran

Ghasem Ronoud*, Parviz Fatehi, Ali A. Darvishsefat, Erkki Tomppo, Jaan Praks, Michael E. Schaepman

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand of Fagus Orientalis located in the Hyrcanian forest of Iran was selected as the study area. The performance of a parametric approach, i.e., Multiple Linear Regression (MLR) model and non-parametric approaches, i.e., k-Nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR), were also evaluated for AGB estimations. Our results indicated that among S2 metrics, the FAPAR canopy biophysical index and NDVI index based on the red-edge band (NIR-b8a) have the highest correlation coefficient (r) of 0.420 and 0.417, respectively. The results of AGB estimation showed that a combination of S2 and S1 datasets using the k-NN algorithm had the best accuracy (R 2 of 0.57 and rRMSE of 14.68%). The best rRMSE using L8, S2, and S1 datasets was 18.95, 16.99, and 19.17% using k-NN, k-NN, and MLR algorithms, respectively. The combination of L8 with S1 dataset also improved the rRMSE relative to L8 and S1 separately by 0.96 and 1.18%, respectively. We concluded that the combination of optical data (L8 or S2) with SAR data (S1) improves the broadleaved Hyrcanian AGB estimation.

Original languageEnglish
Number of pages17
JournalCanadian Journal of Remote Sensing
Early online date6 Sep 2021
Publication statusE-pub ahead of print - 6 Sep 2021
MoE publication typeA1 Journal article-refereed


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