This paper presents and validates a remote sensing methodology for afforestation and reforestation monitoring. This methodology is based on an integrated analysis of modern radar datasets, including interferometric data from the Alaska SAR Facility (ASF) and others. The study area is located in Eastern Siberia, where young trees are surrounded by mature pine stands. The proposed approach combines two key components: 1) Biomass dynamics assessment using L-band SAR data (co- and cross-polarization); 2) Canopy height monitoring via two interferometric techniques. Comparative analysis of biomass dynamics revealed that temporal analysis of backscatter (σ°) provides more accurate biomass estimates than dual-polarization H-Alpha decomposition. Furthermore, backscatter time-series processing can be automated using the Google Earth Engine (GEE) platform. For canopy height, both interferometric methods demonstrated their efficacy. Particularly, L-band InSAR (co-polarized HH) with an extremely long 2114-d baseline detected a 2–3 cm increase in scattering phase center height for young trees. This demonstrates the fundamental possibility of using L-band interferometric pairs with very long time baselines to monitor the radial growth of boreal forest main branches. C-band Stacking-InSAR was applied for the first time to estimate vertical growth rates in young coniferous stands, revealing a canopy height increase of up to 3.5 cm/yr during periods of 54% higher precipitation. The proposed framework, leveraging multi-frequency SAR datasets, enables comprehensive and near-real-time monitoring of reforestation processes. Results on biomass and height dynamics refine carbon sequestration estimates, supporting climate modeling and sustainable forest management.
Бондур et al. (Wed,) studied this question.