Above-ground forest biomass is a significant variable in the terrestrial carbon budget, but is still estimated with relatively large uncertainty. Remote sensing methods can improve the characterization of the spatial distribution and estimation accuracy of biomass; in this respect, it is important to examine the potential offered by new sensors. To assess the contribution of the TanDEM-X mission, eighteen interferometric Synthetic Aperture Radar (SAR) image pairs acquired over the hemi-boreal test site of Remningstorp in Sweden were investigated. Three models were used for interpretation of TanDEM-X signatures and above-ground biomass retrieval: Interferometric Water Cloud Model (IWCM), Random Volume over Ground (RVoG) model, and a simple model based on penetration depth (PD). All use an allometric expression to relate above-ground biomass to forest height measured by TanDEM-X. The retrieval was assessed on 201 forest stands with a minimum size of 1 ha, and ranging from 6 to 267 Mg/ha (mean biomass of 105 Mg/ha) equally divided into a model training dataset and a validation test dataset. Biomass retrieved using the IWCM resulted in a Root Mean Square Error (RMSE) between 17% and 33%, depending on acquisition date and image acquisition geometry (angle of incidence, interferometric baseline, and orbit type). The RMSE in the case of the RVoG and the PD models were slightly higher. A multitemporal estimate of the above-ground biomass using all eighteen acquisitions resulted in an RMSE of 16% with R² = 0.93. These results prove the capability of TanDEM-X interferometric data to estimate forest aboveground biomass in the boreal zone.

The boreal forest is an important global carbon sink, but its response to drought remains uncertain. Here, we compiled biometric- and chamber-based flux data from 50 boreal forest stands to assess the impact of the 2018 European summer drought on net ecosystem production (NEP) across a 68 km² managed landscape in northern Sweden. Our results reveal a non-uniform reduction in NEP (on average by 80 ± 16 g C m⁻² yr⁻¹ or 57 ± 13%) across the landscape, which was greatest in young stands of 20–50 years (95 ± 39 g C m⁻² yr⁻¹), but gradually decreased towards older stands (54 ± 57 g C m⁻² yr⁻¹). This pattern was attributed to the higher sensitivity of forest-floor understorey to drought and its decreasing contribution to production relative to trees during stand development. This suggests that an age-dependent shift in understorey–tree composition with increasing stand age drives the drought response of the boreal forest NEP. Thus, our study advocates the need for partitioning ecosystem responses to improve empirical and modelling assessments of carbon cycle–climate feedbacks in boreal forests. It further implies that the forest age structure may strongly determine the carbon sink response to the projected increase in drought events across the managed boreal landscape.

Boreal forests are important global carbon (C) sinks and, therefore, considered as a key element in climate change mitigation policies. However, their actual C sink strength is uncertain and under debate, particularly for the actively managed forests in the boreal regions of Fennoscandia. In this study, we use an extensive set of biometric‐ and chamber‐based C flux data collected in 50 forest stands (ranging from 5 to 211 years) over 3 years (2016–2018) with the aim to explore the variations of the annual net ecosystem production (NEP; i.e., the ecosystem C balance) across a 68 km² managed boreal forest landscape in northern Sweden. Our results demonstrate that net primary production rather than heterotrophic respiration regulated the spatio‐temporal variations of NEP across the heterogeneous mosaic of the managed boreal forest landscape. We further find divergent successional patterns of NEP in our managed forests relative to naturally regenerating boreal forests, including (i) a fast recovery of the C sink function within the first decade after harvest due to the rapid establishment of a productive understory layer and (ii) a sustained C sink in old stands (131–211 years). We estimate that the rotation period for optimum C sequestration extends to 138 years, which over multiple rotations results in a long‐term C sequestration rate of 86.5 t C ha⁻¹ per rotation. Our study highlights the potential of forest management to maximize C sequestration of boreal forest landscapes and associate climate change mitigation effects by developing strategies that optimize tree biomass production rather than heterotrophic soil C emissions.

Advanced Land Observing Satellite (ALOS) Phased Array L-band type Synthetic Aperture Radar (PALSAR) backscatter images with 50 m pixel size (strip images) at HV-polarization were used to map clear-cuts at a regional and national level in Sweden. For a set of 31 clear-cuts, on average 59.9% of the pixels within each clear-cut were correctly detected. When compared with a one-pixel edge-eroded version of the reference dataset, the accuracy increased to 88.9%. With respect to statistics from the Swedish Forest Agency, county-wise clear-felled areas were underestimated by the ALOS PALSAR dataset (between 25% and 60%) due to the coarse resolution. When compared with statistics from the Swedish National Forest Inventory, the discrepancies were larger, partly due to the estimation errors from the plot-wise forest inventory data. In Sweden, for the time frame of 2008–2010, the total area felled was estimated to be 140,618 ha, 172,532 ha and 194,586 ha using data from ALOS PALSAR, the Swedish Forest Agency and the Swedish National Forest Inventory, respectively. ALOS PALSAR strip images at HV-polarization appear suitable for detection of clear-felled areas at a national level; nonetheless, the pixel size of 50 m is a limiting factor for accurate delineation of clear-felled areas.

A study was undertaken to assess Envisat Advanced Synthetic Aperture Radar (ASAR) ScanSAR data for quantifying forest growing stock volume (GSV) across three boreal regions with varying forest types, composition, and structure (Sweden, Central Siberia, and Québec). Estimates of GSV were obtained using hyper-temporal observations of the radar backscatter acquired by Envisat ASAR with the BIOMASAR algorithm. In total, 5.3×10⁶ km² were mapped with a 0.01° pixel size to obtain estimates representative for the year of 2005. Comparing the SAR-based estimates to spatially explicit datasets of GSV, generated from forest field inventory and/or Earth Observation data, revealed similar spatial distributions of GSV. Nonetheless, the weak sensitivity of C-band backscatter to forest structural parameters introduced significant uncertainty to the estimated GSV at full resolution. Further discrepancies were observed in the case of different scales of the ASAR and the reference GSV and in areas of fragmented landscapes. Aggregation to 0.1° and 0.5° was then undertaken to generate coarse scale estimates of GSV. The agreement between ASAR and the reference GSV datasets improved; the relative difference at 0.5° was consistently within a magnitude of 20–30%. The results indicate an improvement of the characterization of forest GSV in the boreal zone with respect to currently available information.

The last decade has seen launches of radar satellite missions operating in X-band with the sensors acquiring images with spatial resolutions on the order of 1 m. This study uses digital surface models (DSMs) extracted from stereo synthetic aperture radar images and a reference airborne laser scanning digital terrain model to calculate the above-ground biomass and tree height. The resulting values are compared to in situ data. Analyses were undertaken at the Swedish test sites Krycklan (64°N) and Remningstorp (58°N), which have different site conditions. The results showed that, for 459 forest stands in Remningstorp, biomass estimation at the stand level could be performed with 22.9% relative root mean square error, while the height estimation showed 9.4%. Many factors influenced the results and it was found that the topography has a significant effect on the generated DSMs and should therefore be taken into consideration when the stand level mean slope is above four degrees. Different tree species did not have a major effect on the models during leaf-on conditions. Moreover, correct estimation within young forest stands was problematic. The intersection angles resulting in the best results were in the range 8–16°. Based on the results in this study, radargrammetry appears to be a promising potential remote sensing technique for future forest applications.