The estimation of above-ground biomass (AGB) in boreal forests is of special concern as it constitutes the highest carbon pool in the northern hemisphere. Particularly, monitoring of the forests in the Russian Federation is important as some regions have not been inventoried for many years. This study explores the combination of multi-frequency, multi-polarization, and multi-temporal radar data as one key approach to provide an accurate estimate of forest biomass. The data from L-band Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-Band Synthetic Aperture Radar 2 (PALSAR-2), together with C-band RADARSAT-2 data, were applied for AGB estimation. Backscatter coefficients from L- and C-band radar were used independently and in combination with a non-parametric model to retrieve AGB data for a boreal forest in Siberia (Krasnoyarskiy Kray). AGB estimation was performed using the random forests machine learning algorithm. The results demonstrated that high estimation accuracies can be achieved at a spatial resolution of 0.25 ha. When the L-band data alone were used for the retrieval, a corrected root-mean-square error (RMSE_cor) of 29.4 t ha⁻¹ was calculated. A marginal decrease in RMSE_cor was observed when only the filtered L-band backscatter data, without ratio and texture, were used (29.1 t ha⁻¹). The inclusion of the C-band data reduced the over and underestimation; the bias was reduced from 5.5 t ha⁻¹ to 4.7 t ha⁻¹; and a RMSE_cor of 30.2 t ha⁻¹ was calculated.

In order to assess the potentiality of ALOS L-band fully polarimetric radar data for forestry applications, we investigated a four-component decomposition method to characterize the polarization response of Siberian forest. The decomposition powers of surface scattering, double-bounce and volume scattering, derived with and without rotation of coherency matrix, were compared with Growing Stock Volume (GSV). To compensate for topographic effects an adaptive rotation of the coherency matrix was accomplished. After the rotation, the correlation between GSV and double-bounce increased significantly. Volume scattering remained same and the surface scattering power decreased slightly. The volume scattering power and double-bounce power increased as the GSV increased, whereas the surface scattering power decreased. In sparse forest, at unfrozen conditions the surface scattering was higher than volume scattering, while volume scattering was dominant in dense forest. The scenario was different at frozen conditions for dense forest where the surface scattering was higher than volume scattering. Moreover, a slight impact of tree species on polarimetric decomposition powers has been observed. Larch was differed from aspen, birch and pine by +2 dB surface scattering power and also by −1.5 dB and −1.2 dB volume scattering power and double-bounce scattering power respectively at unfrozen conditions.

The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks.

The main objective of this paper is to investigate the effectiveness of two recently popular non-parametric models for aboveground biomass (AGB) retrieval from Synthetic Aperture Radar (SAR) L-band backscatter intensity and coherence images. An area in Siberian boreal forests was selected for this study. The results demonstrated that relatively high estimation accuracy can be obtained at a spatial resolution of 50 m using the MaxEnt and the Random Forests machine learning algorithms. Overall, the AGB estimation errors were similar for both tested models (approximately 35 t∙ha⁻¹). The retrieval accuracy slightly increased, by approximately 1%, when the filtered backscatter intensity was used. Random Forests underestimated the AGB values, whereas MaxEnt overestimated the AGB values.

The field of Earth Observation (EO) and Geoinformation (GI) is gaining more and more importance due to the increasing number of data and data processing algorithms to respond even more accurately to a variety of challenges in many application areas. In order to follow recent activities and align the exponential evolution of datasets and recent processing trends with market and academic training requirements, the EO*GI sector needs an updated and new definition of knowledge and skills. To this goal, a specific body of knowledge for Earth Observation and Geoinformation (EO4GEO BoK) is currently being implemented with the aim of providing interconnected concepts and job-oriented skills. One novelty of the BoK is to include topics related to Earth Observation, in particular to introduce the different sensors used in this field. Active optical sensors are at the crossroad between Earth Observation and close-range photogrammetry and have not been described in any other existing BoK. This paper introduces therefore the part of the EO4GEO BoK that is dedicated to active optical sensors. Such systems are used in various job-oriented applications such as archeology, mobile mapping, indoor or outdoor, or for surveying purposes, just to name a few. The structure of this part of the BoK is explained and specific descriptions and relationships between the identified concepts are given. Finally, the skills acquired by completing these BoK concepts are presented and discussed in terms of how they can be used in a professional context or in the definition of job-oriented learning paths.