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. In 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 (RMSEcor) of 29.4 t ha−1 was calculated. A marginal decrease in RMSEcor 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 RMSEcor of 30.2 t ha−1 was calculated.

Abstract: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. OPEN ACCESSRemote Sens. 2013, 5 5726

Abstract: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.

A body of knowledge (BoK) is an inventory of knowledge or concepts of a domain that serves as a reference vocabulary for various purposes, such as the development of curricula, the preparation of job descriptions, and the description of occupational profiles. To fulfill its purpose, a BoK needs to be up‐to‐date and ideally widely accepted by academia as well as the private and public sectors. This article presents the initiative taken in the Earth observation and geographic information (EO*GI) domain to provide a current, comprehensive education‐ and business‐oriented EO*GI BoK called EO4GEO BoK. In particular, an approach to strengthen the business‐oriented perspective in the EO4GEO BoK is presented. This approach is based on the analysis of professional tasks and the mapping of these tasks to concepts and skills contained in the BoK. A critical reflection of the proposed approach that is based on the experiences gained during a workshop complements this article.

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