Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests

Abstract

Abstract: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.

Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

“…In Vastaranta et al [19], height, diameter, basal area, stem volume, and biomass were predicted with RMSE of 11.2%, 21.7%, 23.6%, 24.5%, and 23.7%, respectively, from AI data, whereas respective accuracies for the ALS-based predictions were 7.8%, 19.1%, 17.8%, 17.9%, and 17.5% using datasets similar to those used in Järnstedt et al [18]. In Persson and Fransson [24], biomass at the stand level were estimated with 22.9% relative RMSE, while the height estimation showed 9.4% using TSX radargrammetry in a boreal forest. The better accuracy in their study is contributed to larger size, i.e., stand level estimates compared to plot size in this study.…”

“…For example, Karjalainen et al [23] used TerraSAR-X radargrammetry to derive estimates for the mean height and stem volume (the relative RMSE of 34% for stem volume) at the plot level in a boreal forest zone. Persson and Fransson [24] obtained RMSE of 22.9% for biomass and 9.4% for height at the stand level using TerraSAR-X radargrammetry data. Solberg et al [26] and Karila et al [27] have also demonstrated the successful use of InSAR in estimation of forest attributes in the Boreal forest zone.…”

Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations

“…The radargrammetric observable in Table 3 refers to the elevation estimated from the parallaxes between two images that were acquired at different look angles [12]. The usefulness of radargrammetric height in the context of forest biomass estimation could be assessed so far only thanks to TerraSAR-X data acquired with intersection angles of 8 • or more [13]. Retrieval of biomass was undertaken in 144 studies; in 42 research papers, instead, the focus was on signature analysis or modeling.…”

Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data

“…The second widely-adopted family of approaches uses interferometric SAR (InSAR) images to estimate forest height, which is an important forest variable. Furthermore, radargrammetry, a technique that relies on a reference ALS-based DTM, is giving promising results for potential applications in large-area boreal forest AGB mapping [12][13][14]. The radar estimated forest height can be applied for deriving aboveground forest biomass [15][16][17][18][19], canopy density [20], estimating carbon flux [21], detecting changes in forests [22] or improving the inventory data for remote areas [23].…”