Search

Abstract The RMoG (Random-Motion-over-Ground) model is commonly used to obtain tree height values from PolInSAR images. The RMoG model borrows its structure function from conventional RVoG (Random-Volume-over-Ground) model which is limited for modelling structural variety in canopy layer. This paper extends the RMoG model to improve tree height estimation accuracy by using a Fourier-Legendre polynomial as the structure function. The new model is denoted by the RMoG L model. The proposed modification makes height estimation less prone to errors by enabling more flexibility in representing the vertical structure of the vegetation layer. We applied the RMoG L model on airborne P- and L-band PolInSAR images from the Remingstorp test site in southern Sweden. We compared it with the RMoG and the conventional RVoG models using Lidar height map and field data for validation. For P-band, the relative error was equal to 37.5% for the RVoG model, to 23.7% for the RMoG model, and to 18.5% for the RMoG L model. For L-band it was equal to 30.54% for the RVoG model, to 20.02% for the RMoG model, and to 21.63% for the RMoG L. We concluded that the RMoG L model estimates tree height more accurately in P-band, while in L-band the RMoG model was equally good. The RMoG L model is of a great value for future SAR sensors that are more focused than before on tree height and biomass estimation. [ABSTRACT FROM AUTHOR]

Longer wavelength synthetic aperture radars (SARs) are precious in the remote sensing of forested areas, being sensitive to contributions from the whole vegetation layer and from the ground below. The electromagnetic properties of such contributions are retrieved from multipolarimetric acquisitions, whereas their vertical structure is retrieved from multibaseline acquisitions through tomographic imaging. Combining baseline and polarization diversity provides most information, allowing the decomposition of the SAR signal into ground- and volume-only contributions. A formal treatment of this problem is provided with the algebraic synthesis technique, which extends the concepts of PolInSAR. The decomposition, however, is shown to be ambiguous in that different solutions are equally consistent with the data. The main goal of this paper is to discuss this topic in light of the experimental results from a tomographic and polarimetric analysis of the boreal forest within the Krycklan River catchment, Northern Sweden, investigated at P- and L-bands during the ESA campaign BioSAR 2008. Different solutions to the decomposition problem will be discussed by examining the corresponding vertical structures accessible through tomographic techniques. Elements are shown supporting the idea that ground-volume interactions play a nonnegligible role at P-band, and a solution is proposed to isolate contributions from direct volume backscattering. The retrieval of forest top height is discussed as well, leading to the conclusion that such parameter is robust against erroneous choices in the identification of volume-only contributions, thus corroborating the PolInSAR approach for the analysis of single-baseline data. [ABSTRACT FROM AUTHOR]

Estimation of carbon stocks in forests and tracking their dynamics over time requires repeated and spatially dense observations. Sweden is one of the major forested countries on Earth with a carbon pool of approximately 1 PgC. Statistics on forest resources are regularly quantified by data collected at field inventory plots by the Swedish National Forest Inventory (NFI). Nonetheless, the sampling by the inventory does not provide detailed spatial knowledge of carbon stored in forests. Thanks to one of the worldwide densest datasets of L-band satellite synthetic aperture radar (SAR) images collected over the years, we assessed the estimation of carbon density on two years, i.e., 2010 and 2015, at pixel (25 m), landscape, county and national level, and investigated the reliability of changes over five years. While the maps of carbon density were obtained with a well-established approach, the uncertainty of the estimates were obtained with a novel procedure that accounted for observational variances and co-variances. The high spatial resolution of the maps revealed the small-scale patterns of carbon density in Swedish forests. However, the substantial uncertainties at the pixel level (25%–62% of the estimated value) prevented from a reliable estimate of carbon density difference at the pixel level. Average values at landscape and county scale were instead unbiased and in strong agreement with corresponding numbers from the Swedish NFI. The uncertainty at county level was between 3% and 12% of the reference mean value. Overall, the net carbon balance for Sweden was positive, with a magnitude comparable to estimates derived from data collected by the NFI. Between 2010 and 2015, Swedish forests acted as a carbon sink, with a net increase of carbon stocks by 2.5%. The high level of confidence in our results indicate that satellite L-band observations are potentially suitable for tracking large-scale forest carbon dynamics in boreal forests provided that multiple observations are available per year and that they are acquired with the same viewing geometry. • First assessment of forest carbon dynamics with L-band SAR data in Sweden. • Full characterization of per-pixel carbon density estimates and uncertainties. • Poor reliability of estimates at pixel level (25%–62% uncertainty). • Unbiased estimates and reduced uncertainty (3% - 12%) for averages at county level. • Carbon dynamics in Sweden from remote sensing agree with inventory-based statistics. [ABSTRACT FROM AUTHOR]

Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m. [ABSTRACT FROM AUTHOR]