Forest biomass estimation using polarimetric SAR interferometry (original) (raw)
Related papers
2011
World forests contain a significant amount of carbon that is, in consequence of natural and human induced deforestation and regrowth processes, affected by rapid changes and therefore difficult to quantify in terms of biomass/carbon storage. This uncertainty remains because of the lack of reliable and frequent information of biomass levels on a global scale. Spaceborne Synthetic Aperture Radar (SAR) could provide the required global and temporal coverage of the world's forested areas. Innovative new inversion approaches allow today accurate meassurement of vegetation structure parameters such as forest height and biomass [1][2] . The coherent combination of polarimetric and interferometric SAR at lower frequencies by means of Pol-InSAR is sensitive to the vertical distribution of scattering processes within a resolution cell and can be used for model-based inversion of forest height and structural parameters.
IEEE Transactions on Geoscience and Remote Sensing, 2012
Biomass estimation performance using polarimetric interferometric synthetic aperture radar (PolInSAR) data is evaluated at Land P-band frequencies over boreal forest. PolInSAR data are decomposed into ground and volume contributions, retrieving vertical forest structure and polarimetric layer characteristics. The sensitivity of biomass to the obtained parameters is analyzed, and a set of these parameters is used for biomass estimation, evaluating one parametric and two non-parametric methodologies: multiple linear regression, support vector machine, and random forest. The methodology is applied to airborne SAR data over the Krycklan Catchment, a boreal forest test site in northern Sweden. The average forest biomass is 94 tons/ha and goes up to 183 tons/ha at forest stand level (317 tons/ha at plot level). The results indicate that the intensity at HH-VV is more sensitive to biomass than any other polarization at L-band. At P-band, polarimetric scattering mechanism type indicators are the most correlated with biomass. The combination of polarimetric indicators and estimated structure information, which consists of forest height and ground-volume ratio, improved the root mean square error (rmse) of biomass estimation by 17%-25% at L-band and 5%-27% at P-band, depending on the used parameter set. Together with additional ground and volume polarimetric characteristics, the rmse was improved up to 27% at L-band and 43% at P-band. The cross-validated biomass rmse was reduced to 20 tons/ha in the best case. Non-parametric estimation methods did not improve the cross-validated rmse of biomass estimation, but could provide a more realistic distribution of biomass values.
Forest biomass change estimated from height change in interferometric SAR height models
Carbon balance and management, 2014
Background: There is a need for new satellite remote sensing methods for monitoring tropical forest carbon stocks. Advanced RADAR instruments on board satellites can contribute with novel methods. RADARs can see through clouds, and furthermore, by applying stereo RADAR imaging we can measure forest height and its changes. Such height changes are related to carbon stock changes in the biomass. We here apply data from the current Tandem-X satellite mission, where two RADAR equipped satellites go in close formation providing stereo imaging. We combine that with similar data acquired with one of the space shuttles in the year 2000, i.e. the socalled SRTM mission. We derive height information from a RADAR image pair using a method called interferometry. Results: We demonstrate an approach for REDD based on interferometry data from a boreal forest in Norway. We fitted a model to the data where above-ground biomass in the forest increases with 15 t/ha for every m increase of the height of the RADAR echo. When the RADAR echo is at the ground the estimated biomass is zero, and when it is 20 m above the ground the estimated above-ground biomass is 300 t/ha. Using this model we obtained fairly accurate estimates of biomass changes from 2000 to 2011. For 200 m 2 plots we obtained an accuracy of 65 t/ha, which corresponds to 50% of the mean above-ground biomass value. We also demonstrate that this method can be applied without having accurate terrain heights and without having former in-situ biomass data, both of which are generally lacking in tropical countries. The gain in accuracy was marginal when we included such data in the estimation. Finally, we demonstrate that logging and other biomass changes can be accurately mapped. A biomass change map based on interferometry corresponded well to a very accurate map derived from repeated scanning with airborne laser. Conclusions: Satellite based, stereo imaging with advanced RADAR instruments appears to be a promising method for REDD. Interferometric processing of the RADAR data provides maps of forest height changes from which we can estimate temporal changes in biomass and carbon.
Estimating Forest Biomass in Temperate Forests Using Airborne Multi-frequency Polarimetric SAR Data
isprs.org
We used multi-sensor, multi-frequency and multi-polarization SAR data for biophysical parameter retrieval in plantation forests of Northern Japan. The statistical relationships with different biophysical parameters are quite robust for certain frequencies-polarization combination. A combination of different frequencies and polarizations facilitate the retrieval of these parameters with R 2 of 0.95 and rms error of 15.19 tons ha -1 . Further, a large sample of 186 stand age from coniferous species showed a robust relationship for all the three polarizations of the L-band data up to 40 years of age. L-band data provided very good retrieval accuracy for the dry biomass with the SEE = 22.52 tons ha -1 .
Estimation and Monitoring of Tropical Forest Biomass Using Polarimetric Interferometric SAR Data
The purpose of the proposed work is to examine the feasibility of using Polarimetric Interferometric SAR (PolInSAR) techniques on ALOS PALSAR data to extract forest canopy heights with the ultimate objective of deriving biomass estimates. Previous work [1] has shown that in homogeneous European forest stands, tree height is a reasonably robust estimator of biomass through a simple allometric relationship. Moreover PolInSAR has proven itself as a valuable technology for tree height estimation at L-Band frequencies. The RVoG model (Random Volume over Ground) proposed in and elaborated in [3], permits a separation of ground and canopy scattering components of the interferometric phase. Through model inversion, the canopy height can be derived . Additionally, the bare earth elevation beneath canopy can also be recovered at least in airborne repeat-pass cases where temporal de-correlation is not prohibitive. While the PolInSAR results to date from airborne repeat-pass L-Band campaigns have been impressive, it is not clear to what extent it will be possible to derive similar results from ALOS data, owing to the much longer temporal baseline between acquisitions.
Forest Biomass Estimation using Multi-Polarization SAR Data Coupled with Optical Data
Current Science, 2020
This study was carried out to estimate biomass extraction from multi-frequency and multi-polarization of Synthetic Aperture Radar (SAR) data coupled with optical data. Further, the estimated biomass was validated with field-observed data. ALOS-2/PALSAR was utilized for retrieval of forest above-ground biomass (AGB) biophysical parameters. Subsequently, Sentinel-2 optical data and 90 m TanDEM were used to identify the bare ground area for calculating pseudo height. Ground-truth data were utilized for estimation and validation of the modelled biomass from radar data. In this study, five allometric models were used. Multivariate regression models were trained using backscatter from the same acquisition (date) on 10 randomly selected samples from 21 field plots. The validation was carried out on the remaining 11 field plots. Co-validation method was used to validate these models. Biomass was estimated from radar data using regression models. Since the objective of the study was to present generalized biomass estimation models using backscatter information and AGB, the AGB value range 100-400 tonne/ha was estimated/mapped. Combined backscatter and height inputs were better than backscatter models. In the estimation of AGB, polarimetric information content and backscatter information played a significant role.
Estimating spruce and pine biomass with interferometric X-band SAR
Remote Sensing of Environment, 2010
The primary aim of this study was to investigate the suitability of interferometric X-band SAR (InSAR) for inventory of boreal forest biomass. We investigated the relationship between SRTM X-band InSAR height and above-ground biomass in a study area in southern Norway. We generated biomass reference data for each SRTM pixel from a field inventory in combination with airborne laser scanning (ALS). One set of forest inventory plots served for calibrating ALS based biomass models, and another set of field plots was used to validate these models. The biomass values obtained in this way ranged up to 250 t/ha at the stand level. The relationship between biomass and InSAR height was linear, no apparent saturation effect was present, and the accuracy was high (RMSE = 19%). The relationship differed between Norway spruce and Scots pine, where an increase in InSAR height of 1 m corresponded to an increase in biomass of 9.9 and 7.0 t/ha, respectively. Using a high-quality terrain model from ALS enabled biomass to be estimated with a higher accuracy as compared to using a terrain model from topographic maps. Interferometric X-band SAR appears to be a promising method for forest biomass monitoring.
Parametric and non-parametric forest biomass estimation from PolInSAR data
2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
Biomass estimation performance from model-based polarimetric SAR interferometry (PolInSAR) using generic parametric and non-parametric regression methods is evaluated at Land P-band frequencies over boreal forest. PolInSAR data is decomposed into ground and volume contributions, estimating vertical forest structure, and using a set of obtained parameters for biomass regression. The considered estimation methods include multiple linear regression, support vector machine and random forest. The biomass estimation performance is evaluated on DLR's airborne SAR data at Land P-bands over Krycklan Catchment, a boreal forest test site in Northern Sweden. The combination of polarimetric indicators and estimated structure information has improved the root mean square error (RMSE) of biomass estimation up to 28% at L-band and up to 46% at P-band. The cross-validated biomass RMSE was reduced to 20 tons/ha.
INDREX-II - Tropical forest height estimation with L and P band polarimetric interferometric SAR
J Texture Stud, 2006
Tropical forests are complex, heterogeneous, dense, remote and changing forest ecosystems. Low frequency synthetic aperture Radar (SAR) techniques allow monitoring and potentially estimation of key forest parameters such as vertical structure (height) and biomass. In the frame of INDREX-II repeat-pass SAR data in quad-pol mode (at L, and P Band) SAR data were acquired and simultaneously ground measurements have been collected. One of the most important -for a wide range of applications -forest parameter is biomass. Biomass appears to be more or less directly related to forest height, which can be estimated from model based inversion of polarimetric and interferometric SAR (Pol-InSAR) data. Indeed, successful height inversion has been demonstrated in several airborne experiments over temperate and boreal forests. In this paper results of model-based L and P Band Pol-InSAR data inversion over tropical forest are shown, including validation against ground measurements.
Comparison of Methods to Derive Forest Height from Polarimetric SAR Interferometry
2008
Six forest tree height inversion methods of polarimetric SAR interferometry were validated using repeat pass E-SAR datasets and the corresponding ground measured forest stand height. We show that SINC, three stage full model and three stage SINC inversions have good performance, and the SINC inversion with the first optimum coherence (OPT1) produces the best result among the eleven coherence types validated. Taking SINC inversion as one example, two inversion correction methods were investigated, RMSE can be reduced below 4.0m after correction. The phase and coherence inversion method achieved the second lowest RMSE among the six inversion methods validated, but the estimated tree height of it has very low correlation with ground truth, and this drawback was found caused by the poor performance of RVoG phase inversion method involved.