Mapping boreal forest biomass with imagery from polarimetric and semi-polarimetric SAR sensors Mapeamento da biomassa fl orestal boreal com imagens dos sensores SAR polarimétricos e semi-polarimétricos (original) (raw)
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International Journal of Remote Sensing, 2002
Airborne polarimetric Synthetic Aperture Radar (SAR) is used for estimating stem volume of a Finnish boreal forest by comparing different empirical models. Its capability for retrieval of snow water equivalent is then explored. Fully polarimetric Land C-band data were acquired over a Finnish test site in March and May 1995. The information content was explored qualitatively by inspecting polarimetric colour composites, and by applying decomposition algorithms to the polarimetric covariance matrices at individual frequencies. Three families of quantitative models were fitted to estimate stem volume: 1) F1P1 models, using a single frequency and a single polarisation; 2) F2P1 models, using the difference between HV polarisation at C-and L-band related to stem volume; 3) F1P4 models, based on a single frequency and the full polarimetric information, selected by stepwise multiple regression with stem volume; Stem volume estimates from SAR are compared with digital stem volume data by the Finnish Forest Research Institute. Prior information about the stem volume distribution addresses the saturation problem of the microwave response. The L-band F1P4 models in March and May 1995 have the smallest root mean square (rms) errors, around 22 m 3 /ha. Three multiple regression models to retrieve snow water equivalent from backscatter are presented: 1) EU model, an explorative, uncorrected multiple regression model; 2) EC model, an explorative, stem volume corrected multiple regression model; 3) CC model, a statistically conservative, stem volume corrected multiple regression model. The accuracy of snow water equivalent estimates was improved significantly by a simple linear correction for stem volume. The statistically conservative CC model showed that only L-band in HH polarisation explained a significant (P<0.05) proportion of snow water equivalent (r 2 =0.51). The explorative EC model resulted in r 2 =0.68 (P>0.05). Conclusions are: 1) Decomposition algorithms of the polarimetric covariance matrix result in information on scattering mechanisms in the vegetation canopy and on the ground, so being potentially of great value for land cover mapping; 2) Satellite polarimetric SARs, for example those to be flown on Envisat and ALOS, will be able to estimate stem volume on continental and global scales; 3) L-band SAR has a potential for snow cover mapping and runoff prediction.
Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests
Remote Sensing, 2017
Here, we examined multitemporal behavior of fully polarimetric SAR (PolSAR) parameters at L-band in relation to the stem volume of boreal forests. The PolSAR parameters were evaluated in terms of their temporal consistency, interdependence and suitability for forest stem volume estimation across several seasonal conditions (frozen, thaw and unfrozen). The satellite SAR data were represented by a time series of PolSAR images acquired during several seasons in the years 2006 to 2009 by the ALOS PALSAR sensor. The study area was in central Finland, and represented a managed area in typical boreal mixed forest land. Utility of different PolSAR parameters, their temporal stability and cross-correlations were studied along with reference stand-level stem volume data from forest inventory. Further, two polarimetric parameters, cross-polarization backscatter and co-polarization coherence, were chosen for further investigation and stem volume retrieval. A relationship between forest stem volume and PolSAR parameters was established using the kNN regression approach. Ways of optimally combining PolSAR images were evaluated as well. For a single scene, best results were observed with polarimetric coherence (RMSE ≈ 38.8 m 3 /ha) for scene acquired in frozen conditions. An RMSE of 40.8 m 3 /ha (42.9%, R 2 = 0.66) was achieved for cross-polarization backscatter in the best case. Cross-polarization backscatter was a better predictor than polarimetric coherence for few summer scenes. Multitemporal aggregation of selected PolSAR scenes improved estimates for both studied PolSAR parameters. Stronger improvement was observed for coherence with RMSE down to 34 m 3 /ha (35.8%, R 2 = 0.77) compared to 38.8-51.6 m 3 /ha (40.8-54.3%) from separate scenes. Finally, the accuracy statistics reached RMSE of 32.2 m 3 /ha (34%, R 2 = 0.79) when multitemporal HHVV coherence was combined with multitemporal HV-backscatter.
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.
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.
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 .
ALOS PALSAR data in boreal forest monitoring and biomass mapping
Polarimetric Palsar data were ortho-rectified for analysis of polarimetric signatures of forested stands. A strong positive correlation of 0.93 was found between Palsar HV amplitude and forest stem volume in stands with a stem volume of 150 m 3 /ha or below. For HH data, the correlation coefficient was 0.82.
Forest biomass estimation using polarimetric SAR interferometry
IEEE International Geoscience and Remote Sensing Symposium, 2002
Forest biomass is one of the most important parameters for global carbon stock modelling yet can only be estimated with great uncertainties. Unfortunately, conventional remote sensing techniques for the estimation of forest biomass are not able to provide estimates on a global scale. An alternative approach is based on forest height estimates from single frequency polarimetric-interferometric SAR data. Here, forest biomass must be converted from forest height through allometric height-biomass relations. Based on the achieved forest height accuracy, this paper shall critically discuss the accuracy of the forest height-biomass relations as derived from standard forestry tables for temperate European forests. The potential of this approach shall be demonstrated by applying the forest height-biomass allometry to convert a forest height-map -acquired from experimental airborne SAR data over the Fichtelgebirge test site (Germany) -into a forest biomass-map.
Multi-temporal JERS SAR data in boreal forest biomass mapping
Remote Sensing of Environment, 2005
Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
The capability of L-band radar backscatter to penetrate through the forest canopy is useful for mapping the forest structure, including above ground biomass (AGB) estimation. Recent studies confirmed that the empirical AGB models generated from the L-band radar backscatter can provide favourable estimation results, especially if the data has dual-polarization configuration. Using dual polarimetry SAR data the backscatter signal is more sensitive to forest biomass and forest structure because of tree trunk scattering, thus showing better discriminations of different forest successional stages. These SAR approaches, however, need to be further studied for the application in tropical peatlands ecosystem We aims at estimating forest carbon stocks and stand biophysical properties using combination of multi-temporal and multi-polarizations (quad-polarimetric) L-band SAR data and focuses on tropical peat swamp forest over Kampar Peninsula at Riau Province, Sumatra, Indonesia which is one of the most peat abundant region in the country. Applying radar backscattering (Sigma nought) to model the biomass we found that co-polarizations (HH and VV) band are more sensitive than cross-polarization channels (HV and VH). Individual HH polarization channel from April 2010 explained > 86% of AGB. Whereas VV polarization showed strong correlation coefficients with LAI, tree height, tree diameter and basal area. Surprisingly, polarimetric anisotropy feature from April 2007 SAR data show relatively high correlations with almost all forest biophysical parameters. Polarimetric anisotropy, which explains the ratio between the second and the first dominant scattering mechanism from a target has reduced at some extent the randomness of scattering mechanism, thus improve the predictability of this particular feature in estimating the forest properties. These results may be influenced by local seasonal variations of the forest as well as moisture, but available quad-pol SAR data were unable to show these patterns, since all the SAR data were acquired during the rainy season. The results of multi-regression analysis in predicting above ground biomass shows that ALOS PALSAR data acquired in 2010 has outperformed other time series data. This is probably due to the fact that land cover change in the area from 2007 -2009 was highly dynamic, converting natural forests into rubber and Acacia plantations, thus SAR data of 2010 which was acquired in between of two field campaigns has provided significant results (F = 40.7, P < 0.005). In general, we found that polarimetric features have improved the models performance in estimating AGB. Surprising results come from single HH polarization band from April 2010 that has a strong correlation with AGB (r = 0.863). Also, HH polarization band of 2009 SAR image resulted in a moderate correlation with AGB (r = 0.440).
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.