Retrieval of timber volume and snow water equivalent over a Finnish boreal forest from airborne polarimetric Synthetic Aperture Radar (original) (raw)
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Polarimetric RADARSAT-2 imagery for soil moisture retrieval in alpine areas
Canadian Journal of Remote Sensing, 2011
In this work, the polarimetric capability of RADARSAT-2 images is exploited in the aim of soil moisture content retrieval in Alpine meadows and pastures. Three feature extraction methods are investigated: the simple polarimetric intensity and phase processing, the H/A/a polarimetric decomposition, and the Independent Component Analysis (ICA). The features extracted according to these strategies were assessed for their capability to improve the soil moisture estimation by considering both quantitative performance on a set of reference samples and qualitative analysis of the corresponding output soil moisture content maps. The method proposed for the soil moisture estimation was based on the Support Vector Regression technique combined with an innovative multi-objective model selection strategy. The results indicated that the use of polarimetric features such as HH and HV channels improved the estimation of soil moisture content in the investigated mountain area, especially because the HV channel was able to disentangle the vegetation effect on the radar signal. From the preliminary results presented in this paper, the use of the H/A/a polarimetric decomposition and the ICA technique seem to not determine a significant improvement in the soil moisture estimation.
Investigation of snow and forest properties by using airborne SAR data
1998
The objective of our project is to investigate the relationship between the backscattering signal and the forest properties in various snow situations for varying incidence angle. The airborne SAR data collected from Northern Finland during the EMAC'95 campaign was analyzed on agricultural and forested areas.
Retrieving snow water equivalence on C- and L-band SAR data for dry snow
IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174), 1998
The airborne synthetic aperture radar (SAR) data were acquired by EMISAR (C-and L-band) of Technical University of Denmark in Northern Finland during EMAC'95 (European Multisensor Airborne Campaign-95). The multifrequency polarimetric S A R data have been analyzed on retrieving snow water equivalence. We have modeled the response of the backscattering coefficient to snow water equivalence. Correlation between the measured data and model are computed. The results indicate that at C-band (1) dry snow cover can be discriminated from a bare surface and short vegetation; (2) snow water equivalence may be estimated by using semi-empirical models when snow is dry.
Monitoring snow parameters in boreal forest using multi-frequency SAR data
The northern hemisphere is characterized by the presence of boreal forest, a nearly continuous belt of coniferous trees across North America and Eurasia. This region is characterized by a subarctic and cold continental climate, showing severe winters and short summers. Precipitation varies, from about 20 cm of precipitation per year to over 200 cm and for the higher percentage is in the form of snow. Recent studies, which were carried out within the framework of ESA's CoReH2O Phase-A mission, demonstrate that multi-frequency SAR data are able to quantify the amount of snow mass (SWE) on land or glaciers. On the other-hand the presence of forest has a significant impact on the propagation of the radar signal, depending on its structure, biomass, water content and cover fraction. In particular for dense forest scattering of vegetation strongly hides the signal from snow and, consequently, compromises the sensitivity to snow parameters. A method to compensate the vegetation effect and then to retrieve snow in forested areas is presented here. The method is based on the development of an e.m. model for a snow-covered vegetated terrain and the availability of some ancillary data about forest characteristics. An example of the SWE retrieval is provided using SAR airborne data collected over a boreal test site in Finland.
Snow extent mapping in alpine areas using polarimetric SAR data
EARSeL …, 2006
This paper proposes an original technique to map the snow extent in the French Alps from SAR measurements using SIR-C fully polarimetric winter/summer data sets at L and C-bands. The method relies on a multi-frequency two-step approach. Basic natural media types, like surface and forest, are first discriminated over the scene under observation from a polarimetric analysis of summer measurements at L-band. Secondly, the presence of snow is then detected separately over each media type using the H-A-α polarimetric decomposition theorem. An original PCVE (polarimetric contrast variation enhancement) procedure provides optimal emission/reception polarisation states that maximise the C-band winter to summer response of snow-covered areas. The results are compared to Landsat optical images acquired simultaneously.
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.
The Use of Polarimetric SAR Data for Forest Disturbance Monitoring
Sensing and Imaging: An International Journal, 2010
7 Abstract Tropical forest degradation has been a major area of interest for the 8 remote sensing community. Various sensors have been dedicated to monitor its 9 changes; however, due to widespread cloud cover, limited information could be 10 retrieved through optical datasets. Synthetic Aperture Radar (SAR) sensors provide 11 an alternative for such purpose. This paper discusses an application of SAR 12 polarimetry data coupled with the Cloude-Pottier decomposition theorem as a non-13 invasive method for the assessment of degraded forests in Indonesia. It was found 14 that Cloude-Pottier feature space provides a convenient way to describe degradation 15 levels, especially using P-band datasets. Both L-and P-band data provided appre-16 ciable classification accuracy through Support Vector Machine methods. Results 17 suggest that fully polarimetric SAR data combined with polarimetric parameters can 18 be useful for operational monitoring. 19 20 Keywords SAR polarimetry Á Cloude-pottier decomposition Á 21 Support vector machine Á Forest Á Mining 22 23 1 Introduction 24 In addition to agriculture, remote sensing has provided invaluable information to 25 forestry. At first, remote sensing images were used to the map spatial extent of forest 26 resources, generally within a context of land use change . With the rising importance of global climate studies, the dynamics of forested sites have been 28 monitored through multi-temporal data sets. In this case, particular attention has 29 been focused on detecting forest degradation, which is strongly related to the carbon 30 pool in the atmosphere [2]. 31
Comparison of SAR-Based Snow-Covered Area Estimation Methods for the Boreal Forest Zone
IEEE Geoscience and Remote Sensing Letters, 2000
Spaceborne synthetic aperture radar data have been utilized for regional-scale snow-covered area (SCA) monitoring for several years. Different methods have been developed and demonstrated for different geographical regions. A method utilizing a single reference image for SCA estimation has been shown to function well on mountainous and nonforested regions. For the boreal forest zone, a method using two reference images and a forest compensation procedure has been previously utilized. The single-reference-image method is evaluated here for the boreal forest zone, and its performance is compared with the Helsinki University of Technology (TKK) SCA method that is specifically developed for boreal forest regions. The SCA evaluations are carried out using Radarsat-1 data for the snow-melt seasons of 2004-2007. The SCA estimation accuracies for the radar-based methods are determined using optical satellite-based SCA data as reference. The results show that SCA estimation using a single reference image is usable for the boreal forest zone, although the accuracy is significantly weaker than that of the TKK-developed boreal forest-specific SCA method. The best accuracy obtained shows a root-mean-square error (rmse) of 0.176 for the single-reference-image method and an rmse of 0.123 for the TKK SCA method.
A semi empirical backscattering model of forest canopy covered by snow using SAR data
… and Remote Sensing …, 2000
The multifrequency polarimetric EMISAR data at 5.3 GHz and ground-truth data such as forest and snow measurements were conducted in Northern Finland during EMAC95. The radar response to forest and snow characteristics, such as the stem volume and the snow water equivalent is analyzed. A semi-empirical backscattering model of forest canopy covered by snow which is a function of the forest stem volume and the snow water equivalent is developed.