Leif E.B. Eriksson

Assessment of stand-wise stem volume retrieval in boreal forest from JERS-1 L-band SAR backscatter

JERS‐1 L‐band SAR backscatter from test sites in Sweden, Finland and Siberia has been investigated to determine the accuracy level achievable in the boreal zone for stand‐wise forest stem volume retrieval using a model‐based approach. The extensive ground‐data and SAR imagery datasets available allowed analysis of the backscatter temporal dynamics. In dense forests the backscatter primarily depended on the frozen/unfrozen state of the canopy, showing a ∼4 dB difference. In sparse forests, the backscatter depended primarily on the dielectric properties of the forest floor, showing smaller differences throughout the year. Backscatter modelling as a function of stem volume was carried out by means of a simple L‐band Water Cloud related scattering model. At each test site, the model fitted the measurements used for training irrespective of the weather conditions. Of the three a priori unknown model parameters, the forest transmissivity coefficient was most affected by seasonal conditions and test site specific features (stand structure, forest management, etc.). Several factors determined the coefficients estimate, namely weather conditions at acquisition, structural heterogeneities of the forest stands within a test site, forest management practice and ground data accuracy. Stem volume retrieval was strongly influenced by these factors. It performed best under unfrozen conditions and results were temporally consistent. Multi‐temporal combination of single‐image estimates eliminated outliers and slightly decreased the estimation error. Retrieved and measured stem volumes were in good agreement up to maximum levels in Sweden and Finland. For the intensively managed test site in Sweden a 25% relative rms error was obtained. Higher errors were achieved in the larger and more heterogeneous forest test sites in Siberia. Hence, L‐band backscatter can be considered a good candidate for stand‐wise stem volume retrieval in boreal forest, although the forest site conditions play a fundamental role for the final accuracy. When the article was submitted L. Eriksson was at the Department of Geoinformatics and Remote Sensing, Friedrich‐Schiller University, D‐07743 Jena, Germany.

Detection of forest changes using ALOS PALSAR satellite images

A controlled experiment has been performed to quantify the ability to detect clear-cuts using ALOS PALSAR data. The experiment consisted of 8 old spruce dominated stands, each with a size of about 1.5 ha, located at a test site in southern Sweden. Four of the stands were clear-felled and the remaining stands were left untreated for reference. A time series of PALSAR images was acquired prior to, during, and after treatment, including 7 fine beam single polarization (FBS, look angle 34.3deg, HH-polarization) SAR images. The results clearly show that the clear-felled stands could be separated from the reference stands. The drop in backscattering coefficient between the reference and the clear-felled stands was on average 2.1 dB. This implies that ALOS PALSAR data potentially can be used for large-scale mapping of changes in forest cover.

Clear-Cut Detection in Swedish Boreal Forest Using Multi-Temporal ALOS PALSAR Backscatter Data

An extensive dataset of images acquired by the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) is investigated for clear-cut detection in the county of Västerbotten, Sweden. Strong forest/non-forest contrast and temporal consistency were found for the Fine Beam Dual HV-polarized backscatter in summer/fall. In consequence of a clear-cut between image acquisitions, the HV-backscatter dropped in most cases between 2 and 3 dB. Thus, a simple thresholding algorithm that exploits the temporal consistency of time series of HV-backscatter measurements has been developed for clear-cut detection. The detection algorithm was applied at pixel level to ALOS PALSAR strip images with a pixel size of 50 m. The performance of the detection algorithm was tested with three different threshold values (2.0, 2.5 and 3.0 dB). The classification accuracy increased from 57.4% to 78.2% for decreasing value of the threshold. Conversely, the classification error increased from 3.0% to 9.7%. For about 90% of the clear-felled polygons used for accuracy assessment the proportion of pixels correctly detected as clear-felled was above 50% when using a threshold value of 2.0 dB. For the threshold values of 2.5 and 3.0 dB the corresponding figures were 80% and 65%, respectively. The total area classified as clear-felled during the time frame of the ALOS PALSAR data differed by 5% compared to an estimate of notified fellings for the same period of time when using a detection threshold of 2.5 dB. The performance of the simple detection algorithm is reasonable when aiming at detecting clear-cuts, whereas there are shortcomings in terms of delineation.

Evaluation of new spaceborne SAR sensors for sea-ice monitoring in the Baltic Sea

In this study, synthetic aperture radar (SAR) data from the Advanced Land Observing Satellite (ALOS) and the Envisat, RADARSAT-2, and TerraSAR-X satellites were compared to evaluate their usefulness for sea-ice monitoring in the Baltic Sea. Radar signature characteristics at different frequencies, polarizations, and spatial resolutions are presented for three examples from 2009. C-band like-polarization data, which have been used for operational sea-ice mapping since the early 1990s, serve as a reference. Advantages and disadvantages were identified for the different SAR systems and imaging modes. One conclusion is that cross-polarized data improve the discrimination between sea ice and open water. Another observation is that it is easier to identify ice ridges in L-band data than in images from shorter wavelengths. The information content of X- and C-band images is largely equivalent, whereas L-band data provide complementary information. L-band SAR also seems to be less sensitive to wet snow cover on the ice.

SIR-C polarimetric radar results from the Weddell Sea, Antarctica

Polarimetric radar data acquired by SIR-C in the Eastern Weddell Sea have been analyzed to investigate techniques for discriminating thin, recently formed sea-ice from thicker first year ice and open water. Several different parameters from both the L-band and the C-band have been studied to understand the scattering processes for different forms of ice. Results indicate that it is possible to differentiate between all the primary ice types appearing in the area (except between brash ice and deformed first year ice) by comparing a combination of two or three of the studied parameters. Several different combinations can be used, but the best results are found when the L-band VV-polarized backscatter and HH- and VV-pol correlation coefficient, and the co-pol ratio HH/VV in C-band are combined. Thanks to the high quality of the SIR-C data, small differences are observed which make it possible to discriminate at least two distinctive thin ice sub-groups. This increases the possibility to use these data to study how new ice evolves under these unique Southern Ocean conditions.

Reviewing ALOS PALSAR Backscatter Observations for Stem Volume Retrieval in Swedish Forest

Between 2006 and 2011, the Advanced Land Observing Satellite (ALOS) Phased Array L-type Synthetic Aperture Radar (PALSAR) instrument acquired multi-temporal datasets under several environmental conditions and multiple configurations of look angle and polarization. The extensive archive of SAR backscatter observations over the forest test sites of Krycklan (boreal) and Remningstorp (hemi-boreal), Sweden, was used to assess the retrieval of stem volume at stand level. The retrieval was based on the inversion of a simple Water Cloud Model with gaps; single estimates of stem volume are then combined to obtain the final multi-temporal estimate. The model matched the relationship between the SAR backscatter and the stem volume under all configurations. The retrieval relative Root Mean Square Error (RMSE) differed depending upon environmental conditions, polarization and look angle. Stem volume was best retrieved in Krycklan using only HV-polarized data acquired under unfrozen conditions with a look angle of 34.3° (relative RMSE: 44.0%). In Remningstorp, the smallest error was obtained using only HH-polarized data acquired under predominantly frozen conditions with a look angle of 34.3° (relative RMSE: 35.1%). The relative RMSE was below 30% for stands >20 ha, suggesting high accuracy of ALOS PALSAR estimates of stem volumes aggregated at moderate resolution.

Investigation of a Hybrid Algorithm for Sea Ice Drift Measurements Using Synthetic Aperture Radar Images

Areal matching by phase correlation and feature tracking are two complementary methods used to measure sea ice drift between synthetic aperture radar images. This paper evaluates a new algorithm that combines the two methods. Areal matching is improved by new methods to handle large motions and rotated ice. It is shown that areal rotation can be resolved using a frequency-domain approach. Image segmentation is a prerequisite for feature tracking and achieved by a new method that performs better than Otsus method for two-component Gaussian mixture distributions. A circular weighted median filter is found to be suitable for the filtering of the motion field. The algorithm is evaluated through a thorough analysis of the response and sensitivity to various algorithm settings. The accuracy of the algorithm varies by up to 50% for one image pair within the studied range of parameter settings, thus indicating the need for a proper initialization of the algorithm.

SAR Algorithm for Sea Ice Concentration—Evaluation for the Baltic Sea

A new sea ice concentration algorithm has been developed for C-band synthetic aperture radar data. Detailed autocorrelation statistics are derived and adopted by the algorithm, and a neural network is utilized for training against 41 sea ice charts. The charts are produced by ice analysts at the Swedish Ice Service and cover the Baltic Sea. The classification of open water pixels is accurate to 94% on average, and the classification of sea ice pixels has an accuracy of 87%. This results in a root-mean-square error of 6.7 percentage points in estimating the sea ice concentration.

Measurements of Faraday Rotation Using Polarimetric PALSAR Images

For spaceborne synthetic aperture radar (SAR) systems operating at L-band frequencies or lower, the ionosphere may have a significant impact on the SAR images. The largest effect at L-band is caused by Faraday rotation (FR). Several studies have modeled the effect of FR and/or devised models to measure and correct FR. With the launch of the fully polarimetric L-band system Phased Array-type L-band SAR (PALSAR), it has become possible to test both models and measurement techniques on real SAR data. In this letter, the quality of calibrated polarimetric PALSAR data is assessed, and FR is measured. It is found that residual crosstalk and channel imbalance are small in the PALSAR data. Two methods are used to measure FR, the first using in-scene distributed targets and the second using large trihedrals. The two methods show very good agreement. The measurements are compared with values of the total electron content using a linear model. It is found that the model and measurements are in good agreement, with a root-mean-square error of 0.3deg or 15% of the mean FR angle.

Mapping of wind-thrown forests in Southern Sweden using space- and airborne SAR

Space- and airborne SAR have a potential for providing timely information of forest storm damage. In this paper, we compare Envisat, Radarsat and CARABAS with aerial photography and field observations after a severe storm event. The spaceborne C-band images were not able to detect the storm- damaged areas due to unfavorable frequency band and coarse resolution. The airborne CARABAS VHF SAR, on the other hand, detects most storm-damaged forest areas and even partly damaged which are often not detected in the aerial photography.