Estimation of Forest Height Using Spaceborne Repeat-Pass L-Band InSAR Correlation Magnitude over the US State of Maine

This paper describes a novel, simple and efficient approach to estimate forest height over a wide region utilizing spaceborne repeat-pass InSAR correlation magnitude data at L-band. We start from a semi-empirical modification of the RVoG model that characterizes repeat-pass InSAR correlation with large temporal baselines (e.g., 46 days for ALOS) by taking account of the temporal change effect of dielectric fluctuation and random motion of scatterers. By assuming (1) the temporal change parameters and forest backscatter profile/extinction coefficient follow some mean behavior across each inteferogram; (2) there is minimal ground scattering contribution for HV-polarization; and (3) the vertical wavenumber is small, a simplified inversion approach is developed to link the observed HV-polarized InSAR correlation magnitude to forest height and validated using ALOS/PALSAR repeat-pass observations against LVIS lidar heights over the Howland Research Forest in central Maine, US (with RMSE \u3c 4 m at a resolution of 32 hectares). The model parameters derived from this supervised regression are used as the basis for propagating the estimates of forest height to available interferometric pairs for the entire state of Maine, thus creating a state-mosaic map of forest height. The present approach described here serves as an alternative and complementary tool for other PolInSAR inversion techniques when full-polarization data may not be available. This work is also meant to be an observational prototype for NASA’s DESDynI-R (now called NISAR) and JAXA’s ALOS-2 satellite missions

Electromagnetic Scattering Models for InSAR Correlation Measurements of Vegetation and Snow

Interferometric Synthetic Aperture Radar (InSAR) has proved successful and efficient in measuring the vertical structure of the distributed targets such as vegetation and snow, which are dominated by volume scattering. In particular, the InSAR correlation measurement has been utilized to retrieve the target vertical structural information. One existing and well-known electromagnetic scattering model of the InSAR correlation was first brought forward focusing on the single-pass InSAR observation of a sparse random medium like vegetation. However, the lack of the adaption of this InSAR scattering model for repeat-pass InSAR observation of vegetation as well as for single-pass InSAR observation of snow by considering its dense medium characteristics, essentially constrain fully exploiting InSAR\u27s capability of measuring sparse and dense medium characteristics. In this work, the well-known InSAR scattering model will be adapted to accommodate the two scenarios: 1) repeat-pass InSAR observation of vegetation and 2) single-pass InSAR observation of snow and considering its dense medium characteristics. Theoretical model derivations as well as parameter retrieval approaches are demonstrated for both of the applications, respectively. Both of the simulated and ground validation results are also presented. The InSAR scattering models along with the parameter retrieval analysis described in this work will expand InSAR\u27s capability as well as the range of vegetation and snow characteristics that can be retrieved by single-pass and/or repeat-pass InSAR systems

Identifying areas of neotectonic activity using radar remote sensing in the northern foothills of the Alaska Range

Thesis (M.S.) University of Alaska Fairbanks, 2013The tectonically active northern foothills of the Alaska Range display obvious uplift and deformation, making the area an attractive place to conduct research. Research has been done in this area of Alaska in the recent past, most of which required intensive fieldwork. This study analyzes if modern radar remote sensing technology is useful in identifying neotectonic activity and in determining where future work should be conducted. Radar remote sensing data is used in two ways to support the identification of tectonically active areas: First, I incorporated available geologic maps with polarimetric and interferometric radar remote sensing data to create a classification scheme to identify and map the preserved depositional surface of the Nenana Gravel. This surface, successfully mapped and overlain on a newly available high-resolution DEM, highlighted the topographic expression of deformation in the area. Second, the high-resolution DEMs were used to create and analyze longitudinal river profiles, and a Stream Length-gradient Index Map, both of which correlate well with known active structures. This study indicates that radar remote sensing can be used to identify tectonically active areas before employing extensive fieldwork and used in combination with traditional geological procedures enhances the amount and quality of the derived information

A change detector based on an optimization with polarimetric SAR imagery

The possibility to detect changes in land cover with remote sensing is particularly valuable considering the current availability of long time series of data. SAR can play an important role in this context, since it can acquire complete time series without limitations of cloud cover. Additionally, polarimetry has the potential to improve significantly the detection capability allowing the discrimination between different polarimetric targets. This paper is focused on developing two new methodologies for testing the stability of observed targets (i.e. Equi-Scattering Mechanisms hypothesis) and change detection. Both the algorithms adopt a Lagrange optimization, which can be performed with two eigen-problems. Interestingly, the two optimizations share the same eigenvectors. Three statistical tests are proposed to set the threshold for the change detector. Two of them are mostly aimed at point targets and one is more suited for distributed targets. All the algorithms and procedures developed in this paper are tested on two different quad-polarimetric dataset acquired by the E-SAR DLR system in L-band (SARTOM 2006 and AGRISAR 2006 campaigns). The dataset are accompanied by ground surveys. The detectors are able to identify targets and areas with validated changes or showing clear differences in the images. The theoretical pdf exploited to model the optimum ratio fits adequately the data and therefore has been used for the statistical tests. Regarding the output of the tests, two of them provided good results, while one needs more care and adjustments

Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Constant false alarm rate (CFAR) algorithms using a local training window are widely used for ship detection with synthetic aperture radar (SAR) imagery. However, when the density of the targets is high, such as in busy shipping lines and crowded harbors, the background statistics may be contaminated by the presence of nearby targets in the training window. Recently, a robust CFAR detector based on truncated statistics (TS) was proposed. However, the truncation of data in the format of polarimetric covariance matrices is much more complicated with respect to the truncation of intensity (single polarization) data. In this article, a polarimetric whitening filter TS CFAR (PWF-TS-CFAR) is proposed to estimate the background parameters accurately in the contaminated sea clutter for PolSAR imagery. The CFAR detector uses a polarimetric whitening filter (PWF) to turn the multidimensional problem to a 1-D case. It uses truncation to exclude possible statistically interfering outliers and uses TS to model the remaining background samples. The algorithm does not require prior knowledge of the interfering targets, and it is performed iteratively and adaptively to derive better estimates of the polarimetric covariance matrix (although this is computationally expensive). The PWF-TS-CFAR detector provides accurate background clutter modeling, a stable false alarm property, and improves the detection performance in high-target-density situations. RadarSat2 data are used to verify our derivations, and the results are in line with the theory