Classification and Monitoring of Reed Belts Using Dual-Polarimetric TerraSAR-X Time Series

Iris Heine,Thomas Jagdhuber,Sibylle Itzerott

doi:10.3390/rs8070552

Abstract

Synthetic aperture radar polarimetry (PolSAR) and polarimetric decomposition techniques have proven to be useful tools for wetland mapping. In this study we classify reed belts and monitor their phenological changes at a natural lake in northeastern Germany using dual-co-polarized (HH, VV) TerraSAR-X time series. The time series comprises 19 images, acquired between August 2014 and May 2015, in ascending and descending orbit. We calculated different polarimetric indices using the HH and VV intensities, the dual-polarimetric coherency matrix including dominant and mean alpha scattering angles, and entropy and anisotropy (normalized eigenvalue difference) as well as combinations of entropy and anisotropy for the analysis of the scattering scenarios. The image classifications were performed with the random forest classifier and validated with high-resolution digital orthophotos. The time series analysis of the reed belts revealed significant seasonal changes for the double-bounce–sensitive parameters (intensity ratio HH/VV and intensity difference HH-VV, the co-polarimetric coherence phase and the dominant and mean alpha scattering angles) and in the dual-polarimetric coherence (amplitude), anisotropy, entropy, and anisotropy-entropy combinations; whereas in summer dense leaves cause volume scattering, in winter, after leaves have fallen, the reed stems cause predominately double-bounce scattering. Our study showed that the five most important parameters for the classification of reed are the intensity difference HH-VV, the mean alpha scattering angle, intensity ratio HH/VV, and the coherence (phase). Due to the better separation of reed and other vegetation (deciduous forest, coniferous forest, meadow), winter acquisitions are preferred for the mapping of reed. Multi-temporal stacks of winter images performed better than summer ones. The combination of ascending and descending images also improved the result as it reduces the influence of the sensor look direction. However, in this study, only an accuracy of ~50% correct classified reed areas was reached. Whereas the shorelines with reed areas (>10 m broad) could be detected correctly, the actual reed areas were significantly overestimated. The main source of error is probably the challenging data geocoding causing geolocation inaccuracies, which need to be solved in future studies.

Highlights

The common reed (Phragmites australis) is a perennial wetland grass that grows typically in large and dense communities, so-called reed belts [1,2]
The “true reed” area is the correct classified reed area that denotes the intersection of the reed area of the random forest (RF) classification of the parameter stacks
This study investigates the potential of dual-polarimetric TSX data for mapping and monitoring of reed belts: 13 of the 16 calculated parameters (Table 3) show a significant difference between summer and winter acquisitions caused by the phenology of reed

Summary

Introduction

The common reed (Phragmites australis) is a perennial wetland grass that grows typically in large and dense communities, so-called reed belts [1,2]. Most of the shallow lake areas in northeastern Germany are covered by reed, but Remote Sens. 2016, 8, 552 fluctuations of the lake levels can reduce the plant stocks temporarily [4]. Despite their importance, reed vegetation is not regularly monitored: the last biotope mapping at Lake Fürstenseer was carried out in 1991. Very high resolution optical images are often expensive and have the inherent disadvantage of weather and illumination dependence: a regular monitoring of lakes in northeastern Germany is not feasible due to frequent cloud coverage and low sun elevation angles in winter [9]. Synthetic aperture radar (SAR) sensors do not have these limitations and are the method of choice for regular, all-year monitoring

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