A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region

Abstract

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Object-Based Image Analysis in Wetland Research: A Review

“…Depending on the sensor wavelength, polarization, incident angle and structure of vegetation, the backscatter response may be dominated by surface scattering, volume scattering, or by "double-bounce" effect caused by the signal's interaction with multiple surfaces inside deeper canopies, e.g., tree trunks and water in flooded forests [37,[74][75][76]. SAR wavelength plays an important role in object-based classifications; for instance, the shorter-wavelength C-band was useful for detecting herbaceous marsh vegetation [37,78] but had limited sensitivity in forests [37,103]; while the longer L-band wavelength was sensitive to size of scattering elements in woody canopies and thus especially informative in forested wetlands [77,82]. In all the studies using SAR data, signal properties at the object level were typically more important than non-spectral attributes for differentiating ecosystems with diverse plant morphology and canopy structure, such as flooded forests, herbaceous marshes, man-made wetlands and rice paddies [27,37,71,74,75,78,80,82,104].…”

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

“…The integration of multi-source data to improve RF model performance has been reported in several studies in tropical environments [30,93,94]. Optical and SAR imagery provide complementary information and are often used in combination, while addition of topographic variables has also been shown to improve wetland and other land cover classification [15,21,95,96].…”

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

“…None of the three best models or the RDL model were significantly different from each other (at an alpha level of 0.05), but all four were a statistical improvement over the NWI (Table 10). Sub-classifying wetlands accurately required ancillary soils and topographic data, as well as increasing the temporal and spectral coverage of remotely sensed data with optical and L-band radar, the latter undoubtedly because of deeper canopy penetration and increased interaction of the signal which has been known to be useful for distinguishing differences in vegetative land cover [26][27][28]88].…”

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