Analysis of multi-frequency and multi-polarization SAR data for wetland mapping in Hamoun-e-Hirmand wetland

Maleki, Saeideh; Baghdadi, Nicolas; Soffianian, Alireza; Hajj, Mohammad El; Rahdari, Vahid

doi:10.1080/01431161.2019.1688414

Cited by 14 publications

(5 citation statements)

References 46 publications

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“…The most appropriate SAR frequency to characterize wetlands varies according to the physiognomy of the existing vegetation. In accordance with previous studies (Mahdianpari, Salehi, Mohammadimanesh, and Motagh 2017;Maleki et al 2020;, our results indicate that model accuracy is higher in the X-band frequency than in the C-band frequency for herbaceous vegetations, especially when only one date is available. However, the findings show that C-band is slightly more relevant than X-band to discriminate woody vegetations and ponds.…”

Section: Is the Influence Of Frequency Polarization And Number Of Acquisition Dates On Model Accuracy Regardless Of Vegetation Type?supporting

confidence: 92%

“…band frequency, polarization mode, number of acquisition dates) on discrimination of wetland vegetation has been studied extensively in recent years (Mahdavi, Salehi Sr, et al 2017;Wohlfart et al 2018). Regarding band frequency, the advantage of the X-band over the C-band (Maleki et al 2020;S-band (van Beijma, Comber, andLamb 2014) for discriminating wetland vegetation types has been described, as has the advantage of the Lband over the C-band for mapping emergent (Simioni et al 2019) or woody (Amani et al 2019;Mahdianpari, Salehi, Mohammadimanesh, and Motagh 2017) vegetations. The literature also describes the advantage of quad-polarized data over dual-polarized data to discriminate woody (de Almeida Furtado, Silva, and de Moraes Novo 2016; Mahdianpari, Salehi, Mohammadimanesh, and Motagh 2017) (de Almeida Furtado et al, 2016, Mahdianpari et al, 2017a or boreal (Mahdianpari, Salehi, and Mohammadimanesh 2017) wet-land vegetation types.…”

Section: Lemna Minormentioning

confidence: 99%

“…5). A possible explanation for this is that the dual-pol configuration includes two frequencies (X-band and C-band) exhibiting contrasting responses of herbaceous vegetations (Mahdianpari, Salehi, Mohammadimanesh, and Motagh 2017;Maleki et al 2020;Mohammadimanesh, Salehi, Mahdianpari, Brian Brisco, and Motagh 2018), whereas the quad-pol configuration includes only one C-band frequency.…”

Section: Is the Influence Of Frequency Polarization And Number Of Acquisition Dates On Model Accuracy Regardless Of Vegetation Type?mentioning

confidence: 99%

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SAR analysis of wetland ecosystems: Effects of band frequency, polarization mode and acquisition dates

Rapinel

Betbeder

Denize

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

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The availability of high spatial resolution synthetic aperture radar (SAR) sensors with a wide range of acquisition modes has increased greatly over the past decade and contributed significantly to the study of wetland ecosystems. However, the relative influence of acquisition configurations (i.e. band frequency, polarization mode, number of acquisition dates) in wetland analysis remains poorly explored. This article investigated the relative influence of X-/C-band frequency, dual-/quad-polarization and single-/multiacquisition features on discrimination of vegetation types in 632 ha Ramsar-protected temperate riverine marshes (Mont-Saint-Michel Bay, France). Three SAR datasets (i.e. quad-pol/C-band, dual-pol/C-band and dual-pol/X-band) were generated from five pairs of TerraSAR-X and RADARSAT-2 images. First, a set of 25 SAR features, including backscattering coefficients and polarimetric parameters, was extracted from the SAR datasets. Second, correlation between each pair of images was calculated using the polarimetric parameter Shannon entropy to select the most similar pairs in the time series. Third, the importance of each SAR feature and modeling accuracy were calculated using a conditional random forest model for each of the three datasets. Finally, analysis of variance was performed to assess the impact of band frequency, polarization mode and number of acquisition dates on the classification of vegetation types. The results highlighted that although the time-shift of each pair of TerraSAR-X and RADARSAT-2 images was short (3-11 days), only three pairs were sufficiently similar, highlighting the high variability in wetland ecosystems. The polarimetric parameter Shannon entropy was the most discriminating feature, regardless of the frequency or polarization. Most variance in the model accuracy was explained by the number of acquisition dates (68%), followed by the frequency (23%), while polarization explained little. This article will help select the most suitable SAR sensor acquisition modes for wetland conservation.

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Section: Is the Influence Of Frequency Polarization And Number Of Acquisition Dates On Model Accuracy Regardless Of Vegetation Type?supporting

confidence: 92%

Section: Lemna Minormentioning

confidence: 99%

Section: Is the Influence Of Frequency Polarization And Number Of Acquisition Dates On Model Accuracy Regardless Of Vegetation Type?mentioning

confidence: 99%

See 1 more Smart Citation

SAR analysis of wetland ecosystems: Effects of band frequency, polarization mode and acquisition dates

Rapinel

Betbeder

Denize

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

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“…and polarizations. There are many different characteristics associated with each of these frequencies and polarizations that make them more suitable and informative for a specific application [210], [211]. • Due to the higher wavelength of the microwave signals, SAR can penetrate into shallow water, snow, ground [212], [213].…”

Section: • Sar Images Can Be Acquired In Different Frequenciesmentioning

confidence: 99%

Synthetic Aperture Radar (SAR) for Ocean: A Review

Asiyabi,

Ghorbanian,

Tameh

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Oceans cover approximately 71% of the Earth's surface and provide numerous services to the environment and humans. Precise, real-time, and large-scale monitoring of the oceanographic parameters is essential for ocean conservation and understanding the interactions between oceans and the atmosphere. In this regard, Synthetic Aperture Radar (SAR) systems, with unique capabilities (e.g., day-night and almost allweather data acquisition), provide valuable datasets for ocean studies. Many studies have exploited the applications of SAR imagery for oceans and have proposed numerous methods to study oceanographic parameters. In this study, a brief introduction to SAR and the interaction between microwave signals and the ocean surface are initially provided. Then, the important spaceborne and airborne SAR systems for oceanographic applications are summarized. Subsequently, 12 different applications of SAR systems in the ocean are comprehensively discussed, and the advantages and disadvantages of SAR systems for ocean studies are extensively explored. Finally, the research trend on SAR applications in the ocean is provided by analyzing all the relevant papers published between 1973 and the end of December 2022, and the existing challenges are discussed for future studies.

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“…Fully utilizing characteristics of wetlands in SAR images is the key to accurately classify and effectively monitor wetlands. Firstly, the backscattering intensity can describe the response difference between targets on radar pulse [12][13][14][15][16][17]. Secondly, textures can supplement the SAR information dimension, but those ones used in SAR wetland classification were calculated by Gray-level Co-occurrence Matrix (GLCM) commonly [18,19], which seldom considers the statistical distribution characteristics of SAR data.…”

Section: Introductionmentioning

confidence: 99%

Mapping Dongting Lake Wetland Utilizing Time Series Similarity, Statistical Texture, and Superpixels With Sentinel-1 SAR Data

Yang

Liu

Chen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Wetland is of significant ecological value, which is very important and challenging for large-scale mapping. Sentinel-1 can continuously record wetland changes with its all-day, allweather working capability. How to fully utilize multidimensional information, such as time and statistical texture of Synthetic Aperture Radar (SAR) image, to classify wetlands more accurately has become a research focus. Thus, this paper constructed Time Series Similarity parameters to describe the temporal change information of targets in wetland and introduced G 0 statistical texture parameters to describe texture characteristics of SAR images. Combining multi-features and superpixels, we proposed a classification method based on Random Forest (RF) to map Dongting Lake wetland. The overall classification accuracy was 97.57%, and the Kappa coefficient was 0.97. The classification accuracy of reed beach, grass marsh etc. was above 95%. The results showed the proposed SAR Time Series Similarity features could effectively utilize dynamical information among classes and was helpful to identify mudflat, grass marsh and reed beach with high dynamics in wetland. The introduced statistical texture features expressed the heterogeneity of targets, and enhanced the recognition and extraction ability of forest beach, mudflat and reed beach. Compared with Support Vector Machine (SVM), Decision Tree (DT) and RF classification methods, RF with superpixels optimized not only got high precision but also could effectively reduce the pepper-salt error in classification, because of the consideration of superpixels context information.

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Analysis of multi-frequency and multi-polarization SAR data for wetland mapping in Hamoun-e-Hirmand wetland

Cited by 14 publications

References 46 publications

SAR analysis of wetland ecosystems: Effects of band frequency, polarization mode and acquisition dates

SAR analysis of wetland ecosystems: Effects of band frequency, polarization mode and acquisition dates

Synthetic Aperture Radar (SAR) for Ocean: A Review

Mapping Dongting Lake Wetland Utilizing Time Series Similarity, Statistical Texture, and Superpixels With Sentinel-1 SAR Data

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