The AfriSAR Campaign: Tomographic Analysis With Phase-Screen Correction for <i>P</i>-Band Acquisitions

Wasik, Valentine; Dubois-Fernandez, Pascale; Taillandier, Cédric; Saatchi, Sassan

doi:10.1109/jstars.2018.2831441

Cited by 19 publications

(8 citation statements)

References 27 publications

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“…The Capon profiles at these three ROIs were not similar to those found from P-band TomoSAR in [37]. The study carried out over Gabon forests using P-band TomoSAR data in [37] showed that the HH ground contributions were stronger than HV for the analyzed ROIs, while HV canopy contributions were larger than those of HH. Indeed, this behavior is expected for P-band imaging.…”

Section: Discussionmentioning

confidence: 58%

L-Band UAVSAR Tomographic Imaging in Dense Forests: Gabon Forests

et al. 2019

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Developing and enhancing strategies to characterize actual forests structure is a timely challenge, particularly for tropical forests. P-band synthetic aperture radar (SAR) tomography (TomoSAR) has previously been demonstrated as a powerful tool for characterizing the 3-D vertical structure of tropical forests, and its capability and potential to retrieve tropical forest structure has been discussed and assessed. On the other hand, the abilities of L-band TomoSAR are still in the early stages of development. Here, we aim to provide a better understanding of L-band TomoSAR capabilities for retrieving the 3-D structure of tropical forests and estimating the top height in dense forests. We carried out tomographic analysis using L-band UAVSAR data from the AfriSAR campaign conducted over Gabon Lopé Park in February 2016. First, it was found that L-band TomoSAR was able to penetrate into and through the canopy down to the ground, and thus the canopy and ground layers were detected correctly. The resulting TomoSAR vertical profiles were validated with a digital terrain model and canopy height model extracted from small-footprint Lidar (SFL) data. Second, there was a strong correlation between the L-band Capon beam forming profile in HH and HV polarizations with Land Vegetation Ice Sensor (LVIS) Level 1B waveform Lidar over different kinds of forest in Gabon Lopé National Park. Finally, forest top height from the L-band data was estimated and validated with SFL data, resulting in a root mean square error of 3 m and coefficient of determination of 0.92. The results demonstrate that L-band TomoSAR is capable of characterizing 3-D structure of tropical forests.

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Section: Discussionmentioning

confidence: 58%

L-Band UAVSAR Tomographic Imaging in Dense Forests: Gabon Forests

et al. 2019

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“…The Mondah forest area was studied by two flights in 2015 and 2016 [33,34]. The campaign was shared between ONERA (dry season, July 2015) and the German Aerospace Center (DLR) (wet season, February 2016).…”

Section: Tomographic P-band Sar Datamentioning

confidence: 99%

Potential of P-Band SAR Tomography in Forest Type Classification

Minh

Ngo²,

Lê

2021

Remote Sensing

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Forest type classification using spaceborne remote sensing is a challenge. Low-frequency Synthetic Aperture Radar (SAR) signals (i.e., P-band, ∼0.69 m wavelength) are needed to penetrate a thick vegetation layer. However, this measurement alone does not guarantee a good performance in forest classification tasks. SAR tomography, a technique employing multiple acquisitions over the same areas to form a three-dimensional image, has been demonstrated to improve SAR’s capability in many applications. Our study shows the potential value of SAR tomography acquisitions to improve forest classification. By using P-band tomographic SAR data from the German Aerospace Center F-SAR sensor during the AfriSAR campaign in February 2016, the vertical profiles of five different forest types at a tropical forest site in Mondah, Gabon (South Africa) were analyzed and exploited for the classification task. We demonstrated that the high sensitivity of SAR tomography to forest vertical structure enables the improvement of classification performance by up to 33%. Interestingly, by using the standard Random Forest technique, we found that the ground (i.e., at 5–10 m) and volume layers (i.e., 20–40 m) play an important role in identifying the forest type. Together, these results suggested the promise of the TomoSAR technique for mapping forest types with high accuracy in tropical areas and could provide strong support for the next Earth Explorer BIOMASS spaceborne mission which will collect P-band tomographic SAR data.

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“…Early research for the distributed media application was generally aimed at tomographic image reconstruction, including first extensive application of super-resolution Capon Tomography [26,27] to distributed targets [44], and at extensive a phenomenological interpretation of tomographic data, to complement traditional radiometric, polarimetric, and interferometric measurements [27,35,36,52,63]. Later, TomoSAR was demonstrated to be a most valuable tool to retrieve forest canopy height [69], and, by virtue of the penetration capabilities of P-Band waves, sub-canopy terrain topography [54,60,67]. In parallel, research on Tomography was carried out at L-Band (wavelength ≈ 25 cm) as well, motivated by the proposal of the L-Band bistatic SAR systems Tandem-L and SAOCOM-CS [37,56].…”

Section: Subproject 2: Multi-baseline Sar Processing For 3d/4d Reconstruction (Mbsar)mentioning

confidence: 99%

Three- and Four-Dimensional Topographic Measurement and Validation

Rocca

Tebaldini

et al. 2021

Remote Sensing

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This paper reports on the activities carried out in the context of “Dragon project 32278: Three- and Four-Dimensional Topographic Measurement and Validation”. The research work was split into three subprojects and encompassed several activities to deliver accurate characterization of targets on land surfaces and deepen the current knowledge on the exploitation of Synthetic Aperture Radar (SAR) data. The goal of Subproject 1 was to validate topographic mapping accuracy of various ESA, TPM, and Chinese satellite system on test sites in the EU and China; define and improve validation methodologies for topographic mapping; and develop and setup test sites for the validation of different surface motion estimation techniques. Subproject 2 focused on the specific case of spatially and temporally decorrelating targets by using multi-baseline interferometric (InSAR) and tomographic (TomoSAR) SAR processing. Research on InSAR led to the development of robust retrieval techniques to estimate target displacement over time. Research on TomoSAR was focused on testing or defining new processing methods for high-resolution 3D imaging of the interior of forests and glaciers and the characterization of their temporal behavior. Subproject 3 was focused on near-real-time motion estimation, considering efficient algorithms for the digestion of new acquisitions and for changes in problem parameterization.

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The AfriSAR Campaign: Tomographic Analysis With Phase-Screen Correction for P-Band Acquisitions

Cited by 19 publications

References 27 publications

L-Band UAVSAR Tomographic Imaging in Dense Forests: Gabon Forests

L-Band UAVSAR Tomographic Imaging in Dense Forests: Gabon Forests

Potential of P-Band SAR Tomography in Forest Type Classification

Three- and Four-Dimensional Topographic Measurement and Validation

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