Potential of P-Band SAR Tomography in Forest Type Classification
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 classif...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/4/696 |
| _version_ | 1797396544418217984 |
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| author | Dinh Ho Tong Minh Yen-Nhi Ngo Thu Trang Lê |
| author_facet | Dinh Ho Tong Minh Yen-Nhi Ngo Thu Trang Lê |
| author_sort | Dinh Ho Tong Minh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:52:54Z |
| format | Article |
| id | doaj.art-c2d94bddae9a44799a449e48f0b3c0ae |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T00:52:54Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c2d94bddae9a44799a449e48f0b3c0ae2023-12-11T17:05:19ZengMDPI AGRemote Sensing2072-42922021-02-0113469610.3390/rs13040696Potential of P-Band SAR Tomography in Forest Type ClassificationDinh Ho Tong Minh0Yen-Nhi Ngo1Thu Trang Lê2UMR TETIS, INRAE, University of Montpellier, 34090 Montpellier, FranceIndependent Researcher, 34090 Montpellier, FranceDepartment of Photogrammetry and Remote Sensing, Hanoi University of Mining and Geology, 18 Vien Street, Hanoi 11910, VietnamForest 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.https://www.mdpi.com/2072-4292/13/4/696P-bandTomoSARBIOMASSforest typesclassificationAfriSAR |
| spellingShingle | Dinh Ho Tong Minh Yen-Nhi Ngo Thu Trang Lê Potential of P-Band SAR Tomography in Forest Type Classification Remote Sensing P-band TomoSAR BIOMASS forest types classification AfriSAR |
| title | Potential of P-Band SAR Tomography in Forest Type Classification |
| title_full | Potential of P-Band SAR Tomography in Forest Type Classification |
| title_fullStr | Potential of P-Band SAR Tomography in Forest Type Classification |
| title_full_unstemmed | Potential of P-Band SAR Tomography in Forest Type Classification |
| title_short | Potential of P-Band SAR Tomography in Forest Type Classification |
| title_sort | potential of p band sar tomography in forest type classification |
| topic | P-band TomoSAR BIOMASS forest types classification AfriSAR |
| url | https://www.mdpi.com/2072-4292/13/4/696 |
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