Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-Band
Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonp...
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MDPI AG
2021-05-01
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| Online Access: | https://www.mdpi.com/2072-4292/13/11/2147 |
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| author | Xing Peng Xinwu Li Yanan Du Qinghua Xie |
| author_facet | Xing Peng Xinwu Li Yanan Du Qinghua Xie |
| author_sort | Xing Peng |
| collection | DOAJ |
| description | Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m. |
| first_indexed | 2024-03-10T10:53:38Z |
| format | Article |
| id | doaj.art-6bb7fc3207e34aa0a3a75aa18a4d2ca2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:53:38Z |
| publishDate | 2021-05-01 |
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| spelling | doaj.art-6bb7fc3207e34aa0a3a75aa18a4d2ca22023-11-21T22:03:28ZengMDPI AGRemote Sensing2072-42922021-05-011311214710.3390/rs13112147Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-BandXing Peng0Xinwu Li1Yanan Du2Qinghua Xie3School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSchool of Geographical Sciences, Guangzhou University, Guangzhou 510006, ChinaSchool of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaForest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m.https://www.mdpi.com/2072-4292/13/11/2147forest heighttomographic synthetic aperture radar (TomoSAR)iterative adaptive approach (IAA)robust iterative adaptive approach (RIAA)L-band |
| spellingShingle | Xing Peng Xinwu Li Yanan Du Qinghua Xie Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-Band Remote Sensing forest height tomographic synthetic aperture radar (TomoSAR) iterative adaptive approach (IAA) robust iterative adaptive approach (RIAA) L-band |
| title | Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-Band |
| title_full | Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-Band |
| title_fullStr | Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-Band |
| title_full_unstemmed | Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-Band |
| title_short | Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-Band |
| title_sort | forest height estimation from a robust tomosar method in the case of small tomographic aperture with airborne dataset at l band |
| topic | forest height tomographic synthetic aperture radar (TomoSAR) iterative adaptive approach (IAA) robust iterative adaptive approach (RIAA) L-band |
| url | https://www.mdpi.com/2072-4292/13/11/2147 |
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