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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ftdoajarticles:oai:doaj.org/article:6bb7fc3207e34aa0a3a75aa18a4d2ca2 2023-05-15T17:44:59+02:00 Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-band Xing Peng Xinwu Li Yanan Du Qinghua Xie 2021-05-01T00:00:00Z https://doi.org/10.3390/rs13112147 https://doaj.org/article/6bb7fc3207e34aa0a3a75aa18a4d2ca2 EN eng MDPI AG https://www.mdpi.com/2072-4292/13/11/2147 https://doaj.org/toc/2072-4292 doi:10.3390/rs13112147 2072-4292 https://doaj.org/article/6bb7fc3207e34aa0a3a75aa18a4d2ca2 Remote Sensing, Vol 13, Iss 2147, p 2147 (2021) forest height tomographic synthetic aperture radar (TomoSAR) iterative adaptive approach (IAA) robust iterative adaptive approach (RIAA) L-band Science Q article 2021 ftdoajarticles https://doi.org/10.3390/rs13112147 2022-12-31T15:17:45Z 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. Article in Journal/Newspaper Northern Sweden Directory of Open Access Journals: DOAJ Articles Remote Sensing 13 11 2147 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
forest height tomographic synthetic aperture radar (TomoSAR) iterative adaptive approach (IAA) robust iterative adaptive approach (RIAA) L-band Science Q |
spellingShingle |
forest height tomographic synthetic aperture radar (TomoSAR) iterative adaptive approach (IAA) robust iterative adaptive approach (RIAA) L-band Science Q 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 |
topic_facet |
forest height tomographic synthetic aperture radar (TomoSAR) iterative adaptive approach (IAA) robust iterative adaptive approach (RIAA) L-band Science Q |
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. |
format |
Article in Journal/Newspaper |
author |
Xing Peng Xinwu Li Yanan Du Qinghua Xie |
author_facet |
Xing Peng Xinwu Li Yanan Du Qinghua Xie |
author_sort |
Xing Peng |
title |
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_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_sort |
forest height estimation from a robust tomosar method in the case of small tomographic aperture with airborne dataset at l-band |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13112147 https://doaj.org/article/6bb7fc3207e34aa0a3a75aa18a4d2ca2 |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_source |
Remote Sensing, Vol 13, Iss 2147, p 2147 (2021) |
op_relation |
https://www.mdpi.com/2072-4292/13/11/2147 https://doaj.org/toc/2072-4292 doi:10.3390/rs13112147 2072-4292 https://doaj.org/article/6bb7fc3207e34aa0a3a75aa18a4d2ca2 |
op_doi |
https://doi.org/10.3390/rs13112147 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
11 |
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2147 |
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1766147688804384768 |