Building blocks for semiempirical models for forest parameter extraction from interferometric X-band SAR images

Jaan Praks, Aire Olesk, Kaupo Voormansik, Oleg Antropov, Karlis Zalite, Mart Noorma

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

Abstract

In this work we provide basic building blocks for semi-empirical models to be applied mainly for forest height extraction from X-band interferometric SAR images. The work uses Random Volume over Ground model as the main theoretical framework, and relies on the measurement data represented by over 3000 measurements points collected in Estonia in 2011 and 2012. Here we demonstrate that the best argument for empirical models which relate coherence and forest parameters is relative interferometric tree height (tree height divided by InSAR Height of ambiguity). Our results suggest that a very simple linear model with no additional a priori parameters can be used as a first approach for estimation of forest height. However, if more extensive dataset are available, a zero extinction model can provide improvement. Moreover, proposed semi-empirical models can also be used to predict forest properties related to forest extinction coefficient. All the derived model approximations are demonstrated by model simulations and verified with extensive dataset of forest measurements. Relation of semi-empirical parameters to physics based model parameters is discussed and the models accuracy is analyzed based on empirical dataset.
Original languageEnglish
Title of host publicationGeoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages736-739
ISBN (Electronic)978-1-5090-3332-4 , 978-1-5090-3331-7
ISBN (Print)978-1-5090-3332-4
DOIs
Publication statusPublished - 3 Nov 2016
MoE publication typeA4 Article in a conference publication
Event36th IEEE International Geoscience and Remote Sensing Symposium: Advancing the understanding of our living planet - Beijing, China
Duration: 10 Jul 201615 Jul 2016
Conference number: 36

Conference

Conference36th IEEE International Geoscience and Remote Sensing Symposium
Abbreviated titleIGARSS
CountryChina
CityBeijing
Period10/07/1615/07/16

Fingerprint

synthetic aperture radar
parameter
extinction coefficient
physics
extinction
simulation

Keywords

  • above ground biomass
  • forest tree height
  • SAR interferometry
  • semi-empirical models

Cite this

Praks, J., Olesk, A., Voormansik, K., Antropov, O., Zalite, K., & Noorma, M. (2016). Building blocks for semiempirical models for forest parameter extraction from interferometric X-band SAR images. In Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International (pp. 736-739). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/IGARSS.2016.7729185
Praks, Jaan ; Olesk, Aire ; Voormansik, Kaupo ; Antropov, Oleg ; Zalite, Karlis ; Noorma, Mart. / Building blocks for semiempirical models for forest parameter extraction from interferometric X-band SAR images. Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International . Institute of Electrical and Electronic Engineers IEEE, 2016. pp. 736-739
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abstract = "In this work we provide basic building blocks for semi-empirical models to be applied mainly for forest height extraction from X-band interferometric SAR images. The work uses Random Volume over Ground model as the main theoretical framework, and relies on the measurement data represented by over 3000 measurements points collected in Estonia in 2011 and 2012. Here we demonstrate that the best argument for empirical models which relate coherence and forest parameters is relative interferometric tree height (tree height divided by InSAR Height of ambiguity). Our results suggest that a very simple linear model with no additional a priori parameters can be used as a first approach for estimation of forest height. However, if more extensive dataset are available, a zero extinction model can provide improvement. Moreover, proposed semi-empirical models can also be used to predict forest properties related to forest extinction coefficient. All the derived model approximations are demonstrated by model simulations and verified with extensive dataset of forest measurements. Relation of semi-empirical parameters to physics based model parameters is discussed and the models accuracy is analyzed based on empirical dataset.",
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Praks, J, Olesk, A, Voormansik, K, Antropov, O, Zalite, K & Noorma, M 2016, Building blocks for semiempirical models for forest parameter extraction from interferometric X-band SAR images. in Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International . Institute of Electrical and Electronic Engineers IEEE, pp. 736-739, 36th IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7729185

Building blocks for semiempirical models for forest parameter extraction from interferometric X-band SAR images. / Praks, Jaan; Olesk, Aire; Voormansik, Kaupo; Antropov, Oleg; Zalite, Karlis; Noorma, Mart.

Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International . Institute of Electrical and Electronic Engineers IEEE, 2016. p. 736-739.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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AU - Olesk, Aire

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AU - Zalite, Karlis

AU - Noorma, Mart

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N2 - In this work we provide basic building blocks for semi-empirical models to be applied mainly for forest height extraction from X-band interferometric SAR images. The work uses Random Volume over Ground model as the main theoretical framework, and relies on the measurement data represented by over 3000 measurements points collected in Estonia in 2011 and 2012. Here we demonstrate that the best argument for empirical models which relate coherence and forest parameters is relative interferometric tree height (tree height divided by InSAR Height of ambiguity). Our results suggest that a very simple linear model with no additional a priori parameters can be used as a first approach for estimation of forest height. However, if more extensive dataset are available, a zero extinction model can provide improvement. Moreover, proposed semi-empirical models can also be used to predict forest properties related to forest extinction coefficient. All the derived model approximations are demonstrated by model simulations and verified with extensive dataset of forest measurements. Relation of semi-empirical parameters to physics based model parameters is discussed and the models accuracy is analyzed based on empirical dataset.

AB - In this work we provide basic building blocks for semi-empirical models to be applied mainly for forest height extraction from X-band interferometric SAR images. The work uses Random Volume over Ground model as the main theoretical framework, and relies on the measurement data represented by over 3000 measurements points collected in Estonia in 2011 and 2012. Here we demonstrate that the best argument for empirical models which relate coherence and forest parameters is relative interferometric tree height (tree height divided by InSAR Height of ambiguity). Our results suggest that a very simple linear model with no additional a priori parameters can be used as a first approach for estimation of forest height. However, if more extensive dataset are available, a zero extinction model can provide improvement. Moreover, proposed semi-empirical models can also be used to predict forest properties related to forest extinction coefficient. All the derived model approximations are demonstrated by model simulations and verified with extensive dataset of forest measurements. Relation of semi-empirical parameters to physics based model parameters is discussed and the models accuracy is analyzed based on empirical dataset.

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Praks J, Olesk A, Voormansik K, Antropov O, Zalite K, Noorma M. Building blocks for semiempirical models for forest parameter extraction from interferometric X-band SAR images. In Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International . Institute of Electrical and Electronic Engineers IEEE. 2016. p. 736-739 https://doi.org/10.1109/IGARSS.2016.7729185