Forest height estimation from TanDEM-X images with semi-empirical coherence models

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

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Forest height estimation from TanDEM-X images with semi-empirical coherence models. / Praks, Jaan; Antropov, Oleg; Olesk, Aire; Voormansik, Kaupo.

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. p. 8805-8808 (IEEE International Symposium on Geoscience and Remote Sensing IGARSS).

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

Harvard

Praks, J, Antropov, O, Olesk, A & Voormansik, K 2018, Forest height estimation from TanDEM-X images with semi-empirical coherence models. in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE International Symposium on Geoscience and Remote Sensing IGARSS, IEEE, pp. 8805-8808, International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22/07/2018. https://doi.org/10.1109/IGARSS.2018.8519569

APA

Praks, J., Antropov, O., Olesk, A., & Voormansik, K. (2018). Forest height estimation from TanDEM-X images with semi-empirical coherence models. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 8805-8808). (IEEE International Symposium on Geoscience and Remote Sensing IGARSS). IEEE. https://doi.org/10.1109/IGARSS.2018.8519569

Vancouver

Praks J, Antropov O, Olesk A, Voormansik K. Forest height estimation from TanDEM-X images with semi-empirical coherence models. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE. 2018. p. 8805-8808. (IEEE International Symposium on Geoscience and Remote Sensing IGARSS). https://doi.org/10.1109/IGARSS.2018.8519569

Author

Praks, Jaan ; Antropov, Oleg ; Olesk, Aire ; Voormansik, Kaupo. / Forest height estimation from TanDEM-X images with semi-empirical coherence models. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. pp. 8805-8808 (IEEE International Symposium on Geoscience and Remote Sensing IGARSS).

Bibtex - Download

@inproceedings{868d569edc5a4dfdabcc3c9f6d52c675,
title = "Forest height estimation from TanDEM-X images with semi-empirical coherence models",
abstract = "In this study we compare semi-empirical interferometric coherence models, proposed in ill, for tree height estimation from TanDEM-X coherence scenes. The models are derived from Random Volume over Ground model, by applying simplifications and introducing empirical parameters at different complexity levels so that the models can be adapted to available ancillary data. Several different TandDEM-X interferometric scenes from Estonia are used to test the model performance in various conditions. All the results are compared with highly accurate canopy height models measured using airborne laser scanning. We demonstrate that models which are very simple to invert, produce accurate tree height estimates when the conditions are most favorable. Best results can be seen for winter images for frozen and dry snow conditions. Simple parametric sinc model can produce accurate tree height maps over large areas with pixel-wise deviation only few meters.",
author = "Jaan Praks and Oleg Antropov and Aire Olesk and Kaupo Voormansik",
year = "2018",
doi = "10.1109/IGARSS.2018.8519569",
language = "English",
isbn = "978-1-5386-7151-1",
series = "IEEE International Symposium on Geoscience and Remote Sensing IGARSS",
publisher = "IEEE",
pages = "8805--8808",
booktitle = "IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",

}

RIS - Download

TY - GEN

T1 - Forest height estimation from TanDEM-X images with semi-empirical coherence models

AU - Praks, Jaan

AU - Antropov, Oleg

AU - Olesk, Aire

AU - Voormansik, Kaupo

PY - 2018

Y1 - 2018

N2 - In this study we compare semi-empirical interferometric coherence models, proposed in ill, for tree height estimation from TanDEM-X coherence scenes. The models are derived from Random Volume over Ground model, by applying simplifications and introducing empirical parameters at different complexity levels so that the models can be adapted to available ancillary data. Several different TandDEM-X interferometric scenes from Estonia are used to test the model performance in various conditions. All the results are compared with highly accurate canopy height models measured using airborne laser scanning. We demonstrate that models which are very simple to invert, produce accurate tree height estimates when the conditions are most favorable. Best results can be seen for winter images for frozen and dry snow conditions. Simple parametric sinc model can produce accurate tree height maps over large areas with pixel-wise deviation only few meters.

AB - In this study we compare semi-empirical interferometric coherence models, proposed in ill, for tree height estimation from TanDEM-X coherence scenes. The models are derived from Random Volume over Ground model, by applying simplifications and introducing empirical parameters at different complexity levels so that the models can be adapted to available ancillary data. Several different TandDEM-X interferometric scenes from Estonia are used to test the model performance in various conditions. All the results are compared with highly accurate canopy height models measured using airborne laser scanning. We demonstrate that models which are very simple to invert, produce accurate tree height estimates when the conditions are most favorable. Best results can be seen for winter images for frozen and dry snow conditions. Simple parametric sinc model can produce accurate tree height maps over large areas with pixel-wise deviation only few meters.

U2 - 10.1109/IGARSS.2018.8519569

DO - 10.1109/IGARSS.2018.8519569

M3 - Conference contribution

SN - 978-1-5386-7151-1

T3 - IEEE International Symposium on Geoscience and Remote Sensing IGARSS

SP - 8805

EP - 8808

BT - IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

PB - IEEE

ER -

ID: 31399641