Fusing radar and optical remote sensing for biomass prediction in mountainous tropical forests

Melissa Fedrigo; Patrick Meir; Douglas Sheil; Miriam Van Heist; Iain H. Woodhouse; Edward T.A. Mitchard

doi:10.1109/IGARSS.2013.6721325

Fusing radar and optical remote sensing for biomass prediction in mountainous tropical forests

Melissa Fedrigo, Patrick Meir, Douglas Sheil, Miriam Van Heist, Iain H. Woodhouse, Edward T.A. Mitchard

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Academic › peer-review

6 Citations (Scopus)

Abstract

Field measured estimates of aboveground biomass (AGB) in the mountainous region of Bwindi Impenetrable National Park ('Bwindi'), Uganda were used to train remote sensing models in order to estimate AGB within the park. AGB estimates were extrapolated using dual-polarization radar satellite data from ALOS PALSAR, optical imagery from Landsat 7 and a fusion of both, and compared to field estimates as indicators of the model prediction strength. Significant geolocation errors existed in the radar data due to the extreme terrain. Fusing the radar and optical data using the non-parametric algorithm Random Forest (RF) in R, provided lower error than using either radar or optical data alone (RMSE ∼120 Mg ha^-1), however, saturation at higher biomass levels was evident. The AGB in Bwindi was estimated at 8.91 Tg ± 0.39 Tg (260.9 Mg ha^-1 ± 11.4 Mg ha^-1).

Original language	English
Title of host publication	2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
Pages	975-978
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS.2013.6721325
Publication status	Published - 2013
Externally published	Yes
Event	2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Melbourne, VIC, Australia Duration: 21 Jul 2013 → 26 Jul 2013

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference/symposium

Conference/symposium	2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
Country/Territory	Australia
City	Melbourne, VIC
Period	21/07/13 → 26/07/13

Keywords

ALOS PALSAR
Biomass
forestry
radar remote sensing
tropical forest

Access to Document

10.1109/IGARSS.2013.6721325

Cite this

Fedrigo, M., Meir, P., Sheil, D., Van Heist, M., Woodhouse, I. H., & Mitchard, E. T. A. (2013). Fusing radar and optical remote sensing for biomass prediction in mountainous tropical forests. In 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings (pp. 975-978). Article 6721325 (International Geoscience and Remote Sensing Symposium (IGARSS)). https://doi.org/10.1109/IGARSS.2013.6721325

@inproceedings{03b39dc0dfa044f9884cdcf9d51d58ae,

title = "Fusing radar and optical remote sensing for biomass prediction in mountainous tropical forests",

abstract = "Field measured estimates of aboveground biomass (AGB) in the mountainous region of Bwindi Impenetrable National Park ('Bwindi'), Uganda were used to train remote sensing models in order to estimate AGB within the park. AGB estimates were extrapolated using dual-polarization radar satellite data from ALOS PALSAR, optical imagery from Landsat 7 and a fusion of both, and compared to field estimates as indicators of the model prediction strength. Significant geolocation errors existed in the radar data due to the extreme terrain. Fusing the radar and optical data using the non-parametric algorithm Random Forest (RF) in R, provided lower error than using either radar or optical data alone (RMSE ∼120 Mg ha-1), however, saturation at higher biomass levels was evident. The AGB in Bwindi was estimated at 8.91 Tg ± 0.39 Tg (260.9 Mg ha-1 ± 11.4 Mg ha-1).",

keywords = "ALOS PALSAR, Biomass, forestry, radar remote sensing, tropical forest",

author = "Melissa Fedrigo and Patrick Meir and Douglas Sheil and {Van Heist}, Miriam and Woodhouse, {Iain H.} and Mitchard, {Edward T.A.}",

year = "2013",

doi = "10.1109/IGARSS.2013.6721325",

language = "English",

isbn = "9781479911141",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

pages = "975--978",

booktitle = "2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings",

note = "2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 ; Conference date: 21-07-2013 Through 26-07-2013",

}

Fedrigo, M, Meir, P, Sheil, D , Van Heist, M, Woodhouse, IH & Mitchard, ETA 2013, Fusing radar and optical remote sensing for biomass prediction in mountainous tropical forests. in 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings., 6721325, International Geoscience and Remote Sensing Symposium (IGARSS), pp. 975-978, 2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013, Melbourne, VIC, Australia, 21/07/13. https://doi.org/10.1109/IGARSS.2013.6721325

Fusing radar and optical remote sensing for biomass prediction in mountainous tropical forests. / Fedrigo, Melissa; Meir, Patrick; Sheil, Douglas et al.
2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings. 2013. p. 975-978 6721325 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Academic › peer-review

TY - GEN

T1 - Fusing radar and optical remote sensing for biomass prediction in mountainous tropical forests

AU - Fedrigo, Melissa

AU - Meir, Patrick

AU - Sheil, Douglas

AU - Van Heist, Miriam

AU - Woodhouse, Iain H.

AU - Mitchard, Edward T.A.

PY - 2013

Y1 - 2013

N2 - Field measured estimates of aboveground biomass (AGB) in the mountainous region of Bwindi Impenetrable National Park ('Bwindi'), Uganda were used to train remote sensing models in order to estimate AGB within the park. AGB estimates were extrapolated using dual-polarization radar satellite data from ALOS PALSAR, optical imagery from Landsat 7 and a fusion of both, and compared to field estimates as indicators of the model prediction strength. Significant geolocation errors existed in the radar data due to the extreme terrain. Fusing the radar and optical data using the non-parametric algorithm Random Forest (RF) in R, provided lower error than using either radar or optical data alone (RMSE ∼120 Mg ha-1), however, saturation at higher biomass levels was evident. The AGB in Bwindi was estimated at 8.91 Tg ± 0.39 Tg (260.9 Mg ha-1 ± 11.4 Mg ha-1).

AB - Field measured estimates of aboveground biomass (AGB) in the mountainous region of Bwindi Impenetrable National Park ('Bwindi'), Uganda were used to train remote sensing models in order to estimate AGB within the park. AGB estimates were extrapolated using dual-polarization radar satellite data from ALOS PALSAR, optical imagery from Landsat 7 and a fusion of both, and compared to field estimates as indicators of the model prediction strength. Significant geolocation errors existed in the radar data due to the extreme terrain. Fusing the radar and optical data using the non-parametric algorithm Random Forest (RF) in R, provided lower error than using either radar or optical data alone (RMSE ∼120 Mg ha-1), however, saturation at higher biomass levels was evident. The AGB in Bwindi was estimated at 8.91 Tg ± 0.39 Tg (260.9 Mg ha-1 ± 11.4 Mg ha-1).

KW - ALOS PALSAR

KW - Biomass

KW - forestry

KW - radar remote sensing

KW - tropical forest

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DO - 10.1109/IGARSS.2013.6721325

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SN - 9781479911141

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

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BT - 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings

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Y2 - 21 July 2013 through 26 July 2013

ER -

Fedrigo M, Meir P, Sheil D , Van Heist M, Woodhouse IH, Mitchard ETA. Fusing radar and optical remote sensing for biomass prediction in mountainous tropical forests. In 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings. 2013. p. 975-978. 6721325. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2013.6721325

Fusing radar and optical remote sensing for biomass prediction in mountainous tropical forests

Abstract

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Keywords

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