Estimation of Woody Biomass of Pine Savanna Woodlands From ALOS PALSAR Imagery

Dimitrios Michelakis, Neil Stuart, Matthew Brolly, Iain Woodhouse, German Lopez, Vinicio Linares

Research output: Contribution to journalArticleResearchpeer-review

Abstract

We present an adapted woody biomass retrieval approach for tropical savanna areas appropriate for use with satellite acquired L-band SAR imagery. We use the semiempirical water cloud model to describe the interaction between the SAR signal and vegetation and re-arrange the model to predict biomass. Estimations are made using dual polarization SAR imagery collected by ALOS PALSAR during 2008 in combination with community woodland inventory data from pine savanna areas in Belize. Estimation accuracy is assessed internally by the fit of the model to the ground training data, and then validated against an independent external dataset, quality controlled using Worldview II imagery. The internal validation shows a biomass estimation with an RMSE of 25 t/ha and a coefficient of determination R2 of 0.70, while the external validation indicates an RMSE of 13 t/ha with R2 of 0.53. This approach to biomass estimation appears to be most influenced by the plots with higher tree numbers and where the trees were more homogeneous. The existence of many similar sized individuals in those plots influence the SAR backscatter and is predicted to be the cause the elevated level of saturation found in this study (>100t/ha) with complete saturation predicted as a result of number density increases, and concurrently increasing basal area, both not exclusively dependent on biomass.
Original languageEnglish
Pages (from-to)244-254
Number of pages11
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume8
Issue number1
DOIs
Publication statusPublished - 6 Feb 2015

Fingerprint

PALSAR
ALOS
savanna
woodland
imagery
synthetic aperture radar
biomass
saturation
cloud water
basal area
backscatter
polarization
vegetation

Bibliographical note

© 2014 IEEE

Keywords

  • Carbon
  • forestry
  • radar imaging
  • satellite applications
  • synthetic aperture radar (SAR)

Cite this

Michelakis, Dimitrios ; Stuart, Neil ; Brolly, Matthew ; Woodhouse, Iain ; Lopez, German ; Linares, Vinicio. / Estimation of Woody Biomass of Pine Savanna Woodlands From ALOS PALSAR Imagery. In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015 ; Vol. 8, No. 1. pp. 244-254.
@article{0971ae35964b49618b49d1b84d877717,
title = "Estimation of Woody Biomass of Pine Savanna Woodlands From ALOS PALSAR Imagery",
abstract = "We present an adapted woody biomass retrieval approach for tropical savanna areas appropriate for use with satellite acquired L-band SAR imagery. We use the semiempirical water cloud model to describe the interaction between the SAR signal and vegetation and re-arrange the model to predict biomass. Estimations are made using dual polarization SAR imagery collected by ALOS PALSAR during 2008 in combination with community woodland inventory data from pine savanna areas in Belize. Estimation accuracy is assessed internally by the fit of the model to the ground training data, and then validated against an independent external dataset, quality controlled using Worldview II imagery. The internal validation shows a biomass estimation with an RMSE of 25 t/ha and a coefficient of determination R2 of 0.70, while the external validation indicates an RMSE of 13 t/ha with R2 of 0.53. This approach to biomass estimation appears to be most influenced by the plots with higher tree numbers and where the trees were more homogeneous. The existence of many similar sized individuals in those plots influence the SAR backscatter and is predicted to be the cause the elevated level of saturation found in this study (>100t/ha) with complete saturation predicted as a result of number density increases, and concurrently increasing basal area, both not exclusively dependent on biomass.",
keywords = "Carbon, forestry, radar imaging, satellite applications, synthetic aperture radar (SAR)",
author = "Dimitrios Michelakis and Neil Stuart and Matthew Brolly and Iain Woodhouse and German Lopez and Vinicio Linares",
note = "{\circledC} 2014 IEEE",
year = "2015",
month = "2",
day = "6",
doi = "10.1109/JSTARS.2014.2365253",
language = "English",
volume = "8",
pages = "244--254",
journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
issn = "1939-1404",
number = "1",

}

Estimation of Woody Biomass of Pine Savanna Woodlands From ALOS PALSAR Imagery. / Michelakis, Dimitrios; Stuart, Neil; Brolly, Matthew; Woodhouse, Iain; Lopez, German; Linares, Vinicio.

In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, No. 1, 06.02.2015, p. 244-254.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Estimation of Woody Biomass of Pine Savanna Woodlands From ALOS PALSAR Imagery

AU - Michelakis, Dimitrios

AU - Stuart, Neil

AU - Brolly, Matthew

AU - Woodhouse, Iain

AU - Lopez, German

AU - Linares, Vinicio

N1 - © 2014 IEEE

PY - 2015/2/6

Y1 - 2015/2/6

N2 - We present an adapted woody biomass retrieval approach for tropical savanna areas appropriate for use with satellite acquired L-band SAR imagery. We use the semiempirical water cloud model to describe the interaction between the SAR signal and vegetation and re-arrange the model to predict biomass. Estimations are made using dual polarization SAR imagery collected by ALOS PALSAR during 2008 in combination with community woodland inventory data from pine savanna areas in Belize. Estimation accuracy is assessed internally by the fit of the model to the ground training data, and then validated against an independent external dataset, quality controlled using Worldview II imagery. The internal validation shows a biomass estimation with an RMSE of 25 t/ha and a coefficient of determination R2 of 0.70, while the external validation indicates an RMSE of 13 t/ha with R2 of 0.53. This approach to biomass estimation appears to be most influenced by the plots with higher tree numbers and where the trees were more homogeneous. The existence of many similar sized individuals in those plots influence the SAR backscatter and is predicted to be the cause the elevated level of saturation found in this study (>100t/ha) with complete saturation predicted as a result of number density increases, and concurrently increasing basal area, both not exclusively dependent on biomass.

AB - We present an adapted woody biomass retrieval approach for tropical savanna areas appropriate for use with satellite acquired L-band SAR imagery. We use the semiempirical water cloud model to describe the interaction between the SAR signal and vegetation and re-arrange the model to predict biomass. Estimations are made using dual polarization SAR imagery collected by ALOS PALSAR during 2008 in combination with community woodland inventory data from pine savanna areas in Belize. Estimation accuracy is assessed internally by the fit of the model to the ground training data, and then validated against an independent external dataset, quality controlled using Worldview II imagery. The internal validation shows a biomass estimation with an RMSE of 25 t/ha and a coefficient of determination R2 of 0.70, while the external validation indicates an RMSE of 13 t/ha with R2 of 0.53. This approach to biomass estimation appears to be most influenced by the plots with higher tree numbers and where the trees were more homogeneous. The existence of many similar sized individuals in those plots influence the SAR backscatter and is predicted to be the cause the elevated level of saturation found in this study (>100t/ha) with complete saturation predicted as a result of number density increases, and concurrently increasing basal area, both not exclusively dependent on biomass.

KW - Carbon

KW - forestry

KW - radar imaging

KW - satellite applications

KW - synthetic aperture radar (SAR)

U2 - 10.1109/JSTARS.2014.2365253

DO - 10.1109/JSTARS.2014.2365253

M3 - Article

VL - 8

SP - 244

EP - 254

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

SN - 1939-1404

IS - 1

ER -