Improved mapping of tropical forests with optical and SAR imagery: Part II: Above ground biomass estimation

Tuomas Häme, Yrjö Rauste, Oleg Antropov, Heikki Ahola, Jorma Kilpi

Research output: Contribution to journalArticleScientificpeer-review

36 Citations (Scopus)

Abstract

Performance of the above ground (dry) biomass estimation with the medium resolution optical (ALOS AVNIR) data and radar (ALOS PALSAR) data was evaluated on a tropical forest site in Lao PDR (Laos). The average biomass of ground reference plots was relatively low, 78 t/ha, due to strong anthropogenic influence in most of the study area. The biomass estimates were computed using linear regression analysis and the Probability method that combines unsupervised clustering and fuzzy estimation. The predictions were validated with independent field plot data. With all the methods and data types, the root mean square error (RMSE) ranged from 33.6 t/ha to 40.1 t/ha (44.2% and 52.8% of mean biomass, respectively). The Probability method produced a larger dynamic range to the predictions than the regression models, which saturated at approximately 100 t/ha. Large errors for higher biomass plots increased the RMSE of Probability over the RMSE of the regression models. The bias ranged from -0.8 to 3.9% except with the Probability model for PALSAR data where the bias was 12.5%. Our study showed that PALSAR data were nearly as good for the biomass estimation as the AVNIR data. A combination of mono-temporal ALOS PALSAR and ALOS AVNIR data did not improve biomass estimation over the performance obtained with AVNIR data alone. For the Probability method, ground reference data should be more representative than that available in this study.
Original languageEnglish
Pages (from-to)92-101
Number of pages9
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume6
Issue number1
DOIs
Publication statusPublished - 2013
MoE publication typeA1 Journal article-refereed

Fingerprint

aboveground biomass
tropical forest
synthetic aperture radar
Biomass
imagery
AVNIR
PALSAR
ALOS
Mean square error
biomass
Linear regression
Regression analysis
Radar
prediction
regression analysis
radar
method

Keywords

  • ALOS AVNIR
  • ALOS PALSAR
  • accuracy assessment
  • REDD
  • SAR
  • biomass
  • optical
  • tropical forest

Cite this

@article{f92de1782c1c44e6bfc1525a604870f3,
title = "Improved mapping of tropical forests with optical and SAR imagery: Part II: Above ground biomass estimation",
abstract = "Performance of the above ground (dry) biomass estimation with the medium resolution optical (ALOS AVNIR) data and radar (ALOS PALSAR) data was evaluated on a tropical forest site in Lao PDR (Laos). The average biomass of ground reference plots was relatively low, 78 t/ha, due to strong anthropogenic influence in most of the study area. The biomass estimates were computed using linear regression analysis and the Probability method that combines unsupervised clustering and fuzzy estimation. The predictions were validated with independent field plot data. With all the methods and data types, the root mean square error (RMSE) ranged from 33.6 t/ha to 40.1 t/ha (44.2{\%} and 52.8{\%} of mean biomass, respectively). The Probability method produced a larger dynamic range to the predictions than the regression models, which saturated at approximately 100 t/ha. Large errors for higher biomass plots increased the RMSE of Probability over the RMSE of the regression models. The bias ranged from -0.8 to 3.9{\%} except with the Probability model for PALSAR data where the bias was 12.5{\%}. Our study showed that PALSAR data were nearly as good for the biomass estimation as the AVNIR data. A combination of mono-temporal ALOS PALSAR and ALOS AVNIR data did not improve biomass estimation over the performance obtained with AVNIR data alone. For the Probability method, ground reference data should be more representative than that available in this study.",
keywords = "ALOS AVNIR, ALOS PALSAR, accuracy assessment, REDD, SAR, biomass, optical, tropical forest",
author = "Tuomas H{\"a}me and Yrj{\"o} Rauste and Oleg Antropov and Heikki Ahola and Jorma Kilpi",
note = "Project code: 107021",
year = "2013",
doi = "10.1109/JSTARS.2013.2241020",
language = "English",
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pages = "92--101",
journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
issn = "1939-1404",
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}

Improved mapping of tropical forests with optical and SAR imagery : Part II: Above ground biomass estimation. / Häme, Tuomas; Rauste, Yrjö; Antropov, Oleg; Ahola, Heikki; Kilpi, Jorma.

In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 6, No. 1, 2013, p. 92-101.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Improved mapping of tropical forests with optical and SAR imagery

T2 - Part II: Above ground biomass estimation

AU - Häme, Tuomas

AU - Rauste, Yrjö

AU - Antropov, Oleg

AU - Ahola, Heikki

AU - Kilpi, Jorma

N1 - Project code: 107021

PY - 2013

Y1 - 2013

N2 - Performance of the above ground (dry) biomass estimation with the medium resolution optical (ALOS AVNIR) data and radar (ALOS PALSAR) data was evaluated on a tropical forest site in Lao PDR (Laos). The average biomass of ground reference plots was relatively low, 78 t/ha, due to strong anthropogenic influence in most of the study area. The biomass estimates were computed using linear regression analysis and the Probability method that combines unsupervised clustering and fuzzy estimation. The predictions were validated with independent field plot data. With all the methods and data types, the root mean square error (RMSE) ranged from 33.6 t/ha to 40.1 t/ha (44.2% and 52.8% of mean biomass, respectively). The Probability method produced a larger dynamic range to the predictions than the regression models, which saturated at approximately 100 t/ha. Large errors for higher biomass plots increased the RMSE of Probability over the RMSE of the regression models. The bias ranged from -0.8 to 3.9% except with the Probability model for PALSAR data where the bias was 12.5%. Our study showed that PALSAR data were nearly as good for the biomass estimation as the AVNIR data. A combination of mono-temporal ALOS PALSAR and ALOS AVNIR data did not improve biomass estimation over the performance obtained with AVNIR data alone. For the Probability method, ground reference data should be more representative than that available in this study.

AB - Performance of the above ground (dry) biomass estimation with the medium resolution optical (ALOS AVNIR) data and radar (ALOS PALSAR) data was evaluated on a tropical forest site in Lao PDR (Laos). The average biomass of ground reference plots was relatively low, 78 t/ha, due to strong anthropogenic influence in most of the study area. The biomass estimates were computed using linear regression analysis and the Probability method that combines unsupervised clustering and fuzzy estimation. The predictions were validated with independent field plot data. With all the methods and data types, the root mean square error (RMSE) ranged from 33.6 t/ha to 40.1 t/ha (44.2% and 52.8% of mean biomass, respectively). The Probability method produced a larger dynamic range to the predictions than the regression models, which saturated at approximately 100 t/ha. Large errors for higher biomass plots increased the RMSE of Probability over the RMSE of the regression models. The bias ranged from -0.8 to 3.9% except with the Probability model for PALSAR data where the bias was 12.5%. Our study showed that PALSAR data were nearly as good for the biomass estimation as the AVNIR data. A combination of mono-temporal ALOS PALSAR and ALOS AVNIR data did not improve biomass estimation over the performance obtained with AVNIR data alone. For the Probability method, ground reference data should be more representative than that available in this study.

KW - ALOS AVNIR

KW - ALOS PALSAR

KW - accuracy assessment

KW - REDD

KW - SAR

KW - biomass

KW - optical

KW - tropical forest

U2 - 10.1109/JSTARS.2013.2241020

DO - 10.1109/JSTARS.2013.2241020

M3 - Article

VL - 6

SP - 92

EP - 101

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 -