Multi-temporal JERS SAR data in boreal forest biomass mapping

Research output: Contribution to journalArticleScientificpeer-review

94 Citations (Scopus)

Abstract

Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects. In single-date regression analysis between backscatter amplitude and stem volume, summer scenes from July to October produced correlation coefficients (r) between 0.63 and 0.81. Backscatter level and the slope of the (linear) regression line were stable from scene to scene. Winter scenes acquired in very cold and dry winter conditions had a very low correlation. One winter scene acquired in conditions where snow is not completely frozen produced a correlation coefficient similar to summer scenes. Multivariate regression analysis with a 6-date JERS SAR dataset produced correlation coefficient of 0.85. A combined JERS–optical regression analysis improved the correlation coefficient to 0.89 and also alleviated the saturation, which affects both SAR and optical data. The stability of the regression results in summer scenes suggests that a simple constant model could be used in wide-area forest biomass mapping if accuracy requirements are low and if biomass estimates are aggregated to large areal units.

Original languageEnglish
Pages (from-to)263 - 275
Number of pages13
JournalRemote Sensing of Environment
Volume97
Issue number2
DOIs
Publication statusPublished - 2005
MoE publication typeA1 Journal article-refereed

Fingerprint

boreal forests
Regression analysis
boreal forest
synthetic aperture radar
Biomass
biomass
regression analysis
backscatter
winter
Snow
summer
Linear regression
topographic effect
snow
Finland
Processing
JERS
stem
saturation
stems

Keywords

  • remote sensing
  • biomass
  • microwaves
  • boreal forest
  • carbon cycles
  • carbon capture
  • climate change
  • CCS

Cite this

@article{4c82dc0aac0d4a65aee6fdf8e271c853,
title = "Multi-temporal JERS SAR data in boreal forest biomass mapping",
abstract = "Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects. In single-date regression analysis between backscatter amplitude and stem volume, summer scenes from July to October produced correlation coefficients (r) between 0.63 and 0.81. Backscatter level and the slope of the (linear) regression line were stable from scene to scene. Winter scenes acquired in very cold and dry winter conditions had a very low correlation. One winter scene acquired in conditions where snow is not completely frozen produced a correlation coefficient similar to summer scenes. Multivariate regression analysis with a 6-date JERS SAR dataset produced correlation coefficient of 0.85. A combined JERS–optical regression analysis improved the correlation coefficient to 0.89 and also alleviated the saturation, which affects both SAR and optical data. The stability of the regression results in summer scenes suggests that a simple constant model could be used in wide-area forest biomass mapping if accuracy requirements are low and if biomass estimates are aggregated to large areal units.",
keywords = "remote sensing, biomass, microwaves, boreal forest, carbon cycles, carbon capture, climate change, CCS",
author = "Yrj{\"o} Rauste",
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language = "English",
volume = "97",
pages = "263 -- 275",
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}

Multi-temporal JERS SAR data in boreal forest biomass mapping. / Rauste, Yrjö.

In: Remote Sensing of Environment, Vol. 97, No. 2, 2005, p. 263 - 275.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Multi-temporal JERS SAR data in boreal forest biomass mapping

AU - Rauste, Yrjö

PY - 2005

Y1 - 2005

N2 - Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects. In single-date regression analysis between backscatter amplitude and stem volume, summer scenes from July to October produced correlation coefficients (r) between 0.63 and 0.81. Backscatter level and the slope of the (linear) regression line were stable from scene to scene. Winter scenes acquired in very cold and dry winter conditions had a very low correlation. One winter scene acquired in conditions where snow is not completely frozen produced a correlation coefficient similar to summer scenes. Multivariate regression analysis with a 6-date JERS SAR dataset produced correlation coefficient of 0.85. A combined JERS–optical regression analysis improved the correlation coefficient to 0.89 and also alleviated the saturation, which affects both SAR and optical data. The stability of the regression results in summer scenes suggests that a simple constant model could be used in wide-area forest biomass mapping if accuracy requirements are low and if biomass estimates are aggregated to large areal units.

AB - Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects. In single-date regression analysis between backscatter amplitude and stem volume, summer scenes from July to October produced correlation coefficients (r) between 0.63 and 0.81. Backscatter level and the slope of the (linear) regression line were stable from scene to scene. Winter scenes acquired in very cold and dry winter conditions had a very low correlation. One winter scene acquired in conditions where snow is not completely frozen produced a correlation coefficient similar to summer scenes. Multivariate regression analysis with a 6-date JERS SAR dataset produced correlation coefficient of 0.85. A combined JERS–optical regression analysis improved the correlation coefficient to 0.89 and also alleviated the saturation, which affects both SAR and optical data. The stability of the regression results in summer scenes suggests that a simple constant model could be used in wide-area forest biomass mapping if accuracy requirements are low and if biomass estimates are aggregated to large areal units.

KW - remote sensing

KW - biomass

KW - microwaves

KW - boreal forest

KW - carbon cycles

KW - carbon capture

KW - climate change

KW - CCS

U2 - 10.1016/j.rse.2005.05.002

DO - 10.1016/j.rse.2005.05.002

M3 - Article

VL - 97

SP - 263

EP - 275

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

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ER -