Remote Sensing for Aboveground Biomass Estimation in Boreal Forests

Stelmaszczuk-Górska, Martyna A.; Thiel, Christian; Schmullius, C.

doi:10.1002/9781118793787.ch3

Cited by 6 publications

(8 citation statements)

References 106 publications

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“…For HV-backscatter, scenes acquired over forests in "non-frozen" state were more suitable, as also supported by previous studies [5,7,42]. Overall, polarimetric coherence appeared to be a better predictor of the forest biomass for "frozen" scenes, as well as in winter conditions in the boreal zone (wet snow on March 2007 scene), while for other scenes the difference was quite marginal, aside from August 2006 scene (summer conditions) when the cross-pol backscatter performed better.…”

Section: Forest Stem Volume Estimationsupporting

confidence: 80%

“…Most recent research on L-band SAR based forest biomass (AGB, growing stock volume, stem volume) estimation is well summarized in ( [5], Table 3.1) and in ([6], Table 2). Among recent studies, in [40], the RMSE over natural taiga forest varied 25-32% of the mean biomass (R 2 in the range of 0.35-0.49); and, in [72], RMSE varied 31-46% of the mean biomass value and R 2 was 0.4-0.6 in forest stands in southern Sweden.…”

Section: Relative Performance Of Biomass Estimation Approachmentioning

confidence: 99%

“…Remote sensing of forest structure and biomass with synthetic aperture radar (SAR) bears significant potential for mapping and understanding forest ecological processes [5][6][7]. These techniques could serve as valuable methods for biomass assessment of heterogeneous complex biophysical environments.…”

Section: Introductionmentioning

confidence: 99%

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Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

et al. 2017

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Here, we examined multitemporal behavior of fully polarimetric SAR (PolSAR) parameters at L-band in relation to the stem volume of boreal forests. The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence and suitability for forest stem volume estimation across several seasonal conditions (frozen, thaw and unfrozen). The satellite SAR data were represented by a time series of PolSAR images acquired during several seasons in the years 2006 to 2009 by the ALOS PALSAR sensor. The study area was in central Finland, and represented a managed area in typical boreal mixed forest land. Utility of different PolSAR parameters, their temporal stability and cross-correlations were studied along with reference stand-level stem volume data from forest inventory. Further, two polarimetric parameters, cross-polarization backscatter and co-polarization coherence, were chosen for further investigation and stem volume retrieval. A relationship between forest stem volume and PolSAR parameters was established using the kNN regression approach. Ways of optimally combining PolSAR images were evaluated as well. For a single scene, best results were observed with polarimetric coherence (RMSE ≈ 38.8 m 3 /ha) for scene acquired in frozen conditions. An RMSE of 40.8 m 3 /ha (42.9%, R 2 = 0.66) was achieved for cross-polarization backscatter in the best case. Cross-polarization backscatter was a better predictor than polarimetric coherence for few summer scenes. Multitemporal aggregation of selected PolSAR scenes improved estimates for both studied PolSAR parameters. Stronger improvement was observed for coherence with RMSE down to 34 m 3 /ha (35.8%, R 2 = 0.77) compared to 38.8-51.6 m 3 /ha (40.8-54.3%) from separate scenes. Finally, the accuracy statistics reached RMSE of 32.2 m 3 /ha (34%, R 2 = 0.79) when multitemporal HHVV coherence was combined with multitemporal HV-backscatter.

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Section: Forest Stem Volume Estimationsupporting

confidence: 80%

Section: Relative Performance Of Biomass Estimation Approachmentioning

confidence: 99%

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Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

et al. 2017

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“…In particular, synthetic aperture radar (SAR) systems are of interest as they provide systematic, weather and sun independent observations. Moreover, SAR shows higher sensitivity to AGB in comparison with the optical sensors [9]. The latest results of biomass estimation using optical data (Landsat) showed measures of moderate accuracy, with a relative mean absolute error (rMAE) of approximately 36%, in the boreal zone [10].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Above-Ground Biomass over Boreal Forests in Siberia Using Updated In Situ, ALOS-2 PALSAR-2, and RADARSAT-2 Data

et al. 2018

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The estimation of above-ground biomass (AGB) in boreal forests is of special concern as it constitutes the highest carbon pool in the northern hemisphere. In particularly, monitoring of the forests in the Russian Federation is important as some regions have not been inventoried for many years. This study explores the combination of multi-frequency, multi-polarization, and multi-temporal radar data as one key approach to provide an accurate estimate of forest biomass. The data from L-band Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-Band Synthetic Aperture Radar 2 (PALSAR-2), together with C-band RADARSAT-2 data, were applied for AGB estimation. Backscatter coefficients from L- and C-band radar were used independently and in combination with a non-parametric model to retrieve AGB data for a boreal forest in Siberia (Krasnoyarskiy Kray). AGB estimation was performed using the random forests machine learning algorithm. The results demonstrated that high estimation accuracies can be achieved at a spatial resolution of 0.25 ha. When the L-band data alone were used for the retrieval, a corrected root-mean-square error (RMSEcor) of 29.4 t ha−1 was calculated. A marginal decrease in RMSEcor was observed when only the filtered L-band backscatter data, without ratio and texture, were used (29.1 t ha−1). The inclusion of the C-band data reduced the over and underestimation; the bias was reduced from 5.5 t ha−1 to 4.7 t ha−1; and a RMSEcor of 30.2 t ha−1 was calculated.

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“…A comparison of this work to other biomass estimates is difficult in part because biomass data is fairly limited. We refer readers to the excellent review articles [2] and [6] for numerous AGB studies with radar remote sensing. We note that our reference data set provides orders of magnitude more biomass samples than those found in field measurement AGB studies as our reference data set is lidar-derived.…”

Section: Introductionmentioning

confidence: 99%

A Regional L-Band High Biomass Estimation Framework Leveraging Spaceborne Lidar and Interferometric Data to Overcome Backscatter Saturation

Marshak

Simard

Duncanson

et al. 2020

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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We propose a framework to estimate high above ground biomass (AGB) from L-band SAR imagery leveraging spaceborne lidars such as GEDI or ICESat-2 and repeat-pass coherence. Our results indicate we are able to overcome model saturation typically associated with purely backscatter methodologies. We validate our approach using lidar-derived AGB maps from the AfriSAR datasets at Mondah, Ogooue, and Lope. We apply our framework to UAVSAR and ALOS-2 imagery to obtain 50 meter resolution biomass maps. We obtain < 60% nRMSE (in some cases much better) with negligible relative bias using a multiscale random forest model. We illustrate that the inclusion of coherence can significantly improve high AGB estimation particularly at the coastal site Mondah.

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Remote Sensing for Aboveground Biomass Estimation in Boreal Forests

Cited by 6 publications

References 106 publications

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

Estimation of Above-Ground Biomass over Boreal Forests in Siberia Using Updated In Situ, ALOS-2 PALSAR-2, and RADARSAT-2 Data

A Regional L-Band High Biomass Estimation Framework Leveraging Spaceborne Lidar and Interferometric Data to Overcome Backscatter Saturation

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