Assessment of stand‐wise stem volume retrieval in boreal forest from JERS‐1 L‐band SAR backscatter

Santoro, Maurizio; Eriksson, Leif E. B.; Askne, J.; Schmullius, C.

doi:10.1080/01431160600646037

Cited by 95 publications

(86 citation statements)

References 35 publications

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“…To retrieve stem volume from the ALOS PALSAR backscatter data, we used a model-based approach exploiting a Water Cloud Model with gaps [5,39]. The individual estimates of stem volume obtained from each SAR backscatter image by inverting the trained model were then combined with a linear weighted function, referred to as multi-temporal combination [5].…”

Section: Stem Volume Retrieval Methodologymentioning

confidence: 99%

Section: Stem Volume Retrieval Methodologymentioning

confidence: 99%

“…The individual estimates of stem volume obtained from each SAR backscatter image by inverting the trained model were then combined with a linear weighted function, referred to as multi-temporal combination [5]. The modeling and retrieval approach was presented and discussed extensively for L-band in previous research papers [5,12,39]. An assessment of the performance of this approach to retrieve forest variables from L-band backscatter was recently presented with respect to other existing parametric and non-parametric approaches [40] did not show significant shortcomings of the Water Cloud Model with gaps.…”

Section: Stem Volume Retrieval Methodologymentioning

confidence: 99%

“…Single-pass interferometric X-band data yielded a relative RMSE of 23% (average over RMSEs from 18 image pairs); a multi-temporal combination of single-image estimates improved the retrieval error to 16% [25]. Multi-temporal retrieval of stem volume using L-band backscatter in Swedish boreal forest was investigated at the test site of Kä ttböle with nine backscatter images acquired by JERS-1 in 1997-1998 in single polarization (HH) and with a fixed look angle of 34.3° [5]. The retrieval was most accurate under unfrozen conditions, did not present systematic errors due to backscatter saturation in high stem volume forest and the RMSE was 25%.…”

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confidence: 99%

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Reviewing ALOS PALSAR Backscatter Observations for Stem Volume Retrieval in Swedish Forest

2015

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Between 2006 and 2011, the Advanced Land Observing Satellite (ALOS) Phased Array L-type Synthetic Aperture Radar (PALSAR) instrument acquired multi-temporal datasets under several environmental conditions and multiple configurations of look angle and polarization. The extensive archive of SAR backscatter observations over the forest test sites of Krycklan (boreal) and Remningstorp (hemi-boreal), Sweden, was used to assess the retrieval of stem volume at stand level. The retrieval was based on the inversion of a simple Water Cloud Model with gaps; single estimates of stem volume are then combined to obtain the final multi-temporal estimate. The model matched the relationship between the SAR backscatter and the stem volume under all configurations. The retrieval relative Root Mean Square Error (RMSE) differed depending upon environmental conditions, polarization and look angle. Stem volume was best retrieved in Krycklan using only HV-polarized data acquired under unfrozen conditions with a look angle of 34.3° (relative RMSE: 44.0%). In Remningstorp, the smallest error was obtained using only HH-polarized data acquired under predominantly frozen conditions with a look angle of 34.3° (relative RMSE: 35.1%). The relative RMSE was below 30% for stands >20 ha, suggesting high accuracy of ALOS PALSAR estimates of stem volumes aggregated at moderate resolution.

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Section: Stem Volume Retrieval Methodologymentioning

confidence: 99%

Section: Stem Volume Retrieval Methodologymentioning

confidence: 99%

Section: Stem Volume Retrieval Methodologymentioning

confidence: 99%

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confidence: 99%

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Reviewing ALOS PALSAR Backscatter Observations for Stem Volume Retrieval in Swedish Forest

2015

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“…Data from the Japanese Earth Resources Satellite (JERS) and the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), the former operational until 1998 and the latter until 2011, were extensively employed to estimate AGB in different forest ecosystems [15][16][17][18][19][20][21]. Previous literature results suggests that there is a saturation level above which there is a loss of sensitivity between AGB and backscattered signal [22].…”

Section: Introductionmentioning

confidence: 99%

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

et al. 2016

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Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R 2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R 2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar.

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Remote Sensing for Aboveground Biomass Estimation in Boreal Forests

Stelmaszczuk-Górska

Thiel

Schmullius

2017

Earth Observation for Land and Emergency Monitoring

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Assessment of stand‐wise stem volume retrieval in boreal forest from JERS‐1 L‐band SAR backscatter

Cited by 95 publications

References 35 publications

Reviewing ALOS PALSAR Backscatter Observations for Stem Volume Retrieval in Swedish Forest

Reviewing ALOS PALSAR Backscatter Observations for Stem Volume Retrieval in Swedish Forest

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

Remote Sensing for Aboveground Biomass Estimation in Boreal Forests

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