Properties of ERS-1/2 coherence in the Siberian boreal forest and implications for stem volume retrieval

Santoro, Maurizio; Shvidenko, А.; McCallum, Ian; Askne, J.; Schmullius, Christiane

doi:10.1016/j.rse.2006.08.004

Cited by 72 publications

(52 citation statements)

References 25 publications

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“…The IWCM has been used for C-band data in order to derive stem volume from coherence, and it was found to be suitable for the retrieval of stem volume at several test sites in Sweden and Finland. For the one-day repeat-pass interval of the European Remote Sensing ERS-1/2 mission, the coherence for stable winter conditions was found to be useful for stem volume estimation [13,14,36,37], while the interferometric phase height was found unstable [38]. As will be illustrated below, it is instead primarily the interferometric phase height which has the highest sensitivity to biomass for TanDEM-X.…”

Section: Interferometric Water Cloud Modelmentioning

confidence: 96%

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

et al. 2013

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Above-ground forest biomass is a significant variable in the terrestrial carbon budget, but is still estimated with relatively large uncertainty. Remote sensing methods can improve the characterization of the spatial distribution and estimation accuracy of biomass; in this respect, it is important to examine the potential offered by new sensors. To assess the contribution of the TanDEM-X mission, eighteen interferometric Synthetic Aperture Radar (SAR) image pairs acquired over the hemi-boreal test site of Remningstorp in Sweden were investigated. Three models were used for interpretation of TanDEM-X signatures and above-ground biomass retrieval: Interferometric Water Cloud Model (IWCM), Random Volume over Ground (RVoG) model, and a simple model based on penetration depth (PD). All use an allometric expression to relate above-ground biomass to forest height measured by TanDEM-X. The retrieval was assessed on 201 forest stands with a minimum size of 1 ha, and ranging from 6 to 267 Mg/ha (mean biomass of 105 Mg/ha) equally divided into a model training dataset and a validation test dataset. Biomass retrieved using the IWCM resulted in a Root Mean Square Error (RMSE) between 17% OPEN ACCESS Remote Sens. 2013, 5 5575 and 33%, depending on acquisition date and image acquisition geometry (angle of incidence, interferometric baseline, and orbit type). The RMSE in the case of the RVoG and the PD models were slightly higher. A multitemporal estimate of the above-ground biomass using all eighteen acquisitions resulted in an RMSE of 16% with R 2 = 0.93. These results prove the capability of TanDEM-X interferometric data to estimate forest aboveground biomass in the boreal zone.

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Section: Interferometric Water Cloud Modelmentioning

confidence: 96%

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

et al. 2013

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“…The limited capability of the ALS first/last return data to explain the GSV variability due to forest structural differences in the horizontal forest growth dimension for stands with similar CH entails that, in order to produce GSV estimates without systematic bias when extrapolating from field data to ALS transects and eventually to the area covered by the spaceborne imagery, a rather large in situ dataset is required to cover not only the full range of GSV but also the regional distribution of RS, which influences the allometric relationship between CH and GSV (cf. [60]). For example, when models for the prediction of GSV are developed for stands with high RS (> 80%) and then used to estimate the GSV for stands with lower RS (<60%), the estimates are strongly biased (RMSE = 85.2 m 3 /ha, RMSEr = 36%, R 2 = 0.79, bias = 60 m 3 /ha) whereas the CH retrieval remains unbiased (RMSE= 2.1 m, RMSEr = 9.0%, R 2 = 0.89, bias = 0.02 m).…”

Section: Information Content Of the Als Canopy Structure Indicesmentioning

confidence: 99%

Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+

et al. 2012

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Abstract:We have investigated for forest plantations in Chile the stand-level retrieval of canopy height (CH) and growing stock volume (GSV) using Airborne Laser Scanner (ALS), ALOS PALSAR and Landsat. In a two-stage up-scaling approach, ensemble regression tree models (randomForest) were used to relate a suite of ALS canopy structure indices to stand-level in situ measurements of CH and GSV for 319 stands. The retrieval of CH and GSV with ALS yielded high accuracies with R 2 s of 0.93 and 0.81, respectively. A second set of randomForest models was developed using multi-temporal ALOS PALSAR intensities and repeat-pass coherences in two polarizations as well as Landsat data as predictor and stand-level ALS based estimates of CH and GSV as response variables. At three test sites, the retrieval of CH and GSV with PALSAR/Landsat reached promising accuracies with R 2 s in the range of 0.7 to 0.85. We show that the combined use of multi-temporal PALSAR intensity, coherence and Landsat yields higher retrieval accuracies than the retrieval with any of the datasets alone. Potential limitations for the large-area application of the fusion approach included (1) the low sensitivity of ALS first/last return data to forest horizontal structure, affecting the retrieval of GSV in less managed types of forest, and (2) the dense ALS sampling required to achieve high retrieval accuracies at larger scale.

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“…In addition, coherence maps from C-band ERS-1/2 including both volume and temporal decorrelation show a relationship with aboveground biomass [21,43] and stem volume [39,44]. Since our experiment employs a zero spatial baseline we can better examine the impacts of temporal decorrelation, in particular the interaction between weather events and forest structure on model fits.…”

Section: Discussionmentioning

confidence: 95%

Using InSAR Coherence to Map Stand Age in a Boreal Forest

2012

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Abstract:The interferometric coherence parameter γ estimates the degree of correlation between two Synthetic Aperture Radar (SAR) images and can be influenced by vegetation structure. Here, we investigate the use of repeat-pass interferometric coherence γ to map stand age, an important parameter for the study of carbon stocks and forest regeneration. In August 2009 NASA's L-band airborne sensor UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) acquired zero-baseline data over Quebec with temporal separation ranging between 45 min and 9 days. Our analysis focuses on a 66 km 2 managed boreal forest and addresses three questions: (i) Can coherence from L-band systems be used to model forest age? (ii) Are models sensitive to weather events and temporal baseline? and (iii) How is model accuracy impacted by the spatial scale of analysis? Linear regression models with 2-day baseline showed the best results and indicated an inverse relationship between γ and stand age. Model accuracy improved at 5 ha scale (R 2 = 0.75, RMSE = 5.3) as compared to 1 ha (R 2 = 0.67, RMSE = 5.8). Our results indicate that coherence measurements from L-band repeat-pass systems can estimate forest age accurately and with no saturation. However, empirical model relationships and their accuracy are sensitive to weather events, temporal baseline, and spatial scale of analysis.

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Properties of ERS-1/2 coherence in the Siberian boreal forest and implications for stem volume retrieval

Cited by 72 publications

References 25 publications

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+

Using InSAR Coherence to Map Stand Age in a Boreal Forest

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