C-band repeat-pass interferometric SAR observations of the forest

Askne, J.; Dammert, Patrik; Ulander, Lars M. H.; Smith, G.

doi:10.1109/36.551931

Cited by 299 publications

(206 citation statements)

References 12 publications

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“…Equation (2) was derived using measurements from test sites in Sweden and Finland and verified by means of 4,188 randomly chosen National Forest Inventory field plots located in different regions of Sweden [11,13]. Combining Equations (1) and (2), an allometric relationship between biomass and height was obtained:…”

Section: Field Observationsmentioning

confidence: 99%

“…Equation (4) shows that β characterizes the transition from dominant ground scattering to dominant vegetation scattering. If the variation of scattering with height is only determined by the attenuation, the volume decorrelation is determined by, [11]:…”

Section: Interferometric Water Cloud Modelmentioning

confidence: 99%

“…The IWCM was introduced to explain the coherence of forest at C-band [11,12,14]. The model assumes that the medium, characterized by a certain forest height, h, and stem volume, V, can be described by a random vegetation layer like the Water Cloud Model [33], with uniform scatterer density but generalized to include gaps.…”

Section: Interferometric Water Cloud Modelmentioning

confidence: 99%

“…For this, single polarized (VV), bistatic interferometric TanDEM-X data acquired between June 2011 and August 2012 over Remningstorp, a hemi-boreal test site situated in southern Sweden, were used. Three InSAR models were employed and evaluated: Interferometric Water Cloud Model (IWCM) [11][12][13][14], Random Volume over Ground model (RVoG) [15][16][17] and a simple model based on the penetration depth (PD) of X-band microwaves. As reference, biomass estimates derived from LiDAR scanning data acquired during the BioSAR 2010 campaign [18,19], performed within the BIOMASS phase-A study [20] were used.…”

Section: Introductionmentioning

confidence: 99%

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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: Field Observationsmentioning

confidence: 99%

Section: Interferometric Water Cloud Modelmentioning

confidence: 99%

Section: Interferometric Water Cloud Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2013

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“…The retrieval was based on a Water Cloud type of model linking the forest GSV, V, in m 3 /ha to the forest backscatter coefficient, σ 0 for , in linear scale [33,36,51,52]. This modeling solution allows for a complete characterization of the backscattered intensity from a forest at C-band in terms of its main contributions with a simple formulation.…”

Section: Forest Backscatter Model and Estimation Of Model Parametersmentioning

confidence: 99%

Estimates of Forest Growing Stock Volume for Sweden, Central Siberia, and Québec Using Envisat Advanced Synthetic Aperture Radar Backscatter Data

et al. 2013

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A study was undertaken to assess Envisat Advanced Synthetic Aperture Radar (ASAR) ScanSAR data for quantifying forest growing stock volume (GSV) across three boreal regions with varying forest types, composition, and structure (Sweden, Central Siberia, and Québec). Estimates of GSV were obtained using hyper-temporal observations of the radar backscatter acquired by Envisat ASAR with the BIOMASAR algorithm. In total, 5.3×106 km2 were mapped with a 0.01° pixel size to obtain estimates representative for the year of 2005. Comparing the SAR-based estimates to spatially explicit datasets of GSV, generated from forest field inventory and/or Earth Observation data, revealed similar spatial distributions of GSV. Nonetheless, the weak sensitivity of C-band backscatter to forest structural parameters introduced significant uncertainty to the estimated GSV at full resolution. Further discrepancies were observed in the case of different scales of the ASAR and the reference GSV and in areas of fragmented landscapes. Aggregation to 0.1° and 0.5° was then undertaken to generate coarse scale estimates of GSV. The agreement between ASAR and the reference GSV datasets improved; the relative difference at 0.5° was consistently within a magnitude of 20–30%. The results indicate an improvement of the characterization of forest GSV in the boreal zone with respect to currently available information

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Computer Processing of Remotely‐Sensed Images

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C-band repeat-pass interferometric SAR observations of the forest

Cited by 299 publications

References 12 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

Estimates of Forest Growing Stock Volume for Sweden, Central Siberia, and Québec Using Envisat Advanced Synthetic Aperture Radar Backscatter Data

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