Biomass estimation from polarimetric SAR interferometry over heterogeneous forest terrain

Mette, Tobias; Papathanassiou, K.; Hajnsek, Irena

doi:10.1109/igarss.2004.1369076

Cited by 63 publications

(40 citation statements)

References 8 publications

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“…Furthermore, a patch-level analysis helps attenuate high-frequency noise in image data. For example, ALOS Phased Array type L-band Synthetic Aperture Radar (PALSAR) backscatter has significant pixel-level speckle (Le Toan et al, 2011;Mette et al, 2004;Shamsoddini and Trinder, 2012) which, when grouped with coincident HRSI patch boundaries, can be averaged to reduce the noise and quantified further with a variance estimate.…”

Section: A Conceptual Model Of the Tte: Forest Patches Ecotone Formmentioning

confidence: 99%

Spaceborne potential for examining taiga–tundra ecotone form and vulnerability

et al. 2016

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Abstract. In the taiga-tundra ecotone (TTE), site-dependent forest structure characteristics can influence the subtle and heterogeneous structural changes that occur across the broad circumpolar extent. Such changes may be related to ecotone form, described by the horizontal and vertical patterns of forest structure (e.g., tree cover, density, and height) within TTE forest patches, driven by local site conditions, and linked to ecotone dynamics. The unique circumstance of subtle, variable, and widespread vegetation change warrants the application of spaceborne data including high-resolution (< 5 m) spaceborne imagery (HRSI) across broad scales for examining TTE form and predicting dynamics. This study analyzes forest structure at the patch scale in the TTE to provide a means to examine both vertical and horizontal components of ecotone form. We demonstrate the potential of spaceborne data for integrating forest height and density to assess TTE form at the scale of forest patches across the circumpolar biome by (1) mapping forest patches in study sites along the TTE in northern Siberia with a multi-resolution suite of spaceborne data and (2) examining the uncertainty of forest patch height from this suite of data across sites of primarily diffuse TTE forms. Results demonstrate the opportunities for improving patch-scale spaceborne estimates of forest height, the vertical component of TTE form, with HRSI. The distribution of relative maximum height uncertainty based on prediction intervals is centered at ∼ 40 %, constraining the use of height for discerning differences in forest patches. We discuss this uncertainty in light of a conceptual model of general ecotone forms and highlight how the uncertainty of spaceborne estimates of height can contribute to the uncertainty in identifying TTE forms. A focus on reducing the uncertainty of height estimates in forest patches may improve depiction of TTE form, which may help explain variable forest responses in the TTE to climate change and the vulnerability of portions of the TTE to forest structure change.

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Section: A Conceptual Model Of the Tte: Forest Patches Ecotone Formmentioning

confidence: 99%

Spaceborne potential for examining taiga–tundra ecotone form and vulnerability

et al. 2016

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“…In the case of forest cover, the random volume over ground (RVoG) model proposed in [5] and [6] has been successfully inverted for forest height and biomass [3], [7]. Instead, when working with agricultural crops, the vertical orientation of the stems and other preferred orienta- tions of different plant parts suggest that the best candidate model corresponds to an oriented VoG (OVoG) [5], [6], [8].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the model is also very simple from the electromagnetic point of view since the interaction between the vegetation elements is simplified in the formulation. (4) according to the framework proposed in [3]. z denotes the vertical coordinate, hv is the vegetation height (i.e., the depth of the layer), and θ 0 is the mean incidence angle.…”

Section: Introductionmentioning

confidence: 99%

“…T HE ESTIMATION of biophysical parameters of vegetation cover from radar remote sensors with interferometric and polarimetric capabilities [polarimetric interferometric synthetic aperture radar (PolInSAR)] has been demonstrated recently at different frequencies: P-band [1], L-band [2], [3], and even X-band [4]. In the case of forest cover, the random volume over ground (RVoG) model proposed in [5] and [6] has been successfully inverted for forest height and biomass [3], [7].…”

Section: Introductionmentioning

confidence: 99%

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Model Limitations and Parameter-Estimation Methods for Agricultural Applications of Polarimetric SAR Interferometry

López-Sánchez

Ballester-Berman

Márquez-Moreno

2007

IEEE Trans. Geosci. Remote Sensing

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Abstract-Application of polarimetric synthetic aperture radar interferometry to the retrieval of geophysical parameters from vegetated scenes is based on simple direct models of such scenes. The first part of this paper presents an analysis of the correspondence between these simple models, namely, the random volume over ground and the oriented volume over ground (OVoG), and experimental data from samples of two agricultural crops (maize and rice) acquired in controlled conditions. Although an overall agreement between model and data is clear, some discrepancies have been found as a consequence of two assumptions in the model formulation: vertical homogeneity of the vegetation volume and absence of multiple scattering effects inside the volume. This paper presents the shape and location of the visible region of the experimental coherences on the complex plane and compares it with the feasible region predicted by the model. This comparison has also pointed out the low sensitivity of the direct model to extinction coefficients. In the second part, two different strategies for a complete inversion (i.e., estimation of all model parameters) of the OVoG model are proposed and compared, using the same data set. The first one is based on a combination of geometrical and numerical approaches (genetic algorithms) and the second one on a dual-baseline configuration. In all cases, ground topography is accurately estimated, with a maximum error of 10 cm. Vegetation height estimates are accurate up to 30 cm, with some bands and baseline configurations providing errors below 15 cm. However, results obtained for the extinction coefficients are not stable with frequency and exhibit high variability.Index Terms-Agriculture, dual baseline, genetic algorithms, parameter retrieval, polarimetric synthetic aperture radar interferometry, vegetation.

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“…The recently development new radar techniques, Polarimetric and Interferometric SAR (POLINSAR), give the multitude observers to describe the physical and geometry characteristics which used in digital elevation model (DEM) estimation, vegetation structure estimation and the target recognition, .etc (Saatchi, S., 1997;Neumann, M., 2010;Mette, T.,2004).…”

Section: Introductionmentioning

confidence: 99%

The Low Backscattering Targets Classification in Urban Areas

Shi

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The Polarimetric and Interferometric Synthetic Aperture Radar (POLINSAR) is widely used in urban area nowadays. Because of the physical and geometric sensitivity, the POLINSAR is suitable for the city classification, power-lines detection, building extraction, etc. As the new X-band POLINSAR radar, the china prototype airborne system, XSAR works with high spatial resolution in azimuth (0.1m) and slant range (0.4m). In land applications, SAR image classification is a useful tool to distinguish the interesting area and obtain the target information. The bare soil, the cement road, the water and the building shadow are common scenes in the urban area. As it always exists low backscattering sign objects (LBO) with the similar scattering mechanism (all odd bounce except for shadow) in the XSAR images, classes are usually confused in Wishart-H-Alpha and Freeman-Durden methods. It is very hard to distinguish those targets only using the general information. To overcome the shortage, this paper explores an improved algorithm for LBO refined classification based on the Pre-Classification in urban areas. Firstly, the PreClassification is applied in the polarimetric datum and the mixture class is marked which contains LBO. Then, the polarimetric covariance matrix C3 is re-estimated on the Pre-Classification results to get more reliable results. Finally, the occurrence space which combining the entropy and the phase-diff standard deviation between HH and VV channel is used to refine the PreClassification results. The XSAR airborne experiments show the improved method is potential to distinguish the mixture classes in the low backscattering objects.

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Biomass estimation from polarimetric SAR interferometry over heterogeneous forest terrain

Cited by 63 publications

References 8 publications

Spaceborne potential for examining taiga–tundra ecotone form and vulnerability

Spaceborne potential for examining taiga–tundra ecotone form and vulnerability

Model Limitations and Parameter-Estimation Methods for Agricultural Applications of Polarimetric SAR Interferometry

The Low Backscattering Targets Classification in Urban Areas

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