S‐RVoG model for forest parameters inversion over underlying topography

Lu, Hongxi; Suo, Zhiyong; Guo, Rui; Bao, Zheng

doi:10.1049/el.2012.4467

Cited by 40 publications

(40 citation statements)

References 6 publications

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“…The S-RVoG model (Lu, 2013) based on RVoG model describes the dependence of coherence on forest height, average extinction and especially the local range terrain slope. By aligning the reference frame along the local terrain slope and changing the corresponding radar geometrical configuration, a significant model complexity reduction is achieved.…”

Section: S-rvog Modelmentioning

confidence: 99%

Research on Inversion Models for Forest Height Estimation Using Polarimetric Sar Interferometry

Zhang

Duan

Zou

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The forest height is an important forest resource information parameter and usually used in biomass estimation. Forest height extraction with PolInSAR is a hot research field of imaging SAR remote sensing. SAR interferometry is a well-established SAR technique to estimate the vertical location of the effective scattering center in each resolution cell through the phase difference in images acquired from spatially separated antennas. The manipulation of PolInSAR has applications ranging from climate monitoring to disaster detection especially when used in forest area, is of particular interest because it is quite sensitive to the location and vertical distribution of vegetation structure components. However, some of the existing methods can't estimate forest height accurately. Here we introduce several available inversion models and compare the precision of some classical inversion approaches using simulated data. By comparing the advantages and disadvantages of these inversion methods, researchers can find better solutions conveniently based on these inversion methods.

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Section: S-rvog Modelmentioning

confidence: 99%

Research on Inversion Models for Forest Height Estimation Using Polarimetric Sar Interferometry

Zhang

Duan

Zou

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…So far, the RVoG model depicting the forest vertical backscatter profile with an exponential function has been widely taken advantage of in forest height estimation [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Moroever, when referring to temporal decorrelation [21][22][23][24][25], canopy filling [12,26] and terrain fluctuations [6,[27][28][29][30][31][32], some modified models were proposed to complement the RVoG model. Under the circumstance of the Quad-pol single-baseline observation, the forest height estimation based on the RVoG model is a six-dimensional parameter optimization problem [1,3,5,10,12].…”

Section: Introductionmentioning

confidence: 99%

S-RVoG Model Inversion Based on Time-Frequency Optimization for P-Band Polarimetric SAR Interferometry

et al. 2019

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This paper investigates the potential of the time-frequency optimization on the basis of the sublook decomposition for forest height estimation. The optimization is deemed to be capable of extracting a relatively accurate volume contribution when P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) systems are adopted to observe forest-covered areas. The highest and the lowest phase centers acquired by the time-frequency optimization modify the conventional three-stage inversion process. This paper presents, for the first time, a performance assessment of the time-frequency optimization on P-band Pol-InSAR data over boreal forests. Simultaneously, to alleviate the model inversion errors caused by topographic fluctuations, forest height is estimated based on the sloped Random Volume over Ground (S-RVoG) model in which the incidence angle is corrected with the terrain slope. The E-SAR P-band Pol-InSAR data acquired during the BIOSAR 2008 campaign in Northern Sweden is utilized to evaluate the performance of the proposed method. From the results of the forest height estimation preprocessed with time-frequency optimization, the root mean square error (RMSE) of Random Volume over Ground (RVoG) and S-RVoG model on negative slope are 5.09 m and 4.71 m, respectively. It is concluded that the time-frequency processing and negative terrain slope compensation improve the inversion performance by 41 . 49 % and 11 . 96 % , respectively.

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“…The RVoG model depicts the scattering process of forest scene as a random volume over ground [9]. In the past few years, this model has been developed to improve the accuracy of forest height inversion, such as the RVoG + TD model considering temporal decorrelation [18][19][20][21][22], the RVoG+CFF model [23] considering canopy-fill-factor, and the S-RVoG model [24,25] considering the slope of topography.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data

Wang

et al. 2016

Remote Sensing

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Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground (RVoG) model. However, the RVoG model is characterized by a lot of parameters, which influence its applicability in forest height retrieval. Forest density, as an important biophysical parameter, is one of those main influencing factors. However, its influence to the RVoG model has been ignored in relating researches. For this paper, we study the applicability of the RVoG model in forest height retrieval with different forest densities, using the simulated and real Polarimetric Interferometric SAR data. P-band ESAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign were selected for experiments. The test site was located in Krycklan River catchment in Northern Sweden. The experimental results show that the forest density clearly affects the inversion accuracy of forest height and ground phase. For the four selected forest stands, with the density increasing from 633 to 1827 stems/Ha, the RMSEs of inversion decrease from 4.6 m to 3.1 m. The RVoG model is not quite applicable for forest height retrieval especially in sparsely vegetated areas. We conclude that the forest stand density is positively related to the estimation accuracy of the ground phase, but negatively correlates to the ground-to-volume scattering ratio.

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S‐RVoG model for forest parameters inversion over underlying topography

Cited by 40 publications

References 6 publications

Research on Inversion Models for Forest Height Estimation Using Polarimetric Sar Interferometry

Research on Inversion Models for Forest Height Estimation Using Polarimetric Sar Interferometry

S-RVoG Model Inversion Based on Time-Frequency Optimization for P-Band Polarimetric SAR Interferometry

The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data

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