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Forest canopy height is a basic metric characterizing forest growth and carbon sink capacity. Based on full-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) data, this study used Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technology to estimate forest canopy height. In total the four methods of differential DEM (digital elevation model) algorithm, coherent amplitude algorithm, coherent phase-amplitude algorithm and three-stage random volume over ground algorithm (RVoG_3) were proposed to obtain canopy height and their accuracy was compared in consideration of the impacts of coherence coefficient and range slope levels. The influence of the statistical window size on the coherence coefficient was analyzed to improve the estimation accuracy. On the basis of traditional algorithms, time decoherence was performed on ALOS/PALSAR data by introducing the change rate of Landsat NDVI (Normalized Difference Vegetation Index). The slope in range direction was calculated based on SRTM (Shuttle Radar Topography Mission) DEM data and then introduced into the s-RVoG (sloped-Random Volume over Ground) model to optimize the canopy height estimation model and improve the accuracy. The results indicated that the differential DEM algorithm underestimated the canopy height significantly, while the coherent amplitude algorithm overestimated the canopy height. After removing the systematic coherence, the overestimation of the RVoG_3 model was restrained, and the absolute error decreased from 23.68 m to 4.86 m. With further time decoherence, the determination coefficient increased to 0.2439. With the introduction of range slope, the s-RVoG model shows improvement compared to the RVoG model. Our results will provide a reference for the appropriate algorithm selection and optimization for forest canopy height estimation using full-polarized L-band synthetic aperture radar (SAR) data for forest ecosystem monitoring and management.

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.

This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest ground-to-volume amplitude ratio (GVR) by employing an additional baseline to constrain the inversion procedure. In this paper, we present for the first time an assessment of such a method on real PolInSAR data over boreal forest. Additionally, we propose an improvement on the original DBPI method by incorporating the sloped random volume over ground (S-RVoG) model in order to reduce the range terrain slope effect. Therefore, a digital elevation model (DEM) is needed to provide the slope information in the proposed method. Three scenes of P-band airborne PolInSAR data acquired by E-SAR and light detection and ranging (LIDAR) data available in the BioSAR2008 campaign are employed for testing purposes. The performance of the SBPI, DBPI, and modified DBPI methods is compared. The results show that the DBPI method extracts forest heights with an average root mean square error (RMSE) of 4.72 m against LIDAR heights for trees of 18 m height on average. It presents a significant improvement of forest height accuracy over the SBPI method (with a stand-level mean improvement of 42.86%). Concerning the modified DBPI method, it consistently improves the accuracy of forest height inversion over sloped areas. This improvement reaches a stand-level mean of 21.72% improvement (with a mean RMSE of 4.63 m) for slopes greater than 10°.

Since the terrain slope cannot be neglected for forest height inversion with polarimetric synthetic aperture radar interferometry (PolInSAR), a sloped random volume over ground (S-RVoG) model is proposed to correct the terrain distortion for forest parameters estimation. A significant model complexity reduction is achieved by aligning the reference frame along the local terrain slope and changing the corresponding radar geometrical configuration. The proposed S-RVoG model inversion promises to provide much more accurate estimation of forest parameters and is validated with L-band PolInSAR data produced by PolSARpro software developed by the European Space Agency (ESA).

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.

P237; 针对经典的PolInSAR森林高度三阶段几何反演算法在单基线条件容易受到地体幅度比假设以及地形坡度影响的问题,从测量平差角度提出了基于S-RVoG模型的PolInSAR非线性复数最小二乘森林高度反演算法.该算法不再需要假设某一个极化通道地体幅度比为零,且采用考虑地形坡度影响的S-RV oG模型作为平差模型.为了验证算法,本文采用欧空局Bi oSAR2008项目提供的3景P波段极化干涉SAR数据进行两组单基线森林高度反演试验.结果表明,在单基线条件下,基于RV oG模型的非线性复数最小二乘算法反演结果优于三阶段几何反演算法,而基于S-RV oG模型的非线性复数最小二乘算法进一步提高反演精度,对于坡度较大区域(坡度>10°),精度平均提高了18.48％.

针对极化干涉SAR植被高度反演中RVOG 模型未考虑地形影响,且三阶段算法受到地面相位估计误差和纯体相干性估计误差影响,提出了一种植被高度反演思路,采用考虑地形因素的SGRVOG 模型作为反演模型校正地形影响,同时引入PD 相干最优算法用于改善三阶段算法中直线拟合地表相位估计和纯体相干性估计精度.为验证算法的有效性,首先采用欧空局提供的PolSARpro软件模拟了不同地形坡度水平的PolInSAR数据进行仿真试验,然后采用德国宇航局提供的EGSAR 机载全极化SAR数据进行真实植被场景测试,并进行了定性和定量分析.结果表明,本文方法对于不同坡度水平数据,均能有效改善传统RVOG 反演模型中地形影响和三阶段算法自身误差影响,反演精度更高.