Polarimetric Parameters for Growing Stock Volume Estimation Using ALOS PALSAR L-Band Data over Siberian Forests

Chowdhury, Tanvir Ahmed; Thiel, Christian; Schmullius, Christiane; Stelmaszczuk-Górska, Martyna A.

doi:10.3390/rs5115725

Cited by 29 publications

(30 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One good candidate parameter is HV-backscatter widely used for assessment of forest biomass [65,66], as it is highly correlated to forest stem volume. Another representative PolSAR parameter we adopt is polarimetric coherence investigated in several earlier studies [42,43]. Another rationale for such choice is incorporating two PolSAR parameters with distinctly different dependence on stem volume, into the stem volume prediction.…”

Section: Temporal Dependence Of Polsar Parameters and Relation To Stementioning

confidence: 99%

“…Another problem is suboptimal inversion scenario in model-based approaches, which may complicate routine stem volume estimation due to non-physical, negative or unrealistically high estimates of AGB, as discussed in [39]. Chowdhury et al used PolSAR variables in linear regression fitting [43] and polarimetric coherence in semi-empirical model inversion [42] to recover growing stock volume of Siberian forests. Results were encouraging with up to 33 m 3 /ha RMSE.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

et al. 2017

View full text Add to dashboard Cite

Here, we examined multitemporal behavior of fully polarimetric SAR (PolSAR) parameters at L-band in relation to the stem volume of boreal forests. The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence and suitability for forest stem volume estimation across several seasonal conditions (frozen, thaw and unfrozen). The satellite SAR data were represented by a time series of PolSAR images acquired during several seasons in the years 2006 to 2009 by the ALOS PALSAR sensor. The study area was in central Finland, and represented a managed area in typical boreal mixed forest land. Utility of different PolSAR parameters, their temporal stability and cross-correlations were studied along with reference stand-level stem volume data from forest inventory. Further, two polarimetric parameters, cross-polarization backscatter and co-polarization coherence, were chosen for further investigation and stem volume retrieval. A relationship between forest stem volume and PolSAR parameters was established using the kNN regression approach. Ways of optimally combining PolSAR images were evaluated as well. For a single scene, best results were observed with polarimetric coherence (RMSE ≈ 38.8 m 3 /ha) for scene acquired in frozen conditions. An RMSE of 40.8 m 3 /ha (42.9%, R 2 = 0.66) was achieved for cross-polarization backscatter in the best case. Cross-polarization backscatter was a better predictor than polarimetric coherence for few summer scenes. Multitemporal aggregation of selected PolSAR scenes improved estimates for both studied PolSAR parameters. Stronger improvement was observed for coherence with RMSE down to 34 m 3 /ha (35.8%, R 2 = 0.77) compared to 38.8-51.6 m 3 /ha (40.8-54.3%) from separate scenes. Finally, the accuracy statistics reached RMSE of 32.2 m 3 /ha (34%, R 2 = 0.79) when multitemporal HHVV coherence was combined with multitemporal HV-backscatter.

show abstract

Section: Temporal Dependence Of Polsar Parameters and Relation To Stementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Moreover, synthetic aperture radar (SAR) data coupled with quad-polarimetric techniques have a stronger ability to account for the propagation and scattering mechanism than the single polarimetric SAR images [6]. With the capacity to penetrate forest canopies and interact with forest structures, quad-polarimetric SAR images provide great potential to improve the accuracy of forest GSV monitoring and assessment [6][7][8][9].Polarimetric characteristics that are highly sensitive to forest GSV can be used to accurately estimate forest GSV [6][7][8]10,11]. There are three kinds of polarimetric characteristics associated with forest GSV.…”

mentioning

confidence: 99%

“…There are three kinds of polarimetric characteristics associated with forest GSV. The first one is the trio of backscattering coefficients-horizontal-horizontal (HH), horizontal-vertical (HV) and vertical-vertical (VV) [7,8,12,13]. The backscattering coefficients of the dual and quad-polarization SAR images have been proved to be appropriate for estimating forest GSV [7,8,[11][12][13][14][15][16][17].…”

mentioning

confidence: 99%

Mapping Growing Stem Volume of Chinese Fir Plantation Using a Saturation-based Multivariate Method and Quad-polarimetric SAR Images

et al. 2019

View full text Add to dashboard Cite

For the planning and sustainable management of forest resources, well-managed plantations are of great significance to mitigate the decrease of forested areas. Monitoring these planted forests is essential for forest resource inventories. In this study, two ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images and ground measurements were employed to estimate growing stem volume (GSV) of Chinese fir plantations located in a hilly area of southern China. To investigate the relationships between forest GSV and polarization characteristics, single and fused variables were derived by the Yamaguchi decomposition and the saturation value of GSV was estimated using a semi-exponential empirical model as a base model. Based on the estimated saturation values and relative root mean square error (RRMSE), the single and fused characteristics and corresponding models were selected and integrated, which led to a novel saturation-based multivariate method used to improve the GSV estimation and mapping of Chinese fir plantations. The new findings included: (1) All the original polarimetric characteristics, statistically, were not significantly correlated with the forest GSV, and their logarithm and ratio transformation fused variables greatly improved the correlations, thus the estimation accuracy of the forest GSV. (2) The logarithm transformation of surface scattering resulted in the greatest saturation, value but the logarithm transformation of double-bounce scattering resulted in the smallest RRMSE of the GSV estimates. (3) Compared with the single transformations, the fused variables led to more reasonable saturation values and obviously reduced the values of RRMSE. (4) The saturation-based multivariate method led to more accurate estimates of the forest GSV than the univariate method, with the smallest value (29.64%) of RRMSE achieved using the set of six variables. This implied that the novel saturation-based multivariate method provided greater potential to improve the estimation and mapping of the forest GSV.during the past four decades. However, it is often difficult to directly obtain forest above ground biomass (AGB) for large areas. Rather than what is done for natural forests, instead, the AGB of plantations is usually derived by first measuring tree heights and diameters at breast height and calculating tree growing stem volumes (GSV) using allometric equations and then multiplying GSV with biomass expansion factors; that is, gravity coefficients by tree species. Thus, GSV is a basic and key factor in the planning and sustainable management of planted forest resources at regional scales [1-3].However, GSV is often obtained by ground measurements by sampling, which is time-consuming, labor-intensive and costly [4,5]. The topographic complexity of mountainous regions makes the ground-based survey even more difficult and costly. Remote sensing images have been widely used to map GSV on regional scales by combining them with field measurements of GSV at plot level. Because of the impacts of clouds, fog...

show abstract

“…Kwok et al [11] demonstrated the usability of the co-and cross-polarization signature for characterizing changes in the relative scattering contributions in the radar signal between frozen and thawed conditions. Recently, several studies applied polarimetric processing techniques to characterize wetland classes in Canada [16] and to estimate the growing stock volume in Siberian forest [21,22].…”

Section: Introductionmentioning

confidence: 99%

Variations of Microwave Scattering Properties by Seasonal Freeze/Thaw Transition in the Permafrost Active Layer Observed by ALOS PALSAR Polarimetric Data

Park

2015

Remote Sensing

View full text Add to dashboard Cite

Radar backscattering properties can be extremely sensitive to the freeze/thaw states of the ground surface. This study aims to evaluate the changes of L-band microwave scattering characteristics between thawed and frozen conditions by using polarimetric scattering mechanism indicators. ALOS PALSAR polarimetric mode data acquired in winter and spring seasons over Eastern Siberia are used in this study. Experimental results show that the actual scattering mechanisms and their seasonal variations over various forested and non-forested permafrost ecosystems can be successfully characterized by the polarimetric target decomposition parameters and the polarimetric coherences. In addition, fully polarimetric radar observations exhibit great potential for mapping land cover types and surficial features in the permafrost active layer. Particularly, the co-polarization coherences on the HV-polarization basis and circular-polarization basis were found to be very useful for discriminating different surficial geocryological characteristics in recently burnt forests and thermokarst regions.

show abstract

Polarimetric Parameters for Growing Stock Volume Estimation Using ALOS PALSAR L-Band Data over Siberian Forests

Cited by 29 publications

References 52 publications

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests

Mapping Growing Stem Volume of Chinese Fir Plantation Using a Saturation-based Multivariate Method and Quad-polarimetric SAR Images

Variations of Microwave Scattering Properties by Seasonal Freeze/Thaw Transition in the Permafrost Active Layer Observed by ALOS PALSAR Polarimetric Data

Contact Info

Product

Resources

About