Potential of Space-Borne PolInSAR for Forest Canopy Height Estimation Over India—A Case Study Using Fully Polarimetric L-, C-, and X-Band SAR Data

Khati, Unmesh; Singh, Gulab; Kumar, Shashi

doi:10.1109/jstars.2018.2835388

Cited by 40 publications

(25 citation statements)

References 33 publications

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“…The TSI model is an extension of the fully polarimetric SAR data-based expression of a two-layer model [66] to estimate the vertical structure of tree species [41]. The PolInSAR-based TSI model is very popular for forest height retrieval [49,58,[67][68][69][70]. The TSI model includes the identification of the ground phase and volumetric coherence of the effective scattering center of forest vegetation for inversion-based forest height retrieval.…”

Section: Three-stage Inversion (Tsi) Modellingmentioning

confidence: 99%

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

et al. 2020

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Spaceborne and airborne polarimetric synthetic-aperture radar interferometry (PolInSAR) data have been extensively used for forest parameter retrieval. The PolInSAR models have proven their potential in the accurate measurement of forest vegetation height. Spaceborne monostatic multifrequency data of different SAR missions and the Global Ecosystem Dynamics Investigation (GEDI)-derived forest canopy height map were used in this study for vegetation height retrieval. This study tested the performance of PolInSAR complex coherence-based inversion models for estimating the vegetation height of the forest ranges of Doon Valley, Uttarakhand, India. The inversion-based forest height obtained from the three-stage inversion (TSI) model had higher accuracy than the coherence amplitude inversion (CAI) model-based estimates. The vegetation height values of GEDI-derived canopy height map did not show good relation with field-measured forest height values. It was found that, at several locations, GEDI-derived forest height values underestimated the vegetation height. The statistical analysis of the GEDI-derived estimates with field-measured height showed a high root mean square error (RMSE; 5.82 m) and standard error (SE; 5.33 m) with a very low coefficient of determination (R2; 0.0022). An analysis of the spaceborne-mission-based forest height values suggested that the L-band SAR has great potential in forest height retrieval. TSI-model-based forest height values showed lower p-values, which indicates the significant relation between modelled and field-measured forest height values. A comparison of the results obtained from different SAR systems is discussed, and it is observed that the L-band-based PolInSAR inversion gives the most reliable result with low RMSE (2.87 m) and relatively higher R2 (0.53) for the linear regression analysis between the modelled tree height and the field data. These results indicate that higher wavelength PolInSAR datasets are more suitable for tree canopy height estimation using the PolInSAR inversion technique.

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Section: Three-stage Inversion (Tsi) Modellingmentioning

confidence: 99%

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

et al. 2020

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“…However, due to the limited spatial coverage, airborne SAR is not suitable for estimating the height of large-scale forests. Spaceborne platforms can acquire SAR images covering a large area with repeat-pass configurations, but the acquired InSAR or PolInSAR data have not been widely used for forest height inversion because of the temporal decorrelation and low sensitivity to forest height attributed to the long temporal baseline and short spatial baseline, respectively [ 20 , 28 , 29 , 30 ]. Yang et al [ 31 ] proposed a forest height inversion framework based on a modified RVoG model, which takes into account the characteristics of a spaceborne repeat-pass SAR system.…”

Section: Introductionmentioning

confidence: 99%

Sub-Canopy Topography Estimation from TanDEM-X DEM by Fusing ALOS-2 PARSAR-2 InSAR Coherence and GEDI Data

Tan

Zhu

et al. 2020

Sensors

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This paper develops a framework for extracting sub-canopy topography from the TanDEM-X digital elevation model (DEM) by fusing ALOS-2 PARSAR-2 interferometric synthetic aperture radar (InSAR) coherence and Global Ecosystem Dynamics Investigation (GEDI) data. The main idea of this method is to estimate the forest height signals caused by the limited penetration of the X-band into the canopy from the TanDEM-X DEM. To achieve this goal, a spaceborne repeat-pass InSAR coherent scattering model is first used to estimate the forest height by the ALOS-2 PARSAR-2 InSAR coherence (APIC), taking the GEDI canopy height as the reference. Then, a linear regression model of the TanDEM-X DEM Vegetation Bias (TDVB) depending on the forest height and the fraction of vegetation cover (FVC) is established and used to estimate the sub-canopy topography. The proposed method was validated by the data of the Amazon rainforest and a boreal forest in Canada. The results showed that the proposed method extracted the sub-canopy topography at the study sites in the tropical forest and boreal forest with the root mean square error of 4.0 m and 6.33 m, respectively, and improved the TanDEM-X DEM accuracy by 75.7% and 39.7%, respectively.

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“…Only a few studies investigated the transferability of methods to other environments and the impact of forest stratification on model performance [34]. The majority of studies was on boreal [8,14,16,18,19,21,24] and tropical [15,22,25,28,33] forests. In addition, the quality and accuracy of a model depend on the definition of the reference forest height that is investigated.…”

Section: Introductionmentioning

confidence: 99%

“…This is the main reason why most applications of scattering and vertical structure model inversion rely on low frequency like L-or P-band PolInSAR data [7,14]. The effect of wavelength on retrieving forest height from the random volume over ground (RVoG), a two-layer and vertical structure model, and its inversion was recently demonstrated by [15] who compared X-, C-, and L-band and found that the RMSE of forest height inversion increased with increasing frequency. Nevertheless, single-polarized X-band InSAR data are used in scattering models under simplified assumptions or when ancillary data are available to replace missing scattering information of the ground layer [16][17][18][19][20].…”

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Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests

et al. 2019

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Synthetic aperture radar (SAR) satellite data provide a valuable means for the large-scale and long-term monitoring of structural components of forest stands. The potential of TanDEM-X interferometric SAR (InSAR) for the assessment of forest structural properties has been widely verified. However, present studies are mostly restricted to homogeneous forests and do not account for stratification in assessing model performance. A systematic sensitivity analysis of the TanDEM-X SAR signal to forest structural parameters was carried out with emphasis on different strata of forest stands (location of the study site, forest type, and development stage). Forest structure was parameterized by forest height metrics and stem volume. Results show that X-band volume coherence is highly sensitive to the forest canopy. Volume scattering within the canopy is dependent on the vertical heterogeneity of the forest stand. In general, TanDEM-X coherence is more sensitive to forest vertical structure compared to backscatter. The relations between TanDEM-X volume coherence and forest structural properties were significant at the level of a single test site as well as across sites in temperate forests in Germany. Forest type does not affect the overall relationship between the SAR signal and the forests' vertical structure. The prediction of forest structural parameters based on the outcome of the sensitivity analysis yielded model accuracies between 15% (relative root mean square error) for Lorey's height and 32% for stem volume. The global database of single-polarized bistatic TanDEM-X data provides an important source for mapping structural parameters in temperate forests at large scale, irrespective of forest type. and ranging (LiDAR), and synthetic aperture radar (SAR) data. A comprehensive summary of studies that aim at quantifying forest biomass from satellite sensors was compiled by [3]. AGB estimates from optical remote sensing data mostly rely on vegetation indices that parameterize the photosynthetic activity of vegetation. They imply a relationship between the foliage and the total AGB of a vegetation stand, which is further used to estimate AGB. Since the major part of AGB in forests is composed of non-photosynthetic (woody) components, such approaches imply high uncertainties. The major challenge in AGB mapping from optical data is the saturation of the signal after canopy closure which especially hampers the estimation of high-level AGB [4]. However, recent studies confirmed the general ability of optical systems like Landsat to map AGB at biomass levels around 70 Mg/ha [5].The saturation problem can be overcome by sensors, which are able to penetrate through the canopy and interact with the forest constituents. For instance, LiDAR and interferometric SAR (InSAR) systems generally provide the necessary technique because they are able to penetrate the canopy of a forest and deliver information about the vertical structure of a forest stand. LiDAR data produce high-accuracy canopy height models (CHM) but the main drawback ...

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Potential of Space-Borne PolInSAR for Forest Canopy Height Estimation Over India—A Case Study Using Fully Polarimetric L-, C-, and X-Band SAR Data

Cited by 40 publications

References 33 publications

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

Sub-Canopy Topography Estimation from TanDEM-X DEM by Fusing ALOS-2 PARSAR-2 InSAR Coherence and GEDI Data

Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests

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