Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest

Kumar, Shashi; Khati, Unmesh; Chandola, Shreya; Agrawal, Shefali; Kushwaha, S. P. S.

doi:10.1016/j.asr.2017.04.018

Cited by 56 publications

(22 citation statements)

References 35 publications

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“…If # » (Cloude, 2005(Cloude, , 2010. Moreover, L is the total number of pixels averaged in the range and azimuth directions which can be replaced by the ensemble averaging operation following the statistical ergodicity assumption (Hanssen, 2001;Hoen & Zebker, 2000;Kugler et al, 2015;Kumar et al, 2017;Papathanassiou & Cloude, 2001). Additionally, φ w f lat ∈ [0, 2π) is the wrapped flat-earth phase obtained from the estimated absolute flat-earth phase, φ f lat and has to be removed from I ( # » w 1 , # » w 2 ) as shown in Eq.…”

Section: Single-baseline Pol-insar Specificsmentioning

confidence: 99%

“…∆Z s,max , should be similar to h 2π (Kugler et al, 2015;Hajnsek et al, 2009;Kumar et al, 2017), k z has to be rescaled to an optimum range for effectively estimating the SSD. Hence, the modified vertical wavenumber, k z , is given by Eq.…”

Section: Single-baseline Pol-insar Specificsmentioning

confidence: 99%

“…Since, in-situ data from only two ground stations are available, the conventional way of accuracy assessment through regression plots (Kugler et al, 2015;Leinss et al, 2014;Kumar et al, 2017) is infeasible in this context. Moreover, the Kothi AWS (Fig 3.1) area falls in the layover region for the descending pass acquisitions and hence, only the Dhundi region which is free from layover, shadow and foreshortening effects, is used for validation.…”

Section: Validation Processmentioning

confidence: 99%

“…As part of future work, it is recommended to use the multi-baseline Pol-InSAR technique (Cloude, 2010) wherein k z can be simulated (instead of scaling by η ) after an appropriate accuracy assessment (Kumar et al, 2017). Similarly, the effect of different window shapes (square or rectangular) and sizes can be considered for the ensemble averaging operation.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

“…In this context, the Pol-InSAR technique works on the coherent combination of both PolSAR and InSAR observations, thereby enabling the interferogram generation in arbitrary transmit and receive channels (Papathanassiou & Cloude, 2001;Cloude, 2005Cloude, , 2010. It has been widely used for estimating tree height in forested regions and can be effectively applied to natural or artificial volume scatterers including snow and ice (Leinss et al, 2014;Hajnsek et al, 2009;Kugler et al, 2015;Kumar et al, 2017;Papathanassiou & Cloude, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

Majumdar¹,

Thakur²,

Chang³

et al. 2019

Preprint

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Snow depth (SD) and Snow Water Equivalent (SWE) constitute essential physical properties of snow and find extensive usage in the hydrological modelling domain. However, the prominent impact of the hydrometeorological conditions and difficult terrain conditions inhibit accurate measurement of the SD and SWE— an ongoing research problem in the cryosphere paradigm. In this context, spaceborne synthetic aperture radar (SAR) systems benefit from global coverage at sufficiently high spatial and temporal resolutions. Still, existing polarimetric and interferometric SAR techniques are susceptible to high volume scattering resulting from the increased snow grain sizes due to the standing (or old) snow formation driven by the temperature induced snow metamorphosis process. Hence, to model this volume decorrelation, the polarimetric SAR interferometry (Pol-InSAR) technique can be effectively applied. In this work, the standing snow depth (SSD) and its corresponding standing snow water equivalent (SSWE) are estimated using the single-baseline Pol-InSAR based hybrid Digital Elevation Model (DEM) differencing and coherence amplitude inversion model. To achieve this, six TerraSAR-X, TanDEM-X Coregistered Single look Slant range Complex (CoSSC) bistatic quad-pol acquisitions between December 2015 and January 2016 over Dhundi (situated in the Beas watershed, northwestern Himalayas, India) are used. Due to the associated problems of model parameter tuning, complex topographical conditions, and limited ground-truth measurements, appropriate sensitivity analyses have been carried out for the parameter optimisation. Furthermore, the uncertainty sources are identified by performing a summer (June 8, 2017) and wintertime (January 8, 2016) comparative analysis of the study area which quantitatively highlights the changes in the percentages of the surface and volume scatterings. Evidently, the improved model displays sufficiently high overall SSD accuracy with coefficient of determination (R^2) ≈ 0.96, Mean Absolute Error (MAE) ≈ 1.61 cm, and Root Mean Square Error (RMSE) ≈ 2.16 cm. Additionally, the respective SSWEs have been calculated by assuming a fixed snow density for each epoch wherein the overall error metrics are R^2 ≈ 0.71, MAE ≈ 5.19 mm, and RMSE ≈ 6.84 mm. Therefore, this research successfully demonstrates the practicability of the improved Pol-InSAR model for SD estimation over rugged terrains.

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Section: Single-baseline Pol-insar Specificsmentioning

confidence: 99%

Section: Single-baseline Pol-insar Specificsmentioning

confidence: 99%

Section: Validation Processmentioning

confidence: 99%

Section: Conclusion and Future Scopementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

Majumdar¹,

Thakur²,

Chang³

et al. 2019

Preprint

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Responses of Multi‐Frequency Remote Sensing to Forest Biomass

Sinha

Santra

Sharma

et al. 2021

Advances in Remote Sensing for Natural Resource Monitoring

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Leveraging the next generation of spaceborne Earth observations for fuel monitoring and wildland fire management

Leite,

Amaral,

Neigh

et al. 2024

Remote Sens Ecol Conserv

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Managing fuels is a key strategy for mitigating the negative impacts of wildfires on people and the environment. The use of satellite‐based Earth observation data has become an important tool for managers to optimize fuel treatment planning at regional scales. Fortunately, several new sensors have been launched in the last few years, providing novel opportunities to enhance fuel characterization. Herein, we summarize the potential improvements in fuel characterization at large scale (i.e., hundreds to thousands of km2) with high spatial and spectral resolution arising from the use of new spaceborne instruments with near‐global, freely‐available data. We identified sensors at spatial resolutions suitable for fuel treatment planning, featuring: lidar data for characterizing vegetation structure; hyperspectral sensors for retrieving chemical compounds and species composition; and dense time series derived from multispectral and synthetic aperture radar sensors for mapping phenology and moisture dynamics. We also highlight future hyperspectral and radar missions that will deliver valuable and complementary information for a new era of fuel load characterization from space. The data volume that is being generated may still challenge the usability by a diverse group of stakeholders. Seamless cyberinfrastructure and community engagement are paramount to guarantee the use of these cutting‐edge datasets for fuel monitoring and wildland fire management across the world.

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Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest

Cited by 56 publications

References 35 publications

Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

Responses of Multi‐Frequency Remote Sensing to Forest Biomass

Leveraging the next generation of spaceborne Earth observations for fuel monitoring and wildland fire management

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