AfriSAR: Gridded Forest Biomass and Canopy Metrics Derived from LVIS, Gabon, 2016

Armston, John; Tang, Hao; Hancock, S.; Marselis, S.; Duncanson, Laura; Kellner, James R.; Hofton,; Blair, J. B.; Fatoyinbo, Temilola; Dubayah, Ralph

doi:10.3334/ornldaac/1775

Cited by 4 publications

(1 citation statement)

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“…In 2016, NASA, the ESA, and the Gabonese Space Agency collaborated on the AfriSAR project, in which NASA's unmanned aerial vehicle synthetic aperture radar (UAVSAR) and airborne LiDAR sensors acquired L-band multibaseline, fully polarized PolInSAR data and fullwaveform LVIS LiDAR datasets, respectively. From this survey, eight stacks of UAVSAR data processed by polarization calibration, baseline fine coregistration, and spectral filtering were made publicly available free of charge as single-look complexes [27]; these data were used in combination with the relative height variable RH100 of LVIS LiDAR data to validate the forest canopy height derived from TomoSAR [28]. In this study, we only tested the TomoSAR performance of the HH channel.…”

Section: Study Area and Datamentioning

confidence: 99%

Improving Forest Canopy Height Estimation Using a Semi-Empirical Approach to Overcome TomoSAR Phase Errors

Luo

Yuan

Chen

2023

Forests

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Forest canopy height is an important forest indicator parameter. Synthetic aperture radar tomography (TomoSAR) is an effective method to characterize forest canopy height and describe forest 3D structure; however, the residual phase error of TomoSAR affects the focus of the relative reflectance and can lead to errors in forest canopy height estimation. Therefore, this paper proposes a semi-empirical method to overcome the residual phase effects on forest canopy height estimation. In this study, we used airborne multi-baseline UAVSAR data to estimate forest canopy height via TomoSAR techniques and applied a semi-empirical method to improve forest canopy height estimation without phase calibration to mitigate the effects of phase error. The process is divided into three stages: the first step uses a semi-empirical method to initially determine the optimal relative reflectance loss threshold (K) by excluding the inverse extremes; in the second and third steps, the percentile height was used to gradually reduce the height interval between the upper and lower envelopes to minimize overestimation of extreme values and the lower vegetation. When the root mean square error (RMSE) was minimized, the percentile combinations were determined between the inversion results and a LiDAR dataset of the area. The results show that the canopy height estimation results are not satisfactory when relying solely on the K value to estimate the height difference between the envelope at the top of the forest and the ground; the best result was obtained when K = 0.4, but the corresponding R2 value was only 0.13, and the RMSE was 15.23 m. In our proposed method, the K value is determined as 0.3 by excluding the extreme values of the inversion result in the initial step—the corresponding R2 and RMSE values were 0.59 and 10.73 m, respectively, representing an RMSE decrease of 29.54% relative to the initial K value. After two steps of correction overestimation, the inversion accuracy was significantly improved with an R2 value of 0.65 and an RMSE of 9.69 m, corresponding to an RMSE decrease of 36.38%. Overall, the findings of the study represent an important reference for optimizing future spaceborne TomoSAR forest canopy height estimates.

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Section: Study Area and Datamentioning

confidence: 99%

Improving Forest Canopy Height Estimation Using a Semi-Empirical Approach to Overcome TomoSAR Phase Errors

Luo

Yuan

Chen

2023

Forests

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Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations

Liu,

Neigh,

Pardini

et al. 2024

International Journal of Remote Sensing

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Improving Forest Height-To-Biomass Allometry With Structure Information: A Tandem-X Study

Choi¹,

Pardini²,

Heym³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Allometric relations that link forest above ground biomass to top forest (i.e. canopy) height are of particular significance in the context of lidar and interferometric Synthetic Aperture Radar (SAR) remote sensing, as both techniques allow accurate height measurements at ecologically relevant spatial scales. Besides the often unknown allometry itself, its spatial variation in heterogenous forest environments restricts the performance when using a single fixed height-to-biomass allometric relation.This paper addresses how forest structure information derived from interferometric TanDEM-X data can be used to locally adapt the height-to-biomass allometry in heterogeneous forests, and to improve biomass estimation performance. The analysis is carried out using TanDEM-X interferometric measurements in three tropical forest test sites in Gabon. A structure index expressing forest density is derived from the TanDEM-X data. Then, a continuous relationship between the structure index and the allometric level that defines the forest height-to-biomass allometry is reconstructed from the available lidar data, and used to vary the height-to-biomass relationship. Finally, the potential of the derived structure index to support an allometric relationship common to all sites is evaluated. The experimental results show the appropriateness of TanDEM-X data for characterizing structure and in this way improving the biomass estimation performance.

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AfriSAR: Gridded Forest Biomass and Canopy Metrics Derived from LVIS, Gabon, 2016

Cited by 4 publications

References 0 publications

Improving Forest Canopy Height Estimation Using a Semi-Empirical Approach to Overcome TomoSAR Phase Errors

Improving Forest Canopy Height Estimation Using a Semi-Empirical Approach to Overcome TomoSAR Phase Errors

Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations

Improving Forest Height-To-Biomass Allometry With Structure Information: A Tandem-X Study

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