Comparative Analysis of GEDI’s Elevation Accuracy from the First and Second Data Product Releases over Inland Waterbodies

Fayad, Ibrahim; Baghdadi, Nicolas; Frappart, Frédéric

doi:10.3390/rs14020340

Cited by 11 publications

(13 citation statements)

References 23 publications

(40 reference statements)

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“…In contrast, with uncorrected GEDI estimates, the RMSE on the water level estimates ranged from 0.57 m (Lake Erie) to 0.68 m (Lake Ontario), with a bias that ranged from 0.43 m (Lake Erie) to 0.61 m (Lake Ontario). On the other hand, the uncertainties obtained with GEDI were consistent with other studies, such as the study of Xiang et al [13] over the five Great Lakes, or the studies of Fayad et al [14,18] and the study of Frappart et al [6] over several lakes in France and Switzerland. Therefore, model-free GEDI elevation estimates are not recommended for the retrieval of water surface levels.…”

Section: Discussionsupporting

confidence: 89%

“…In contrast, using ICESat-2 data, RMSEs of 0.06 (biases = −0.01 ± 0.05 m) and 0.12 m (biases = −0.08 ± 0.07 m) were obtained for water level estimates over the five Great Lakes and the lower Mississippi river, respectively. Finally, in the study of Fayad et al [14], the comparison between the second version of GEDI elevation estimates and gauge station readings over Lake Geneva showed an estimation bias of 0.63 ± 0.24 m.…”

Section: Introductionmentioning

confidence: 91%

“…(3) the number of detected modes (num_detectedmodes); (4) the width of the Gaussian fit to the received waveform (rx_gwidth); (5) the amplitude of the smoothed waveforms lowest detected mode (zcross_amp); (6) the viewing angle (VA) at acquisition time [14]; finally, (7) the signal to noise ratio (SNR) [14].…”

Section: Gedi Data Productsmentioning

confidence: 99%

“…These factors can be classed into three main groups: (1) instrumental, (2) atmospheric, and (3) water surface state factors. Instrumental factors include, but are not limited to, the viewing angle (VA) [15], the acquiring laser or beam (i.e., coverage or full-power lasers in the case of GEDI) [14], the amplitude of the echoed water surface return [14], the width of the echoed water surface return [14], and the signal to noise ratio (SNR) [14]. Atmospheric factors mostly include the type and composition of clouds at the acquisition time [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Correcting GEDI Water Level Estimates for Inland Waterbodies Using Machine Learning

et al. 2022

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The Global Ecosystem Dynamics Investigation (GEDI) LiDAR on the International Space Station has acquired more than 35 billion shots globally in the period between April 2019 and August 2021. The acquired shots could offer a significant database for the measure and monitoring of inland water levels over the Earth’s surface. Nonetheless, previous and current studies have shown that the provided GEDI elevation estimates are significantly less accurate than any available radar or LiDAR altimeter. Indeed, our analysis of GEDI’s altimetric capabilities to retrieve water levels over the five North American Great Lakes presented estimates with a bias that ranged between 0.26 and 0.35 m and a root mean squared error (RMSE) ranging between 0.54 and 0.68 m. Therefore, our objective in this study is to post-process the original GEDI water level estimates from an error model taking instrumental, atmospheric, and lakes surface state factors as proxies, which affect the physical shape of the waveforms, hence introducing uncertainties on the elevation estimates. The first tested model, namely a random forest regressor (RFICW) with the instrumental, atmospheric, and water surface state factors as inputs, was validated temporally (trained on a given year and validated on another) and spatially (trained on a given lake and validated on the remaining four). The results showed a significant decrease in elevation estimation errors both temporally and spatially. The temporally validated models showed an RMSE on the corrected elevation estimates of 0.18 m. Concerning the spatially validated model, the results varied based on the lake data used for training. Indeed, the most accurate spatially validated model showed an RMSE of 0.17 m, while the least accurate model showed an RMSE of 0.26 m. Finally, given that an elevation correction model using all the factors (instrumental, atmospheric, and water surface state factors) presents a best-case scenario, as water surface state factors are only available over a selected number of lakes globally, three additional models based on random forest were tested. The first, RFI, uses only instrumental factors as correction factors, RFIC uses both instrumental and atmospheric factors, while the third, RFIW, uses instrumental and water surface state factors. The temporal validation of these models showed that the model using instrumental factors, while less accurate than the remaining two models, was capable of correcting the original GEDI elevation estimates by a factor of two across the five lakes. On the other hand, the RFIC model was the most accurate between the three, with a slight degradation in comparison to the full model. Indeed, the RFIC model showed an RMSE on the estimation of water levels of 0.21 m.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 91%

Section: Gedi Data Productsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correcting GEDI Water Level Estimates for Inland Waterbodies Using Machine Learning

et al. 2022

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“…A high sensitivity allows for the signal to penetrate denser canopies and thus to reach the ground. Secondly, SNR is a commonly used measure that compares the level of a desired signal to the level of background noise [28]. A high SNR implies a better extraction of the useful parts of the signal to perform the metrics computation.…”

Section: Direct Estimationmentioning

confidence: 99%

Influence of GEDI Acquisition and Processing Parameters on Canopy Height Estimates over Tropical Forests

et al. 2022

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LiDAR technology has been widely used to characterize structural parameters of forest ecosystems, which in turn are valuable information for forest monitoring. GEDI is a spaceborne LiDAR system specifically designed to measure vegetation’s vertical structure, and it has been acquiring waveforms on a global scale since April 2019. In particular, canopy height is an important descriptor of forest ecosystems, as it allows for quantifying biomass and other inventory information. This paper analyzes the accuracy of canopy height estimates from GEDI data over tropical forests in French Guiana and Gabon. The influence of various signal acquisition and processing parameters is assessed to highlight how they impact the estimation of canopy heights. Canopy height models derived from airborne LiDAR data are used as reference heights. Several linear and non-linear approaches are tested given the richness of the available GEDI information. The results show that the use of regression models built on multiple GEDI metrics allows for reaching improved accuracies compared to a direct estimation from a single GEDI height metric. In a notable way, random forest improves the canopy height estimation accuracy by almost 80% (in terms of RMSE) compared to the use of rh_95 as a direct proxy of canopy height. Additionally, convolutional neural networks calibrated on GEDI waveforms exhibit similar results to the ones of other regression models. Beam type as well as beam sensitivity, which are related to laser penetration, appear as parameters of major influence on the data derived from GEDI waveforms and used as input for canopy height estimation. Therefore, we recommend the use of only power and high-sensitivity beams when sufficient data are available. Finally, we note that regression models trained on reference data can be transferred across study sites that share identical environmental conditions.

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Improving mean water lake surface elevation estimates using dense lidar measurements from the GEDI satellite mission

Frappart,

Tong Minh,

Baghdadi

et al. 2024

Remote Sensing Applications: Society and Environment

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Comparative Analysis of GEDI’s Elevation Accuracy from the First and Second Data Product Releases over Inland Waterbodies

Cited by 11 publications

References 23 publications

Correcting GEDI Water Level Estimates for Inland Waterbodies Using Machine Learning

Correcting GEDI Water Level Estimates for Inland Waterbodies Using Machine Learning

Influence of GEDI Acquisition and Processing Parameters on Canopy Height Estimates over Tropical Forests

Improving mean water lake surface elevation estimates using dense lidar measurements from the GEDI satellite mission

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