Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation

Li, Yanyan; Li, Linye; Chen, Chuanfa; Liu, Yan

doi:10.1080/17538947.2023.2203953

Cited by 4 publications

(2 citation statements)

References 75 publications

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“…Notably, Evergreen Broadleaved Forests demonstrate the highest ME and RMSE measurements of 5.77 m and 14.31 m correspondingly. In contrast, Deciduous Broadleaved Forests closely follow with ME and RMSE values of 5.20 m and 14.07 m. In contrast, Evergreen Coniferous Forests exhibits the lowest error rates, with a ME of 1.18 m and a RMSE of 11.94 m. It is precisely because, Evergreen Broadleaved Forests typically boast the highest average tree height and present challenges for radar signal penetration due to their canopy and foliage characteristics [27]. In the case of Deciduous Broadleaved Forests, lower ME and RMSE values can be responsible for the timing of SRTM DEM acquisition during the deciduous stage, reduced canopy coverage [31], as well as decreased air moisture content and relatively cooler air temperatures during this period.…”

Section: The Influence Of Different Vegetation Types On Srtm Dem Erro...mentioning

confidence: 84%

“…Currently, artificial neural networks, being the predominant algorithm in machine learning, have been extensively applied to enhance SRTM DEMs within forested regions. Li, et al [27] in their study proposed a method for rectifying forest DEMs using the back-propagation neural network (BPNN). This approach considers elevation spatial autocorrelation and mitigates vegetation bias (VB) present in DEMs across different types of forests.…”

Section: Introductionmentioning

confidence: 99%

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SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

Sun,

Zhou,

Xie

et al. 2023

Forests

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The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is extensively utilized in various fields, such as forestry, oceanography, geology, and hydrology. However, due to limitations in radar side-view imaging, the SRTM DEM still contains gaps and anomalies, particularly in areas with an intricate topography, like forests. To enhance the accuracy of the SRTM DEM in forested regions, commonly employed approaches include regression analysis and artificial neural networks (ANN). Nevertheless, existing regression methods struggle to accurately capture the intricate nonlinear relationship between the error and influencing factors. Additionally, traditional ANN models are susceptible to overfitting, resulting in subpar accuracy. Deep Belief Network (DBN) is a highly precise algorithm in deep learning. However, the intricate combination of hyperparameters often leads to limited generalization ability and model robustness when correcting DEM. The present study proposes an error prediction model based on the DBN optimized by Particle Swarm Optimization (PSO) for SRTM DEM correction. By utilizing the PSO algorithm, we aim to identify the optimal combination of hyperparameters of DBN, including the number of neurons in the hidden layer and the learning rates. The experiment focuses on two regions in Hunan Province, China, characterized by abundant vegetation cover. The reference data utilized for comparison is ICESat/GLAS data. The experimental results demonstrate that the mean error (ME) and root mean square error (RMSE) of the SRTM DEM corrected by the proposed algorithm in these two regions are significantly reduced by 93.5%–96.0% and 21.5%–23.5%, respectively. Moreover, there is an improvement of over 26.1% in accuracy within complex terrain areas. Specifically, in broadleaf forest, the PSO-DBN method exhibits a remarkable accuracy improvement of 26.2%, while the DBN-corrected SRTM DEM shows an improvement of 15.3%. In coniferous forest, the PSO-DBN method achieves an accuracy improvement of 14.8%, whereas the DBN-corrected SRTM DEM demonstrates a gain of 5.8%. The approach provides a more effective and robust tool for correcting SRTM DEM or other similar DEMs over vegetated mountain areas.

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Section: The Influence Of Different Vegetation Types On Srtm Dem Erro...mentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

Sun,

Zhou,

Xie

et al. 2023

Forests

View full text Add to dashboard Cite

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Explainable artificial intelligence framework for urban global digital elevation model correction based on the SHapley additive explanation-random forest algorithm considering spatial heterogeneity and factor optimization

Chen,

Liu,

et al. 2024

International Journal of Applied Earth Observation and Geoinfor

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Composite Electromagnetic Scattering Characteristics of Actual Terrain and Moving Rockets

Peng,

Ren,

Guo

et al. 2024

International Journal of Antennas and Propagation

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This research involved studying the characteristics of composite electromagnetic scattering from a rocket moving above actual terrain using the finite-difference time-domain method. The angular distribution curve of the composite scattering coefficient was obtained, and the influences of factors such as the angle of incidence, the frequency of the incident electromagnetic wave, the content of moisture in soil, the dielectric constant of the rocket-shell material, the height of the rocket, and the undulation of the terrain on the composite scattering coefficient were investigated. Results show that the composite scattering coefficient oscillates with the scattering angle and increases in the direction of mirror reflection. It also decreases with increasing angle of incidence, frequency of the incident wave, and altitude of the rocket, while it increases with increasing soil moisture and dielectric constant of the rocket-shell materials. Although the influence of different terrain undulations on the composite scattering coefficient is noticeable, it follows no fixed pattern.

show abstract

Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation

Cited by 4 publications

References 75 publications

SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

Explainable artificial intelligence framework for urban global digital elevation model correction based on the SHapley additive explanation-random forest algorithm considering spatial heterogeneity and factor optimization

Composite Electromagnetic Scattering Characteristics of Actual Terrain and Moving Rockets

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