High-resolution sub-canopy topography mapping via TanDEM-X DEM combined with future P-band BIOMASS PolInSAR data

Zhu, Jianjun; Liu, Zhiwei; Fu, Haiqiang; Zhou, Cui; Zhou, Yi; Wang, Huiqiang; Xie, Yanzhou

doi:10.1007/s00190-023-01807-0

Cited by 2 publications

(1 citation statement)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As two current advanced deformation monitoring technologies, interferometric synthetic aperture radar (InSAR) and Global Navigation Satellite System (GNSS) are the main sources of high-precision deformation monitoring data (Takada et al 2018;Xu et al 2018;Yan et al 2022b;Zhang et al 2023;Zhu et al 2023). GNSS sites are mostly deployed with deep anchors in bedrock or anchored through reinforced concrete structures and stable buildings (Xi et al 2021;Xu et al 2022); thus, these monitoring sites have fixed location properties.…”

Section: Introductionmentioning

confidence: 99%

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

Yan,

Dai,

et al. 2024

Earth Planets Space

View full text Add to dashboard Cite

Correcting interferometric synthetic aperture radar (InSAR) interferograms using Global Navigation Satellite System (GNSS) data can effectively improve their accuracy. However, most of the existing correction methods utilize the difference between GNSS and InSAR data for surface fitting; these methods can effectively correct overall long-wavelength errors, but they are insufficient for multiple medium-wavelength errors in localized areas. Based on this, we propose a method for correcting InSAR interferograms using GNSS data and the K-means spatial clustering algorithm, which is capable of obtaining correction information with high accuracy, thus improving the overall and localized area error correction effects and contributing to obtaining high-precision InSAR deformation time series. In an application involving the Central Valley of Southern California (CVSC), the experimental results show that the proposed correction method can effectively compensate for the deficiency of surface fitting in capturing error details and suppress the effect of low-quality interferograms. At the nine GNSS validation sites that are not included in the modeling process, the errors in the ascending track 137A and descending track 144D are mostly less than 15 mm, and the average root mean square error values are 11.8 mm and 8.0 mm, respectively. Overall, the correction method not only realizes effective interferogram error correction, but also has the advantages of high accuracy, high efficiency, ease of promotion, and can effectively address large-scale and high-precision deformation monitoring scenarios. Graphical Abstract

show abstract

Section: Introductionmentioning

confidence: 99%

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

Yan,

Dai,

et al. 2024

Earth Planets Space

View full text Add to dashboard Cite

show abstract

A Pseudo-Waveform-Based Method for Grading ICESat-2 ATL08 Terrain Estimates in Forested Areas

Zhao,

Hu,

Liu

et al. 2024

Forests

View full text Add to dashboard Cite

The ICESat-2 Land and Vegetation Height (ATL08) product is a new control point dataset for large-scale topographic mapping and geodetic surveying. However, its elevation accuracy is typically affected by multiple factors. The study aims to propose a new approach to classify ATL08 terrain estimates into different accuracy levels and extract reliable ground control points (GCPs) from ICESat-2 ATL08. Specifically, the methodology is divided into three stages. First, the ATL08 terrain estimates are matched with the raw ATL03 photon cloud data, and the ATL08 terrain estimates are used to fit a continuous terrain curve. Then, using the fitted continuous terrain curve and raw ATL03 photon cloud data, a pseudo-waveform is generated for grading the ATL08 terrain estimates. Finally, all the ATL08 terrain estimates are graded based on the peak characteristics of the generated pseudo-waveform. To validate the feasibility of the proposed method, four study areas from the National Ecological Observatory Network (NEON), characterized by various terrain features and forest types were selected. High-accuracy airborne lidar data were used to evaluate the accuracy of graded ICESat-2 terrain estimates. The results demonstrate that the method effectively classified all ATL08 terrain estimates into different accuracy levels and successfully extracted high-accuracy GCPs. The root mean square errors (RMSEs) of the first accuracy level in the four selected study areas were 0.99 m, 0.51 m, 1.88 m, and 0.65 m, representing accuracy improvement of 51.7%, 58.2%, 83.1%, and 68.8%, respectively, compared to the original ATL08 terrain estimates before classifying. Additionally, a comparison with the conventional threshold-based GCP extraction method demonstrated the superior performance of our proposed approach. This study introduces a new approach to extract high-quality elevation control points from ICESat-2 ATL08 data, particularly in forested areas.

show abstract

High-resolution sub-canopy topography mapping via TanDEM-X DEM combined with future P-band BIOMASS PolInSAR data

Cited by 2 publications

References 69 publications

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

A Pseudo-Waveform-Based Method for Grading ICESat-2 ATL08 Terrain Estimates in Forested Areas

Contact Info

Product

Resources

About