[Retracted] Interpolation Parameters in Inverse Distance‐Weighted Interpolation Algorithm on DEM Interpolation Error

Liu, Zhiqiang; Xu, Bo; Cheng, Bo; Hu, Xiaomei

doi:10.1155/2021/3535195

Cited by 10 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent study highlights similar results for the inverse distance-weighted interpolation, whose optimum parameters depend on the study area [14].…”

Section: )supporting

confidence: 63%

See 1 more Smart Citation

Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models

Riazanoff,

Corseaux,

Albinet

et al. 2024

IJGI

View full text Add to dashboard Cite

In recent decades, an important number of regional and global digital elevation models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs need to be expressed in the same coordinate reference system (CRS) and sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention) with heights relative to the same vertical reference system (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements or disparities may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect, or curvature. Therefore, before any comparison, the control of DEM planimetric accuracy is certainly a very important task to perform. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have already been computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been introduced through bicubic interpolation in an 11 × 11 grid with a 0.1-pixel step in both directionsThis validation method has been applied in four approximately 10 km × 10 km DEMIX tiles showing different roughness (height distribution). Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with typical errors (eb) ranging from 12 to 20% of the pixel size (with the worst case in Croatia). These errors in displacement retrievals are not equally distributed in the 11 × 11 grid, and the overall error Eb depends on the roughness encountered in the different tiles. The second aim of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d = 1 of the interpolated point) on the accuracy of the displacement retrieval. By considering Eb as a quality indicator, tests have been performed in the four DEMIX tiles, making the bicubic parameter vary between −1.5 and 0.0 by a step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four Eb minimal values. This BBC parameter b* is low for flat areas (around −0.95) and higher in mountainous areas (around −0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r = 0.717). The logarithmic regression formula b~σslope estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC b~.

show abstract

“…A recent study highlights similar results for the inverse distance-weighted interpolation, whose optimum parameters depend on the study area [14].…”

Section: )supporting

confidence: 63%

“…This error is expressed in radians because of the geographic CRS. 𝑒 (𝑙, 𝑝) = 𝑑 (𝑙, 𝑝) − 𝑠 λ 𝑒 (𝑙, 𝑝) = 𝑑 (𝑙, 𝑝) − 𝑠 (11) Then, the error is converted in metres ( 12) by considering the radius of the WGS84 ellipsoid at given latitude φ (13) and (14).…”

Section: Error Matrices and Surfacesmentioning

confidence: 99%

Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models

Riazanoff,

Corseaux,

Albinet

et al. 2024

IJGI

View full text Add to dashboard Cite

show abstract

Section: Interpolation Methodsmentioning

confidence: 99%

“…The inverse distance weighting (IDW) method assigns weights as per the distance of known points, that is, the weight is inversely proportional to the distance. The closer the predicted point to the known point, the greater the assigned weight (Liu et al, 2021; Wen et al, 2022). Further, the spline function method is used to generate a smooth surface by limiting the range of the surrounding data points, selecting a mathematical function that expresses spatial variation, and assigning values to unknown points, in which the interpolation results of the tensile spline function (TS) are more closely restricted to the range of values of the sample points.…”

Section: Methodsmentioning

confidence: 99%

Spatial and temporal groundwater dynamics in extreme arid basins

Wei,

Guo,

Zhou

et al. 2024

Hydrological Processes

View full text Add to dashboard Cite

To accurately obtain the spatial distribution characteristics of groundwater level in an extremely arid zone and its dynamic change patterns under the influence of human activities, based on the data of 55 groundwater observation wells in the middle and lower reaches of the Kriya River, spatial interpolation of regional groundwater level data were performed using the inverse distance weight, spline function, trend surface, and the ordinary kriging methods. The optimal interpolation method was selected by its accuracy to spatially interpolate the groundwater level data in the study area from 2019 to 2021. The results show that: (1) the ordinary kriging method has the highest interpolation accuracy (MAE = 7.1393, MRE = 0.0058, RMSE = 9.4314) and reflects the spatial and temporal variability and distribution characteristics of groundwater levels with great accuracy. (2)The relationship between surface water–groundwater recharge and discharge in different areas of the river channel in the desert section varies depending on geological structure, surface water seepage, and other elements. (3) Groundwater in the Taklamakan Desert has little effect on groundwater recharge in the Dariyabui Oasis, and changes in groundwater dynamics in the oasis are predominantly influenced by surface runoff. (4) Monthly changes in groundwater levels in the Yutian Oasis are continuous, with ‘V’ shaped fluctuations, a declining trend in the southern part, no significant change in the central part, and a slight increase in the northern part. These results contribute to the sustainable management of water resources in the Kriya River Basin, provide a basis for groundwater prediction, and offer a reference for studies of other, similar extreme desert area basins.

show abstract

“…To address the underestimation caused by missing ozone data, we corrected the AOT40 values to account for full-time coverage (Text S3 in Supporting Information S1). And then, we utilized the inverse distance-weighted spatial interpolation method (Liu et al, 2021), a commonly used approach, to estimate AOT40 values among the 10 km grid cells. Previous studies have used AOT40 to assess the adverse effects of ozone on vegetation in China (Hu et al, 2023;Ren et al, 2011), implicating the effectivity of the AOT40 value in assessing ozone pollution's impact on NPP.…”

Section: Data Setsmentioning

confidence: 99%

Adverse Effects of Ozone Pollution on Net Primary Productivity in the North China Plain

Long,

Han,

Wang

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

Tropospheric ozone significantly damages vegetation and reduces net primary productivity (NPP). We developed a stable linear NPP response model based on accumulated ozone exposure over a threshold of 40 parts per billion (ppb) (AOT40). We then estimated the effects of regional ozone damage on NPP for different vegetation types. The study suggests an average decrease in NPP of 24.7% due to ozone pollution in the North China Plain, similar to previous estimates ranging from 10.1% to 24.7%, with a maximum reduction exceeding 200 [g C m‐2 yr‐1] and more than 50%. Vegetation types such as broadleaf forests, needleleaf forest, crops, and grasses showed significant NPP decreases of 47.1%, 37.8%, 36.7%, and 44.6%, respectively. Declining NPP also had negative impacts on several Chinese crop species. Our work highlights the need for urgent and effective action to mitigate ozone pollution's substantial detrimental effects on ecosystem health and productivity.

show abstract

[Retracted] Interpolation Parameters in Inverse Distance‐Weighted Interpolation Algorithm on DEM Interpolation Error

Cited by 10 publications

References 27 publications

Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models

Best BiCubic Method to Compute the Planimetric Misregistration between Images with Sub-Pixel Accuracy: Application to Digital Elevation Models

Spatial and temporal groundwater dynamics in extreme arid basins

Adverse Effects of Ozone Pollution on Net Primary Productivity in the North China Plain

Contact Info

Product

Resources

About