A short review of interpolation methods used
for terrain modeling

Gradka, Robert; Wrocławska, Politechnika; Kwinta, Andrzej

doi:10.15576/gll/2018.4.29

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…The global polynomial interpolation fits a mathematically derived surface to the entire study area based on all observed data points [146,149], as shown in Figure A1A. GPI is suitable for slowly varying parameters [52].…”

Section: -2017mentioning

confidence: 99%

Comparing Remote Sensing and Geostatistical Techniques in Filling Gaps in Rain Gauge Records and Generating Multi-Return Period Isohyetal Maps in Arid Regions—Case Study: Kingdom of Saudi Arabia

Helmi,

Farouk,

Hassan

et al. 2024

Water

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Arid regions are susceptible to flash floods and severe drought periods, therefore there is a need for accurate and gap-free rainfall data for the design of flood mitigation measures and water resource management. Nevertheless, arid regions may suffer from a shortage of precipitation gauge data, whether due to improper gauge coverage or gaps in the recorded data. Several alternatives are available to compensate for deficiencies in terrestrial rain gauge records, such as satellite data or utilizing geostatistical interpolation. However, adequate assessment of these alternatives is mandatory to avoid the dramatic effect of using improper data in the design of flood protection works and water resource management. The current study covers 75% of the Kingdom of Saudi Arabia’s area and spans the period from 1967 to 2014. Seven satellite precipitation datasets with daily, 3-h, and 30-min temporal resolutions, along with 43 geostatistical interpolation techniques, are evaluated as supplementary data to address the gaps in terrestrial gauge records. The Normalized Root Mean Square Error by the mean value of observation (NRMSE) is selected as a ranking criterion for the evaluated datasets. The geostatistical techniques outperformed the satellite datasets with 0.69 and 0.8 NRMSE for the maximum and total annual records, respectively. The best performance was found in the areas with the highest gauge density. PERSIANN-CDR and GPM IMERG V7 satellite datasets performed better than other satellite datasets, with 0.8 and 0.82 NRMSE for the maximum and total annual records, respectively. The spatial distributions of maximum and total annual precipitation for every year from 1967 to 2014 are generated using geostatistical techniques. Eight Probability Density Functions (PDFs) belonging to the Gamma, Normal, and Extreme Value families are assessed to fit the gap-filled datasets. The PDFs are ranked according to the Chi-square test results and Akaike information criterion (AIC). The Gamma, Extreme Value, and Normal distribution families had the best fitting over 56%, 34%, and 10% of the study area gridded data, respectively. Finally, the selected PDF at each grid point is utilized to generate the maximum annual precipitation for 2, 5, 10, 25, 50, and 100-year rasters that can be used directly as a gridded precipitation input for hydrological studies.

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Section: -2017mentioning

confidence: 99%

Comparing Remote Sensing and Geostatistical Techniques in Filling Gaps in Rain Gauge Records and Generating Multi-Return Period Isohyetal Maps in Arid Regions—Case Study: Kingdom of Saudi Arabia

Helmi,

Farouk,

Hassan

et al. 2024

Water

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“…The Global Polynomial Interpolation method involves fitting a smooth surface, defined by a mathematical function (polynomial), to the data points of the input [22]. The method of global polynomial interpolation is classified as an inaccurate interpolator because the mathematical function rarely passes through all the actual measured points.…”

Section: Global Polynomial Interpolationmentioning

confidence: 99%

“…It can be seen that Kriging is often chosen. It is flexible and includes many parameters, which can be both-positive, as it allows precise modeling, and negative, as using Kriging forces a detailed and relatively complicated data analysis, searching trends and modeling [22][23][24]. This causes more workload than in the case of other methods, but on the other hand, the faster analysis does not have to be more accurate.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Selected Geostatistical Methods for Bottom Surface Modeling

Biernacik

Kazimierski

Wlodarczyk–Sielicka³

2023

Sensors

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Digital bottom models are commonly used in many fields of human activity, such as navigation, harbor and offshore technologies, or environmental studies. In many cases, they are the basis for further analysis. They are prepared based on bathymetric measurements, which in many cases have the form of large datasets. Therefore, various interpolation methods are used for calculating these models. In this paper, we present the analysis in which we compared selected methods for bottom surface modeling with a particular focus on geostatistical methods. The aim was to compare five variants of Kriging and three deterministic methods. The research was performed with real data acquired with the use of an autonomous surface vehicle. The collected bathymetric data were reduced (from about 5 million points to about 500 points) and analyzed. A ranking approach was proposed to perform a complex and comprehensive analysis integrating typically used error statistics—mean absolute error, standard deviation and root mean square error. This approach allowed the inclusion of various views on methods of assessment while integrating various metrics and factors. The results show that geostatistical methods perform very well. The best results were achieved with the modifications of classical Kriging methods, which are disjunctive Kriging and empirical Bayesian Kriging. For these two methods, good statistics were calculated compared to other methods (for example, the mean absolute error for disjunctive Kriging was 0.23 m, while for universal Kriging and simple Kriging, it was 0.26 m and 0.25 m, respectively). However, it is worth mentioning that interpolation based on radial basis function in some cases is comparable to Kriging in its performance. The proposed ranking approach was proven to be useful and can be utilized in the future for choosing and comparing DBMs, mostly in mapping and analyzing seabed changes, for example in dredging operations. The research will be used during the implementation of the new multidimensional and multitemporal coastal zone monitoring system using autonomous, unmanned floating platforms. The prototype of this system is at the design stage and is expected to be implemented.

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“…Upscaling soil GHG fluxes using the RF algorithm required spatial raster maps of the soil physico-chemical predictor parameters. Thus, we interpolated our measured point data to continuous landscape maps using the inverse distance weighted (IDW) approach in the System for Automated Geoscientific Analyses software (SAGA: QGIS) with a distance coefficient power of 1 (Gradka & Kwinta 2018). The spatial interpolations were performed per land use (forest, grassland, and arable land) and for each season (summer and autumn) due to significant variations in soil parameters such as soil moisture or inorganic N content across land uses and seasons (see Wangari et al, 2022).…”

Section: Spatial Interpolation Of Soil Parametersmentioning

confidence: 99%

Identifying landscape hot and cold spots of soil GHG fluxes by combining field measurements and remote sensing data

Wangari

Mwanake

Houska

et al. 2023

Preprint

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Abstract. Upscaling chamber measurements of soil greenhouse gas (GHG) fluxes from points to landscape scales remain challenging due to high variability of fluxes in space and time. This study measured GHG fluxes and soil parameters at selected point locations (n=268), thereby implementing a stratified sampling approach on a mixed land-use landscape (~5.8 km2). Based on these field-based measurements and remotely-sensed data on landscape and vegetation properties, we used Random Forest models to predict GHG fluxes at a landscape scale (1 m resolution) in summer and autumn. The results showed improved GHG flux prediction performance when combining field-measured soil parameters with remotely-sensed data. Available satellite data products from Sentinel-2 on vegetation cover and water content played a more significant role than attributes derived from a digital elevation model, possibly due to their ability to capture both spatial and seasonal changes of ecosystem parameters within the landscape. Similar seasonal patterns of higher soil/ecosystem respiration (SR/ER-CO2) and nitrous oxide (N2O) fluxes in summer and higher methane (CH4) uptake in autumn were observed in both the measured and predicted landscape fluxes. Based on the upscaled fluxes, we also assessed the contribution of hot spots to total landscape fluxes. The identified emission hot spots occupied a small landscape area (7 to 16 %) but accounted for up to 42 % of the landscape GHG fluxes. Our study showed that combining remotely-sensed data with chamber measurements and soil properties is a promising approach for identifying spatial patterns and hot spots of GHG fluxes across heterogeneous landscapes. Such information may be used to inform targeted mitigation strategies at landscape-scale.

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A short review of interpolation methods used for terrain modeling

Cited by 10 publications

References 18 publications

Comparing Remote Sensing and Geostatistical Techniques in Filling Gaps in Rain Gauge Records and Generating Multi-Return Period Isohyetal Maps in Arid Regions—Case Study: Kingdom of Saudi Arabia

Comparing Remote Sensing and Geostatistical Techniques in Filling Gaps in Rain Gauge Records and Generating Multi-Return Period Isohyetal Maps in Arid Regions—Case Study: Kingdom of Saudi Arabia

Comparative Analysis of Selected Geostatistical Methods for Bottom Surface Modeling

Identifying landscape hot and cold spots of soil GHG fluxes by combining field measurements and remote sensing data

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