scholarly journals Drought Index Mapping at Different Spatial Units

2008 ◽  
Vol 9 (6) ◽  
pp. 1523-1534 ◽  
Author(s):  
Jinyoung Rhee ◽  
Gregory J. Carbone ◽  
James Hussey
Keyword(s):  
Spatial Interpolation ◽  
Drought Index ◽  
Cross Validation ◽  
Interpolation Method ◽  
Inverse Distance Weighted ◽  
Frequency Distributions ◽  
Distance Weighted ◽  
Cross Validation Error ◽  
Index Mapping ◽  
Inverse Distance

Abstract This paper investigates the influence of spatial interpolation and aggregation of data to depict drought at different spatial units relevant to and often required for drought management. Four different methods for drought index mapping were explored, and comparisons were made between two spatial operation methods (simple unweighted average versus spatial interpolation plus aggregation) and two calculation procedures (whether spatial operations are performed before or after the calculations of drought index values). Deterministic interpolation methods including Thiessen polygons, inverse distance weighted, and thin-plate splines as well as a stochastic and geostatistical interpolation method of ordinary kriging were compared for the two methods that use interpolation. The inverse distance weighted method was chosen based on the cross-validation error. After obtaining drought index values for different spatial units using each method in turn, differences in the empirical binned frequency distributions were tested between the methods and spatial units. The two methods using interpolation and aggregation introduced fewer errors in cross validation than the two simple unweighted average methods. Whereas the method performing spatial interpolation and aggregation before calculating drought index values generally provided consistent drought information between various spatial units, the method performing spatial interpolation and aggregation after calculating drought index values reduced errors related to the calculations of precipitation data.

scholarly journals Comparison of Inverse Distance Weighted and Natural Neighbor Interpolation Method at Air Temperature Data in Malang Region

CAUCHY ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. 48 ◽  
Author(s):  
Jaka Pratama Musashi ◽  
Henny Pramoedyo ◽  
Rahma Fitriani
Keyword(s):  
Air Temperature ◽  
Spatial Data ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Inverse Distance Weighted ◽  
Natural Neighbor ◽  
Comparison Results ◽  
Data Record ◽  
Distance Weighted ◽  
Inverse Distance

The purpose of this study was to compare the results of Inverse Distance Weighted (IDW) and Natural Neighbor interpolation methods for spatial data of air temperature in the Malang Region.  Interpolation is one way to determine a point of events from several points around the known value.  Spatial interpolation can be used to estimate an area that does not have a data record using the value of its known surroundings.  38 points observation air temperature of Malang Region in 2016 is used as a sample point to interpolate the surrounding air temperature.  Obtained optimum parameter power value is 2 for IDW interpolation method.  The RMSE comparison results show that IDW method is better to be used than the Natural Neighbor Interpolation method with the RMSE values of 1,2292 for the IDW method and 1,6173 for the NN method.

scholarly journals AN IMPROVED TEMPERATURE SPATIAL INTERPOLATION METHOD FOR SPACEBORNE LIDAR ATMOSPHERIC CORRECTION

2021 ◽  
Vol V-4-2021 ◽  
pp. 83-88
Author(s):  
M. Zhou ◽  
K. Li ◽  
M. Pan ◽  
J. Chen ◽  
C. Li ◽  
...  
Keyword(s):  
Spatial Interpolation ◽  
Interpolation Method ◽  
Meteorological Parameter ◽  
Atmospheric Correction ◽  
Absolute Error ◽  
Lapse Rate ◽  
Interpolation Methods ◽  
Spatial Interpolation Method ◽  
Meteorological Elements ◽  
Inverse Distance

Abstract. As one of the most important meteorological elements, temperature is an indispensable meteorological parameter for the atmospheric correction of spaceborne LiDAR ranging. Given a limited number of surface meteorological observation stations, the temperature values for all region of LiDAR observation need to be interpolated using appropriate spatial interpolation methods. In this paper, based on the monthly surface observation values in individual years (1981–2010) of Sichuan province observation stations, we firstly analyze the effects of three common interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK) and gradient plus inverse distance squared (GIDS). To solve the problem of low interpolation accuracy in severely undulating terrain area, an improved gradient distance inverse square method based on the adiabatic lapse rate (GIDS-ALR) is proposed. The experimental results show that the GIDS-ALR has an obvious improvement in the effect of severely undulating terrain, where the absolute error has been improved by more than 43% in average. Additionally, the temperature-interpolated MAE is reduced by more than 30%. The effectiveness and applicability of the proposed method is verified in this paper.

scholarly journals A Gridded Database for the Spatiotemporal Analysis of Rainfall in Southern Italy (Calabria Region)

2020 ◽  
Vol 2 (1) ◽  
pp. 6
Author(s):  
Tommaso Caloiero ◽  
Roberto Coscarelli ◽  
Gaetano Pellicone
Keyword(s):  
Southern Italy ◽  
Interpolation Method ◽  
Grid Point ◽  
Spatiotemporal Analysis ◽  
Annual Rainfall ◽  
Inverse Distance Weighted ◽  
Calabria Region ◽  
Increasing Trend ◽  
Distance Weighted ◽  
Inverse Distance

In this work, a gridded database was obtained from a rainfall dataset of 129 monthly series collected for the period 1951–2016 in the Calabria region (southern Italy). The Inverse Distance Weighted (IDW) interpolation method was applied to build 603 rainfall grid series with a spatial resolution of 5 km × 5 km. In order to detect possible trends, for each grid point, the seasonal and annual rainfall series were analyzed with the Mann–Kendall non-parametric test and the Theil–Sen estimator. Results showed a decreasing trend for the annual and winter–autumn rainfall and an increasing trend for the summer one.

scholarly journals The Utilization of Remote Sensing Imagery and Inverse Distance Weighted Scheme to Simulate the White Oil Effects on Soil Geotechnical Properties

2021 ◽  
pp. 2824-2833
Author(s):  
L. A. Jawad ◽  
H. W. Abdulwadud ◽  
Z. A. Hameed
Keyword(s):  
Spatial Interpolation ◽  
Liquid Limit ◽  
Soil Samples ◽  
Strength Parameter ◽  
Undisturbed Soil ◽  
Inverse Distance Weighted ◽  
Area Of Interest ◽  
Clayey Silt ◽  
Distance Weighted ◽  
Inverse Distance

     This research aims to utilize a complementarity of field excavations and laboratory works with spatial analyses techniques for a highly accurate modeling of soil geotechniques properties (i.e. having lower root mean square error value for the spatial interpolation). This was conducted, for a specified area of interest, firstly by adopting spatially sufficient and  well distributed samples (cores). Then, in the second step, a simulation is performed for the variations in properties when soil is contaminated with commonly used industrial material, which is white oil in our case. Cohesive (disturbed and undisturbed) soil samples were obtained from three various locations inside Baghdad University campus in AL-Jadiriya section of Baghdad, Iraq. The unified soil categorization system (USCS) was adopted and soil was categorized  as clayey silt of low plasticity (CL). The cores were contaminated in a synthetically manner using two specified values of white oil (5 and 10 % of its dry weight). Then, the samples were left for three days to certify homogeneity. The results of laboratory tests were enhanced by spatial interpolation mapping, using Inverse Distance Weighted scheme for normal soil samples and those with synthetic pollution. The liquid limit rates were raised slightly as contamination rates raised, while particle size was reduced; in contrary, shear strength parameter values were decreased.

Data Interpolation Methods by Inverse Distance Weighted Average Method between Multidisciplines

2014 ◽  
Vol 1044-1045 ◽  
pp. 620-623 ◽  
Author(s):  
Fan Yang ◽  
Wei Zhu Yang
Keyword(s):  
Average Method ◽  
Data Exchange ◽  
Interpolation Method ◽  
Weighted Average ◽  
Exponential Parameter ◽  
Inverse Distance Weighted ◽  
Weighted Average Method ◽  
Interpolation Methods ◽  
Distance Weighted ◽  
Inverse Distance

In this article, to handle data exchange between different grid systems efficiently and accuracy, the accuracy of inverse distance weighted average method is researched by different searching radius and exponent parameter. The result is compared with other two interpolation methods, radial basis function interpolation and local triangular projection method. The result shows that the search radius and exponential parameter of inverse distance weighted average interpolation method have not significant influence on interpolation result when radius is large.

Application of Inverse Distance Weighted Interpolation Method in Finite Temperature Point Temperature Field

2014 ◽  
Vol 599-601 ◽  
pp. 1268-1271 ◽  
Author(s):  
Fu Cai Zhang ◽  
Xiao Gang Sun
Keyword(s):  
Temperature Field ◽  
Interpolation Method ◽  
Interpolation Point ◽  
Limited Data ◽  
Weighted Interpolation ◽  
Theoretical Derivation ◽  
Inverse Distance Weighted ◽  
Distance Weighted ◽  
Inverse Distance Weighted Interpolation ◽  
Inverse Distance

The paper introduces a kind of inverse distance weighted interpolation theoretical derivation and its solving way. The basic principle of this method is that to use known information limited data were interpolated to the unknown information for point, through the obtained interpolation point value to calculate the new interpolation point value. Thus, it is solved that the problem that limited number of temperature points can not be comprehensive description of temperature field, and the method is applied in the process of solving the unknown temperature points in the two dimensional temperature field. The characteristics of this method are simple, practical, less amount of calculation.

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