The Spatial Interpolation Method Research of Vegetation Characteristics Model Establishment Based on DTM - A Case Study of Chongzhou Forest Farm

2011 ◽  
Vol 403-408 ◽  
pp. 2378-2382
Author(s):  
Dong Li Chen ◽  
Lei Huang
Keyword(s):  
Spatial Data ◽  
Spatial Interpolation ◽  
Production Management ◽  
Interpolation Method ◽  
Kriging Interpolation ◽  
Final Conclusion ◽  
Kriging Interpolation Method ◽  
Vegetation Characteristics ◽  
Sample Points ◽  
Forest Farm

The Vegetation Characteristics Model is based on the principle of DTM. The property values is the data of forest coverage of every small spot of forestry, By spatial interpolation, we can access continuous, accurate characteristic values of the data model which covers the entire forest farm forestry. This article describes three interpolation methods in detail. They are used to build Vegetation Characteristics Model, are compared each other. we can get the final conclusion: Inverse distance weighting method produces samples of the surface does not exceed the maximum and minimum data; if less sample points, using the Kriging interpolation method, we need to add some sample points in those changing areas; Spline, the main problem is when the area is lack of data, then there will be “steep slopes”, it is often referred to as "overshoot" ,this model does not work effectively. The Vegetation Characteristics Model can simulate the surface of the real situation in the forest region. This model can support spatial data for Forestry production, management and spatial analysis.

scholarly journals INTERPOLASI SPASIAL DENGAN METODE ORDINARY KRIGING MENGGUNAKAN SEMIVARIOGRAM ISOTROPIK PADA DATA SPASIAL (Studi Kasus: Curah Hujan di Kabupaten Karangasem)

2015 ◽  
Vol 4 (1) ◽  
pp. 26
Author(s):  
PUTU MIRAH PURNAMA D. ◽  
KOMANG GDE SUKARSA ◽  
KOMANG DHARMAWAN
Keyword(s):  
Spatial Data ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Kriging Interpolation ◽  
Control Points ◽  
Kriging Method ◽  
3D Data ◽  
Kriging Interpolation Method ◽  
Relationship Of

Spatial data is data that is presented in the geographic of an object, related to the location, shape and relationship of the earth in space. One of example of spatial data is rainfall. To determine the value of rainfall in an area, built to predict rain post information regarding rainfall. Spatial interpolation is used to estimate rainfall by collecting rainfall values held rain heading around. Assessment methods used in the estimate the rainfall in the Karangasem district is ordinary kriging using isotropic semivariogram that takes into account height on spatial data. Isotropic semivariogram which only takes into account the distance alone. Ordinary kriging method using isotropic semivariogram that takes into account height  value estimated rainfall is much different to the values at the control points Amlapura and Besakih. Interpolation on 3D data are not suitable for use on ordinary kriging method, grouping should be done at the data into a few weeks to application of ordinary kriging interpolation method using anisotropic semivariogram on 3D data.

Research of Critical Precipitation Distribution in Sichuan Province Based on Spatial Interpolation Method

2013 ◽  
Vol 726-731 ◽  
pp. 3469-3474
Author(s):  
Zhao Xian Wang ◽  
Wen Bo Xu ◽  
Shao Cai Jing ◽  
Li Mei Zhou ◽  
Wen Zhi Zhang
Keyword(s):  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Sichuan Province ◽  
Kriging Interpolation ◽  
Distribution Map ◽  
Rainfall Distribution ◽  
Kriging Interpolation Method ◽  
Critical Rainfall ◽  
Spatial Interpolation Method

Critical rainfall is one of the most important parameters of prediction, forecasting and early warning on mountain torrents disasters with important meanings for preventing mountain torrents disasters. In this paper, the measured rainfall method was taken to calculate the critical rainfall in typical regions based on the rainfall data of typical regional meteorological stations. Then, the Inverse Distance Weighting spatial interpolation method and Ordinary Kriging Interpolation method are taken based on the typical regional critical rainfall to get the critical rainfall distribution map of Sichuan Province. The critical rainfall distribution maps, which obtained from the two kinds of interpolation methods, both have universality and operability. Among them, the critical rainfall distribution map mapped by the Ordinary Kriging Interpolation method was more consistent with the practical situation.

Numerical Investigation of the Identifiability of Elastomer Mechanical Properties by Nano-Indentation and Shape-Manifold Approach

2021 ◽  
Author(s):  
Oumaima Ezzaamari ◽  
Guénhaël Le Quilliec ◽  
Florian Lacroix ◽  
Stéphane Méo
Keyword(s):  
Numerical Study ◽  
Interpolation Method ◽  
Structural Characteristics ◽  
Numerical Approach ◽  
Kriging Interpolation ◽  
Nano Indentation ◽  
Kriging Interpolation Method ◽  
Elastomeric Materials ◽  
Hyper Elastic ◽  
Characterization Test

ABSTRACT Various research is covering instrumented nano-indentation in the literature. However, studies on this characterization test remain limited when it comes to the local mechanical behavior of elastomeric materials. The application of nano-indentation on these materials is a difficult task given their complex mechanical and structural characteristics. We try to overcome these experimental limitations and find an effective numerical approach for local mechanical characterization of hyper-elastic materials. For such needs, we carried out a numerical study based on model reduction and shape manifold approach to investigate the parameters identification of different hyper-elastic constitutive laws by using instrumented indentation. Similarly, we studied the influence of the indenter geometry, the friction coefficient variation, and finally the indented material height effect. To this end, we constructed a reduced order model through a design of experiments by proper orthogonal decomposition combined with the kriging interpolation method.

scholarly journals Estimation of Ice Thickness and the Features of Subglacial Media Detected by Ground Penetrating Radar at the Baishui River Glacier No. 1 in Mt. Yulong, China

2020 ◽  
Vol 12 (24) ◽  
pp. 4105
Author(s):  
Jing Liu ◽  
Shijin Wang ◽  
Yuanqing He ◽  
Yuqiang Li ◽  
Yuzhe Wang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Interpolation Method ◽  
Kriging Interpolation ◽  
Ice Thickness ◽  
Grid Data ◽  
Differential Gps ◽  
Landsat Images ◽  
Kriging Interpolation Method ◽  
Bedrock Surface ◽  
Ground Penetrating

Using ground-penetrating radar (GPR), we measured and estimated the ice thickness of the Baishui River Glacier No. 1 of Yulong Snow Mountain. According to the position of the reflected media from the GPR image, combined with the radar waveform amplitude and polarity change information, the ice thickness and the changing medium position at the bottom of this temperate glacier were identified. Water paths were found in the measured ice, including ice caves and crevasses. A debris-rich ice layer was found at the bottom of the glacier, which produces strong abrasion and ploughing action at the bedrock surface. This results in the formation of different detrital layers stagnated at the ice-bedrock interface and numerous crevasses on the bedrock surface. Based on the obtained ice thickness and differential GPS data, combined with Landsat images, the kriging interpolation method was used to obtain grid data. The average ice thickness was 52.48 m and between 4740 and 4890 m above sea level, with a maximum depth of 92.83 m. The bedrock topography map of this area was drawn using digital elevation model from the Shuttle Radar Topography Mission. The central part of the glacier was characterized by small ice basins with distributed ice steps and ice ridges at the upper and lower parts.

Numerical Modeling of Fracture Process Using Element-Free Method

2013 ◽  
Vol 427-429 ◽  
pp. 146-149
Author(s):  
Cheng Fan
Keyword(s):  
Interpolation Method ◽  
Kriging Interpolation ◽  
Computational Accuracy ◽  
Simple Method ◽  
Solution Domain ◽  
Kriging Interpolation Method ◽  
Finite Covers ◽  
Crack Problems ◽  
Cover Systems ◽  
Element Free

A new element-free formulation of Kriging interpolation procedure based on finite covers technique and Kriging interpolation method which integrates the flexibilities of the manifold method in dealing with discontinuity and the element-free features of the moving Kriging interpolation. Two cover systems are employed in this method. Mathematical cover of the solution domain under consideration are used to construct shape function and physical cover is used to reproduce the geometry of the solution domain. The mathematical covers can take any types of shape and is much easily formed compared with those in the conventional MM. The presented method can overcome some difficulties in conventional element-free Galerkin methods in treating discontinuous crack problems. The fundamental theory of this procedure is illustrated and numerical analyses of examples show that the proposed procedure is an effective and simple method with higher computational accuracy.

Integration of statistical and spatial methods for distributing precipitation in tropical areas

2012 ◽  
Vol 44 (6) ◽  
pp. 982-994 ◽  
Author(s):  
Mandana Abedini ◽  
Md Azlin Md Said ◽  
Fauziah Ahmad
Keyword(s):  
Multivariate Regression ◽  
Interpolation Method ◽  
Rainfall Data ◽  
Environmental Research ◽  
Kriging Interpolation ◽  
Slope Aspect ◽  
Annual Average ◽  
Rain Gauges ◽  
Kriging Interpolation Method ◽  
Topographical Factors

The high spatial resolution of precipitation distribution is a major concern for experts in environmental research and planning. This paper establishes a combination of multivariate regression algorithm and spatial analysis to predict distribution of precipitation, considering the four topographical factors of altitude, slope, aspect and location. Annual average and seasonal rainfall data were collected in nine rain gauges in Ulu Kinta Catchment in East Malaysia from 1974 to 2010. To examine records and fill gaps from long-term rain gauges, homogeneity analysis was performed using the double-mass curve method. Estimated missing rainfall data were also tested using index gauges from network rainfall stations. Multivariate regression analysis was conducted to propose an empirical equation for the study area. Topographical factors were considered from a 90 m resolution digital elevation model. The multivariate regression model was found to clarify 74% of spatial variability of precipitation on annual average and 78% during wet season. However, the correlation coefficient for the dry season decreased sharply to 63%. By using the kriging interpolation method, the estimated annual average improved to 78.4%; the average improved to 65.2 and 80.3% in the dry and wet seasons, respectively. This confirms the efficiency and significance of the model and its potential for use in other tropical catchments.

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